Technology Governance Wiki - User contributions [en]

Economies of Scale (Jihae)

2026-10-06T20:29:30Z

Park Jihae: /* Economies of Scale */

== '''Economies of Scale''' ==
[[image:img1791336432117.png|thumb|300px]]
=== '''Definition''' ===
'''Economies of scale''' take place when the cost of producing a product reduces as production volume increases. Simply put, it is referring to the cost advantages a business gains when it increases production. By spreading both fixed and variable costs over more units, it will help decreasing the average cost per unit. There are two types of economies of scale, '''internal '''and '''external''':
*'''Internal economies of scale''' take place within the company when there are improvements to the production process, management, or technology. * ''Technical economies'' by using automation or digital system to increase the outputs but reduce total costs ''* Managerial economies'' by streamlining decision-making process to improve work efficiency * ''Financial and marketing economies'' by reducing borrowing and promotion costs as the business scales * ''Network economies'' by creating value as each new user enhances the experience for others
*'''External economies of scale '''derive from outside factors, such as industry growth, shared infrastructure, or access to skilled labor, which could reduce costs for all market participants.
However, when the growth reaches the limit, this could result in '''diseconomies of scale''', where''' '''it’s actually expanding either too quickly or inefficiently.[https://online.hbs.edu/blog/post/economies-of-scale [1]]

=== '''Tech Example for Economies of Scale: Instagram''' ===
Regardless of the number of users they have, Meta will have fixed cost in terms of maintaining its servers and datacenters, workforce (software engineers and other staff members), cyber security, app development, content moderation, etc. However, this cost will not increase proportionally with the number of users. In fact, once the system is settled, adding some more users will not make Meta to create a new server for them. This means fixed costs will spread out across the users, leading to economies of scale.
It’s also interesting to note the network economies here. Increase in the number of users result in creating value by improving user experience for the new comers. This also helps instagram to gain good reputation in the platform industry, drawing even more users to bring them more revenue.

=== '''Governance Implication''' ===
For a large-scale platform like Instagram, more users with lower average cost will lead to more investment for them to provide better service, hence attracting even more users and becoming greater in scale. With 3 billion monthly active users in 2026[https://www.icuc.social/resources/blog/instagram-statistics [2]], the barriers are so high for competitors to join the game. This is how economies of scale contribute to market concentration.
Such large-scale platform with a billion-scale user base is no longer simply a company, but it is an infrastructure in which many actors are at play, namely creators, advertisers, businesses, etc. A governance problem may arise by exercising its rule-making power. For instance, Instagram make their users to follower their own rules such as terms of service, advertising rules, content policies, account restrictions, etc.

=== '''An Open Question''' ===
The idea of economies of scale from the example of Instagram fails to address the part that it could also lead to the influx of black users. There are growing risks of scams, inappropriate contents, and frauds along with the increase in the number of users. This could also mean more investment and cost in order to tackle such problems.
<div></div>

Economies of Scale (Jihae)

2026-10-06T20:29:08Z

Park Jihae: Created page with "== '''Economies of Scale''' == 300px === '''Definition''' === '''Economies of scale''' take place when the cost of producing a product reduces as production volume increases. Simply put, it is referring to the cost advantages a business gains when it increases production. By spreading both fixed and variable costs over more units, it will help decreasing the average cost per unit. There are two types of economies of scale, '''inter..."

== '''Economies of Scale''' ==
[[image:img1791336432117.png|thumb|300px]]
=== '''Definition''' ===
'''Economies of scale''' take place when the cost of producing a product reduces as production volume increases. Simply put, it is referring to the cost advantages a business gains when it increases production. By spreading both fixed and variable costs over more units, it will help decreasing the average cost per unit. There are two types of economies of scale, '''internal '''and '''external''':
*'''Internal economies of scale''' take place within the company when there are improvements to the production process, management, or technology. * ''Technical economies'' by using automation or digital system to increase the outputs but reduce total costs ''* Managerial economies'' by streamlining decision-making process to improve work efficiency * ''Financial and marketing economies'' by reducing borrowing and promotion costs as the business scales * ''Network economies'' by creating value as each new user enhances the experience for others
*'''External economies of scale '''derive from outside factors, such as industry growth, shared infrastructure, or access to skilled labor, which could reduce costs for all market participants.
However, when the growth reaches the limit, this could result in '''diseconomies of scale''', where''' '''it’s actually expanding either too quickly or inefficiently.[https://online.hbs.edu/blog/post/economies-of-scale [1]] 
=== '''Tech Example for Economies of Scale: Instagram''' ===
Regardless of the number of users they have, Meta will have fixed cost in terms of maintaining its servers and datacenters, workforce (software engineers and other staff members), cyber security, app development, content moderation, etc. However, this cost will not increase proportionally with the number of users. In fact, once the system is settled, adding some more users will not make Meta to create a new server for them. This means fixed costs will spread out across the users, leading to economies of scale.It’s also interesting to note the network economies here. Increase in the number of users result in creating value by improving user experience for the new comers. This also helps instagram to gain good reputation in the platform industry, drawing even more users to bring them more revenue. 
=== '''Governance Implication''' ===
For a large-scale platform like Instagram, more users with lower average cost will lead to more investment for them to provide better service, hence attracting even more users and becoming greater in scale. With 3 billion monthly active users in 2026[https://www.icuc.social/resources/blog/instagram-statistics [2]], the barriers are so high for competitors to join the game. This is how economies of scale contribute to market concentration.Such large-scale platform with a billion-scale user base is no longer simply a company, but it is an infrastructure in which many actors are at play, namely creators, advertisers, businesses, etc. A governance problem may arise by exercising its rule-making power. For instance, Instagram make their users to follower their own rules such as terms of service, advertising rules, content policies, account restrictions, etc. 
=== '''An Open Question''' ===
The idea of economies of scale from the example of Instagram fails to address the part that it could also lead to the influx of black users. There are growing risks of scams, inappropriate contents, and frauds along with the increase in the number of users. This could also mean more investment and cost in order to tackle such problems.<div></div>

File:Img1791336432117.png

2026-10-06T20:24:45Z

Park Jihae:

Governance by markets

2026-10-06T20:24:11Z

Park Jihae: /* Economies of Scale (Jihae) */

= Glossary =
= [https://wiki.ivir.nl/index.php?title=Data_as_an_input_to_market_power Data as an input to market power] (Sergey) =In the present digital economy, data has emerged as a valuable economic commodity that entities leverage to gain a competitive edge over their rivals [[https://repository.nls.ac.in/cgi/viewcontent.cgi?article=1478&context=nlsblr 1]]. '''Data''' is an input into production, so businesses that control data can have market power and impede the entry of potential competitors. '''Market power''' refers to a firm's ability to influence the price, output, or other market conditions in its favour, without being constrained by competitive forces [[https://www.tutor2u.net/economics/reference/what-is-meant-by-market-power 2]].'''Data as an input to market power''' means that access to information can help a business limit the competitive pressure it faces. Information about searches, clicks or purchases can improve services and advertising. If competitors cannot obtain comparable information at a reasonable cost, they may struggle to offer an attractive alternative. This can allow a platform to raise prices or worsen quality or privacy protections without losing enough business to make doing so unprofitable. This matters even when a service is free, because competition also concerns advertising charges, personal information and the choices available. Data can be used repeatedly and by several businesses without being used up. However, access can be restricted through contracts and technical controls. Nevertheless, holding a large dataset alone does not establish market power.'''Google Search''' illustrates this mechanism. Google controls over 91% of global search engine traffic [[https://www.statista.com/statistics/1381664/worldwide-all-devices-market-share-of-search-engines/ 3]] and uses information about users’ searches and the results they click to improve the relevance of its rankings. A larger volume of searches gives it more opportunities to learn, including about unusual queries that smaller search engines encounter less often. Better results can attract further use, producing additional information and reinforcing the advantage. A new search engine may therefore struggle to attract users while lacking the information needed to improve its service. Data access can constitute a significant barrier to entry when this type of analytics is necessary to participate in a market, particularly when alternatives are limited. A platform’s control over data access can shape who is able to compete with it. By determining whether, and on what terms, rivals can access that information, a platform can influence the competitive opportunities available to others. This suggests that regulation should examine access to strategically important data alongside prices and market shares. Article 6(11) of the EU Digital Markets Act [[https://eur-lex.europa.eu/eli/reg/2022/1925/oj/eng#:~:text=giving%20their%20consent.-,11.%C2%A0,-The%20gatekeeper%20shall 4]] reflects this approach: certain large search providers must, on request, share specified search data with rival search providers on fair and non-discriminatory terms, with relevant personal data anonymised. The aim is to help rivals compete while protecting users. A data advantage does not, by itself, establish unlawful conduct.Would access to equivalent data enable a rival to compete effectively? The concept cannot answer this on its own. Additional data may eventually add little value, become outdated, or have substitutes. Firms also need the skills and computing resources to use it. Google’s default positions in browsers and devices can further restrict rivals’ access to users even if data become available. The unresolved issue is how much competitive advantage comes from data itself and how much comes from these other resources and arrangements. 

= Two Sided Markets (Ceren) =In two-sided markets, two sides of a market interact through an intermediary. These markets typically have some externality,<code class="mwt-code">Carl Shapiro and Hal R Varian, ''Information Rules: A Strategic Guide to the Network Economy'' (Harvard Business School Press 2010), 183.</code> where the decisions of the sides affect the other. The intermediary also has multitudes of effects on both sides, mainly identified in the related literature through its pricing.An example of a two sided market is payment systems, whereby the credit card providing bank is the intermediary between the customers and merchants. In this example, banks provide credit cards to customers to be utilized in their payments, and merchants accept payment via multiple credit card systems to accommodate payments from customers holding credit cards from such providers[https://doi.org/10.1257/jep.23.3.125 .]The main power of the intermediary materializes in their pricing toward the merchant (and the merchant’s bank in interchange fees in case of payment being transferred through different banks). Customers will be expected to be more loyal, holding a few credit cards, whereas merchants accept payment from many banks to accommodate all customers’ payment methods. Banks will provide more subsidies to customers to attract them and compete aggressively on the customer side, making their platform more desirable to work with by merchants, who will in turn be more accepting of the platform’s fees to gain the business of the larger volume of customers. However, dynamic pricing (increasing fees after gaining entry and loyalty) may be a way for these platforms to abuse their power.The concept may be derailed from these logical / analytical systems of operation or expected outcomes when public policy interferes with the market. For example, net neutrality rules regulate what internet service providers (the intermediary) can charge content providers (one side) who aim to reach the consumers (the other side). In this case the above considerations and the concept are halted.

= Switching costs and lock-in (Kate) =
'''Definition '''When you move from one product or service to another, switching costs are everything you have to either give up or spend. This can include money, time, lost contacts, etc. Lock-in is when you feel (and are) trapped by these costs that add up and you decide instead of moving products or services, it’s just easier to stay with a service, not because you want to but because it’s just too much of a hassle. This is true even if a less expensive or better service exists all because leaving the product/service just costs too much in terms of time, effort, and money. Shapiro and Varian (2010) argue that switching costs are normal in information markets and that the full cost of a switch includes what you as the customer pay and what a new supplier spends to win that customer over.  '''Example: Apple''' The more Apple products you own, the harder it is to leave. As an avid Apple user, I am absolutely stuck in their ecosystem. I have my photos in Apple’s iCloud, my group chats in iMessage, and my Apple Watch only works with an iPhone. Leaving Apple is not easy. So much so that in March 2024, the US Department of Justice sued Apple alleging it raises costs of leaving/switching phones by making messaging worse and less secure when you’re messaging with non iPhones. In June 2025, Judge Julien Neals denied Apple’s motion to dismiss and the case is still in pre-trial proceedings.  '''Governance implication''' Lock-in makes users dependent on a platform and because of that, once we’re locked in, the platform (Meta, Apple, Google, etc) can raise prices, show more ads or even provide lower quality terms. So in this way, the platform can control users because if a user wants to leave, it’s going to cost you in that the platform has already designed your exit including how you can export your data, licenses and APIs. Under the EU’s Digital Markets Act, the European Commission adopted decisions in March 2025 making Apple open nine iOS connectivity features, mostly used by devices like smartwatches, headphones and televisions. These decisions are legally binding and they focus on lock-in by lowering the cost of choosing non-Apple products. It’s worth noting, however, that they only apply in the EU. When the Commission adopted the decisions, Apple said (reported by MacTech) “It’s bad for our products and for our European users.” '''Open Question''' So what happens when the cost of leaving isn’t just your own costs as a user but also the cost of leaving family and friends behind?  If a user leaves, let’s say, Instagram, they won’t be charged to leave (they will, however, have some friction to leaving in that they’ll be asked why they’re leaving and if there’s anything Instagram can do to make them stay), but they will be leaving their family and friends that stay on the app. This is where data portability can’t really help because while you can export your photos, you can’t make your friends leave. This is part of what makes leaving a network or platform so hard. '''References:''' European Commission (2025, March 19). ''Commission provides guidance under Digital Markets Act to facilitate development of innovative products on Apple’s platforms''. https://digital-markets-act.ec.europa.eu/commission-provides-guidance-under-digital-markets-act-facilitate-development-innovative-products-2025-03-19_en MacTech (2025, March 19). ''Apple isn’t happy with the latest decisions regarding the DMA''. https://www.mactech.com/2025/03/19/apple-isnt-happy-with-the-latest-decisions-regarding-the-dma Shapiro, C. & Varian, H.R. (2010). ''Information Rules: A Strategic Guide to the Network Economy.'' Boston, MA: Harvard Business School Press. The Hill. (2025, June 30). ''Judge rejects Apple’s bid to dismiss DOJ antitrust case''. https://thehill.com/policy/technology/5380007-apple-federal-judge-antitrust-case/ The Mac Observer. (2026, September 12). ''Apple’s other antitrust fights: Where the DOJ case and the UK’s CMA stand now''. https://www.macobserver.com/news/apple-doj-antitrust-case-uk-cma-status/ U.S. Department of Justice (2024, March 21). ''Justice Department Sues Apple for Monopolizing Smartphone Markets'' [Press Release]. https://www.justice.gov/archives/opa/pr/justice-department-sues-apple-monopolizing-smartphone-markets

= Excludability and rivalrousness (Paul)  =
=== Excludability and Rivalrousness ===The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. '''Excludability''': Goods to which economic actors can technically or legally restrict access are considered to have ‘high excludability’. Conversely, goods to which access cannot be restricted are of ‘low excludability’, or, in absolute terms, ‘non-excludable’.'''Rivalrousness''': Rivalrousness on the other hand refers to the finitude of the good in question. The greater the depletion of the stock, quantity, or amount is on account of one party’s consumption, the more rivalrous a good is. Tabulating the characteristics against one another allows for inference and definition of the type of good an object, service, or resource may constitute (see fig. 1). Excludability and Rivalrousness of goods can therethrough serve as a predictor for certain types of market failures: Goods which are non-excludable or of low excludability, but nonetheless costly to provide, are vulnerable to so-called free rider problem. Goods which are non-excludable, but highly rivalrous (common goods), may lead to the tragedy of the commons, that is over-exploitation. 

=== AI Training and the (non-)excludability of information ===The unusual nature of excludability and rivalrousness of data further compromises the applicability of the goods classification built thereon. In traditional economics, a non-excludable and non-rivalrous good would be seen as a public good. This does apply insofar as data is vulnerable to corporate exploitation without commensurate indemnification. Data usage may however actually be anti-rival in the sense that increasing aggregate usage increases the value of the data (better labeling, easier access, previously undrawn conclusions, etc.), rather than diminish the ‘stock’ of data. Conversely, data may be rivalrous not in its absolute depletion by consumption, but rather the benefit or competitive advantage usage of that data may confer: If all AI firms are able to train on the exact same data (all available data), the utility of that data may still increase, but the competitive advantage of possessing that data is exhausted.

==== Free-Rider Problems & Public Data ====As any party can benefit from publicly available data at no cost or low cost, the incentive for private market actors to fund its creation is comparatively weak. Consequently, public bodies (and less frequently open-source foundations and non-profits) often assume the responsibility for the provision of this type of data. A classical example is weather data and its creation by national weather agencies.

= Interoperability (Sisi) =

#What is interoperability?[[https://www.ibm.com/think/topics/interoperability 1]]Interoperability is a “standards-based” approach that enables different IT systems to communicate with minimal end-user interaction to exchange data and “share functionality”. Each system also allows the other system to “read, update, modify, and analyze” data with minimal human interaction. [[https://www.oracle.com/nl/interoperability/ 2]]Interoperability works by having different systems share data by employing the same standards for data formats (e.g., Extensible Markup Language or JavaScript Object Notation), exchange, and vocabulary to title data elements. These standards are agreed upon “in advance” by organizations, industry types, and user groups. Then, software developers build the system based on the model agreed upon.

#Concrete example from a digital technology or platformInteroperable systems are vital in healthcare, commerce, and public safety. A notable example includes the integration of Electronic Health Records (HER). This system allows different healthcare providers to securely access and share personal patient information regardless of the specific EHR system used.[[https://data.europa.eu/en/news-events/news/interoperability-solutions-bridging-systems-connected-future 3]] An example of such a system is the GNU Health Hospital Information System, which is a model employed by small private offices and large national public health systems.[[https://interoperable-europe.ec.europa.eu/collection/gnu-health/solution/gnu-health-hospital-information-system 4]]For example, a health care professional may access lab results and patient history data from various platforms to enable them to make a diagnosis.

#One governance implication: what does this concept explain about how platforms exercise power?Interoperability is a powerful tool because it helps provide a “seamless user experience” and optimizes data sharing between separate systems. It helps companies and organizations access data from multiple sources and improve efficiency, such as accelerating workflows and reducing the possibility of errors by pooling data from its sources.Interoperability standards are mandated by law on the technical side to ensure “critical systems” are compatible, especially in public safety and health. [[https://www.ibm.com/think/topics/interoperability 5]]In Europe, interoperability is governed by the European Health Data Space regulation (EHDS); the “first common EU data space dedicated to a specific sector as part of the European strategy for data.”This Regulation aims to benefit EU citizens such as patients, researchers, healthcare professionals, and industry players by establishing a common framework for the exchange of electronic health data within the EU. In addition, it enables certain data to be “reused” for public purpose and innovative scientific research. [[https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en 6]]

#Where does the concept break down or fail to explain somethingWhile the EDHS harmonizes a legal and technical framework for sharing EU data, its shortcoming is its gradual implementation. The EDHS entered into force in 2025; however, most of its obligations will not apply until 2029. This leaves four years for health care companies to use interoperability in a deregulated manner. [[https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en 7]]Furthermore, there are actual barriers to achieving full interoperability in healthcare, including data silos (which isolate data collections and hinder data exchange), lack of standardization (where one system records blood pressure as free text, another system stores it as a structured numeric value), and deficient infrastructure, which requires significant technological upgrading.The burden remains on health companies to decide which standards to adopt and how much to invest in stable technological infrastructure to strengthen their data governance and interoperability. [[https://www.tigahealth.com/how-to-overcome-healthcare-interoperability-challenges/ 8]]

= [[Economies of Scale (Jihae)]] =

= [[Economies of Scope (Mariona)]] =
= [[Network Effects (Indirect) - Sebastián]] =
= [[Monopsony - (Atmika)]] =
== Definition: ==
 A Monopsony is a market condition where there is one buyer and multiple sellers; the opposite of the better known non-competitive market - Monopoly. Here, the buyer has the power to control the price and set market conditions. 
== Digital platform: Uber ==
'''Definition:''' 
 Uber was released into the market as an online platform to connect drivers with customers - but it’s business model created a huge social and economic impact in the labor sector. (1) Uber engaged drivers on a contract basis and offered short-term, low commitment and flexible jobs, which we characterise as the ‘Gig economy’. Uber offered a better transportation network alternative - dynamic pricing, more operating efficiency and on-demand mobility in contrast to the rigid capacities and fares of conventional transportational means available (mainly taxicabs), which would soon be replaced.(2) Additional social perspectives showed fair, non-discriminatory and safer practices - such as access and availability to lower economic groups.   

 '''How power is exercised '''

 1. Capturing labour force: The primary economic impact in the labour space is with regard to human capital mobilisation; a 2014 survey showed that 36% of 600 Uber drivers reported neither having a job, nor actively seeking work, before signing up to drive for Uber. (3) The presence of Uber resulted in increased regional employment. Drivers valued the flexibility and self-supervision enough to accept lower earnings than in previous jobs.

 2. Control of price and contractual terms: Uber exercises vertical control by dictating terms on which drivers transact and fixed prices - independent contractors are not allowed liberty to negotiate. 

 3. Horizontal friction: Assimilation and use of dominant position in one sectoral space to be able to expand into another such as in the case of Uber Eats. While Uber has since started to reduce it’s region specific operations by selling their holdings to the local leading brand, such as in China with Didi, they maintain equity shares as a control mechanism. 

 '''Where does the concept break down or fail to explain?'''

 A highly non-competitive market structure, is one wherein the buyer and seller cannot freely move within the market, curbed by inefficiency, high barrier of entry and lack of innovation. But modern day monopsony looks alot more different than this traditional concept. For one in this case, Uber was a new innovation brought into the market to outdate the old inefficient model of taxis with more control by its users. Moreover, monopsony can exist even in settings that appear easy to enter or leave.(4)

 While it may not be a pure monopsony, Uber does hold 71% market shares in the TNC space in the United States. However, the lack of barriers for entry saw Lyft introduced at the same time, where both provided similar models and impacts. This would be considered as a oligopsony, but these two players do not collude, instead users often use the applications independently do lack of lock-in. 

 '''References:'''

 (1) Koling, A., Michalek, J., Armanios, D. et al. Effects of Uber and Lyft on jobs, wages and GDP. Nat Cities 3, 700–707 (2026). https://doi.org/10.1038/s44284-026-00478-0 (2) Zha, L., Yin, Y. & Du, Y. Surge pricing and labor supply in the ridesourcing market. Transport. Res. Proc. 23, 2–21 (2017). (3) Levin, A. The Driver Roadmap: Where Uber Driver-Partners Have Been, and Where They’re Going (Benenson Strategy Group, 2014). (4) Manning, A. (2021). Monopsony in Labor Markets: A Review. ILR Review, 74(1), 3-26. https://doi.org/10.1177/0019793920922499.

===== [https://wiki.ivir.nl/index.php?title=Market_tipping_-_Rodrigue Market Tipping - Rodrigue] =====
== Vertical Integration by Pablo Latorre =='''1. Definition'''
“Vertical Integration” is the control by a single corporation of two or more consecutive steps of the same production chain of activity in such a manner that said corporation does not need to rely on others for them (1). In the context of digital markets, “Vertical Integration” usually refers to the position of a platform that operates a marketplace or infrastructure that other enterprises depend on and, at the same time, commercializes products or services in that same space (2). Therefore, in a “Vertical Integration” the platform is both, provider and competitor, of other enterprises.
 
'''2. Example: Amazon as a Marketplace'''
Amazon operates the largest online marketplace (3) in the world and, thus, thousands of small and medium size enterprises depend on it to connect with their costumers. At the same time, Amazon commercializes its own products on its marketplace (4), acting as a retailer and competitor to those enterprises. Such dual role allows Amazon to exercise competitive advantages: first, it can access detailed, non-public data on the enterprises’ sales, including products, prices, and costumer behavior; and second, it can decide the rules that define the products that appear first as the result of a costumer search.
In 2022 Amazon had to commit to the European Commission to not use the enterprises’ non-public data for its retail business and to treat equally rival offers in its “Buy Box” (5). Currently, the Digital Markets Act (DMA) establishes matching general obligations for designated gatekeepers (6).
 
'''3. Governance Implication'''
“Vertical Integration” allows platforms to exercise power over enterprises through digital architecture. When a platform operates a marketplace and competes in it, its ordinary decisions on digital architecture (i.e. on algorithms, searches, products visibility) can become tools for power and competition. In a sense, platforms are the ''de facto'' private regulators of markets in which they have clear commercial interests. Such state of affairs allows to understand a strategy change in antitrust cases. Traditionally, antitrust intervenes after the harm, however regulators are moving towards ''ex ante'' conduct rules (i.e. the DMA) (7).
 
'''4. Open Question'''
“Vertical Integration” clearly explains the incentive structure that drives platforms to favor their own products through the exercise of digital architecture mediated competitive advantages. However, “Vertical Integration” assumes that the line between one product (i.e. the marketplace, the search engine) and the other (i.e. the products, the websites competing on the platform) is clear when, in practice, this is not always so. For instance, nowadays when a user search something in Google Search, Google not only provides links to websites that contain the desired information but also the desired information itself with AI-generated summaries. In this case, two possible perspectives arise: on the one hand, that Google could be abusing its position to capture the online information market by displacing websites that depend on it; on the other hand, that Google could be simply improving its search engine and search itself because what users actually want is information and not links to it. In this example, “Vertical Integration” is incapable of settling the issue because it depends on a clear distinction between one product and the other (or one market and the other) and that is absent in this case. Moreover, even if in the presented case was possible to separate one product from the other (or one market from the other), “Vertical Integration” does not provide a clear criteria to establish when an “integration” is abusive exclusion of competitors and when it is legitimate and necessary innovation.
 
'''References'''
(1) “Vertical integration” (''Cambridge Dictionary'') [https://dictionary.cambridge.org/dictionary/english/vertical-integration https://dictionary.cambridge.org/dictionary/english/vertical-integration] accessed 4 October 2026.
(2) [https://academic.oup.com/antitrust/article/6/3/355/5034073?itm_medium=sidebar&itm_source=trendmd-widget&itm_campaign=Journal_of_Antitrust_Enforcement&itm_content=Journal_of_Antitrust_Enforcement_0 Bostoen Friso, “Online Platforms and Vertical Integration: The Return of Margin Squeeze” (2018) (6) ''Journal of Antitrust Enforcement'' 355.]
(3) Lovell Keir, “Top 10 Ecommerce Marketplaces in the World: 2026 Rankings by GMV, Users & Reach” (''ServeRetail'', 2 January 2026) [https://www.serveretail.com/blog/marketplace/the-10-largest-ecommerce-marketplaces-in-the-world-size-reach-and-cx-strategy/ https://www.serveretail.com/blog/marketplace/the-10-largest-ecommerce-marketplaces-in-the-world-size-reach-and-cx-strategy/] accessed 4 October 2026.
(4) Ellison Mara, “What are Amazon's Brands, Explained” (''Ir Mountaintopuniversity Ng'') [https://ir.mountaintopuniversity.edu.ng/article/what-are-amazon-s-brands-explained https://ir.mountaintopuniversity.edu.ng/article/what-are-amazon-s-brands-explained] accessed 4 October 2026.
(5) [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=oj:JOC_2023_087_R_0005 Summary of Commission Decision of 20 December 2022 Relating to a Proceeding under Article 102 of the Treaty on the Functioning of the European Union and Article 54 of the EEA Agreement (Cases AT.40462 – Amazon Marketplace, and AT.40703 – Amazon Buy Box) (2023) OJ C87.]
(6) [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv%3AOJ.L_.2022.265.01.0001.01.ENG&toc=OJ%3AL%3A2022%3A265%3ATOC Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act) [2022] OJ L265/1.]
(7) OECD, "Ex Ante Regulation and Competition in Digital Markets" (''OECD Roundtables on Competition Policy Papers, OECD Publishing 2021'') [https://doi.org/10.1787/c83e178d-en https://doi.org/10.1787/c83e178d-en] accessed 4 October 2026.

= Multi-homing (Juliette) =

Governance by markets

2026-10-06T16:54:04Z

Park Jihae: /* Economies of Scale (Jihae) */

= Glossary =
= [https://wiki.ivir.nl/index.php?title=Data_as_an_input_to_market_power Data as an input to market power] (Sergey) =In the present digital economy, data has emerged as a valuable economic commodity that entities leverage to gain a competitive edge over their rivals [[https://repository.nls.ac.in/cgi/viewcontent.cgi?article=1478&context=nlsblr 1]]. '''Data''' is an input into production, so businesses that control data can have market power and impede the entry of potential competitors. '''Market power''' refers to a firm's ability to influence the price, output, or other market conditions in its favour, without being constrained by competitive forces [[https://www.tutor2u.net/economics/reference/what-is-meant-by-market-power 2]].'''Data as an input to market power''' means that access to information can help a business limit the competitive pressure it faces. Information about searches, clicks or purchases can improve services and advertising. If competitors cannot obtain comparable information at a reasonable cost, they may struggle to offer an attractive alternative. This can allow a platform to raise prices or worsen quality or privacy protections without losing enough business to make doing so unprofitable. This matters even when a service is free, because competition also concerns advertising charges, personal information and the choices available. Data can be used repeatedly and by several businesses without being used up. However, access can be restricted through contracts and technical controls. Nevertheless, holding a large dataset alone does not establish market power.'''Google Search''' illustrates this mechanism. Google controls over 91% of global search engine traffic [[https://www.statista.com/statistics/1381664/worldwide-all-devices-market-share-of-search-engines/ 3]] and uses information about users’ searches and the results they click to improve the relevance of its rankings. A larger volume of searches gives it more opportunities to learn, including about unusual queries that smaller search engines encounter less often. Better results can attract further use, producing additional information and reinforcing the advantage. A new search engine may therefore struggle to attract users while lacking the information needed to improve its service. Data access can constitute a significant barrier to entry when this type of analytics is necessary to participate in a market, particularly when alternatives are limited. A platform’s control over data access can shape who is able to compete with it. By determining whether, and on what terms, rivals can access that information, a platform can influence the competitive opportunities available to others. This suggests that regulation should examine access to strategically important data alongside prices and market shares. Article 6(11) of the EU Digital Markets Act [[https://eur-lex.europa.eu/eli/reg/2022/1925/oj/eng#:~:text=giving%20their%20consent.-,11.%C2%A0,-The%20gatekeeper%20shall 4]] reflects this approach: certain large search providers must, on request, share specified search data with rival search providers on fair and non-discriminatory terms, with relevant personal data anonymised. The aim is to help rivals compete while protecting users. A data advantage does not, by itself, establish unlawful conduct.Would access to equivalent data enable a rival to compete effectively? The concept cannot answer this on its own. Additional data may eventually add little value, become outdated, or have substitutes. Firms also need the skills and computing resources to use it. Google’s default positions in browsers and devices can further restrict rivals’ access to users even if data become available. The unresolved issue is how much competitive advantage comes from data itself and how much comes from these other resources and arrangements. 

= Two Sided Markets (Ceren) =In two-sided markets, two sides of a market interact through an intermediary. These markets typically have some externality,<code class="mwt-code">Carl Shapiro and Hal R Varian, ''Information Rules: A Strategic Guide to the Network Economy'' (Harvard Business School Press 2010), 183.</code> where the decisions of the sides affect the other. The intermediary also has multitudes of effects on both sides, mainly identified in the related literature through its pricing.An example of a two sided market is payment systems, whereby the credit card providing bank is the intermediary between the customers and merchants. In this example, banks provide credit cards to customers to be utilized in their payments, and merchants accept payment via multiple credit card systems to accommodate payments from customers holding credit cards from such providers[https://doi.org/10.1257/jep.23.3.125 .]The main power of the intermediary materializes in their pricing toward the merchant (and the merchant’s bank in interchange fees in case of payment being transferred through different banks). Customers will be expected to be more loyal, holding a few credit cards, whereas merchants accept payment from many banks to accommodate all customers’ payment methods. Banks will provide more subsidies to customers to attract them and compete aggressively on the customer side, making their platform more desirable to work with by merchants, who will in turn be more accepting of the platform’s fees to gain the business of the larger volume of customers. However, dynamic pricing (increasing fees after gaining entry and loyalty) may be a way for these platforms to abuse their power.The concept may be derailed from these logical / analytical systems of operation or expected outcomes when public policy interferes with the market. For example, net neutrality rules regulate what internet service providers (the intermediary) can charge content providers (one side) who aim to reach the consumers (the other side). In this case the above considerations and the concept are halted.

= Switching costs and lock-in (Kate) =
[[File:Untitled Diagram.drawio (1).png|300px|thumb|Goods Matrix]]

= Excludability and rivalrousness (Paul)  =
=== Excludability and Rivalrousness ===
The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. 
'''Excludability''': Goods to which economic actors can technically or legally restrict access are considered to have ‘high excludability’. Conversely, goods to which access cannot be restricted are of ‘low excludability’, or, in absolute terms, ‘non-excludable’.
'''Rivalrousness''': Rivalrousness on the other hand refers to the finitude of the good in question. The greater the depletion of the stock, quantity, or amount is on account of one party’s consumption, the more rivalrous a good is. 
Tabulating the characteristics against one another allows for inference and definition of the type of good an object, service, or resource may constitute (see fig. 1). Excludability and Rivalrousness of goods can therethrough serve as a predictor for certain types of market failures: Goods which are non-excludable or of low excludability, but nonetheless costly to provide, are vulnerable to so-called free rider problem. Goods which are non-excludable, but highly rivalrous (common goods), may lead to the tragedy of the commons, that is over-exploitation. 

=== AI Training and the (non-)excludability of information ===
The unusual nature of excludability and rivalrousness of data further compromises the applicability of the goods classification built thereon. In traditional economics, a non-excludable and non-rivalrous good would be seen as a public good. This does apply insofar as data is vulnerable to corporate exploitation without commensurate indemnification. Data usage may however actually be anti-rival in the sense that increasing aggregate usage increases the value of the data (better labeling, easier access, previously undrawn conclusions, etc.), rather than diminish the ‘stock’ of data. Conversely, data may be rivalrous not in its absolute depletion by consumption, but rather the benefit or competitive advantage usage of that data may confer: If all AI firms are able to train on the exact same data (all available data), the utility of that data may still increase, but the competitive advantage of possessing that data is exhausted.

==== Free-Rider Problems & Public Data ====
As any party can benefit from publicly available data at no cost or low cost, the incentive for private market actors to fund its creation is comparatively weak. Consequently, public bodies (and less frequently open-source foundations and non-profits) often assume the responsibility for the provision of this type of data. A classical example is weather data and its creation by national weather agencies.

= Interoperability (Sisi) =

#What is interoperability?[[https://www.ibm.com/think/topics/interoperability 1]]Interoperability is a “standards-based” approach that enables different IT systems to communicate with minimal end-user interaction to exchange data and “share functionality”. Each system also allows the other system to “read, update, modify, and analyze” data with minimal human interaction. [[https://www.oracle.com/nl/interoperability/ 2]]Interoperability works by having different systems share data by employing the same standards for data formats (e.g., Extensible Markup Language or JavaScript Object Notation), exchange, and vocabulary to title data elements. These standards are agreed upon “in advance” by organizations, industry types, and user groups. Then, software developers build the system based on the model agreed upon.

#Concrete example from a digital technology or platformInteroperable systems are vital in healthcare, commerce, and public safety. A notable example includes the integration of Electronic Health Records (HER). This system allows different healthcare providers to securely access and share personal patient information regardless of the specific EHR system used.[[https://data.europa.eu/en/news-events/news/interoperability-solutions-bridging-systems-connected-future 3]] An example of such a system is the GNU Health Hospital Information System, which is a model employed by small private offices and large national public health systems.[[https://interoperable-europe.ec.europa.eu/collection/gnu-health/solution/gnu-health-hospital-information-system 4]]For example, a health care professional may access lab results and patient history data from various platforms to enable them to make a diagnosis.

#One governance implication: what does this concept explain about how platforms exercise power?Interoperability is a powerful tool because it helps provide a “seamless user experience” and optimizes data sharing between separate systems. It helps companies and organizations access data from multiple sources and improve efficiency, such as accelerating workflows and reducing the possibility of errors by pooling data from its sources.Interoperability standards are mandated by law on the technical side to ensure “critical systems” are compatible, especially in public safety and health. [[https://www.ibm.com/think/topics/interoperability 5]]In Europe, interoperability is governed by the European Health Data Space regulation (EHDS); the “first common EU data space dedicated to a specific sector as part of the European strategy for data.”This Regulation aims to benefit EU citizens such as patients, researchers, healthcare professionals, and industry players by establishing a common framework for the exchange of electronic health data within the EU. In addition, it enables certain data to be “reused” for public purpose and innovative scientific research. [[https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en 6]]

#Where does the concept break down or fail to explain somethingWhile the EDHS harmonizes a legal and technical framework for sharing EU data, its shortcoming is its gradual implementation. The EDHS entered into force in 2025; however, most of its obligations will not apply until 2029. This leaves four years for health care companies to use interoperability in a deregulated manner. [[https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en 7]]Furthermore, there are actual barriers to achieving full interoperability in healthcare, including data silos (which isolate data collections and hinder data exchange), lack of standardization (where one system records blood pressure as free text, another system stores it as a structured numeric value), and deficient infrastructure, which requires significant technological upgrading.The burden remains on health companies to decide which standards to adopt and how much to invest in stable technological infrastructure to strengthen their data governance and interoperability. [[https://www.tigahealth.com/how-to-overcome-healthcare-interoperability-challenges/ 8]]

= Economies of Scale (Jihae) =
=== Definition ===

= [[Economies of Scope (Mariona)]] =
= [[Network Effects (Indirect) - Sebastián]] =
= [[Monopsony - (Atmika)]] =
===== [https://wiki.ivir.nl/index.php?title=Market_tipping_-_Rodrigue Market Tipping - Rodrigue] =====
== Vertical Integration by Pablo Latorre =='''1. Definition'''“Vertical Integration” is the control by a single corporation of two or more consecutive steps of the same production chain of activity in such a manner that said corporation does not need to rely on others for them (1). In the context of digital markets, “Vertical Integration” usually refers to the position of a platform that operates a marketplace or infrastructure that other enterprises depend on and, at the same time, commercializes products or services in that same space (2). Therefore, in a “Vertical Integration” the platform is both, provider and competitor, of other enterprises. '''2. Example: Amazon as a Marketplace'''Amazon operates the largest online marketplace (3) in the world and, thus, thousands of small and medium size enterprises depend on it to connect with their costumers. At the same time, Amazon commercializes its own products on its marketplace (4), acting as a retailer and competitor to those enterprises. Such dual role allows Amazon to exercise competitive advantages: first, it can access detailed, non-public data on the enterprises’ sales, including products, prices, and costumer behavior; and second, it can decide the rules that define the products that appear first as the result of a costumer search.In 2022 Amazon had to commit to the European Commission to not use the enterprises’ non-public data for its retail business and to treat equally rival offers in its “Buy Box” (5). Currently, the Digital Markets Act (DMA) establishes matching general obligations for designated gatekeepers (6). '''3. Governance Implication'''“Vertical Integration” allows platforms to exercise power over enterprises through digital architecture. When a platform operates a marketplace and competes in it, its ordinary decisions on digital architecture (i.e. on algorithms, searches, products visibility) can become tools for power and competition. In a sense, platforms are the ''de facto'' private regulators of markets in which they have clear commercial interests. Such state of affairs allows to understand a strategy change in antitrust cases. Traditionally, antitrust intervenes after the harm, however regulators are moving towards ''ex ante'' conduct rules (i.e. the DMA) (7). '''4. Open Question'''“Vertical Integration” clearly explains the incentive structure that drives platforms to favor their own products through the exercise of digital architecture mediated competitive advantages. However, “Vertical Integration” assumes that the line between one product (i.e. the marketplace, the search engine) and the other (i.e. the products, the websites competing on the platform) is clear when, in practice, this is not always so. For instance, nowadays when a user search something in Google Search, Google not only provides links to websites that contain the desired information but also the desired information itself with AI-generated summaries. In this case, two possible perspectives arise: on the one hand, that Google could be abusing its position to capture the online information market by displacing websites that depend on it; on the other hand, that Google could be simply improving its search engine and search itself because what users actually want is information and not links to it. In this example, “Vertical Integration” is incapable of settling the issue because it depends on a clear distinction between one product and the other (or one market and the other) and that is absent in this case. Moreover, even if in the presented case was possible to separate one product from the other (or one market from the other), “Vertical Integration” does not provide a clear criteria to establish when an “integration” is abusive exclusion of competitors and when it is legitimate and necessary innovation. '''References'''(1) “Vertical integration” (''Cambridge Dictionary'') [https://dictionary.cambridge.org/dictionary/english/vertical-integration https://dictionary.cambridge.org/dictionary/english/vertical-integration] accessed 4 October 2026.(2) [https://academic.oup.com/antitrust/article/6/3/355/5034073?itm_medium=sidebar&itm_source=trendmd-widget&itm_campaign=Journal_of_Antitrust_Enforcement&itm_content=Journal_of_Antitrust_Enforcement_0 Bostoen Friso, “Online Platforms and Vertical Integration: The Return of Margin Squeeze” (2018) (6) ''Journal of Antitrust Enforcement'' 355.](3) Lovell Keir, “Top 10 Ecommerce Marketplaces in the World: 2026 Rankings by GMV, Users & Reach” (''ServeRetail'', 2 January 2026) [https://www.serveretail.com/blog/marketplace/the-10-largest-ecommerce-marketplaces-in-the-world-size-reach-and-cx-strategy/ https://www.serveretail.com/blog/marketplace/the-10-largest-ecommerce-marketplaces-in-the-world-size-reach-and-cx-strategy/] accessed 4 October 2026.(4) Ellison Mara, “What are Amazon's Brands, Explained” (''Ir Mountaintopuniversity Ng'') [https://ir.mountaintopuniversity.edu.ng/article/what-are-amazon-s-brands-explained https://ir.mountaintopuniversity.edu.ng/article/what-are-amazon-s-brands-explained] accessed 4 October 2026.(5) [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=oj:JOC_2023_087_R_0005 Summary of Commission Decision of 20 December 2022 Relating to a Proceeding under Article 102 of the Treaty on the Functioning of the European Union and Article 54 of the EEA Agreement (Cases AT.40462 – Amazon Marketplace, and AT.40703 – Amazon Buy Box) (2023) OJ C87.](6) [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv%3AOJ.L_.2022.265.01.0001.01.ENG&toc=OJ%3AL%3A2022%3A265%3ATOC Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act) [2022] OJ L265/1.](7) OECD, "Ex Ante Regulation and Competition in Digital Markets" (''OECD Roundtables on Competition Policy Papers, OECD Publishing 2021'') [https://doi.org/10.1787/c83e178d-en https://doi.org/10.1787/c83e178d-en] accessed 4 October 2026.

Governance by markets

2026-10-06T16:47:34Z

Park Jihae: /* "Economies of Scale (Jihae)" */

= Glossary =
= [https://wiki.ivir.nl/index.php?title=Data_as_an_input_to_market_power Data as an input to market power] (Sergey) =In the present digital economy, data has emerged as a valuable economic commodity that entities leverage to gain a competitive edge over their rivals [[https://repository.nls.ac.in/cgi/viewcontent.cgi?article=1478&context=nlsblr 1]]. '''Data''' is an input into production, so businesses that control data can have market power and impede the entry of potential competitors. '''Market power''' refers to a firm's ability to influence the price, output, or other market conditions in its favour, without being constrained by competitive forces [[https://www.tutor2u.net/economics/reference/what-is-meant-by-market-power 2]].'''Data as an input to market power''' means that access to information can help a business limit the competitive pressure it faces. Information about searches, clicks or purchases can improve services and advertising. If competitors cannot obtain comparable information at a reasonable cost, they may struggle to offer an attractive alternative. This can allow a platform to raise prices or worsen quality or privacy protections without losing enough business to make doing so unprofitable. This matters even when a service is free, because competition also concerns advertising charges, personal information and the choices available. Data can be used repeatedly and by several businesses without being used up. However, access can be restricted through contracts and technical controls. Nevertheless, holding a large dataset alone does not establish market power.'''Google Search''' illustrates this mechanism. Google controls over 91% of global search engine traffic [[https://www.statista.com/statistics/1381664/worldwide-all-devices-market-share-of-search-engines/ 3]] and uses information about users’ searches and the results they click to improve the relevance of its rankings. A larger volume of searches gives it more opportunities to learn, including about unusual queries that smaller search engines encounter less often. Better results can attract further use, producing additional information and reinforcing the advantage. A new search engine may therefore struggle to attract users while lacking the information needed to improve its service. Data access can constitute a significant barrier to entry when this type of analytics is necessary to participate in a market, particularly when alternatives are limited. A platform’s control over data access can shape who is able to compete with it. By determining whether, and on what terms, rivals can access that information, a platform can influence the competitive opportunities available to others. This suggests that regulation should examine access to strategically important data alongside prices and market shares. Article 6(11) of the EU Digital Markets Act [[https://eur-lex.europa.eu/eli/reg/2022/1925/oj/eng#:~:text=giving%20their%20consent.-,11.%C2%A0,-The%20gatekeeper%20shall 4]] reflects this approach: certain large search providers must, on request, share specified search data with rival search providers on fair and non-discriminatory terms, with relevant personal data anonymised. The aim is to help rivals compete while protecting users. A data advantage does not, by itself, establish unlawful conduct.Would access to equivalent data enable a rival to compete effectively? The concept cannot answer this on its own. Additional data may eventually add little value, become outdated, or have substitutes. Firms also need the skills and computing resources to use it. Google’s default positions in browsers and devices can further restrict rivals’ access to users even if data become available. The unresolved issue is how much competitive advantage comes from data itself and how much comes from these other resources and arrangements. 

= Two Sided Markets (Ceren) =In two-sided markets, two sides of a market interact through an intermediary. These markets typically have some externality,<code class="mwt-code">Carl Shapiro and Hal R Varian, ''Information Rules: A Strategic Guide to the Network Economy'' (Harvard Business School Press 2010), 183.</code> where the decisions of the sides affect the other. The intermediary also has multitudes of effects on both sides, mainly identified in the related literature through its pricing.An example of a two sided market is payment systems, whereby the credit card providing bank is the intermediary between the customers and merchants. In this example, banks provide credit cards to customers to be utilized in their payments, and merchants accept payment via multiple credit card systems to accommodate payments from customers holding credit cards from such providers[https://doi.org/10.1257/jep.23.3.125 .]The main power of the intermediary materializes in their pricing toward the merchant (and the merchant’s bank in interchange fees in case of payment being transferred through different banks). Customers will be expected to be more loyal, holding a few credit cards, whereas merchants accept payment from many banks to accommodate all customers’ payment methods. Banks will provide more subsidies to customers to attract them and compete aggressively on the customer side, making their platform more desirable to work with by merchants, who will in turn be more accepting of the platform’s fees to gain the business of the larger volume of customers. However, dynamic pricing (increasing fees after gaining entry and loyalty) may be a way for these platforms to abuse their power.The concept may be derailed from these logical / analytical systems of operation or expected outcomes when public policy interferes with the market. For example, net neutrality rules regulate what internet service providers (the intermediary) can charge content providers (one side) who aim to reach the consumers (the other side). In this case the above considerations and the concept are halted.

= Switching costs and lock-in (Kate) =
[[File:Untitled Diagram.drawio (1).png|300px|thumb|Goods Matrix]]

= Excludability and rivalrousness (Paul)  =
=== Excludability and Rivalrousness ===
The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. 
'''Excludability''': Goods to which economic actors can technically or legally restrict access are considered to have ‘high excludability’. Conversely, goods to which access cannot be restricted are of ‘low excludability’, or, in absolute terms, ‘non-excludable’.
'''Rivalrousness''': Rivalrousness on the other hand refers to the finitude of the good in question. The greater the depletion of the stock, quantity, or amount is on account of one party’s consumption, the more rivalrous a good is. 
Tabulating the characteristics against one another allows for inference and definition of the type of good an object, service, or resource may constitute (see fig. 1). Excludability and Rivalrousness of goods can therethrough serve as a predictor for certain types of market failures: Goods which are non-excludable or of low excludability, but nonetheless costly to provide, are vulnerable to so-called free rider problem. Goods which are non-excludable, but highly rivalrous (common goods), may lead to the tragedy of the commons, that is over-exploitation. 

=== AI Training and the (non-)excludability of information ===
The unusual nature of excludability and rivalrousness of data further compromises the applicability of the goods classification built thereon. In traditional economics, a non-excludable and non-rivalrous good would be seen as a public good. This does apply insofar as data is vulnerable to corporate exploitation without commensurate indemnification. Data usage may however actually be anti-rival in the sense that increasing aggregate usage increases the value of the data (better labeling, easier access, previously undrawn conclusions, etc.), rather than diminish the ‘stock’ of data. Conversely, data may be rivalrous not in its absolute depletion by consumption, but rather the benefit or competitive advantage usage of that data may confer: If all AI firms are able to train on the exact same data (all available data), the utility of that data may still increase, but the competitive advantage of possessing that data is exhausted.

==== Free-Rider Problems & Public Data ====
As any party can benefit from publicly available data at no cost or low cost, the incentive for private market actors to fund its creation is comparatively weak. Consequently, public bodies (and less frequently open-source foundations and non-profits) often assume the responsibility for the provision of this type of data. A classical example is weather data and its creation by national weather agencies.

= Interoperability (Sisi) =

#What is interoperability?[[https://www.ibm.com/think/topics/interoperability 1]]Interoperability is a “standards-based” approach that enables different IT systems to communicate with minimal end-user interaction to exchange data and “share functionality”. Each system also allows the other system to “read, update, modify, and analyze” data with minimal human interaction. [[https://www.oracle.com/nl/interoperability/ 2]]Interoperability works by having different systems share data by employing the same standards for data formats (e.g., Extensible Markup Language or JavaScript Object Notation), exchange, and vocabulary to title data elements. These standards are agreed upon “in advance” by organizations, industry types, and user groups. Then, software developers build the system based on the model agreed upon.

#Concrete example from a digital technology or platformInteroperable systems are vital in healthcare, commerce, and public safety. A notable example includes the integration of Electronic Health Records (HER). This system allows different healthcare providers to securely access and share personal patient information regardless of the specific EHR system used.[[https://data.europa.eu/en/news-events/news/interoperability-solutions-bridging-systems-connected-future 3]] An example of such a system is the GNU Health Hospital Information System, which is a model employed by small private offices and large national public health systems.[[https://interoperable-europe.ec.europa.eu/collection/gnu-health/solution/gnu-health-hospital-information-system 4]]For example, a health care professional may access lab results and patient history data from various platforms to enable them to make a diagnosis.

#One governance implication: what does this concept explain about how platforms exercise power?Interoperability is a powerful tool because it helps provide a “seamless user experience” and optimizes data sharing between separate systems. It helps companies and organizations access data from multiple sources and improve efficiency, such as accelerating workflows and reducing the possibility of errors by pooling data from its sources.Interoperability standards are mandated by law on the technical side to ensure “critical systems” are compatible, especially in public safety and health. [[https://www.ibm.com/think/topics/interoperability 5]]In Europe, interoperability is governed by the European Health Data Space regulation (EHDS); the “first common EU data space dedicated to a specific sector as part of the European strategy for data.”This Regulation aims to benefit EU citizens such as patients, researchers, healthcare professionals, and industry players by establishing a common framework for the exchange of electronic health data within the EU. In addition, it enables certain data to be “reused” for public purpose and innovative scientific research. [[https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en 6]]

#Where does the concept break down or fail to explain somethingWhile the EDHS harmonizes a legal and technical framework for sharing EU data, its shortcoming is its gradual implementation. The EDHS entered into force in 2025; however, most of its obligations will not apply until 2029. This leaves four years for health care companies to use interoperability in a deregulated manner. [[https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space-regulation-ehds_en 7]]Furthermore, there are actual barriers to achieving full interoperability in healthcare, including data silos (which isolate data collections and hinder data exchange), lack of standardization (where one system records blood pressure as free text, another system stores it as a structured numeric value), and deficient infrastructure, which requires significant technological upgrading.The burden remains on health companies to decide which standards to adopt and how much to invest in stable technological infrastructure to strengthen their data governance and interoperability. [[https://www.tigahealth.com/how-to-overcome-healthcare-interoperability-challenges/ 8]]

= Economies of Scale (Jihae) =

= [[Economies of Scope (Mariona)]] =
= [[Network Effects (Indirect) - Sebastián]] =
= [[Monopsony - (Atmika)]] =
===== [https://wiki.ivir.nl/index.php?title=Market_tipping_-_Rodrigue Market Tipping - Rodrigue] =====
== Vertical Integration by Pablo Latorre =='''1. Definition'''“Vertical Integration” is the control by a single corporation of two or more consecutive steps of the same production chain of activity in such a manner that said corporation does not need to rely on others for them (1). In the context of digital markets, “Vertical Integration” usually refers to the position of a platform that operates a marketplace or infrastructure that other enterprises depend on and, at the same time, commercializes products or services in that same space (2). Therefore, in a “Vertical Integration” the platform is both, provider and competitor, of other enterprises. '''2. Example: Amazon as a Marketplace'''Amazon operates the largest online marketplace (3) in the world and, thus, thousands of small and medium size enterprises depend on it to connect with their costumers. At the same time, Amazon commercializes its own products on its marketplace (4), acting as a retailer and competitor to those enterprises. Such dual role allows Amazon to exercise competitive advantages: first, it can access detailed, non-public data on the enterprises’ sales, including products, prices, and costumer behavior; and second, it can decide the rules that define the products that appear first as the result of a costumer search.In 2022 Amazon had to commit to the European Commission to not use the enterprises’ non-public data for its retail business and to treat equally rival offers in its “Buy Box” (5). Currently, the Digital Markets Act (DMA) establishes matching general obligations for designated gatekeepers (6). '''3. Governance Implication'''“Vertical Integration” allows platforms to exercise power over enterprises through digital architecture. When a platform operates a marketplace and competes in it, its ordinary decisions on digital architecture (i.e. on algorithms, searches, products visibility) can become tools for power and competition. In a sense, platforms are the ''de facto'' private regulators of markets in which they have clear commercial interests. Such state of affairs allows to understand a strategy change in antitrust cases. Traditionally, antitrust intervenes after the harm, however regulators are moving towards ''ex ante'' conduct rules (i.e. the DMA) (7). '''4. Open Question'''“Vertical Integration” clearly explains the incentive structure that drives platforms to favor their own products through the exercise of digital architecture mediated competitive advantages. However, “Vertical Integration” assumes that the line between one product (i.e. the marketplace, the search engine) and the other (i.e. the products, the websites competing on the platform) is clear when, in practice, this is not always so. For instance, nowadays when a user search something in Google Search, Google not only provides links to websites that contain the desired information but also the desired information itself with AI-generated summaries. In this case, two possible perspectives arise: on the one hand, that Google could be abusing its position to capture the online information market by displacing websites that depend on it; on the other hand, that Google could be simply improving its search engine and search itself because what users actually want is information and not links to it. In this example, “Vertical Integration” is incapable of settling the issue because it depends on a clear distinction between one product and the other (or one market and the other) and that is absent in this case. Moreover, even if in the presented case was possible to separate one product from the other (or one market from the other), “Vertical Integration” does not provide a clear criteria to establish when an “integration” is abusive exclusion of competitors and when it is legitimate and necessary innovation. '''References'''(1) “Vertical integration” (''Cambridge Dictionary'') [https://dictionary.cambridge.org/dictionary/english/vertical-integration https://dictionary.cambridge.org/dictionary/english/vertical-integration] accessed 4 October 2026.(2) [https://academic.oup.com/antitrust/article/6/3/355/5034073?itm_medium=sidebar&itm_source=trendmd-widget&itm_campaign=Journal_of_Antitrust_Enforcement&itm_content=Journal_of_Antitrust_Enforcement_0 Bostoen Friso, “Online Platforms and Vertical Integration: The Return of Margin Squeeze” (2018) (6) ''Journal of Antitrust Enforcement'' 355.](3) Lovell Keir, “Top 10 Ecommerce Marketplaces in the World: 2026 Rankings by GMV, Users & Reach” (''ServeRetail'', 2 January 2026) [https://www.serveretail.com/blog/marketplace/the-10-largest-ecommerce-marketplaces-in-the-world-size-reach-and-cx-strategy/ https://www.serveretail.com/blog/marketplace/the-10-largest-ecommerce-marketplaces-in-the-world-size-reach-and-cx-strategy/] accessed 4 October 2026.(4) Ellison Mara, “What are Amazon's Brands, Explained” (''Ir Mountaintopuniversity Ng'') [https://ir.mountaintopuniversity.edu.ng/article/what-are-amazon-s-brands-explained https://ir.mountaintopuniversity.edu.ng/article/what-are-amazon-s-brands-explained] accessed 4 October 2026.(5) [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=oj:JOC_2023_087_R_0005 Summary of Commission Decision of 20 December 2022 Relating to a Proceeding under Article 102 of the Treaty on the Functioning of the European Union and Article 54 of the EEA Agreement (Cases AT.40462 – Amazon Marketplace, and AT.40703 – Amazon Buy Box) (2023) OJ C87.](6) [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv%3AOJ.L_.2022.265.01.0001.01.ENG&toc=OJ%3AL%3A2022%3A265%3ATOC Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act) [2022] OJ L265/1.](7) OECD, "Ex Ante Regulation and Competition in Digital Markets" (''OECD Roundtables on Competition Policy Papers, OECD Publishing 2021'') [https://doi.org/10.1787/c83e178d-en https://doi.org/10.1787/c83e178d-en] accessed 4 October 2026.

Governance by markets

2026-10-02T14:04:50Z

Park Jihae: Undid revision 124097 by Park Jihae (talk)

= Glossary =
= Data as an input to market power (Sergey) =
Draft In the present digital economy, data has emerged as a valuable economic commodity that entities leverage to gain a competitive edge over their rivals. Today, all enterprises in the digital economy have data-driven business models; that is, these entities thrive by collecting, processing, and analysing data on a large scale. Due to such a data-centric business model, it is crucial to consider the commercial use of data when assessing competitive concerns in digital markets. Economists point to economies of scale in information and the dominance of large, data-intensive firms as evidence that the unequal accumulation of data is responsible for a decline in competition. As a result, the data-rich firm exerts more market power.  Data can be considered as a metric to assess the market power of a digital platform. Market power is a firm’s ability to act independently of its competitors. Data is an input into production, so firms that control data can have market power and impede the entry of would-be competitors. Data affect the competitive dynamics of markets in four main ways. First, they serve as an important input for products and services. Second, they may constitute a dimension of quality from the perspective of consumers (both in terms of the amount of information available to consumers, and the amount of personal data collected from consumers). Third, data may themselves constitute a product that leads to the creation of new markets. Finally, they may be used to facilitate consumer decision making.Data access can constitute a significant barrier to entry when this type of analytics is necessary to participate in a market, particularly when alternatives are limited. For example, Google offers free search engine services to acquire data and uses this data for targeted online advertisements to generate revenue.

....

= Two Sided Markets (Ceren) =
= Switching costs and lock-in (Kate) =
[[File:Untitled Diagram.drawio (1).png|300px|thumb|Goods Matrix]]

= Excludability and rivalrousness (Paul) =
<h3 class="mwt-heading" >Excludability and Rivalrousness</h3>
The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. 
Excludability: Goods to which economic actors can technically or legally restrict access are considered to have ‘high excludability’. Conversely, goods to which access cannot be restricted are of ‘low excludability’, or, in absolute terms, ‘non-excludable’.
Rivalrousness: Rivalrousness on the other hand refers to the finitude of the good in question. The greater the depletion of the stock, quantity, or amount is on account of one party’s consumption, the more rivalrous a good is. 
Tabulating the characteristics against one another allows for inference and definition of the type of good an object, service, or resource may constitute (see fig. 1). Excludability and Rivalrousness of goods can therethrough serve as a predictor for certain types of market failures: Goods which are non-excludable or of low excludability, but nonetheless costly to provide, are vulnerable to so-called free rider problem. Goods which are non-excludable, but highly rivalrous (common goods), may lead to the tragedy of the commons, that is over-exploitation. 
<h3 class="mwt-heading" >AI Training and the (non-)excludability of information.</h3>
The unusual nature of excludability and rivalrousness of data further compromises the applicability of the goods classification built thereon. In traditional economics, a non-excludable and non-rivalrous good would be seen as a public good. This does apply insofar as data is vulnerable to corporate exploitation without commensurate indemnification. Data usage may however actually be anti-rival in the sense that increasing aggregate usage increases the value of the data (better labeling, easier access, previously undrawn conclusions, etc.), rather than diminish the ‘stock’ of data. 

= Interoperability (Sisi) =

Governance by markets

2026-10-02T13:48:41Z

Park Jihae:

== Glossary ==
== Data as an input to market power (Sergey) ==
In the present digital economy, data has emerged as a valuable economic commodity that entities leverage to gain a competitive edge over their rivals. Today, all enterprises in the digital economy have data-driven business models; that is, these entities thrive by collecting, processing, and analysing data on a large scale. Due to such a data-centric business model, it is crucial to consider the commercial use of data when assessing competitive concerns in digital markets. Economists point to economies of scale in information and the dominance of large, data-intensive firms as evidence that the unequal accumulation of data is responsible for a decline in competition. As a result, the data-rich firm exerts more market power.  Data can be considered as a metric to assess the market power of a digital platform. Market power is a firm’s ability to act independently of its competitors. Data is an input into production, so firms that control data can have market power and impede the entry of would-be competitors. Data affect the competitive dynamics of markets in four main ways. First, they serve as an important input for products and services. Second, they may constitute a dimension of quality from the perspective of consumers (both in terms of the amount of information available to consumers, and the amount of personal data collected from consumers). Third, data may themselves constitute a product that leads to the creation of new markets. Finally, they may be used to facilitate consumer decision making.Data access can constitute a significant barrier to entry when this type of analytics is necessary to participate in a market, particularly when alternatives are limited. For example, Google offers free search engine services to acquire data and uses this data for targeted online advertisements to generate revenue.
== Two Sided Markets (Ceren) ==
== Switching costs and lock-in (Kate) ==
[File:Untitled Diagram.drawio (1).png|300px|thumb|Goods Matrix]]
== Excludability and rivalrousness (Paul) ==
=== Excludability and Rivalrousness ===
The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. Excludability: Goods to which economic actors can technically or legally restrict access are considered to have ‘high excludability’. Conversely, goods to which access cannot be restricted are of ‘low excludability’, or, in absolute terms, ‘non-excludable’.Rivalrousness: Rivalrousness on the other hand refers to the finitude of the good in question. The greater the depletion of the stock, quantity, or amount is on account of one party’s consumption, the more rivalrous a good is. Tabulating the characteristics against one another allows for inference and definition of the type of good an object, service, or resource may constitute (see fig. 1). Excludability and Rivalrousness of goods can therethrough serve as a predictor for certain types of market failures: Goods which are non-excludable or of low excludability, but nonetheless costly to provide, are vulnerable to so-called free rider problem. Goods which are non-excludable, but highly rivalrous (common goods), may lead to the tragedy of the commons, that is over-exploitation. 
=== AI Training and the (non-)excludability of information. ===
The unusual nature of excludability and rivalrousness of data further compromises the applicability of the goods classification built thereon. In traditional economics, a non-excludable and non-rivalrous good would be seen as a public good. This does apply insofar as data is vulnerable to corporate exploitation without commensurate indemnification. Data usage may however actually be anti-rival in the sense that increasing aggregate usage increases the value of the data (better labeling, easier access, previously undrawn conclusions, etc.), rather than diminish the ‘stock’ of data. 
== Interoperability (Sisi) ==
== Economies of Scale (Jihae) ==

Infrastructures: Digital Sovereignty

2026-09-25T10:37:13Z

Park Jihae: /* The United States Sovereignty Strategy: AI Dominance */

= Infrastructures: Digital Sovereignty =
Digital Sovereignty in the European Union is a [https://www.tandfonline.com/doi/full/10.1080/17579961.2026.2705118 term used to convey ] a broad array of policy ambitions in relation to digital technology development and use: the urgency to regain control over data, infrastructures, and (critical) technologies, the establishment of strategic autonomy, the conclusion of partnerships with geopolitical allies, and the reduction of critical dependencies. According to the Digital Decade Policy Programme [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52023SC0570 Implementation Report] of 2023, digital sovereignty refers to the EU's "ability to act independently in the digital world and therefore it constitutes a crucial means to safeguard [EU] values".

= Brazil =
In progress
= China =
'''Introduction'''

''''''

“Digital Sovereignty” is an increasingly important concept for understanding how States exercise political and economic control over digital infrastructures and technologies. Since the massive introduction of the Internet in 1994, Chinese academic and political discourse has formulated notions of cyber, information, and network sovereignty in direct relation to digital and technological independence.In the early 2000s, Chinese authorities focused primarily on controlling data flows rather than developing national technological champions. Indeed, at the time Google held 40% of the Chinese Internet search market and companies like Yahoo maintained substantial equity participation in Chinese firms like Alibaba, all of which ended in 2009 when the Urumqi riots triggered China’s decision to permanently block platforms like Facebook and YouTube, prompting Google’s withdrawal from the Internet search market.In 2010 “Cyber Sovereignty” was crystallized as an organizing principle of Chinese digital policy following historical concerns about information control and a national determination to resist foreign influence over China. This political and economic landscape cemented the nation’s ambition to achieve full digital sovereignty.Today Chinese digital sovereignty is incorporated into its legal framework, particularly in the Cybersecurity Law and the Data Security Law (both from 2017). These laws establish data localization requirements and security assessments mechanisms for cross-border transfers. Moreover, they aim for China to develop the autonomy and capacity necessary to exercise full control over its data.

 '''Cybersecurity Law'''

 The Cybersecurity Law is a comprehensive legal framework that regulates cyberspace and information infrastructure security in China, governing information networks build, operation, and use within its territory (article 2). The law covers a wide range of issues, including critical information infrastructure management, cybersecurity, and data protection.In the area of critical information infrastructure management, the law applies to “Critical Information Infrastructures” (CIIs) and “Critical Information Infrastructure Operators” (CIIOs) which include information infrastructure and its operators in sectors such as energy, finance, healthcare, telecommunications, transportation, and governmental services.CIIOs have the obligation to adopt enhanced security measures and protocols to safeguard information infrastructure and their data. Additionally, CIIOs must perform regular security assessments and report their results to State agencies, implement protective measures and protocols, and cooperate with State agencies, among others (articles 23, 36, 37, 38, and 40).In the area of cybersecurity, the law applies to all information infrastructure operators (not only to CIIOs) which have the obligation to adopt comprehensive cybersecurity measures which include monitoring their infrastructure security, safeguarding against data breaches, impeding misuse of their infrastructures for illegal activities, prepare incident response plans, report cybersecurity incidents, and cooperate with State agencies (articles 23, 27, 30 and 51).Finally, in the area of data protection, the law applies to CIIOs in the case of data localization, and to all information infrastructure operators in the case of personal information.CIIOs have the obligation to store within China all critical data and personal information they collect or generate in the country’s territory. Additionally, CIIOs must pass a security evaluation and get State authorization to transfer data abroad (article 39).Moreover, all information infrastructure operators have the obligation to follow guidelines for collecting, processing, and storing personal information. In that sense, information infrastructure operators must secure user consent for gathering personal information and disclose it, and warrant its protection by implementing measures to guard against unauthorized or illegal access and misuse including breach notification (article 42).Additionally, the law establishes obligations for the State including to balance cybersecurity and information infrastructure development (article 4), continuously improve the national cybersecurity strategy (article 5), and deal with cybersecurity risks and threats (article 6).In general, the law provides the State extensive powers to access data held by information infrastructure operators functioning within China, including the authority to require assistance and support in matters of national security or criminal investigations.Infringing the law can result in sanctions for information infrastructure operators which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a threat to public order or national security, infringements can result in criminal prosecution and career suspension (articles 61, 63, 64, 65, 66, 68, 69, 71, and 76).

 '''Data Security Law'''

 The Data Security Law is a comprehensive legal framework that regulates data management for the purposes of national sovereignty, security, and social and economic development.It establishes the obligation of the State to develop a data classification system based on its importance to social and economic development and the damage its alteration, destruction, leakage, or illicit use would cause to national security, public interest, or people or organizations’ rights and interests. Such classification allows the State to apply varying protection levels depending on the data’s sensitivity and relevance, where “central data of the State” or data deemed important to national security, major public interests, critical economic sectors, or important aspects of people livelihood is given the highest level of protection (article 21). Other categories of data are “important data” and “general data” which are not defined or developed in the law but in complementary regulations.The law applies to data management carried out within China’s territory, and generates legal responsibility for data management outside if it harms national security, public interest, or people or organizations’ rights and interests (article 2). Data processors of “important data” have the obligation to designate officers responsible for data security, to conduct risk evaluations periodically reporting the results to State authorities, and to report security incidents to users and State authorities, among others (articles 27, 29 and 30). Moreover, data processors are impeded to transfer data to foreign judicial authorities without authorization (article 36).Additionally, the law establishes obligations for the State including to encourage the use of data for economic and innovation purposes, specifically for the development of new data-driven industries as long as they comply with the law (article 1). Also, it has the obligation to coordinate the national data security, and draft and implement the national data security strategy (article 5).In general the law enables the State to collect data if it is necessary to safeguard national security or investigate crimes, subject to approval procedures (article 35). Data processors and individuals are obliged to cooperate, and refusal is subject to fines (article 48).Infringing the law can result in sanctions which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a crime or a threat to national sovereignty, security, or economic development interests, infringements can result in criminal prosecution (articles 45 and 52).

 '''Personal Information Protection Law'''

 The Personal Information Protection Law of 2021 is a comprehensive legal framework that protects personal information similarly to the European General Data Protection Regulation (GDPR). Although the law is not directly related to China’s digital sovereignty, it establishes obligations relevant to it: State agencies, critical infrastructure operators, and data processors that collect, process, or use data above a certain threshold (defined by State authorities) must store within China all personal information they collect or generate in the country’s territory. Additionally, they must pass a security evaluation and get State authorization to transfer data abroad (article 36 and 40).Moreover, the State is allowed to blacklist individuals and organizations and deny them access to personal information generated or collected in China if it deems that they acted against Chinese people rights or interests, as well as to adopt reciprocal measures against foreign States if it deems that they discriminated against China regarding the protection of personal information (articles 42 and 43).

 '''China’s State Centered Concept of Digital Sovereignty'''

 The Cybersecurity Law and the Data Security Law are integral parts of China’s strategy to strengthen its digital sovereignty.China’s concept of digital sovereignty is characterized for its strong State-centered approach in which the State holds ultimate and supreme authority in the digital space, controlling data flows and content sharing within its borders. In a sense, China’s concept of digital sovereignty is defensive: it is essential to protect China’s social, political, and economic architecture from destabilization attempts by foreign actors (particularly the United States), which explains why the Cybersecurity Law establishes the obligation of private actors to “abide by the Constitution and laws, observe public order, respect social morality, and (...) not (...) use the Internet to engage in activities that endanger national security, honor, or interests (...), incite subversion of State power or overthrow of the socialist system (...), incite secession or undermine national unity (...), (or) fabricate or disseminate false information to disrupt economic and social order” (article 13), and why the the Data Security Law establishes as an objective to “preserve State sovereignty, security, and development interests” (article 1).Such a concept requires the achievement of two policy objectives: territorialization and indigenization. Through territorialization China seeks to replicate its national borders in cyberspace, ensuring that online processes impacting important national interests take place within Chinese boundaries, and unwanted practices or operations can be barred from entering. Through indigenization, in turn, it strives to replace foreign actors and technologies with local equivalents, reducing foreign dependence.In this context, the Cybersecurity Law and the Data Security Law aim to protect China’s digital sovereignty through both policy objectives. In the case of territorialization, they establish the objective of protecting sovereignty in cyberspace and preventing and responding to cybersecurity risks and threats coming from inside and outside China; the obligation of CIIOs to store in China all personal information and important data that they generate or collect inside the country’s borders, and to conduct security evaluations and obtain State permission before transferring them abroad; the obligation of data processors to not transfer data to foreign judicial authorities without State authorization; the responsibility of data processors for activities carried out outside China; and the capacity of the State to block prohibited information, to temporarily restrict communications in specific areas for national security reasons, and to adopt countermeasures against foreign States that discriminate against China in data investment or trade (Cybersecurity Law articles 1, 6, 39, 50 and 60, and Data Security Law articles 2, 26 and 36).In the case of indigenization, the laws establish mechanisms of promotion and access control that indirectly privilege national providers. As an example, they establish the obligation of critical infrastructure operators to purchase information network products and services approved by a national security assessment; the obligation of critical information network products providers to offer products that are certified or comply with national standards; the obligation of the State to support Chinese cybersecurity industries and projects, to support Chinese Artificial Intelligence (AI) industries, to support data development and security industrial systems, to support the creation of national data markets, and to develop a national strategy for big data and data infrastructure (Cybersecurity Law articles 17, 20, 25 and 37, Data Security Law articles 14, 16 and 19).These efforts are related to the “Great Firewall”, the Chinese State system for censoring and controlling Internet traffic within its borders which is, arguably, its most visible instrument of territorialization, drawing an effective Internet frontier between China and the rest of the world.In summary, China's digital sovereignty concept demands asserting control over data generated within its borders and to protect sensitive information from foreign interference and cyber-attacks while simultaneously reinforcing its surveillance capacity and its ability to manage information flows for “social stability”.In this sense, China's data localization mandates are designed to nurture domestic technological independence, and they achieve this by ensuring that valuable data generated within China remains in the hands of Chinese firms rather than foreign competitors. This treats data itself as a strategic economic asset. China simultaneously fuels domestic innovation and strengthens its digital economy and denies foreign firms the informational advantage they would otherwise hold by keeping the economic advantage within its borders.Finally, on the regulation of foreign companies, laws such as the Data Security Law gives Chinese authorities a direct enforcement mechanism over foreign entities operating domestically, compelling compliance with localization requirements and government data access provisions.Therefore, each Chinese law was built to accomplish a specific stated goal, and each demonstrably does so, which makes China’s digital sovereignty regime distinct because China’s legal scheme was constructed with sufficient State control to ensure regulatory instruments and its sovereignty goals remained tightly aligned.

 '''China vs. the European Union: Major Digital Sovereigns'''

 The European Union (EU) and China represent two distinct approaches to digital sovereignty. The EU contrasts to China’s reactive, security-driven model, since EU digital sovereignty emerged from a supranational, rights-based traditional rather than an authoritarian one. The EU’s push to digital sovereignty began after 2013 when Edward Snowden’s revelation of NSA interception of European communications exposed the EU's dependence on foreign technology and triggered a wave of regulatory safeguards. This led to the creation of the GDPR adopted in 2016, which enshrined enforceable individual rights, including access to, and objection to, the processing of European customer data. The EU continued its trajectory by developing two consumer protection directives in 2019, in addition to a series of antitrust proceedings against hyperscale’s such as Google, Amazon, and Apple between 2015 and 2020. In 2024, the AI Act was created to harmonize AI development and deployment rules according to EU values and fundamental rights. More recently, the EU initiated infrastructure initiatives such as Gaia X for cloud sovereignty and the Chips Act for semiconductor independence.Despite this extensive regulatory framework, China's claim to digital sovereignty may be considered more consolidated than the EU's because it fuses legal codification with direct State control over technology, information, and infrastructure in a way the EU market-correcting model does not.As European Commission President von der Leyen stated in 2019, ‘it is not too late to achieve technological sovereignty in some critical technology areas’. In actuality, the EU has been catching up to China, which spent the last decade tightening its state authority over its digital infrastructure before the EU began treating its own technology dependence as a vulnerability. China's more recent legislative activity has extended this consolidation into the international stage as well as domestically. In 2024, the Shanghai Declaration on Global AI governance, issued by China's Ministry of Foreign Affairs, committed signatories to developing AI under principles of safety, reliability, and controllability, while asserting each country's right to regulate AI according to its own national conditions. This language simultaneously projects openness to international cooperation while defending non-foreign interference, the logic underpinning China's cyber-sovereignity doctrine.This declaration is not merely aspirational: it is anchored in binding domestic law, specifically Article 20 of China's 2025 amended Security Law, which directs the state to support AI research, infrastructure, ethical norms, and risk supervision.Therefore, where the EU’s sovereignty is exercised primarily through regulating outside actors within its internal market, China's sovereignty is exercised through direct state-led development, information control, and an explicit fusion of domestic statute and international declaratory positioning. This makes China's model arguably more total in its sovereignty, though less extensive in scope, over the digital domain it governs compared to the EU.

'''References'''

''''''(1) Thumfart, J. & Cong. W., “A Chinese Precursor to the Digital Sovereignty Debate: Digital Anti-Colonialism and Authoritarianism from the Post–Cold War Era to the Tunis Agenda”, Global Studies Quarterly (2022) 2, 1–13.(2) Rivaton, R., “The Origins and Fate of Digital Sovereignty”, Fondation pour L’Innovation Politique, 21 may 2026, https://www.fondapol.org/decryptage/the-origins-and-fate-of-digital-sovereignty/(3) Jiang, M. Models of State Digital Sovereignty From the Global South: Diverging Experiences From China, India and South Africa, Policy & Internet, 15 December 2024, https://doi.org/10.1002/poi3.427''''''

= India =
In progress
== United States ==
=== The Concept of Digital Sovereignty ===
The idea of “digital sovereignty” is not a single concept. There are a number of scholars who attempted to define digital sovereignty. [https://www.academia.edu/99043334/What_does_the_notion_of_sovereignty_mean_when_referring_to_the_digital Couture and Toupin] and [https://www.researchgate.net/publication/343611573_The_Fight_for_Digital_Sovereignty_What_It_Is_and_Why_It_Matters_Especially_for_the_EU Floridi] emphasized the '''control''' over the digital realm, in which Floridi saw a combination of democratic legitimacy with innovative flexibility by a hybrid system of control. On the other hand, [https://techreg.org/article/view/23778?articlesBySimilarityPage=1 Chung] looked into the aspect of '''authority''' by defining digital sovereignty as a state’s ability to define, implement and enforce political and legal decisions across digital systems, platforms and data infrastructures. She also reconceptualized the term by asserting that sovereignty should be understood as governance '''capacity''' rather than exclusive control. There is a variety of perspectives in determining what digital sovereignty means and conflicting definitions based on varying national strategies.The definition of digital sovereignty adopted throughout this study is a merge of authority and practical capacity of a state to set, apply and defend its own rules governing the digital infrastructure, data flows and algorithmic systems that shape its legal and political orders, against interference by outside actors, whether public or private, including other levels of government within the same state.<div></div>
=== The United States Sovereignty Strategy: AI Dominance ===
Unlike the EU who attempts to maintain digital sovereignty under its regulatory control, the idea of securing digital sovereignty is not its priority for the US. Their goal is to ensure geopolitical dominance by entrenching American advantage in the technologies that determine their economic and military standing, against a field of competitors, namely China as the principal one.
From ''[https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf Winning the Race: America’s AI Action Plan]'', Issued on 23 July 2025, the US outlines 103 recommended actions by three pillars:
*Pillar 1: Accelerate AI Innovation – removing “bureaucratic red tapes” which hinder technological advancement by deregulation
*Pillar 2: Build American AI Infrastructure – fostering an environment conducive to AI innovations
*Pillar 3: Lead in International AI Diplomacy and Security – Ensuring its global hegemonic position in AI technology
As explicitly stated from the plan, their core interest is in winning the race and securing its geopolitical primacy relative to other competitors rather than enhancing its authority and capacity over its domestic digital governance. The Plan itself has no legal force, yet the actual implementation is played by executive orders and procurements.

=== Supply Chain Management and Rare Earth Minerals ===
The United States' pursuit of AI dominance rests on a chain of physical dependencies: AI systems require advanced semiconductors, semiconductors require rare earth elements and critical minerals, particularly the neodymium, dysprosium, and gadolinium used in the magnets and components of chip fabrication and data-centre hardware, and the United States currently remains reliant on processing capacity concentrated in China. Where the [https://www.congress.gov/bill/117th-congress/house-bill/4346 CHIPS and Science Act] addresses the fabrication layer, a separate and more fragmented body of law addresses the mineral layer beneath it. Unlike the EU's [https://eur-lex.europa.eu/eli/reg/2024/1252/oj/eng Critical Raw Materials Act], the American framework layers a foundational statute, an executive order, and a narrower defense-procurement statute. 
==== Supply Chain Management and Critical Minerals ====
Section 7002 of the Energy Act of 2020 (codified at [https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=30-USC-185389929-22931526&term_occur=999&term_src= 30 U.S.C. §1606]) supplies the foundational definitions. It defines a "critical mineral" through a delegated standard: a "byproduct" is a critical mineral whose recovery depends on the production of a host mineral not itself designated critical, while a "critical material" is any non-fuel mineral, element, substance, or material the Secretary of Energy determines carries a high risk of supply-chain disruption. This is primarily an administrative and classificatory act: it does not itself compel production, but it creates the taxonomy (which of the 17 rare earth elements are legally "critical") on which subsequent executive and procurement action depends.
==== Executive Order 14241 (2025) ====
[https://www.whitehouse.gov/presidential-actions/2025/03/immediate-measures-to-increase-american-mineral-production/ Executive Order 14241], "Immediate Measures to Increase American Mineral Production," signed March 20, 2025, operationalizes that taxonomy. Its Section 1 purpose clause is unusually candid about the framing: national and economic security are now threatened by dependence on hostile foreign powers. Operationally, Section 3 directs every federal agency involved in permitting to submit to the National Energy Dominance Council a list of all mineral production projects for which a plan of operations or permit application has been submitted, after which agency heads must identify projects that can be immediately approved and expedite them. The Order invokes [https://www.law.cornell.edu/uscode/text/50/4533 Title III of the Defense Production Act] to mobilize financing, and tasks the NEDC, chaired by the Secretary of the Interior, with coordinating implementation across Defense, Energy, Interior, and Agriculture. Unlike the CHIPS Act, it required no congressional appropriation, and unlike the Energy Act, it does not merely classify inputs but seeks to physically build an alternative domestic supply chain, the mining-and-processing analogue to the CRMA's "strategic projects" mechanism. Executive Order 14241 has since been diluted by [https://www.whitehouse.gov/presidential-actions/2025/04/reinvigorating-americas-beautiful-clean-coal-industry-and-amending-executive-order-14241/ Executive Order 14261], which extends designation of criticality to sectorally unrelated materials such as coal.
==== 10 U.S.C. §4872 and the DFARS ====
The narrowest instrument is [https://www.law.cornell.edu/uscode/text/10/4872 10 U.S.C. §4872], which provides that the Secretary of Defense may not procure any covered material melted or produced in any covered nation, or any end item containing such material. [https://www.law.cornell.edu/definitions/uscode.php?def_id=10-USC-1197798417-780045830&term_src=title:10:subtitle:A:part:V:subpart:I:chapter:385:subchapter:III:section:4872 “Covered nations"] are defined as North Korea, China, Russia, and Iran, covered materials include samarium-cobalt and neodymium-iron-boron magnets. Implemented through [https://www.acquisition.gov/dfars/225.7018-2-restriction. DFARS 225.7018-2], the clause currently bars acquisition of any covered material melted or produced in any covered country, tightening in 2027 to cover the entire chain. This statute does not build supply; it leverages the federal government's own purchasing power to exclude adversary-sourced inputs from the defense industrial base, a tool with no direct EU parallel, since the CRMA's diversification targets operate through incentive rather than exclusion.

Infrastructures: Digital Sovereignty

2026-09-25T10:36:45Z

Park Jihae: /* United States */

= Infrastructures: Digital Sovereignty =
Digital Sovereignty in the European Union is a [https://www.tandfonline.com/doi/full/10.1080/17579961.2026.2705118 term used to convey ] a broad array of policy ambitions in relation to digital technology development and use: the urgency to regain control over data, infrastructures, and (critical) technologies, the establishment of strategic autonomy, the conclusion of partnerships with geopolitical allies, and the reduction of critical dependencies. According to the Digital Decade Policy Programme [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52023SC0570 Implementation Report] of 2023, digital sovereignty refers to the EU's "ability to act independently in the digital world and therefore it constitutes a crucial means to safeguard [EU] values".

= Brazil =
In progress
= China =
'''Introduction'''

''''''

“Digital Sovereignty” is an increasingly important concept for understanding how States exercise political and economic control over digital infrastructures and technologies. Since the massive introduction of the Internet in 1994, Chinese academic and political discourse has formulated notions of cyber, information, and network sovereignty in direct relation to digital and technological independence.In the early 2000s, Chinese authorities focused primarily on controlling data flows rather than developing national technological champions. Indeed, at the time Google held 40% of the Chinese Internet search market and companies like Yahoo maintained substantial equity participation in Chinese firms like Alibaba, all of which ended in 2009 when the Urumqi riots triggered China’s decision to permanently block platforms like Facebook and YouTube, prompting Google’s withdrawal from the Internet search market.In 2010 “Cyber Sovereignty” was crystallized as an organizing principle of Chinese digital policy following historical concerns about information control and a national determination to resist foreign influence over China. This political and economic landscape cemented the nation’s ambition to achieve full digital sovereignty.Today Chinese digital sovereignty is incorporated into its legal framework, particularly in the Cybersecurity Law and the Data Security Law (both from 2017). These laws establish data localization requirements and security assessments mechanisms for cross-border transfers. Moreover, they aim for China to develop the autonomy and capacity necessary to exercise full control over its data.

 '''Cybersecurity Law'''

 The Cybersecurity Law is a comprehensive legal framework that regulates cyberspace and information infrastructure security in China, governing information networks build, operation, and use within its territory (article 2). The law covers a wide range of issues, including critical information infrastructure management, cybersecurity, and data protection.In the area of critical information infrastructure management, the law applies to “Critical Information Infrastructures” (CIIs) and “Critical Information Infrastructure Operators” (CIIOs) which include information infrastructure and its operators in sectors such as energy, finance, healthcare, telecommunications, transportation, and governmental services.CIIOs have the obligation to adopt enhanced security measures and protocols to safeguard information infrastructure and their data. Additionally, CIIOs must perform regular security assessments and report their results to State agencies, implement protective measures and protocols, and cooperate with State agencies, among others (articles 23, 36, 37, 38, and 40).In the area of cybersecurity, the law applies to all information infrastructure operators (not only to CIIOs) which have the obligation to adopt comprehensive cybersecurity measures which include monitoring their infrastructure security, safeguarding against data breaches, impeding misuse of their infrastructures for illegal activities, prepare incident response plans, report cybersecurity incidents, and cooperate with State agencies (articles 23, 27, 30 and 51).Finally, in the area of data protection, the law applies to CIIOs in the case of data localization, and to all information infrastructure operators in the case of personal information.CIIOs have the obligation to store within China all critical data and personal information they collect or generate in the country’s territory. Additionally, CIIOs must pass a security evaluation and get State authorization to transfer data abroad (article 39).Moreover, all information infrastructure operators have the obligation to follow guidelines for collecting, processing, and storing personal information. In that sense, information infrastructure operators must secure user consent for gathering personal information and disclose it, and warrant its protection by implementing measures to guard against unauthorized or illegal access and misuse including breach notification (article 42).Additionally, the law establishes obligations for the State including to balance cybersecurity and information infrastructure development (article 4), continuously improve the national cybersecurity strategy (article 5), and deal with cybersecurity risks and threats (article 6).In general, the law provides the State extensive powers to access data held by information infrastructure operators functioning within China, including the authority to require assistance and support in matters of national security or criminal investigations.Infringing the law can result in sanctions for information infrastructure operators which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a threat to public order or national security, infringements can result in criminal prosecution and career suspension (articles 61, 63, 64, 65, 66, 68, 69, 71, and 76).

 '''Data Security Law'''

 The Data Security Law is a comprehensive legal framework that regulates data management for the purposes of national sovereignty, security, and social and economic development.It establishes the obligation of the State to develop a data classification system based on its importance to social and economic development and the damage its alteration, destruction, leakage, or illicit use would cause to national security, public interest, or people or organizations’ rights and interests. Such classification allows the State to apply varying protection levels depending on the data’s sensitivity and relevance, where “central data of the State” or data deemed important to national security, major public interests, critical economic sectors, or important aspects of people livelihood is given the highest level of protection (article 21). Other categories of data are “important data” and “general data” which are not defined or developed in the law but in complementary regulations.The law applies to data management carried out within China’s territory, and generates legal responsibility for data management outside if it harms national security, public interest, or people or organizations’ rights and interests (article 2). Data processors of “important data” have the obligation to designate officers responsible for data security, to conduct risk evaluations periodically reporting the results to State authorities, and to report security incidents to users and State authorities, among others (articles 27, 29 and 30). Moreover, data processors are impeded to transfer data to foreign judicial authorities without authorization (article 36).Additionally, the law establishes obligations for the State including to encourage the use of data for economic and innovation purposes, specifically for the development of new data-driven industries as long as they comply with the law (article 1). Also, it has the obligation to coordinate the national data security, and draft and implement the national data security strategy (article 5).In general the law enables the State to collect data if it is necessary to safeguard national security or investigate crimes, subject to approval procedures (article 35). Data processors and individuals are obliged to cooperate, and refusal is subject to fines (article 48).Infringing the law can result in sanctions which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a crime or a threat to national sovereignty, security, or economic development interests, infringements can result in criminal prosecution (articles 45 and 52).

 '''Personal Information Protection Law'''

 The Personal Information Protection Law of 2021 is a comprehensive legal framework that protects personal information similarly to the European General Data Protection Regulation (GDPR). Although the law is not directly related to China’s digital sovereignty, it establishes obligations relevant to it: State agencies, critical infrastructure operators, and data processors that collect, process, or use data above a certain threshold (defined by State authorities) must store within China all personal information they collect or generate in the country’s territory. Additionally, they must pass a security evaluation and get State authorization to transfer data abroad (article 36 and 40).Moreover, the State is allowed to blacklist individuals and organizations and deny them access to personal information generated or collected in China if it deems that they acted against Chinese people rights or interests, as well as to adopt reciprocal measures against foreign States if it deems that they discriminated against China regarding the protection of personal information (articles 42 and 43).

 '''China’s State Centered Concept of Digital Sovereignty'''

 The Cybersecurity Law and the Data Security Law are integral parts of China’s strategy to strengthen its digital sovereignty.China’s concept of digital sovereignty is characterized for its strong State-centered approach in which the State holds ultimate and supreme authority in the digital space, controlling data flows and content sharing within its borders. In a sense, China’s concept of digital sovereignty is defensive: it is essential to protect China’s social, political, and economic architecture from destabilization attempts by foreign actors (particularly the United States), which explains why the Cybersecurity Law establishes the obligation of private actors to “abide by the Constitution and laws, observe public order, respect social morality, and (...) not (...) use the Internet to engage in activities that endanger national security, honor, or interests (...), incite subversion of State power or overthrow of the socialist system (...), incite secession or undermine national unity (...), (or) fabricate or disseminate false information to disrupt economic and social order” (article 13), and why the the Data Security Law establishes as an objective to “preserve State sovereignty, security, and development interests” (article 1).Such a concept requires the achievement of two policy objectives: territorialization and indigenization. Through territorialization China seeks to replicate its national borders in cyberspace, ensuring that online processes impacting important national interests take place within Chinese boundaries, and unwanted practices or operations can be barred from entering. Through indigenization, in turn, it strives to replace foreign actors and technologies with local equivalents, reducing foreign dependence.In this context, the Cybersecurity Law and the Data Security Law aim to protect China’s digital sovereignty through both policy objectives. In the case of territorialization, they establish the objective of protecting sovereignty in cyberspace and preventing and responding to cybersecurity risks and threats coming from inside and outside China; the obligation of CIIOs to store in China all personal information and important data that they generate or collect inside the country’s borders, and to conduct security evaluations and obtain State permission before transferring them abroad; the obligation of data processors to not transfer data to foreign judicial authorities without State authorization; the responsibility of data processors for activities carried out outside China; and the capacity of the State to block prohibited information, to temporarily restrict communications in specific areas for national security reasons, and to adopt countermeasures against foreign States that discriminate against China in data investment or trade (Cybersecurity Law articles 1, 6, 39, 50 and 60, and Data Security Law articles 2, 26 and 36).In the case of indigenization, the laws establish mechanisms of promotion and access control that indirectly privilege national providers. As an example, they establish the obligation of critical infrastructure operators to purchase information network products and services approved by a national security assessment; the obligation of critical information network products providers to offer products that are certified or comply with national standards; the obligation of the State to support Chinese cybersecurity industries and projects, to support Chinese Artificial Intelligence (AI) industries, to support data development and security industrial systems, to support the creation of national data markets, and to develop a national strategy for big data and data infrastructure (Cybersecurity Law articles 17, 20, 25 and 37, Data Security Law articles 14, 16 and 19).These efforts are related to the “Great Firewall”, the Chinese State system for censoring and controlling Internet traffic within its borders which is, arguably, its most visible instrument of territorialization, drawing an effective Internet frontier between China and the rest of the world.In summary, China's digital sovereignty concept demands asserting control over data generated within its borders and to protect sensitive information from foreign interference and cyber-attacks while simultaneously reinforcing its surveillance capacity and its ability to manage information flows for “social stability”.In this sense, China's data localization mandates are designed to nurture domestic technological independence, and they achieve this by ensuring that valuable data generated within China remains in the hands of Chinese firms rather than foreign competitors. This treats data itself as a strategic economic asset. China simultaneously fuels domestic innovation and strengthens its digital economy and denies foreign firms the informational advantage they would otherwise hold by keeping the economic advantage within its borders.Finally, on the regulation of foreign companies, laws such as the Data Security Law gives Chinese authorities a direct enforcement mechanism over foreign entities operating domestically, compelling compliance with localization requirements and government data access provisions.Therefore, each Chinese law was built to accomplish a specific stated goal, and each demonstrably does so, which makes China’s digital sovereignty regime distinct because China’s legal scheme was constructed with sufficient State control to ensure regulatory instruments and its sovereignty goals remained tightly aligned.

 '''China vs. the European Union: Major Digital Sovereigns'''

 The European Union (EU) and China represent two distinct approaches to digital sovereignty. The EU contrasts to China’s reactive, security-driven model, since EU digital sovereignty emerged from a supranational, rights-based traditional rather than an authoritarian one. The EU’s push to digital sovereignty began after 2013 when Edward Snowden’s revelation of NSA interception of European communications exposed the EU's dependence on foreign technology and triggered a wave of regulatory safeguards. This led to the creation of the GDPR adopted in 2016, which enshrined enforceable individual rights, including access to, and objection to, the processing of European customer data. The EU continued its trajectory by developing two consumer protection directives in 2019, in addition to a series of antitrust proceedings against hyperscale’s such as Google, Amazon, and Apple between 2015 and 2020. In 2024, the AI Act was created to harmonize AI development and deployment rules according to EU values and fundamental rights. More recently, the EU initiated infrastructure initiatives such as Gaia X for cloud sovereignty and the Chips Act for semiconductor independence.Despite this extensive regulatory framework, China's claim to digital sovereignty may be considered more consolidated than the EU's because it fuses legal codification with direct State control over technology, information, and infrastructure in a way the EU market-correcting model does not.As European Commission President von der Leyen stated in 2019, ‘it is not too late to achieve technological sovereignty in some critical technology areas’. In actuality, the EU has been catching up to China, which spent the last decade tightening its state authority over its digital infrastructure before the EU began treating its own technology dependence as a vulnerability. China's more recent legislative activity has extended this consolidation into the international stage as well as domestically. In 2024, the Shanghai Declaration on Global AI governance, issued by China's Ministry of Foreign Affairs, committed signatories to developing AI under principles of safety, reliability, and controllability, while asserting each country's right to regulate AI according to its own national conditions. This language simultaneously projects openness to international cooperation while defending non-foreign interference, the logic underpinning China's cyber-sovereignity doctrine.This declaration is not merely aspirational: it is anchored in binding domestic law, specifically Article 20 of China's 2025 amended Security Law, which directs the state to support AI research, infrastructure, ethical norms, and risk supervision.Therefore, where the EU’s sovereignty is exercised primarily through regulating outside actors within its internal market, China's sovereignty is exercised through direct state-led development, information control, and an explicit fusion of domestic statute and international declaratory positioning. This makes China's model arguably more total in its sovereignty, though less extensive in scope, over the digital domain it governs compared to the EU.

'''References'''

''''''(1) Thumfart, J. & Cong. W., “A Chinese Precursor to the Digital Sovereignty Debate: Digital Anti-Colonialism and Authoritarianism from the Post–Cold War Era to the Tunis Agenda”, Global Studies Quarterly (2022) 2, 1–13.(2) Rivaton, R., “The Origins and Fate of Digital Sovereignty”, Fondation pour L’Innovation Politique, 21 may 2026, https://www.fondapol.org/decryptage/the-origins-and-fate-of-digital-sovereignty/(3) Jiang, M. Models of State Digital Sovereignty From the Global South: Diverging Experiences From China, India and South Africa, Policy & Internet, 15 December 2024, https://doi.org/10.1002/poi3.427''''''

= India =
In progress
== United States ==
=== The Concept of Digital Sovereignty ===
The idea of “digital sovereignty” is not a single concept. There are a number of scholars who attempted to define digital sovereignty. [https://www.academia.edu/99043334/What_does_the_notion_of_sovereignty_mean_when_referring_to_the_digital Couture and Toupin] and [https://www.researchgate.net/publication/343611573_The_Fight_for_Digital_Sovereignty_What_It_Is_and_Why_It_Matters_Especially_for_the_EU Floridi] emphasized the '''control''' over the digital realm, in which Floridi saw a combination of democratic legitimacy with innovative flexibility by a hybrid system of control. On the other hand, [https://techreg.org/article/view/23778?articlesBySimilarityPage=1 Chung] looked into the aspect of '''authority''' by defining digital sovereignty as a state’s ability to define, implement and enforce political and legal decisions across digital systems, platforms and data infrastructures. She also reconceptualized the term by asserting that sovereignty should be understood as governance '''capacity''' rather than exclusive control. There is a variety of perspectives in determining what digital sovereignty means and conflicting definitions based on varying national strategies.The definition of digital sovereignty adopted throughout this study is a merge of authority and practical capacity of a state to set, apply and defend its own rules governing the digital infrastructure, data flows and algorithmic systems that shape its legal and political orders, against interference by outside actors, whether public or private, including other levels of government within the same state.<div></div>
=== The United States Sovereignty Strategy: AI Dominance ===
Unlike the EU who attempts to maintain digital sovereignty under its regulatory control, the idea of securing digital sovereignty is not its priority for the US. Their goal is to ensure geopolitical dominance by entrenching American advantage in the technologies that determine their economic and military standing, against a field of competitors, namely China as the principal one.From ''[//chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf Winning the Race: America’s AI Action Plan]'', Issued on 23 July 2025, the US outlines 103 recommended actions by three pillars:
*Pillar 1: Accelerate AI Innovation – removing “bureaucratic red tapes” which hinder technological advancement by deregulation
*Pillar 2: Build American AI Infrastructure – fostering an environment conducive to AI innovations
*Pillar 3: Lead in International AI Diplomacy and Security – Ensuring its global hegemonic position in AI technology
As explicitly stated from the plan, their core interest is in winning the race and securing its geopolitical primacy relative to other competitors rather than enhancing its authority and capacity over its domestic digital governance. The Plan itself has no legal force, yet the actual implementation is played by executive orders and procurements.
=== Supply Chain Management and Rare Earth Minerals ===
The United States' pursuit of AI dominance rests on a chain of physical dependencies: AI systems require advanced semiconductors, semiconductors require rare earth elements and critical minerals, particularly the neodymium, dysprosium, and gadolinium used in the magnets and components of chip fabrication and data-centre hardware, and the United States currently remains reliant on processing capacity concentrated in China. Where the [https://www.congress.gov/bill/117th-congress/house-bill/4346 CHIPS and Science Act] addresses the fabrication layer, a separate and more fragmented body of law addresses the mineral layer beneath it. Unlike the EU's [https://eur-lex.europa.eu/eli/reg/2024/1252/oj/eng Critical Raw Materials Act], the American framework layers a foundational statute, an executive order, and a narrower defense-procurement statute. 
==== Supply Chain Management and Critical Minerals ====
Section 7002 of the Energy Act of 2020 (codified at [https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=30-USC-185389929-22931526&term_occur=999&term_src= 30 U.S.C. §1606]) supplies the foundational definitions. It defines a "critical mineral" through a delegated standard: a "byproduct" is a critical mineral whose recovery depends on the production of a host mineral not itself designated critical, while a "critical material" is any non-fuel mineral, element, substance, or material the Secretary of Energy determines carries a high risk of supply-chain disruption. This is primarily an administrative and classificatory act: it does not itself compel production, but it creates the taxonomy (which of the 17 rare earth elements are legally "critical") on which subsequent executive and procurement action depends.
==== Executive Order 14241 (2025) ====
[https://www.whitehouse.gov/presidential-actions/2025/03/immediate-measures-to-increase-american-mineral-production/ Executive Order 14241], "Immediate Measures to Increase American Mineral Production," signed March 20, 2025, operationalizes that taxonomy. Its Section 1 purpose clause is unusually candid about the framing: national and economic security are now threatened by dependence on hostile foreign powers. Operationally, Section 3 directs every federal agency involved in permitting to submit to the National Energy Dominance Council a list of all mineral production projects for which a plan of operations or permit application has been submitted, after which agency heads must identify projects that can be immediately approved and expedite them. The Order invokes [https://www.law.cornell.edu/uscode/text/50/4533 Title III of the Defense Production Act] to mobilize financing, and tasks the NEDC, chaired by the Secretary of the Interior, with coordinating implementation across Defense, Energy, Interior, and Agriculture. Unlike the CHIPS Act, it required no congressional appropriation, and unlike the Energy Act, it does not merely classify inputs but seeks to physically build an alternative domestic supply chain, the mining-and-processing analogue to the CRMA's "strategic projects" mechanism. Executive Order 14241 has since been diluted by [https://www.whitehouse.gov/presidential-actions/2025/04/reinvigorating-americas-beautiful-clean-coal-industry-and-amending-executive-order-14241/ Executive Order 14261], which extends designation of criticality to sectorally unrelated materials such as coal.
==== 10 U.S.C. §4872 and the DFARS ====
The narrowest instrument is [https://www.law.cornell.edu/uscode/text/10/4872 10 U.S.C. §4872], which provides that the Secretary of Defense may not procure any covered material melted or produced in any covered nation, or any end item containing such material. [https://www.law.cornell.edu/definitions/uscode.php?def_id=10-USC-1197798417-780045830&term_src=title:10:subtitle:A:part:V:subpart:I:chapter:385:subchapter:III:section:4872 “Covered nations"] are defined as North Korea, China, Russia, and Iran, covered materials include samarium-cobalt and neodymium-iron-boron magnets. Implemented through [https://www.acquisition.gov/dfars/225.7018-2-restriction. DFARS 225.7018-2], the clause currently bars acquisition of any covered material melted or produced in any covered country, tightening in 2027 to cover the entire chain. This statute does not build supply; it leverages the federal government's own purchasing power to exclude adversary-sourced inputs from the defense industrial base, a tool with no direct EU parallel, since the CRMA's diversification targets operate through incentive rather than exclusion.

Infrastructures: Digital Sovereignty

2026-09-25T10:24:59Z

Park Jihae: Undid revision 124010 by Park Jihae (talk)

= Infrastructures: Digital Sovereignty =
Digital Sovereignty in the European Union is a [https://www.tandfonline.com/doi/full/10.1080/17579961.2026.2705118 term used to convey ] a broad array of policy ambitions in relation to digital technology development and use: the urgency to regain control over data, infrastructures, and (critical) technologies, the establishment of strategic autonomy, the conclusion of partnerships with geopolitical allies, and the reduction of critical dependencies. According to the Digital Decade Policy Programme [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52023SC0570 Implementation Report] of 2023, digital sovereignty refers to the EU's "ability to act independently in the digital world and therefore it constitutes a crucial means to safeguard [EU] values".

= Brazil =
In progress
= China =
'''Introduction'''

''''''

“Digital Sovereignty” is an increasingly important concept for understanding how States exercise political and economic control over digital infrastructures and technologies. Since the massive introduction of the Internet in 1994, Chinese academic and political discourse has formulated notions of cyber, information, and network sovereignty in direct relation to digital and technological independence.In the early 2000s, Chinese authorities focused primarily on controlling data flows rather than developing national technological champions. Indeed, at the time Google held 40% of the Chinese Internet search market and companies like Yahoo maintained substantial equity participation in Chinese firms like Alibaba, all of which ended in 2009 when the Urumqi riots triggered China’s decision to permanently block platforms like Facebook and YouTube, prompting Google’s withdrawal from the Internet search market.In 2010 “Cyber Sovereignty” was crystallized as an organizing principle of Chinese digital policy following historical concerns about information control and a national determination to resist foreign influence over China. This political and economic landscape cemented the nation’s ambition to achieve full digital sovereignty.Today Chinese digital sovereignty is incorporated into its legal framework, particularly in the Cybersecurity Law and the Data Security Law (both from 2017). These laws establish data localization requirements and security assessments mechanisms for cross-border transfers. Moreover, they aim for China to develop the autonomy and capacity necessary to exercise full control over its data.

 '''Cybersecurity Law'''

 The Cybersecurity Law is a comprehensive legal framework that regulates cyberspace and information infrastructure security in China, governing information networks build, operation, and use within its territory (article 2). The law covers a wide range of issues, including critical information infrastructure management, cybersecurity, and data protection.In the area of critical information infrastructure management, the law applies to “Critical Information Infrastructures” (CIIs) and “Critical Information Infrastructure Operators” (CIIOs) which include information infrastructure and its operators in sectors such as energy, finance, healthcare, telecommunications, transportation, and governmental services.CIIOs have the obligation to adopt enhanced security measures and protocols to safeguard information infrastructure and their data. Additionally, CIIOs must perform regular security assessments and report their results to State agencies, implement protective measures and protocols, and cooperate with State agencies, among others (articles 23, 36, 37, 38, and 40).In the area of cybersecurity, the law applies to all information infrastructure operators (not only to CIIOs) which have the obligation to adopt comprehensive cybersecurity measures which include monitoring their infrastructure security, safeguarding against data breaches, impeding misuse of their infrastructures for illegal activities, prepare incident response plans, report cybersecurity incidents, and cooperate with State agencies (articles 23, 27, 30 and 51).Finally, in the area of data protection, the law applies to CIIOs in the case of data localization, and to all information infrastructure operators in the case of personal information.CIIOs have the obligation to store within China all critical data and personal information they collect or generate in the country’s territory. Additionally, CIIOs must pass a security evaluation and get State authorization to transfer data abroad (article 39).Moreover, all information infrastructure operators have the obligation to follow guidelines for collecting, processing, and storing personal information. In that sense, information infrastructure operators must secure user consent for gathering personal information and disclose it, and warrant its protection by implementing measures to guard against unauthorized or illegal access and misuse including breach notification (article 42).Additionally, the law establishes obligations for the State including to balance cybersecurity and information infrastructure development (article 4), continuously improve the national cybersecurity strategy (article 5), and deal with cybersecurity risks and threats (article 6).In general, the law provides the State extensive powers to access data held by information infrastructure operators functioning within China, including the authority to require assistance and support in matters of national security or criminal investigations.Infringing the law can result in sanctions for information infrastructure operators which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a threat to public order or national security, infringements can result in criminal prosecution and career suspension (articles 61, 63, 64, 65, 66, 68, 69, 71, and 76).

 '''Data Security Law'''

 The Data Security Law is a comprehensive legal framework that regulates data management for the purposes of national sovereignty, security, and social and economic development.It establishes the obligation of the State to develop a data classification system based on its importance to social and economic development and the damage its alteration, destruction, leakage, or illicit use would cause to national security, public interest, or people or organizations’ rights and interests. Such classification allows the State to apply varying protection levels depending on the data’s sensitivity and relevance, where “central data of the State” or data deemed important to national security, major public interests, critical economic sectors, or important aspects of people livelihood is given the highest level of protection (article 21). Other categories of data are “important data” and “general data” which are not defined or developed in the law but in complementary regulations.The law applies to data management carried out within China’s territory, and generates legal responsibility for data management outside if it harms national security, public interest, or people or organizations’ rights and interests (article 2). Data processors of “important data” have the obligation to designate officers responsible for data security, to conduct risk evaluations periodically reporting the results to State authorities, and to report security incidents to users and State authorities, among others (articles 27, 29 and 30). Moreover, data processors are impeded to transfer data to foreign judicial authorities without authorization (article 36).Additionally, the law establishes obligations for the State including to encourage the use of data for economic and innovation purposes, specifically for the development of new data-driven industries as long as they comply with the law (article 1). Also, it has the obligation to coordinate the national data security, and draft and implement the national data security strategy (article 5).In general the law enables the State to collect data if it is necessary to safeguard national security or investigate crimes, subject to approval procedures (article 35). Data processors and individuals are obliged to cooperate, and refusal is subject to fines (article 48).Infringing the law can result in sanctions which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a crime or a threat to national sovereignty, security, or economic development interests, infringements can result in criminal prosecution (articles 45 and 52).

 '''Personal Information Protection Law'''

 The Personal Information Protection Law of 2021 is a comprehensive legal framework that protects personal information similarly to the European General Data Protection Regulation (GDPR). Although the law is not directly related to China’s digital sovereignty, it establishes obligations relevant to it: State agencies, critical infrastructure operators, and data processors that collect, process, or use data above a certain threshold (defined by State authorities) must store within China all personal information they collect or generate in the country’s territory. Additionally, they must pass a security evaluation and get State authorization to transfer data abroad (article 36 and 40).Moreover, the State is allowed to blacklist individuals and organizations and deny them access to personal information generated or collected in China if it deems that they acted against Chinese people rights or interests, as well as to adopt reciprocal measures against foreign States if it deems that they discriminated against China regarding the protection of personal information (articles 42 and 43).

 '''China’s State Centered Concept of Digital Sovereignty'''

 The Cybersecurity Law and the Data Security Law are integral parts of China’s strategy to strengthen its digital sovereignty.China’s concept of digital sovereignty is characterized for its strong State-centered approach in which the State holds ultimate and supreme authority in the digital space, controlling data flows and content sharing within its borders. In a sense, China’s concept of digital sovereignty is defensive: it is essential to protect China’s social, political, and economic architecture from destabilization attempts by foreign actors (particularly the United States), which explains why the Cybersecurity Law establishes the obligation of private actors to “abide by the Constitution and laws, observe public order, respect social morality, and (...) not (...) use the Internet to engage in activities that endanger national security, honor, or interests (...), incite subversion of State power or overthrow of the socialist system (...), incite secession or undermine national unity (...), (or) fabricate or disseminate false information to disrupt economic and social order” (article 13), and why the the Data Security Law establishes as an objective to “preserve State sovereignty, security, and development interests” (article 1).Such a concept requires the achievement of two policy objectives: territorialization and indigenization. Through territorialization China seeks to replicate its national borders in cyberspace, ensuring that online processes impacting important national interests take place within Chinese boundaries, and unwanted practices or operations can be barred from entering. Through indigenization, in turn, it strives to replace foreign actors and technologies with local equivalents, reducing foreign dependence.In this context, the Cybersecurity Law and the Data Security Law aim to protect China’s digital sovereignty through both policy objectives. In the case of territorialization, they establish the objective of protecting sovereignty in cyberspace and preventing and responding to cybersecurity risks and threats coming from inside and outside China; the obligation of CIIOs to store in China all personal information and important data that they generate or collect inside the country’s borders, and to conduct security evaluations and obtain State permission before transferring them abroad; the obligation of data processors to not transfer data to foreign judicial authorities without State authorization; the responsibility of data processors for activities carried out outside China; and the capacity of the State to block prohibited information, to temporarily restrict communications in specific areas for national security reasons, and to adopt countermeasures against foreign States that discriminate against China in data investment or trade (Cybersecurity Law articles 1, 6, 39, 50 and 60, and Data Security Law articles 2, 26 and 36).In the case of indigenization, the laws establish mechanisms of promotion and access control that indirectly privilege national providers. As an example, they establish the obligation of critical infrastructure operators to purchase information network products and services approved by a national security assessment; the obligation of critical information network products providers to offer products that are certified or comply with national standards; the obligation of the State to support Chinese cybersecurity industries and projects, to support Chinese Artificial Intelligence (AI) industries, to support data development and security industrial systems, to support the creation of national data markets, and to develop a national strategy for big data and data infrastructure (Cybersecurity Law articles 17, 20, 25 and 37, Data Security Law articles 14, 16 and 19).These efforts are related to the “Great Firewall”, the Chinese State system for censoring and controlling Internet traffic within its borders which is, arguably, its most visible instrument of territorialization, drawing an effective Internet frontier between China and the rest of the world.In summary, China's digital sovereignty concept demands asserting control over data generated within its borders and to protect sensitive information from foreign interference and cyber-attacks while simultaneously reinforcing its surveillance capacity and its ability to manage information flows for “social stability”.In this sense, China's data localization mandates are designed to nurture domestic technological independence, and they achieve this by ensuring that valuable data generated within China remains in the hands of Chinese firms rather than foreign competitors. This treats data itself as a strategic economic asset. China simultaneously fuels domestic innovation and strengthens its digital economy and denies foreign firms the informational advantage they would otherwise hold by keeping the economic advantage within its borders.Finally, on the regulation of foreign companies, laws such as the Data Security Law gives Chinese authorities a direct enforcement mechanism over foreign entities operating domestically, compelling compliance with localization requirements and government data access provisions.Therefore, each Chinese law was built to accomplish a specific stated goal, and each demonstrably does so, which makes China’s digital sovereignty regime distinct because China’s legal scheme was constructed with sufficient State control to ensure regulatory instruments and its sovereignty goals remained tightly aligned.

 '''China vs. the European Union: Major Digital Sovereigns'''

 The European Union (EU) and China represent two distinct approaches to digital sovereignty. The EU contrasts to China’s reactive, security-driven model, since EU digital sovereignty emerged from a supranational, rights-based traditional rather than an authoritarian one. The EU’s push to digital sovereignty began after 2013 when Edward Snowden’s revelation of NSA interception of European communications exposed the EU's dependence on foreign technology and triggered a wave of regulatory safeguards. This led to the creation of the GDPR adopted in 2016, which enshrined enforceable individual rights, including access to, and objection to, the processing of European customer data. The EU continued its trajectory by developing two consumer protection directives in 2019, in addition to a series of antitrust proceedings against hyperscale’s such as Google, Amazon, and Apple between 2015 and 2020. In 2024, the AI Act was created to harmonize AI development and deployment rules according to EU values and fundamental rights. More recently, the EU initiated infrastructure initiatives such as Gaia X for cloud sovereignty and the Chips Act for semiconductor independence.Despite this extensive regulatory framework, China's claim to digital sovereignty may be considered more consolidated than the EU's because it fuses legal codification with direct State control over technology, information, and infrastructure in a way the EU market-correcting model does not.As European Commission President von der Leyen stated in 2019, ‘it is not too late to achieve technological sovereignty in some critical technology areas’. In actuality, the EU has been catching up to China, which spent the last decade tightening its state authority over its digital infrastructure before the EU began treating its own technology dependence as a vulnerability. China's more recent legislative activity has extended this consolidation into the international stage as well as domestically. In 2024, the Shanghai Declaration on Global AI governance, issued by China's Ministry of Foreign Affairs, committed signatories to developing AI under principles of safety, reliability, and controllability, while asserting each country's right to regulate AI according to its own national conditions. This language simultaneously projects openness to international cooperation while defending non-foreign interference, the logic underpinning China's cyber-sovereignity doctrine.This declaration is not merely aspirational: it is anchored in binding domestic law, specifically Article 20 of China's 2025 amended Security Law, which directs the state to support AI research, infrastructure, ethical norms, and risk supervision.Therefore, where the EU’s sovereignty is exercised primarily through regulating outside actors within its internal market, China's sovereignty is exercised through direct state-led development, information control, and an explicit fusion of domestic statute and international declaratory positioning. This makes China's model arguably more total in its sovereignty, though less extensive in scope, over the digital domain it governs compared to the EU.

'''References'''

''''''(1) Thumfart, J. & Cong. W., “A Chinese Precursor to the Digital Sovereignty Debate: Digital Anti-Colonialism and Authoritarianism from the Post–Cold War Era to the Tunis Agenda”, Global Studies Quarterly (2022) 2, 1–13.(2) Rivaton, R., “The Origins and Fate of Digital Sovereignty”, Fondation pour L’Innovation Politique, 21 may 2026, https://www.fondapol.org/decryptage/the-origins-and-fate-of-digital-sovereignty/(3) Jiang, M. Models of State Digital Sovereignty From the Global South: Diverging Experiences From China, India and South Africa, Policy & Internet, 15 December 2024, https://doi.org/10.1002/poi3.427''''''

= India =
In progress
= United States =
== Supply Chain Management and Rare Earth Minerals ==
The United States' pursuit of AI dominance rests on a chain of physical dependencies: AI systems require advanced semiconductors, semiconductors require rare earth elements and critical minerals, particularly the neodymium, dysprosium, and gadolinium used in the magnets and components of chip fabrication and data-centre hardware, and the United States currently remains reliant on processing capacity concentrated in China. Where the [https://www.congress.gov/bill/117th-congress/house-bill/4346 CHIPS and Science Act] addresses the fabrication layer, a separate and more fragmented body of law addresses the mineral layer beneath it. Unlike the EU's [https://eur-lex.europa.eu/eli/reg/2024/1252/oj/eng Critical Raw Materials Act], the American framework layers a foundational statute, an executive order, and a narrower defense-procurement statute. 

=== Supply Chain Management and Critical Minerals ===
Section 7002 of the Energy Act of 2020 (codified at [https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=30-USC-185389929-22931526&term_occur=999&term_src= 30 U.S.C. §1606]) supplies the foundational definitions. It defines a "critical mineral" through a delegated standard: a "byproduct" is a critical mineral whose recovery depends on the production of a host mineral not itself designated critical, while a "critical material" is any non-fuel mineral, element, substance, or material the Secretary of Energy determines carries a high risk of supply-chain disruption. This is primarily an administrative and classificatory act: it does not itself compel production, but it creates the taxonomy (which of the 17 rare earth elements are legally "critical") on which subsequent executive and procurement action depends.
=== Executive Order 14241 (2025) ===
[https://www.whitehouse.gov/presidential-actions/2025/03/immediate-measures-to-increase-american-mineral-production/ Executive Order 14241], "Immediate Measures to Increase American Mineral Production," signed March 20, 2025, operationalizes that taxonomy. Its Section 1 purpose clause is unusually candid about the framing: national and economic security are now threatened by dependence on hostile foreign powers. Operationally, Section 3 directs every federal agency involved in permitting to submit to the National Energy Dominance Council a list of all mineral production projects for which a plan of operations or permit application has been submitted, after which agency heads must identify projects that can be immediately approved and expedite them. The Order invokes [https://www.law.cornell.edu/uscode/text/50/4533 Title III of the Defense Production Act] to mobilize financing, and tasks the NEDC, chaired by the Secretary of the Interior, with coordinating implementation across Defense, Energy, Interior, and Agriculture. Unlike the CHIPS Act, it required no congressional appropriation, and unlike the Energy Act, it does not merely classify inputs but seeks to physically build an alternative domestic supply chain, the mining-and-processing analogue to the CRMA's "strategic projects" mechanism. Executive Order 14241 has since been diluted by [https://www.whitehouse.gov/presidential-actions/2025/04/reinvigorating-americas-beautiful-clean-coal-industry-and-amending-executive-order-14241/ Executive Order 14261], which extends designation of criticality to sectorally unrelated materials such as coal.

=== 10 U.S.C. §4872 and the DFARS ===
The narrowest instrument is [https://www.law.cornell.edu/uscode/text/10/4872 10 U.S.C. §4872], which provides that the Secretary of Defense may not procure any covered material melted or produced in any covered nation, or any end item containing such material. [https://www.law.cornell.edu/definitions/uscode.php?def_id=10-USC-1197798417-780045830&term_src=title:10:subtitle:A:part:V:subpart:I:chapter:385:subchapter:III:section:4872 “Covered nations"] are defined as North Korea, China, Russia, and Iran, covered materials include samarium-cobalt and neodymium-iron-boron magnets. Implemented through [https://www.acquisition.gov/dfars/225.7018-2-restriction. DFARS 225.7018-2], the clause currently bars acquisition of any covered material melted or produced in any covered country, tightening in 2027 to cover the entire chain. This statute does not build supply; it leverages the federal government's own purchasing power to exclude adversary-sourced inputs from the defense industrial base, a tool with no direct EU parallel, since the CRMA's diversification targets operate through incentive rather than exclusion.

Infrastructures: Digital Sovereignty

2026-09-25T10:24:41Z

Park Jihae: /* United States */

= Infrastructures: Digital Sovereignty =
Digital Sovereignty in the European Union is a [https://www.tandfonline.com/doi/full/10.1080/17579961.2026.2705118 term used to convey ] a broad array of policy ambitions in relation to digital technology development and use: the urgency to regain control over data, infrastructures, and (critical) technologies, the establishment of strategic autonomy, the conclusion of partnerships with geopolitical allies, and the reduction of critical dependencies. According to the Digital Decade Policy Programme [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52023SC0570 Implementation Report] of 2023, digital sovereignty refers to the EU's "ability to act independently in the digital world and therefore it constitutes a crucial means to safeguard [EU] values".

= Brazil =
In progress
= China =
'''Introduction'''

''''''

“Digital Sovereignty” is an increasingly important concept for understanding how States exercise political and economic control over digital infrastructures and technologies. Since the massive introduction of the Internet in 1994, Chinese academic and political discourse has formulated notions of cyber, information, and network sovereignty in direct relation to digital and technological independence.In the early 2000s, Chinese authorities focused primarily on controlling data flows rather than developing national technological champions. Indeed, at the time Google held 40% of the Chinese Internet search market and companies like Yahoo maintained substantial equity participation in Chinese firms like Alibaba, all of which ended in 2009 when the Urumqi riots triggered China’s decision to permanently block platforms like Facebook and YouTube, prompting Google’s withdrawal from the Internet search market.In 2010 “Cyber Sovereignty” was crystallized as an organizing principle of Chinese digital policy following historical concerns about information control and a national determination to resist foreign influence over China. This political and economic landscape cemented the nation’s ambition to achieve full digital sovereignty.Today Chinese digital sovereignty is incorporated into its legal framework, particularly in the Cybersecurity Law and the Data Security Law (both from 2017). These laws establish data localization requirements and security assessments mechanisms for cross-border transfers. Moreover, they aim for China to develop the autonomy and capacity necessary to exercise full control over its data.

 '''Cybersecurity Law'''

 The Cybersecurity Law is a comprehensive legal framework that regulates cyberspace and information infrastructure security in China, governing information networks build, operation, and use within its territory (article 2). The law covers a wide range of issues, including critical information infrastructure management, cybersecurity, and data protection.In the area of critical information infrastructure management, the law applies to “Critical Information Infrastructures” (CIIs) and “Critical Information Infrastructure Operators” (CIIOs) which include information infrastructure and its operators in sectors such as energy, finance, healthcare, telecommunications, transportation, and governmental services.CIIOs have the obligation to adopt enhanced security measures and protocols to safeguard information infrastructure and their data. Additionally, CIIOs must perform regular security assessments and report their results to State agencies, implement protective measures and protocols, and cooperate with State agencies, among others (articles 23, 36, 37, 38, and 40).In the area of cybersecurity, the law applies to all information infrastructure operators (not only to CIIOs) which have the obligation to adopt comprehensive cybersecurity measures which include monitoring their infrastructure security, safeguarding against data breaches, impeding misuse of their infrastructures for illegal activities, prepare incident response plans, report cybersecurity incidents, and cooperate with State agencies (articles 23, 27, 30 and 51).Finally, in the area of data protection, the law applies to CIIOs in the case of data localization, and to all information infrastructure operators in the case of personal information.CIIOs have the obligation to store within China all critical data and personal information they collect or generate in the country’s territory. Additionally, CIIOs must pass a security evaluation and get State authorization to transfer data abroad (article 39).Moreover, all information infrastructure operators have the obligation to follow guidelines for collecting, processing, and storing personal information. In that sense, information infrastructure operators must secure user consent for gathering personal information and disclose it, and warrant its protection by implementing measures to guard against unauthorized or illegal access and misuse including breach notification (article 42).Additionally, the law establishes obligations for the State including to balance cybersecurity and information infrastructure development (article 4), continuously improve the national cybersecurity strategy (article 5), and deal with cybersecurity risks and threats (article 6).In general, the law provides the State extensive powers to access data held by information infrastructure operators functioning within China, including the authority to require assistance and support in matters of national security or criminal investigations.Infringing the law can result in sanctions for information infrastructure operators which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a threat to public order or national security, infringements can result in criminal prosecution and career suspension (articles 61, 63, 64, 65, 66, 68, 69, 71, and 76).

 '''Data Security Law'''

 The Data Security Law is a comprehensive legal framework that regulates data management for the purposes of national sovereignty, security, and social and economic development.It establishes the obligation of the State to develop a data classification system based on its importance to social and economic development and the damage its alteration, destruction, leakage, or illicit use would cause to national security, public interest, or people or organizations’ rights and interests. Such classification allows the State to apply varying protection levels depending on the data’s sensitivity and relevance, where “central data of the State” or data deemed important to national security, major public interests, critical economic sectors, or important aspects of people livelihood is given the highest level of protection (article 21). Other categories of data are “important data” and “general data” which are not defined or developed in the law but in complementary regulations.The law applies to data management carried out within China’s territory, and generates legal responsibility for data management outside if it harms national security, public interest, or people or organizations’ rights and interests (article 2). Data processors of “important data” have the obligation to designate officers responsible for data security, to conduct risk evaluations periodically reporting the results to State authorities, and to report security incidents to users and State authorities, among others (articles 27, 29 and 30). Moreover, data processors are impeded to transfer data to foreign judicial authorities without authorization (article 36).Additionally, the law establishes obligations for the State including to encourage the use of data for economic and innovation purposes, specifically for the development of new data-driven industries as long as they comply with the law (article 1). Also, it has the obligation to coordinate the national data security, and draft and implement the national data security strategy (article 5).In general the law enables the State to collect data if it is necessary to safeguard national security or investigate crimes, subject to approval procedures (article 35). Data processors and individuals are obliged to cooperate, and refusal is subject to fines (article 48).Infringing the law can result in sanctions which include fines, operational suspensions, and business license revocations (individuals may face personal responsibility). In more extreme cases, where there is a crime or a threat to national sovereignty, security, or economic development interests, infringements can result in criminal prosecution (articles 45 and 52).

 '''Personal Information Protection Law'''

 The Personal Information Protection Law of 2021 is a comprehensive legal framework that protects personal information similarly to the European General Data Protection Regulation (GDPR). Although the law is not directly related to China’s digital sovereignty, it establishes obligations relevant to it: State agencies, critical infrastructure operators, and data processors that collect, process, or use data above a certain threshold (defined by State authorities) must store within China all personal information they collect or generate in the country’s territory. Additionally, they must pass a security evaluation and get State authorization to transfer data abroad (article 36 and 40).Moreover, the State is allowed to blacklist individuals and organizations and deny them access to personal information generated or collected in China if it deems that they acted against Chinese people rights or interests, as well as to adopt reciprocal measures against foreign States if it deems that they discriminated against China regarding the protection of personal information (articles 42 and 43).

 '''China’s State Centered Concept of Digital Sovereignty'''

 The Cybersecurity Law and the Data Security Law are integral parts of China’s strategy to strengthen its digital sovereignty.China’s concept of digital sovereignty is characterized for its strong State-centered approach in which the State holds ultimate and supreme authority in the digital space, controlling data flows and content sharing within its borders. In a sense, China’s concept of digital sovereignty is defensive: it is essential to protect China’s social, political, and economic architecture from destabilization attempts by foreign actors (particularly the United States), which explains why the Cybersecurity Law establishes the obligation of private actors to “abide by the Constitution and laws, observe public order, respect social morality, and (...) not (...) use the Internet to engage in activities that endanger national security, honor, or interests (...), incite subversion of State power or overthrow of the socialist system (...), incite secession or undermine national unity (...), (or) fabricate or disseminate false information to disrupt economic and social order” (article 13), and why the the Data Security Law establishes as an objective to “preserve State sovereignty, security, and development interests” (article 1).Such a concept requires the achievement of two policy objectives: territorialization and indigenization. Through territorialization China seeks to replicate its national borders in cyberspace, ensuring that online processes impacting important national interests take place within Chinese boundaries, and unwanted practices or operations can be barred from entering. Through indigenization, in turn, it strives to replace foreign actors and technologies with local equivalents, reducing foreign dependence.In this context, the Cybersecurity Law and the Data Security Law aim to protect China’s digital sovereignty through both policy objectives. In the case of territorialization, they establish the objective of protecting sovereignty in cyberspace and preventing and responding to cybersecurity risks and threats coming from inside and outside China; the obligation of CIIOs to store in China all personal information and important data that they generate or collect inside the country’s borders, and to conduct security evaluations and obtain State permission before transferring them abroad; the obligation of data processors to not transfer data to foreign judicial authorities without State authorization; the responsibility of data processors for activities carried out outside China; and the capacity of the State to block prohibited information, to temporarily restrict communications in specific areas for national security reasons, and to adopt countermeasures against foreign States that discriminate against China in data investment or trade (Cybersecurity Law articles 1, 6, 39, 50 and 60, and Data Security Law articles 2, 26 and 36).In the case of indigenization, the laws establish mechanisms of promotion and access control that indirectly privilege national providers. As an example, they establish the obligation of critical infrastructure operators to purchase information network products and services approved by a national security assessment; the obligation of critical information network products providers to offer products that are certified or comply with national standards; the obligation of the State to support Chinese cybersecurity industries and projects, to support Chinese Artificial Intelligence (AI) industries, to support data development and security industrial systems, to support the creation of national data markets, and to develop a national strategy for big data and data infrastructure (Cybersecurity Law articles 17, 20, 25 and 37, Data Security Law articles 14, 16 and 19).These efforts are related to the “Great Firewall”, the Chinese State system for censoring and controlling Internet traffic within its borders which is, arguably, its most visible instrument of territorialization, drawing an effective Internet frontier between China and the rest of the world.In summary, China's digital sovereignty concept demands asserting control over data generated within its borders and to protect sensitive information from foreign interference and cyber-attacks while simultaneously reinforcing its surveillance capacity and its ability to manage information flows for “social stability”.In this sense, China's data localization mandates are designed to nurture domestic technological independence, and they achieve this by ensuring that valuable data generated within China remains in the hands of Chinese firms rather than foreign competitors. This treats data itself as a strategic economic asset. China simultaneously fuels domestic innovation and strengthens its digital economy and denies foreign firms the informational advantage they would otherwise hold by keeping the economic advantage within its borders.Finally, on the regulation of foreign companies, laws such as the Data Security Law gives Chinese authorities a direct enforcement mechanism over foreign entities operating domestically, compelling compliance with localization requirements and government data access provisions.Therefore, each Chinese law was built to accomplish a specific stated goal, and each demonstrably does so, which makes China’s digital sovereignty regime distinct because China’s legal scheme was constructed with sufficient State control to ensure regulatory instruments and its sovereignty goals remained tightly aligned.

 '''China vs. the European Union: Major Digital Sovereigns'''

 The European Union (EU) and China represent two distinct approaches to digital sovereignty. The EU contrasts to China’s reactive, security-driven model, since EU digital sovereignty emerged from a supranational, rights-based traditional rather than an authoritarian one. The EU’s push to digital sovereignty began after 2013 when Edward Snowden’s revelation of NSA interception of European communications exposed the EU's dependence on foreign technology and triggered a wave of regulatory safeguards. This led to the creation of the GDPR adopted in 2016, which enshrined enforceable individual rights, including access to, and objection to, the processing of European customer data. The EU continued its trajectory by developing two consumer protection directives in 2019, in addition to a series of antitrust proceedings against hyperscale’s such as Google, Amazon, and Apple between 2015 and 2020. In 2024, the AI Act was created to harmonize AI development and deployment rules according to EU values and fundamental rights. More recently, the EU initiated infrastructure initiatives such as Gaia X for cloud sovereignty and the Chips Act for semiconductor independence.Despite this extensive regulatory framework, China's claim to digital sovereignty may be considered more consolidated than the EU's because it fuses legal codification with direct State control over technology, information, and infrastructure in a way the EU market-correcting model does not.As European Commission President von der Leyen stated in 2019, ‘it is not too late to achieve technological sovereignty in some critical technology areas’. In actuality, the EU has been catching up to China, which spent the last decade tightening its state authority over its digital infrastructure before the EU began treating its own technology dependence as a vulnerability. China's more recent legislative activity has extended this consolidation into the international stage as well as domestically. In 2024, the Shanghai Declaration on Global AI governance, issued by China's Ministry of Foreign Affairs, committed signatories to developing AI under principles of safety, reliability, and controllability, while asserting each country's right to regulate AI according to its own national conditions. This language simultaneously projects openness to international cooperation while defending non-foreign interference, the logic underpinning China's cyber-sovereignity doctrine.This declaration is not merely aspirational: it is anchored in binding domestic law, specifically Article 20 of China's 2025 amended Security Law, which directs the state to support AI research, infrastructure, ethical norms, and risk supervision.Therefore, where the EU’s sovereignty is exercised primarily through regulating outside actors within its internal market, China's sovereignty is exercised through direct state-led development, information control, and an explicit fusion of domestic statute and international declaratory positioning. This makes China's model arguably more total in its sovereignty, though less extensive in scope, over the digital domain it governs compared to the EU.

'''References'''

''''''(1) Thumfart, J. & Cong. W., “A Chinese Precursor to the Digital Sovereignty Debate: Digital Anti-Colonialism and Authoritarianism from the Post–Cold War Era to the Tunis Agenda”, Global Studies Quarterly (2022) 2, 1–13.(2) Rivaton, R., “The Origins and Fate of Digital Sovereignty”, Fondation pour L’Innovation Politique, 21 may 2026, https://www.fondapol.org/decryptage/the-origins-and-fate-of-digital-sovereignty/(3) Jiang, M. Models of State Digital Sovereignty From the Global South: Diverging Experiences From China, India and South Africa, Policy & Internet, 15 December 2024, https://doi.org/10.1002/poi3.427''''''

= India =
In progress
== United States ==
=== Supply Chain Management and Rare Earth Minerals ===
The United States' pursuit of AI dominance rests on a chain of physical dependencies: AI systems require advanced semiconductors, semiconductors require rare earth elements and critical minerals, particularly the neodymium, dysprosium, and gadolinium used in the magnets and components of chip fabrication and data-centre hardware, and the United States currently remains reliant on processing capacity concentrated in China. Where the [https://www.congress.gov/bill/117th-congress/house-bill/4346 CHIPS and Science Act] addresses the fabrication layer, a separate and more fragmented body of law addresses the mineral layer beneath it. Unlike the EU's [https://eur-lex.europa.eu/eli/reg/2024/1252/oj/eng Critical Raw Materials Act], the American framework layers a foundational statute, an executive order, and a narrower defense-procurement statute. 
=== Supply Chain Management and Critical Minerals ===
Section 7002 of the Energy Act of 2020 (codified at [https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=30-USC-185389929-22931526&term_occur=999&term_src= 30 U.S.C. §1606]) supplies the foundational definitions. It defines a "critical mineral" through a delegated standard: a "byproduct" is a critical mineral whose recovery depends on the production of a host mineral not itself designated critical, while a "critical material" is any non-fuel mineral, element, substance, or material the Secretary of Energy determines carries a high risk of supply-chain disruption. This is primarily an administrative and classificatory act: it does not itself compel production, but it creates the taxonomy (which of the 17 rare earth elements are legally "critical") on which subsequent executive and procurement action depends.
=== Executive Order 14241 (2025) ===
[https://www.whitehouse.gov/presidential-actions/2025/03/immediate-measures-to-increase-american-mineral-production/ Executive Order 14241], "Immediate Measures to Increase American Mineral Production," signed March 20, 2025, operationalizes that taxonomy. Its Section 1 purpose clause is unusually candid about the framing: national and economic security are now threatened by dependence on hostile foreign powers. Operationally, Section 3 directs every federal agency involved in permitting to submit to the National Energy Dominance Council a list of all mineral production projects for which a plan of operations or permit application has been submitted, after which agency heads must identify projects that can be immediately approved and expedite them. The Order invokes [https://www.law.cornell.edu/uscode/text/50/4533 Title III of the Defense Production Act] to mobilize financing, and tasks the NEDC, chaired by the Secretary of the Interior, with coordinating implementation across Defense, Energy, Interior, and Agriculture. Unlike the CHIPS Act, it required no congressional appropriation, and unlike the Energy Act, it does not merely classify inputs but seeks to physically build an alternative domestic supply chain, the mining-and-processing analogue to the CRMA's "strategic projects" mechanism. Executive Order 14241 has since been diluted by [https://www.whitehouse.gov/presidential-actions/2025/04/reinvigorating-americas-beautiful-clean-coal-industry-and-amending-executive-order-14241/ Executive Order 14261], which extends designation of criticality to sectorally unrelated materials such as coal.
=== 10 U.S.C. §4872 and the DFARS ===
The narrowest instrument is [https://www.law.cornell.edu/uscode/text/10/4872 10 U.S.C. §4872], which provides that the Secretary of Defense may not procure any covered material melted or produced in any covered nation, or any end item containing such material. [https://www.law.cornell.edu/definitions/uscode.php?def_id=10-USC-1197798417-780045830&term_src=title:10:subtitle:A:part:V:subpart:I:chapter:385:subchapter:III:section:4872 “Covered nations"] are defined as North Korea, China, Russia, and Iran, covered materials include samarium-cobalt and neodymium-iron-boron magnets. Implemented through [https://www.acquisition.gov/dfars/225.7018-2-restriction. DFARS 225.7018-2], the clause currently bars acquisition of any covered material melted or produced in any covered country, tightening in 2027 to cover the entire chain. This statute does not build supply; it leverages the federal government's own purchasing power to exclude adversary-sourced inputs from the defense industrial base, a tool with no direct EU parallel, since the CRMA's diversification targets operate through incentive rather than exclusion.

Representative Search Engine and AI Company in China, Baidu - By Jihae Park

2026-09-23T04:43:09Z

Park Jihae: Undid revision 123943 by Park Jihae (talk)

Baidu is a Chinese multinational technology company and is well-known for being the representative search engine in Chinese market. It incorporates a wide range of internet services ranging from Baidu Wiki(equivalent to Wikipedia) to Baidu App (A flagship app for Baidu Search). For Baidu App, it had 679 million in December 2025, ensuring its place as the dominant search-plus-feed app in Chinese market.[https://ir.baidu.com/baidu-general-business [1]] As the leading AI company in China, Baidu is one of the few companies in the world that could offer a full AI stack of four layers, including cloud infrastructure, in-house developed deep learning framework named PaddlePaddle, a self-developed AI foundation models called ERNIE, and applications.[https://ir.baidu.com/company-overview [2]]

   Simply put, Baidu is a Chinese version of Google which entails a variety internet services and AI tools. It is a public institution, which is dual-listed in both Nasdaq and HKEX (Hong Kong Stock Exchange), also a non-state institution chaired by its founder Robin Li. Hence I will be drawing a holistic analysis of the institutions that Baidu is running at the moment.
== The source of authority and its governing instrument ==
   By the nature of China’s political system, the state always remains as the main regulatory authority in society regardless of being a state or non-state institution. Within this state-centered regulatory environment (China’s cyberspace, data, and platform regulators – the CAC, SAMR, etc.), it is inevitable for all institutions to follow by Chinese state regulation.
   Baidu being a dominant digital platform, it does not just operate within the state regulation system. Drawing from Julia Black’s framework, it also has its own regulatory functions as well. It could set the rules by its search algorithm ranks content, the terms applied to the Baidu Union partner network, the content moderation standards on Haokan and other platforms, and even technical standards for its AI/autonomous-driving ecosystem (Apollo).
   This is a good example of a “self-regulation by a powerful non-state actor” from Black’s analytical framework. Though Baidu can’t be entirely free of the state-centered regulatory environment under Chinese political system, it plays a decentralizing role to a certain extent, away from the central state.

== 2. The interest or value it purports to protect ==
    Since 2017, Baidu officially stated that their mission is “to make the complicated world simpler through technology”. Using AI and search technology, Baidu aims to decrease friction and complexity in accessing information and services through its two-pillar strategy, by strengthening its mobile foundation and leading in artificial intelligence. It has been consistently investing in AI sector since 2010 to achieve its goal. It serves to protect the values and interests of its users (from their online platform services), customers (advertisers), partners (Baidu Union, content creators), and its shareholders. 
== One weakness in its capacity, legitimacy, or accountability ==
   The most significant weakness of Baidu comes from its accountability. Being a dominant search engine and leading AI company in China, its capacity is out of question. In terms of legitimacy, it is just impossible for a Chinese company to survive outside of state control, implying that its legitimacy is secured. Yet there are some doubts regarding its accountability for the information and contents they are providing.
   With the censorship activities frequently implemented by Chinese government, certain information is not even accessible on Baidu. For instance, if you are trying to search about “cultural revolution”, which is one of the significant historical even for its attempt for democratization, you could only find simple information explaining that it was an anti-proletarian movement which posed a serious threat to the state and the Chinese Communist Party. This indicates that the data and information from Baidu is under a strict control of Chinese government. Therefore, the accountability for the information that Baidu portrays is still in question with this regard.

=== References ===
  1. Baidu Inc, 'Baidu General Business' (''Baidu Investor Relations'') https://ir.baidu.com/baidu-general-business accessed 22 September 2026

  2. Baidu Inc, 'Company Overview' (''Baidu Investor Relations'') https://ir.baidu.com/company-overview accessed 22 September 2026

Representative Search Engine and AI Company in China, Baidu - By Jihae Park

2026-09-23T04:41:36Z

Park Jihae: /* 2. The interest or value it purports to protect */

Baidu is a Chinese multinational technology company and is well-known for being the representative search engine in Chinese market. It incorporates a wide range of internet services ranging from Baidu Wiki(equivalent to Wikipedia) to Baidu App (A flagship app for Baidu Search). For Baidu App, it had 679 million in December 2025, ensuring its place as the dominant search-plus-feed app in Chinese market.[https://ir.baidu.com/baidu-general-business [1]] As the leading AI company in China, Baidu is one of the few companies in the world that could offer a full AI stack of four layers, including cloud infrastructure, in-house developed deep learning framework named PaddlePaddle, a self-developed AI foundation models called ERNIE, and applications.[https://ir.baidu.com/company-overview [2]]

   Simply put, Baidu is a Chinese version of Google which entails a variety internet services and AI tools. It is a public institution, which is dual-listed in both Nasdaq and HKEX (Hong Kong Stock Exchange), also a non-state institution chaired by its founder Robin Li. Hence I will be drawing a holistic analysis of the institutions that Baidu is running at the moment.
== The source of authority and its governing instrument ==
   By the nature of China’s political system, the state always remains as the main regulatory authority in society regardless of being a state or non-state institution. Within this state-centered regulatory environment (China’s cyberspace, data, and platform regulators – the CAC, SAMR, etc.), it is inevitable for all institutions to follow by Chinese state regulation.
   Baidu being a dominant digital platform, it does not just operate within the state regulation system. Drawing from Julia Black’s framework, it also has its own regulatory functions as well. It could set the rules by its search algorithm ranks content, the terms applied to the Baidu Union partner network, the content moderation standards on Haokan and other platforms, and even technical standards for its AI/autonomous-driving ecosystem (Apollo).
   This is a good example of a “self-regulation by a powerful non-state actor” from Black’s analytical framework. Though Baidu can’t be entirely free of the state-centered regulatory environment under Chinese political system, it plays a decentralizing role to a certain extent, away from the central state.

== The interest or value it purports to protect ==    Since 2017, Baidu officially stated that their mission is “to make the complicated world simpler through technology”. Using AI and search technology, Baidu aims to decrease friction and complexity in accessing information and services through its two-pillar strategy, by strengthening its mobile foundation and leading in artificial intelligence. It has been consistently investing in AI sector since 2010 to achieve its goal. It serves to protect the values and interests of its users (from their online platform services), customers (advertisers), partners (Baidu Union, content creators), and its shareholders. 

== One weakness in its capacity, legitimacy, or accountability ==
   The most significant weakness of Baidu comes from its accountability. Being a dominant search engine and leading AI company in China, its capacity is out of question. In terms of legitimacy, it is just impossible for a Chinese company to survive outside of state control, implying that its legitimacy is secured. Yet there are some doubts regarding its accountability for the information and contents they are providing.
   With the censorship activities frequently implemented by Chinese government, certain information is not even accessible on Baidu. For instance, if you are trying to search about “cultural revolution”, which is one of the significant historical even for its attempt for democratization, you could only find simple information explaining that it was an anti-proletarian movement which posed a serious threat to the state and the Chinese Communist Party. This indicates that the data and information from Baidu is under a strict control of Chinese government. Therefore, the accountability for the information that Baidu portrays is still in question with this regard.

=== References ===
  1. Baidu Inc, 'Baidu General Business' (''Baidu Investor Relations'') https://ir.baidu.com/baidu-general-business accessed 22 September 2026

  2. Baidu Inc, 'Company Overview' (''Baidu Investor Relations'') https://ir.baidu.com/company-overview accessed 22 September 2026

Representative Search Engine and AI Company in China, Baidu - By Jihae Park

2026-09-23T04:41:19Z

Park Jihae: /* 1. The source of authority and its governing instrument */

Representative Search Engine and AI Company in China, Baidu - By Jihae Park

2026-09-23T04:41:00Z

Park Jihae: /* 3. One weakness in its capacity, legitimacy, or accountability */

Baidu is a Chinese multinational technology company and is well-known for being the representative search engine in Chinese market. It incorporates a wide range of internet services ranging from Baidu Wiki(equivalent to Wikipedia) to Baidu App (A flagship app for Baidu Search). For Baidu App, it had 679 million in December 2025, ensuring its place as the dominant search-plus-feed app in Chinese market.[https://ir.baidu.com/baidu-general-business [1]] As the leading AI company in China, Baidu is one of the few companies in the world that could offer a full AI stack of four layers, including cloud infrastructure, in-house developed deep learning framework named PaddlePaddle, a self-developed AI foundation models called ERNIE, and applications.[https://ir.baidu.com/company-overview [2]]

   Simply put, Baidu is a Chinese version of Google which entails a variety internet services and AI tools. It is a public institution, which is dual-listed in both Nasdaq and HKEX (Hong Kong Stock Exchange), also a non-state institution chaired by its founder Robin Li. Hence I will be drawing a holistic analysis of the institutions that Baidu is running at the moment.
== 1. The source of authority and its governing instrument ==
   By the nature of China’s political system, the state always remains as the main regulatory authority in society regardless of being a state or non-state institution. Within this state-centered regulatory environment (China’s cyberspace, data, and platform regulators – the CAC, SAMR, etc.), it is inevitable for all institutions to follow by Chinese state regulation.   Baidu being a dominant digital platform, it does not just operate within the state regulation system. Drawing from Julia Black’s framework, it also has its own regulatory functions as well. It could set the rules by its search algorithm ranks content, the terms applied to the Baidu Union partner network, the content moderation standards on Haokan and other platforms, and even technical standards for its AI/autonomous-driving ecosystem (Apollo).   This is a good example of a “self-regulation by a powerful non-state actor” from Black’s analytical framework. Though Baidu can’t be entirely free of the state-centered regulatory environment under Chinese political system, it plays a decentralizing role to a certain extent, away from the central state.
== 2. The interest or value it purports to protect ==
    Since 2017, Baidu officially stated that their mission is “to make the complicated world simpler through technology”. Using AI and search technology, Baidu aims to decrease friction and complexity in accessing information and services through its two-pillar strategy, by strengthening its mobile foundation and leading in artificial intelligence. It has been consistently investing in AI sector since 2010 to achieve its goal. It serves to protect the values and interests of its users (from their online platform services), customers (advertisers), partners (Baidu Union, content creators), and its shareholders. 
== One weakness in its capacity, legitimacy, or accountability ==
   The most significant weakness of Baidu comes from its accountability. Being a dominant search engine and leading AI company in China, its capacity is out of question. In terms of legitimacy, it is just impossible for a Chinese company to survive outside of state control, implying that its legitimacy is secured. Yet there are some doubts regarding its accountability for the information and contents they are providing.
   With the censorship activities frequently implemented by Chinese government, certain information is not even accessible on Baidu. For instance, if you are trying to search about “cultural revolution”, which is one of the significant historical even for its attempt for democratization, you could only find simple information explaining that it was an anti-proletarian movement which posed a serious threat to the state and the Chinese Communist Party. This indicates that the data and information from Baidu is under a strict control of Chinese government. Therefore, the accountability for the information that Baidu portrays is still in question with this regard.

=== References ===
  1. Baidu Inc, 'Baidu General Business' (''Baidu Investor Relations'') https://ir.baidu.com/baidu-general-business accessed 22 September 2026

  2. Baidu Inc, 'Company Overview' (''Baidu Investor Relations'') https://ir.baidu.com/company-overview accessed 22 September 2026

Representative Search Engine and AI Company in China, Baidu - By Jihae Park

2026-09-23T04:39:40Z

Park Jihae:

Baidu is a Chinese multinational technology company and is well-known for being the representative search engine in Chinese market. It incorporates a wide range of internet services ranging from Baidu Wiki(equivalent to Wikipedia) to Baidu App (A flagship app for Baidu Search). For Baidu App, it had 679 million in December 2025, ensuring its place as the dominant search-plus-feed app in Chinese market.[https://ir.baidu.com/baidu-general-business [1]] As the leading AI company in China, Baidu is one of the few companies in the world that could offer a full AI stack of four layers, including cloud infrastructure, in-house developed deep learning framework named PaddlePaddle, a self-developed AI foundation models called ERNIE, and applications.[https://ir.baidu.com/company-overview [2]]

   Simply put, Baidu is a Chinese version of Google which entails a variety internet services and AI tools. It is a public institution, which is dual-listed in both Nasdaq and HKEX (Hong Kong Stock Exchange), also a non-state institution chaired by its founder Robin Li. Hence I will be drawing a holistic analysis of the institutions that Baidu is running at the moment.
== 1. The source of authority and its governing instrument ==
   By the nature of China’s political system, the state always remains as the main regulatory authority in society regardless of being a state or non-state institution. Within this state-centered regulatory environment (China’s cyberspace, data, and platform regulators – the CAC, SAMR, etc.), it is inevitable for all institutions to follow by Chinese state regulation.   Baidu being a dominant digital platform, it does not just operate within the state regulation system. Drawing from Julia Black’s framework, it also has its own regulatory functions as well. It could set the rules by its search algorithm ranks content, the terms applied to the Baidu Union partner network, the content moderation standards on Haokan and other platforms, and even technical standards for its AI/autonomous-driving ecosystem (Apollo).   This is a good example of a “self-regulation by a powerful non-state actor” from Black’s analytical framework. Though Baidu can’t be entirely free of the state-centered regulatory environment under Chinese political system, it plays a decentralizing role to a certain extent, away from the central state.
== 2. The interest or value it purports to protect ==
    Since 2017, Baidu officially stated that their mission is “to make the complicated world simpler through technology”. Using AI and search technology, Baidu aims to decrease friction and complexity in accessing information and services through its two-pillar strategy, by strengthening its mobile foundation and leading in artificial intelligence. It has been consistently investing in AI sector since 2010 to achieve its goal. It serves to protect the values and interests of its users (from their online platform services), customers (advertisers), partners (Baidu Union, content creators), and its shareholders. 
== 3. One weakness in its capacity, legitimacy, or accountability ==
   The most significant weakness of Baidu comes from its accountability. Being a dominant search engine and leading AI company in China, its capacity is out of question. In terms of legitimacy, it is just impossible for a Chinese company to survive outside of state control, implying that its legitimacy is secured. Yet there are some doubts regarding its accountability for the information and contents they are providing.   With the censorship activities frequently implemented by Chinese government, certain information is not even accessible on Baidu. For instance, if you are trying to search about “cultural revolution”, which is one of the significant historical even for its attempt for democratization, you could only find simple information explaining that it was an anti-proletarian movement which posed a serious threat to the state and the Chinese Communist Party. This indicates that the data and information from Baidu is under a strict control of Chinese government. Therefore, the accountability for the information that Baidu portrays is still in question with this regard.
=== References ===
  1. Baidu Inc, 'Baidu General Business' (''Baidu Investor Relations'') https://ir.baidu.com/baidu-general-business accessed 22 September 2026

  2. Baidu Inc, 'Company Overview' (''Baidu Investor Relations'') https://ir.baidu.com/company-overview accessed 22 September 2026

Representative Search Engine and AI Company in China, Baidu - By Jihae Park

2026-09-23T02:56:33Z

Park Jihae: /* 1. The source of authority and its governing instrument */

== 1. The source of authority and its governing instrument ==
  Baidu is a Chinese multinational technology company and is well-known for being the representative search engine in Chinese market. It incorporates a wide range of internet services ranging from Baidu Wiki(equivalent to Wikipedia) to Baidu App (A flagship app for Baidu Search). For Baidu App, it had 679 million in December 2025, ensuring its place as the dominant search-plus-feed app in Chinese market.[https://ir.baidu.com/baidu-general-business [1]] As the leading AI company in China, Baidu is one of the few companies in the world that could offer a full AI stack of four layers, including cloud infrastructure, in-house developed deep learning framework named PaddlePaddle, a self-developed AI foundation models called ERNIE, and applications.[https://ir.baidu.com/company-overview [2]] Simply put, Baidu is a Chinese version of Google, so it   Being a highly centralized company, it runs through a conventional top-down structure. This consists of a board of directors chaired by Li, with Robin Yanhong Li serving as chairman since the company's founding in 2000 and CEO since 2004, overseeing strategy and operations, supported by a CFO and a small set of independe nt directors.

== 2. The interest or value it purports to protect ==
== 3. One weakness in its capacity, legitimacy, or accountability ==
=== References ===
  1. Baidu Inc, 'Baidu General Business' (''Baidu Investor Relations'') https://ir.baidu.com/baidu-general-business accessed 22 September 2026

  2. Baidu Inc, 'Company Overview' (''Baidu Investor Relations'') https://ir.baidu.com/company-overview accessed 22 September 2026

Representative Search Engine and AI Company in China, Baidu - By Jihae Park

2026-09-22T20:43:19Z

Park Jihae: Created page with "== 1. The source of authority and its governing instrument ==   Baidu is a Chinese multinational technology company and is known for being the representative search engine in Chinese market. It incorporates a wide range of internet services ranging from Baidu Wiki(equivalent to Wikipedia) to Baidu App (A flagship app for Baidu Search). For Baidu App, it had 679 million in December 2025, ensuring its place as the dominant search-plus-feed app in Chinese market.[https..."

== 1. The source of authority and its governing instrument ==
  Baidu is a Chinese multinational technology company and is known for being the representative search engine in Chinese market. It incorporates a wide range of internet services ranging from Baidu Wiki(equivalent to Wikipedia) to Baidu App (A flagship app for Baidu Search). For Baidu App, it had 679 million in December 2025, ensuring its place as the dominant search-plus-feed app in Chinese market.[https://ir.baidu.com/baidu-general-business [1]] As the leading AI company in China, Baidu is one of the few companies in the world that could offer a full AI stack of four layers, including cloud infrastructure, in-house developed deep learning framework named PaddlePaddle, a self-developed AI foundation models called ERNIE, and applications.[https://ir.baidu.com/company-overview [2]]  Being a highly centralized company, it runs through a conventional top-down structure. This consists of a board of directors chaired by Li, with Robin Yanhong Li serving as chairman since the company's founding in 2000 and CEO since 2004, overseeing strategy and operations, supported by a CFO and a small set of independent directors. 
== 2. The interest or value it purports to protect ==
== 3. One weakness in its capacity, legitimacy, or accountability ==
=== References ===
  1. Baidu Inc, 'Baidu General Business' (''Baidu Investor Relations'') https://ir.baidu.com/baidu-general-business accessed 22 September 2026

  2. Baidu Inc, 'Company Overview' (''Baidu Investor Relations'') https://ir.baidu.com/company-overview accessed 22 September 2026

Governance by institutions

2026-09-22T20:30:28Z

Park Jihae:

[https://wiki.ivir.nl/index.php?title=Antropic%27s_Fable_5.1_(Kate_Davey) Anthropic's Fable 5.1 (Kate Davey)]

*[[social-media platform]] (mariona)[[]]
************
= [[cloud service (Ceren)]] =
* app store
*digital identity system
*online-payment system
*AI model
*public algorithmic system.
[https://wiki.ivir.nl/index.php?title=Decentralized_digital_currency:_BITCOIN_(Sisi) Decentralized Digital Currency: Bitcoin (Sisi)]

[[Submarine Internet Cables - By Pablo Latorre]]

[[Artificial General Intelligence - By Sebastián Muller]]

[https://wiki.ivir.nl/index.php?title=Social-Media_Governance_in_the_EU_-_by_Sergey_Abramov Social-Media Governance - by Sergey Abramov]

= [[Predicitive Policing - Europol (Atmika)]] =
== [[App Store Governance - Google Play (Paul)]]`` ==
<h2 class="mwt-heading" >[[Representative Search Engine and AI Company in China, Baidu - By Jihae Park]]</h2>

= [https://wiki.ivir.nl/index.php?title=Revolut_Regulation Revolut Regulation] =

Governance by institutions

2026-09-22T20:29:10Z

Park Jihae:

[https://wiki.ivir.nl/index.php?title=Antropic%27s_Fable_5.1_(Kate_Davey) Anthropic's Fable 5.1 (Kate Davey)]

*[[social-media platform]] (mariona)[[]]
************
= [[cloud service (Ceren)]] =
* app store
*digital identity system
*online-payment system
*AI model
*public algorithmic system.
[https://wiki.ivir.nl/index.php?title=Decentralized_digital_currency:_BITCOIN_(Sisi) Decentralized Digital Currency: Bitcoin (Sisi)]

[[Submarine Internet Cables - By Pablo Latorre]]

[[Artificial General Intelligence - By Sebastián Muller]]

[https://wiki.ivir.nl/index.php?title=Social-Media_Governance_in_the_EU_-_by_Sergey_Abramov Social-Media Governance - by Sergey Abramov]

= [[Predicitive Policing - Europol (Atmika)]] =
== [[App Store Governance - Google Play (Paul)]]`` ==
[[A Representative Search Engine and AI Company in China, Baidu - By Jihae Park]] 

= [https://wiki.ivir.nl/index.php?title=Revolut_Regulation Revolut Regulation] =

Information Control and Workplace Surveillance - Jihae Park and Mariona Fortuny

2026-09-20T19:44:05Z

Park Jihae: Created page with "== Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané == ==== The Current Governance Issue ==== There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in s..."

== Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané ==

==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

Governance by history

2026-09-20T19:43:06Z

Park Jihae: /* contributions */

[[File:Old internet.jpg|300px|thumb]]

= contributions =
*can
*you 
*please
*create
*individual pages
*from the contributions below
*and link them back here? 
*[[Information Control and Workplace Surveillance - Jihae Park and Mariona Fortuny]]

= links things that popped up during class =
*[https://craphound.com/ Cory Doctorow]
*[https://dougengelbart.org/content/view/209/ The mother of All demos] (first mouse, graphical interface, word processor, etc)
*Can a simple sensor be political? [https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773 well, this "racist" soap dispenser made headlines a couple of years ago.]
*Podcast series: [https://www.the-santiago-boys.com/ Morozov: the Santiago Boys.]
*Anatomy on AN AI. [[File:Ai-anatomy-map.pdf|check out the pdf here]]

= Workplace surveillance =
== Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané ==

==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 
== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 
=== 2. Historical lineage. === 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 
=== 5. Contingency and alternatives. === 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

= [[Platform Dependence in Public Communication (News) - Ceren Isiklar & Rodrigue Kayembe|Platform Dependence in Public Communication (News)]] Ceren Isiklar & Rodrigue Kayembe =

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.=== Politics and distribution ===Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.

=== Contingency and alternatives ===If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.

=== Governance implication ===History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.


=== Bibliography ===
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div>
<div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div>
<div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div>
<div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div>
<div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div>
<div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div>
<div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div>
<div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 ==== Revert to Physical Infrastructure Dependency in the rush to be the best ====The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 ==== Regulation and Decentralization of Information ====Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.
=== Mechanisms of Path Dependence ===Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. ''Energies'', ''19''(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', ''6''(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In ''Computer ethics''(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. ''The American economic review'', ''75''(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-20T19:42:54Z

Park Jihae: /* contributions */

[[File:Old internet.jpg|300px|thumb]]

= contributions =
*can
*you 
*please
*create
*individual pages
*from the contributions below
*and link them back here?   [[Information Control and Workplace Surveillance - Jihae Park and Mariona Fortuny]]

= links things that popped up during class =
*[https://craphound.com/ Cory Doctorow]
*[https://dougengelbart.org/content/view/209/ The mother of All demos] (first mouse, graphical interface, word processor, etc)
*Can a simple sensor be political? [https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773 well, this "racist" soap dispenser made headlines a couple of years ago.]
*Podcast series: [https://www.the-santiago-boys.com/ Morozov: the Santiago Boys.]
*Anatomy on AN AI. [[File:Ai-anatomy-map.pdf|check out the pdf here]]

= Workplace surveillance =
== Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané ==

==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 
== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 
=== 2. Historical lineage. === 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 
=== 5. Contingency and alternatives. === 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

= [[Platform Dependence in Public Communication (News) - Ceren Isiklar & Rodrigue Kayembe|Platform Dependence in Public Communication (News)]] Ceren Isiklar & Rodrigue Kayembe =

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.=== Politics and distribution ===Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.

=== Contingency and alternatives ===If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.

=== Governance implication ===History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.


=== Bibliography ===
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div>
<div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div>
<div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div>
<div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div>
<div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div>
<div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div>
<div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div>
<div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 ==== Revert to Physical Infrastructure Dependency in the rush to be the best ====The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 ==== Regulation and Decentralization of Information ====Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.
=== Mechanisms of Path Dependence ===Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. ''Energies'', ''19''(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', ''6''(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In ''Computer ethics''(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. ''The American economic review'', ''75''(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Jihae and Mariona

2026-09-20T19:41:48Z

Governance by history

2026-09-20T19:41:25Z

Park Jihae: /* contributions */

[[File:Old internet.jpg|300px|thumb]]

= contributions =
*can
*you 
*please
*create
*individual pages
*from the contributions below
*and link them back here?
[[Jihae and Mariona]]

= links things that popped up during class =
*[https://craphound.com/ Cory Doctorow]
*[https://dougengelbart.org/content/view/209/ The mother of All demos] (first mouse, graphical interface, word processor, etc)
*Can a simple sensor be political? [https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773 well, this "racist" soap dispenser made headlines a couple of years ago.]
*Podcast series: [https://www.the-santiago-boys.com/ Morozov: the Santiago Boys.]
*Anatomy on AN AI. [[File:Ai-anatomy-map.pdf|check out the pdf here]]

= Workplace surveillance =
== Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané ==

==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 
== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 
=== 2. Historical lineage. === 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 
=== 5. Contingency and alternatives. === 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

= [[Platform Dependence in Public Communication (News) - Ceren Isiklar & Rodrigue Kayembe|Platform Dependence in Public Communication (News)]] Ceren Isiklar & Rodrigue Kayembe =

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.=== Politics and distribution ===Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.

=== Contingency and alternatives ===If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.

=== Governance implication ===History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.


=== Bibliography ===
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div>
<div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div>
<div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div>
<div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div>
<div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div>
<div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div>
<div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div>
<div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 ==== Revert to Physical Infrastructure Dependency in the rush to be the best ====The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 ==== Regulation and Decentralization of Information ====Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.
=== Mechanisms of Path Dependence ===Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. ''Energies'', ''19''(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', ''6''(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In ''Computer ethics''(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. ''The American economic review'', ''75''(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-20T19:40:57Z

Park Jihae: /* contributions */

[[File:Old internet.jpg|300px|thumb]]

= contributions =
*can
*you 
*please
*create
*individual pages
*from the contributions below
*and link them back here?
[[Mariona and Jihae]]

= links things that popped up during class =
*[https://craphound.com/ Cory Doctorow]
*[https://dougengelbart.org/content/view/209/ The mother of All demos] (first mouse, graphical interface, word processor, etc)
*Can a simple sensor be political? [https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773 well, this "racist" soap dispenser made headlines a couple of years ago.]
*Podcast series: [https://www.the-santiago-boys.com/ Morozov: the Santiago Boys.]
*Anatomy on AN AI. [[File:Ai-anatomy-map.pdf|check out the pdf here]]

= Workplace surveillance =
== Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané ==

==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 
== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 
=== 2. Historical lineage. === 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 
=== 5. Contingency and alternatives. === 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

= [[Platform Dependence in Public Communication (News) - Ceren Isiklar & Rodrigue Kayembe|Platform Dependence in Public Communication (News)]] Ceren Isiklar & Rodrigue Kayembe =

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.=== Politics and distribution ===Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.

=== Contingency and alternatives ===If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.

=== Governance implication ===History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.


=== Bibliography ===
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div>
<div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div>
<div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div>
<div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div>
<div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div>
<div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div>
<div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div>
<div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div>
<div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 ==== Revert to Physical Infrastructure Dependency in the rush to be the best ====The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 ==== Regulation and Decentralization of Information ====Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.
=== Mechanisms of Path Dependence ===Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. ''Energies'', ''19''(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', ''6''(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In ''Computer ethics''(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. ''The American economic review'', ''75''(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T23:03:30Z

Park Jihae: /* 6. Governance Implication */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T23:03:26Z

Park Jihae: /* 5. Contingency and Alternatives */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T23:03:21Z

Park Jihae: /* 4. Politics and Distribution */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T23:03:16Z

Park Jihae: /* 3. Mechanism of Path Dependence */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T23:03:10Z

Park Jihae: /* 2. Historical Lineage */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T23:03:04Z

Park Jihae: /* 1. The Current Governance Issue */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T23:00:42Z

Park Jihae: /* 4. Politics and Distribution */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' ([https://archive.org/details/beniger-james-the-control-revolution/page/65/mode/2up Beniger, 1986, pp. 17, 21]). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:59:07Z

Park Jihae: /* 3. Mechanism of Path Dependence */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:58:54Z

Park Jihae: /* 3. Mechanism of Path Dependence */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working. 
 This means switching costs from an old system to a new one is clearly high considering its time and capital, not necessarily will guarantee increasing returns. Hence, many firms end up maintaining its old strategy, reluctant to make any modifications or changes. 

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:22:42Z

Park Jihae: /* 2. Historical Lineage */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986, p.15-16]) This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:19:55Z

Park Jihae: /* 2. Historical Lineage */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Max Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:17:12Z

Park Jihae: /* 6. Governance implication. */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance Implication ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:17:02Z

Park Jihae: /* 5. Contingency and alternatives. */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and Alternatives ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:16:52Z

Park Jihae: /* 4. Politics and distribution. */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and Distribution ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:16:21Z

Park Jihae: /* 3. Mechanism of path dependence */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of Path Dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and distribution. ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:15:51Z

Park Jihae: /* 2. Historical lineage */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical Lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of path dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and distribution. ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:15:43Z

Park Jihae: /* 1. The current Governance issue */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The Current Governance Issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of path dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and distribution. ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:14:29Z

Park Jihae: /* 2. Historical lineage */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The current Governance issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency.

==== 3. Mechanism of path dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and distribution. ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:13:14Z

Park Jihae: /* References */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The current Governance issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency. 

==== 3. Mechanism of path dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and distribution. ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. [https://archive.org/details/beniger-james-the-control-revolution https://archive.org/details/beniger-james-the-control-revolution]  Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:12:01Z

Park Jihae: /* 1. The current Governance issue */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The current Governance issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. ([https://archive.org/details/beniger-james-the-control-revolution Beniger, 1986]) By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency. 

==== 3. Mechanism of path dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and distribution. ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Ollier-Malaterre, A., Parent-Rocheleau, X., Benkrid, K., & Provost Savard, Y. (2026). Making Sense of Workplace Surveillance: How Workers and Union Representatives Experience, Imagine, and Resist Surveillance Exposure. ''Surveillance & Society'', 24(3), 236–254. [https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>

Governance by history

2026-09-08T22:07:03Z

Park Jihae: /* 1. The current Governance issue */

= [[Workplace surveillance]] =
== [[Information Control and Workplace Surveillance - by Jihae Park and Mariona Fortuny Jané]] ==
==== 1. The current Governance issue ====
There are numerous current governance problems arising from technological innovations, yet here we will specifically focus on the information control and workplace surveillance. Whether intended or unintended, there were certain historical incidents which resulted in social and political consequences. As noticed from the Control Revolution, the dramatic evolution in mass media and telecommunications technologies has led to the reinforcement of the economic and political control during the Industrial Revolution. By applying this logic to a modern example, this is clearly how many corporations are getting in control of their employees through surveillance technologies. Along with the emergence of remote and hybrid work after Covid-19, employers are now relying on “bossware” to analyze the emails, chats, and messages of their employees on platforms such as Teams or Slack. ([https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/19100/13263 Ollier-Malaterre et al., 2026, p.237]) Some companies have strict restrictions on transferring data both internally and externally in protection of confidential information and consistently monitor their workers to the certain extent which could slow down their efficiency. This is problematic as it’s not only violating the privacy of their employees but also depriving employees of rights they should be naturally be able to exercise.



==== 2. Historical lineage ====
In relation to the current issue of the information control and workplace surveillance, Weber’s argument of bureaucracy and rationalization in the middle and late nineteenth century are good historical examples which are still frequently visible in nowadays. Weber stated how the range of control could extend not only by expanding the capability to process information but also by limiting the amount of information to be processed. Here, the former is directly related to the control implemented by bureaucratization (in the mid 19th century) and computerization (in the late 20th century), whereas the latter addresses rationalization. An example from bureaucracy is the expansion of paperwork in response to the growth in information. We can still note these traces of bureaucracy from numerous procedures that we have to go through for a decision-making to take place in corporations. He brings up the example of standardized time zones for rationalization which is simply an example for an omission or a breakdown of information to facilitate its processing. This is also commonly seen from nowadays as some companies may use internal terminologies for the sake of both confidentiality and efficiency. 

==== 3. Mechanism of path dependence ====
Following along the line with the historical lineage, there are certain mechanisms at work of path dependence for information control and workplace surveillance. Bureaucracy is closely related with organizational routines. Many companies have already settled down with a fixed workflow and decision-making process, which makes it more costly for one to change from its conventional way of working.

==== 4. Politics and distribution. ====

As businesses grew too large for managers to oversee everything directly, companies replaced human judgment with formal rule-systems, as we see explained by Beniger's 'intellectual technology' (Beniger, 1986, pp. 17, 21). Algorithmic management continues this: a call once made by a supervisor's judgment is now a rule built into software that runs without one, extending a logic Beniger traces as far back as to Taylorism and the so called 'assembly line'. Winner ([https://www.jstor.org/stable/20024652 1980]) asks whether such systems must concentrate power this way or were driven there, when other designs were equally possible; nothing required shutting workers out of the decision, so leaving them out was a political choice, and not necessarily a technical necessity. Barbrook and Cameron ([https://doi.org/10.1080/09505439609526455 1996]) explain the reasoning why some can defend this 'domination': they show how 1990s tech culture sold new technologies as freedom even as it tightened control, so the rhetoric could become one of closer oversight as flexibility and autonomy. Together, authority accrues to employers, who in some systems can set and revise these oversight rules unilaterally and face less resistance because the arrangement is framed as freedom; workers bear the cost, losing the informal give-and-take they had with a human supervisor for oversight that is harder to see, question, or contest, while being told it grants them more freedom, not less.

==== 5. Contingency and alternatives. ====

A plausible alternative happened on U.S. post WWII: in some workplaces, labor and its pacing was demanded to be set through collective bargaining and enforced jointly, by both management and union stewards monitoring compliance on the line ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, p. 211]). So, in this context, workplace surveillance could have developed as a bargained, dual-administered practice rather than a management monopoly. The 1950 [https://en.wikipedia.org/wiki/Reuther%27s_Treaty_of_Detroit Reuther's Treaty of Detroit ] cut that possibility off: unions won guaranteed wages and benefits in exchange for surrendering any general claim over how work was supervised ([https://doi.org/10.1093/icc/dtaf038 Breznitz et al., 2025, pp. 210–211]). Workers depended on wages to survive, and employers controlled the wage relationship. That asymmetry meant employers, given their position of power, were able to arrange things so that, at the very least, workers would have far less capacity to sustain a fight over control itself. Thus, the "trade" wasn't a fair exchange between equals. This reflects which side could afford to hold out on which issue. Hypothetically speaking, had that joint administration model prevailed instead, this could have greatly impacted norms internationally and good practices with worker-union agreed surveillance would have become a widely accepted standard practice.

==== 6. Governance implication. ====

An interesting proposition for present-day governance that wants to fill a gap in workplace surveillance is what [https://doi.org/10.1016/j.digbus.2025.100120 Kayas, Ong and Belal (2025)] are proposing: They argue that EU law, through the [https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng GDPR] and its required [https://en.wikipedia.org/wiki/Data_protection_impact_assessment data protection impact assessments (DPIAs)], already offers meaningful protection for workers in terms of lawful high-risk data collection and data processing. Nevertheless, these DPIAs are built around data and privacy, so they leave out several dimensions of monitoring itself: its purpose, how intrusive it is, how visible it is to the people being watched, and its effects on workers' autonomy, trust, and standing relative to management. These authors propose a "surveillance impact assessment" scoring a system against these characteristics directly, before any surveillance system is put in place. Although this mechanism "is constrained by its reliance on existing literature and the lack of empirical validation," it feels like a genuine alternative for two reasons: 1. it targets the actual mechanism at issue, and 2. at the same time it builds upon a mechanism of active participatory approach where employees have a direct say in how surveillance is designed ([https://doi.org/10.1016/j.digbus.2025.100120 Kayas et al., 2025, p. 11]), which, combined with the actual assessment, would put workers' own judgment at the centre of the one decision that affects them. It's important to note that this alternative is proposed as worth piloting, not as a proven fix, and it is fair to ask whether another mandatory assessment stacked on top of DPIAs would function as intended or simply read to workers as more compliance paperwork.

==== References ====
 Barbrook, R., & Cameron, A. (1996). The Californian Ideology. ''Science as Culture'', 6(1), 44–72. [https://doi.org/10.1080/09505439609526455 https://doi.org/10.1080/09505439609526455] Beniger, J. R. (1986). ''The Control Revolution: Technological and Economic Origins of the Information Society''. Harvard University Press. Breznitz, D., Levy, K., Lipartito, K., & Zehavi, A. (2025). An equity-focused research agenda for workplace surveillance. ''Industrial and Corporate Change'', 35(1), 206–221. [https://doi.org/10.1093/icc/dtaf038 https://doi.org/10.1093/icc/dtaf038] Kayas, O. G., Ong, C. E., & Belal, H. M. (2025). From humans to algorithms: A sociotechnical framework of workplace surveillance. ''Digital Business'', 5, 100120. [https://doi.org/10.1016/j.digbus.2025.100120 https://doi.org/10.1016/j.digbus.2025.100120] Winner, L. (1980). Do Artifacts Have Politics? ''Daedalus'', 109(1), 121–136. [https://www.jstor.org/stable/20024652 https://www.jstor.org/stable/20024652]

 

== [[Algorithmic management and workplace surveillance - by Sebastián Muller and Kate Davey''' ''']] ==

=== 1. The current issue. ===
 Algorithmic management and workplace surveillance can be defined as the use of computer-programmed procedures to coordinate labor input in an organization ([https://joint-research-centre.ec.europa.eu/scientific-activities/employment/algorithmic-management-and-digital-monitoring-work_en 1]). Simply put, it is a way to control workers, and workers generally have no say in how the system that tracks them is created. As Winner says "different people are differently situated and possess unequal degrees of power as well as unequal levels of awareness" ([https://www.jstor.org/stable/20024652?seq=7 2]).
The population affected is workers; both “white collar” and “blue collar” workers are impacted. Whether it’s Amazon tracking warehouse workers’ “Time Off Task” with handheld scanners ([https://www.wired.com/story/amazon-worker-tracking-details-revealed 3]) or Meta employees having their every click tracked ([https://www.techpolicy.press/metas-worker-surveillance-tests-eu-rules-on-ai-and-labor/ 4]) to train AI, employers have the power to watch and control their employees in an unprecedented way.
It matters legally because workers have very few rights to fight back against these surveillance programs; even the The Electronic Communications Privacy Act of 1986 allows exceptions to surveillance of employees ([https://bja.ojp.gov/program/it/privacy-civil-liberties/authorities/statutes/1285 5]). 

=== 2. Historical lineage. ===
 2.1 Panopticon: it is an architectural apparatus developed by Jeremy Bentham in 1791. The design of this building effectively generated the sense of constant monitoring, which in turn motivated the prisoners to behave as if they were being watched at all times. As human labor became commodified, capitalists began implementing measures to ensure the maximization of productivity, including the replication of this design ([https://doi.org/10.14452/MR-070-09-2019-02_2 6]). While unused today, the panopticon is widely considered as both a theoretical and practical inspiration of other forms of surveillance in the workplace.
 
2.2 Scientific management: this theory attempts to analyze and standardize workflows to improve labor productivity. Frederick Winslow Taylor, its main pioneer, used early forms of data analysis during the XX century to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. ([https://doi.org/10.14452/MR-070-09-2019-02_2 7]). There are many modern forms of data-driven decision-making processes in the workforce. For instance, Amazon warehouses track individual workers' scan rates in real time through handheld scanners, generating data feeds to track and evaluate performance ([https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations 8]).
 
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 ([https://www.computerhistory.org/revolution/personal-computers/17/297 9]), however, it wasn’t until the mid-1980s that the use of personal computers in the workplace became an industry standard. This achievement gave rise to the digital monitoring of work, which in turn allowed employers to monitor employees in real-time, effectively replacing the human eye with a more efficient surveillance mechanism. This represents one of the key aspects of modern-day workplace monitoring. 
 

=== 3. Mechanism of path dependence. ===
''' '''When analyzing the historic lineage of work surveillance, it is possible to point out two main path dependencies mechanisms in place.Firstly, it is clear that many of the technological developments that arose through these events have one major goal, which is to increase wealth within a hierarchical ecosystem. Employers resort to several mechanisms to maximize profits and cut inefficiencies at the expense of workers. 
In addition, standardization can also be singled out as a key mechanism at play. Once one corporation implements any of these developments (such as the digital monitoring of the workplace), the market incentives would force the rest to do the same in order to be profitable and remain competitive.

=== 4. Politics and distribution. ===
 One simple answer to this question would be that capitalists implement and leverage these mechanisms to increase their wealth while the working class remains powerless and unable to withdraw itself from the system in force. However, if we resort to Winner's framework, we see that the technical elements of these technologies are not intrinsically capitalist, since workplace surveillance can also be observed in alternative regimes (including chattel slavery and the Soviet regime). It seems that, rather than being exclusively tied to capitalistic values, the methods in question respond to strong hierarchical structures.
In addition, even under a libertarian framework, structural inequalities are still present, allowing a privileged class to enjoy individual freedoms while segregated groups keep performing low-income jobs ([https://www.metamute.org/editorial/articles/californian-ideology 10]), arguably still subject to surveillance. Thus, not only are these technologies compatible with hierarchical top-down forms of government, but also with structures that allow such hierarchies to form without state intervention.
That said, this form of management seems less compatible with democratic societies, which tend not only to value individual liberties but to turn them into enforceable rights (including workers' privacy, health and integrity), thus establishing boundaries for workplace surveillance. This could explain why studies of American business leaders have found them unhappy with democratic scruples ([https://www.jstor.org/stable/20024652 11]).
 

=== 5. Contingency and alternatives. ===
 
Bush writes about the memex, a machine that could extend a person’s memory and that the memory would belong to the person who owned it. As Bush expands, “For mature thought there is no mechanical substitute" ([https://dl.acm.org/doi/pdf/10.1145/227181.227186 12]). His idea was that a machine could take over the repetitive nature of someone’s job, while the person could record their own thoughts for their own use. Bush’s memex was never created, but the idea inspired products like Microsoft’s Productivity Score that were made for employers (the buyers) not for the person whose memory it used (the worker).
This immediately brings to mind Meta's Model Capability Initiative, which was done to train AI agents the company hopes to sell to other employers as well as watch their employees ([https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 13]). If this was Bush’s memex, it would belong to the employee using it, not Meta. 

=== 6. Governance implication. ===
Democratic oversight of the use of algorithms as a form of surveillance is essential to ensure that workers’ rights are not overstepped in the name of efficiency. As such we propose: all workers should have a legal right to be consulted and approve a surveillance program before a company installs and uses it. A tracking system should be a group decision, not an inevitable, unilateral one. While the United States has very little protections for workers in terms of surveillance, the EU leads the way with the GDPR, specifically Article 22 ([http://data.europa.eu/eli/reg/2016/679/oj 14]), and the AI Act Article 5 ([http://data.europa.eu/eli/reg/2024/1689/oj 15]). It’s also worth noting that in December 2026, the Platform Work Directive ([https://eur-lex.europa.eu/eli/dir/2024/2831/oj/eng 16]) will come into effect which will ban automated termination.

== [[Platform Dependence in Public Communication (News)]] Ceren & Rodrigue ==

Public communication is the conveyance of a message to a large group of people. This includes public announcements, advertisements in public spaces, social media broadcasts, news delivered in reporting, editorials, opinion pieces, and even political campaigns. Platform dependence is the reliance on a third party platform or digital infrastructure to reach the audience of the public communication, the public. Platform dependence in public communication therefore includes reliance on private companies like Meta (Facebook), state television channels, or telecommunications providers who provide and control access to, and develop the infrastructure of such platforms. For the purpose of this exercise, news will be utilized as the main example of public communication. It is very difficult to discuss news without highlighting the deep entanglement it shares with with information. For our purpose 'information' will have the the following characteristics; it can be learned, processed, archived, and it's worth correlates to its rarity.[[#fn1|[1]]] News is the end result of the process of taking this information from one form to another, and transmitting to a wider, typically public audience is what we will anchor our definition of 'news' to be.

Those who are affected by platform dependence in public communication are the communicators and the public alike. News outlets are mainly for-profit organizations. These organizations’ reliance on third party digital platforms grows day-by-day to maintain their popularity and engagement ultimately deciding the revenue generated from advertisers. The public on the other hand are provided catchier and more diluted information, gaining just enough knowledge to grab their attention while not being provided substantial knowledge on matters. Additionally, members of the public who do not have the resources to access such platforms and digital services remain “information poor”. 6 Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.

This is crucial as platform dependence may threaten democracy and free political expression. Platforms, run by private parties, can be run according to their political beliefs. Social media platforms aiming to keep users online utilize algorithms that provide their users with their confirmed preferences, not allowing the conveyance of a truly heterogeneous catalogue of news. The original ideal of the democratic electronic agora is undermined when private corporations control the flow of news and the infrastructure on which they are provided.

=== Historical Lineage ===

Modern form of this can be traced to Renaissance Europe,[[#fn2|[2]]] where a single innovation displaced established networks of collecting, processing and sharing news.The church, merchants, students, and diplomats were core participants in this ecosystem.[[#fn3|[3]]] The invention of printing in the 15th century was a seismic point in history as it offered new possibilities for the dissemination of information and therefore news. Initially, printing benefitted from the existing networks more than the networks benefitted from printing. This was the case until the networks were unrecognisably transformed. A trend we will see come to see throughout the history of the news. A clear instance of this is Johann Carolus's ''Relation aller Fürnemmen und gedenckwürdigen Historien'', a handwritten newspaper delivered weekly to the elite class of the time. The mode of production and the means of distribution were constrained by the available technology rather than strategy. The mode of mode production decreased the cost, time and later revolutionised the network as production could be localised and dependency on transporting was reduced.  

One of the first paying customer for press was the Church, selling indulgences. During this era the Church and the State were in a clear symbiotic relationship making drawing a separation between the two difficult. They both deeply understood Beniger's principle that control depends on information processing and communication working together.[[#fn4|[4]]] By introducing hurdles of licensing the type of content that could be printed on one hand and benefitting from the industrial might of this innovation they had effectively consolidated power and control. Interesting, this contrasts what takes place generations later in the computing revolutions, where proximity to the state purposely unstated in order to appeal to the mass.[[#fn5|[5]]] 

The next evolution of news takes place when transmission is once again innovated with the radio. With radio reach could reach could be decoupled from cost, reliance on aging networks such as the now matured postal system. Control was could now be on content and move closer to physics on licensing who could be broadcast rights. This changed the trajectory of new's as it birth the state broadcaster, responsible for broadcasting news to anyone with a receiver on the appropriate frequency. News outlet now had legitimate diverse alternative methods of production and distribution. The television followed not after, however it mostly followed the trajectory of the radio with the caveat that it dominated due to ability to generate commercial revenue through advertisement and optionality it provided.  

=== Mechanism of Path Dependence ===

The public is generally partakes in the consumption of content on social media. Hence, with its 3 billion monthly active users for example, Instagram has an installed base for news outlets to depend upon (Instagram Revenue and Usage Statistics (2026), n.d.). The audiences of news outlets spend an increasing amount of their time on social media platforms, which gives results in a habitual entrenchment for these news outlets: the means of reaching the audience is dependent on their habits of content consumption. An indirect contractual entrenchment may also be discussed, as news outlets have obligations to their advertisers (who may request the upkeep of views/clicks on the news platforms they pay to advertise in). Cultural entrenchment should not be disregarded: the public expects to receive their news on social media platforms they readily frequent, and many news outlets meet this expectation. Hence, news outlets must partake in building a presence in social media platforms if they wish to compete.

=== Politics and Distribution ===

Barbook argues that although the California Ideology claims that state power will be decreased and individuals will be empowered through decentralized networks enabled by digital technologies, he demonstrates that public communication is in fact not immune to undemocratic powers. These include private corporations: these corporations may gatekeep in choosing when and how to provide access to their platforms, while finetuning how users navigate their platforms. By enabling access to content that users tend to agree with, those who manage these platforms enable public opinion to shift to extremes. Being “fed” the same narrative and not being challenged to see an opposing perspective ultimately provides fewer choices to the consumer of the information, the general public. Therefore, individual agency is constrained by platform architecture and control, not liberated by it.

=== Point Of No Return ===
In examining the lineage of news and its eventual platform dependency, the diminishing returns of technological advancement for the industry become evident. Revenue has not been a particular focus of this article. The broadcast era was the era in which news outlets earned some of their largest revenues and were at their most independent. It was during this time that news outlets initiated the unbundling of one of their main revenue generators, the classified advertisement. By initiating this [https://stratechery.com/2017/the-great-unbundling/ unbundling] without any afterthought on capitalising on its timely advantages, news outlets entered their relationships with social media platforms in a weakened position. Searching for a saviour and giving the platform the credentials and reputation they needed in the early days in return. Reminiscent of the trend identified earlier. An example of a new's outlet that has navigated this journey with more diligence is [https://business.columbia.edu/insights/ai-transformative-tech/how-new-york-times-company-reinventing-itself-digital-age The New York Times].

=== Governance Implication & Proposition ===

It's clear that complete avoidance of these platforms on the part of news outlet is a Heculean request! New's outlets have flocked to these platforms due to their substantial audience and lock on audience. It's virtually impossible for a single organisation to mount a significant rebellion without self harming. A proposal is to approach the problem more as a herd and invest and cultivate more alternative open platforms for example [https://en.wikipedia.org/wiki/Bluesky Bluesky]  and [https://joinmastodon.org Mastadon]. The benefit that these platforms bring about begin with not only optionality from the current dependencies but governance via protocol.

=== Footnotes ===

# Mark Greengrass, Thierry Rentet and Stéphane Gal, ‘The Hinterland of the Newsletter: Handling Information in Space and Time’ in Joad Raymond and Noah Moxham (eds), News Networks in Early Modern Europe (Brill 2016) http://www.jstor.org/stable/10.1163/j.ctt1w8h1ng.34 accessed 7 September 2026; ‘Michel Serres Prononce En Sorbonne La Conférence Inaugurale Du Programme Paris Nouveaux Mondes - La Chancellerie Des Universités de Paris’ (La Chancellerie des Universités de Paris, 1 January 2013) https://www.sorbonne.fr/michel-serres-prononce-en-sorbonne-la-conference-inaugurale-du-programme-paris-nouveaux-mondes/ accessed 7 September 2026. James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986).
# Brendan Dooley, Making News in Renaissance Europe (1st edn, Cambridge University Press 2026) https://doi.org/10.1017/9781009358835.
# Andrew Pettegree, The Invention of News: How the World Came to Know about Itself (Yale University Press 2014). Helmer Helmers, ‘Public Diplomacy in Early Modern Europe: Towards a New History of News’ (2016) 22 Media History 401 https://doi.org/10.1080/13688804.2016.1174570.
# James Ralph Beniger, The Control Revolution: Technological and Economic Origins of the Information Society (Harvard Univ Press 1986) P24-25
# Richard Barbrook and Andy Cameron, The Californian Ideology (1996) 
#Richard Barbrook and Andy Cameron, The Californian Ideology (1996)

= Centralization of AI Compute and Data =

== Centralization of AI Compute and Data - by Sisi Hammoudi & Juliette Chamot ==
=== Governance issue ===
Today, several path-dependence phenomena are emerging across advanced computing technologies. This assignment addresses one of them: the centralisation of AI compute and data. Training and running frontier AI systems now depends on a handful of hyperscale providers, namely Amazon, Microsoft, Google, and Nvidia, which dominates the GPU market. In other words, near monopolies exist throughout the supply chain. This concentration creates a governance problem affecting public and private actors alike, both legally and institutionally. On the institutional side, it prevents public administrations, and states other than the US or China, from reaching sovereign AI capacity. It also raises competition-law concerns, since dominant market positions can prevent the market from delivering the best products at the best prices to consumers. Finally, AI developers, like states, are often forced into contracts with hyperscale providers, creating a structural dependence on them.


=== Historical lineage ===
==== 1945 ====
Wartime scientific instruments should have been redirected to extend human command over knowledge, helping establish the postwar model of sustained federal funding for computing research (Vannevar Bush's, "As We May Think », 1945).


==== 1990s ====
The privatisation of the NSFNET established the idea that critical digital infrastructure, even though publicly subsidised, could legitimately be privately owned. The EU Commission followed the US's lead, recommending privatisation and deregulation of telecommunications and media as the best way to build the "infobahn" though EU members could instead have chosen nationalisation of the infrastructure (Barbrook & Cameron, "The Californian Ideology," 1996).


==== 2006 ====
<div class="s13">Hyperscale cloud computing turned massive fixed capital investment in data centresinto a rentable service, favouring firms able to invest at scale.</div>
<div class="s13">Same period: Nvidia's proprietary CUDA software, released alongside its GPUs, became the de facto standard for machine learning, locking in a decade of tooling and expertise around one firm's hardware.</div>
<div class="s13">

==== From 2022 ====
Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.</div>



=== Mechanisms of path dependance ===
Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.
=== Politics and distribution ===
Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit. 
Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.” These infrastructures privileged wealthy and white communities while restricting poor and Black populations’ choice of mobility. Similarly, the development of industrial machinery increased productivity, simultaneously weakening union and non-union workers’ bargaining power.
In the twentieth century, following the Industrial Revolution, a Control Revolution emerged from the increasing need to manage expanding production, transportation, and information flows requiring strengthened organizational bureaucratic control. This was heavily shaped by the political context of post-World War II warfare.
Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.
AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.
Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain. Today, this is one of the most concentrated industries, with single companies having near-total monopolies over the production of chips necessary for the functioning of AI. This monopoly model threatens to reinforce the existing economic hierarchy.


=== Contingency and alternatives ===
If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.
Instead of allowing control technologies to be monopolized within businesses and states, our societies could have pursued a more democratic and decentralized system. Barbrook and Cameron provide such an alternative path through their idea of the “electronic agora,” where digital networks could encourage community participation and direct democracy, where government could actively engage with the people to shape digital infrastructure and expand access. These alternatives did not actually prevail because corporate and state market participants possessed greater resources and authority to shape technological development at the end of the twentieth century. 
Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements. Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.


=== Governance implication ===
History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning. 
However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”. Firstly, the model would be launched by “subsidizing and negotiating” cloud computing for human AI researchers through “existing” cloud vendors, which would expand on existing AI compute initiatives. Secondly, the compute system would be specifically invested in as a publicly owned infrastructure that would be built and operate similarly to national libraries which own “academic supercomputing facilities”. 
The pilot program would have a “base level of compute access” which would keep costs of processing down and enable it to meet the compute needs of a majority of academic researchers. Absent any strengthened legislation on the use of AI compute and data, the hardware would be accompanied by an AI ethics framework to enable sustainable technological governance. In addition to an ethical staff, the human resources needed to keep an AI compute model running would include data officers, scientists, data center and software engineers, research developers, and system administrators. These positions require specialized training, allowing new types of professionals, from any background, to have access to professional prosperity in the field of AI. 
By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

<h3 class="s16 mwt-heading" style="text-align: justify;" >Bibliography</h3>
<div><div>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</div><div>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</div><div>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</div><div>[5] Anton Korinek, Jai Vipra, Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence, National Bureau of Economic Research, 10.3386/w33139, p. 11, 13-15 (2024)</div></div><div><div>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</div><div>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</div><div>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</div><div>[9] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 81(2022)</div><div>[10] Daniel E. Ho, Jennifer King, Russell C. Wald & Christopher Wan, The Centrality of Data and Compute for AI Innovation: A Blueprint for the National Research Cloud, 3 Notre Dame J. on Emerging Tech. 71, 82 (2022)</div></div>

== Centralization of AI compute and Data - by Atmika Dechamma & Pablo Latorre ==
=== Governance Issue ===
AI computing capacity and data presents the governance problem of an extreme concentration of technological infrastructure and information in a restricted number of companies that privately and globally dominate the market. This affects public and private actors - such as small and medium enterprises, potential new competitors, and Global South countries - that increasingly depend on AI systems outside their control and/or are deterred from competing with established companies because of impenetrable entry barriers - most notably initial investments and operational costs (1). The institutional and legal relevance of this is that it consolidates, reproduces and amplifies asymmetries of power in favor of the “Big Tech”, reverting the technological trend from digital decentralization to physical infrastructure dependency and curbing innovation and market efficiency.

=== Historical Lineage ===
==== Limitation of Machine Learning ====
The AI revolution was built on the limitation of human capabilities to process large information and derive efficient results - where before there existed only facilitative tools to allow for human-enabled processes, AI now offered derivations and cognitive reasoning in moments and at large scales.

 
==== Revert to Physical Infrastructure Dependency in the rush to be the best ====
The present AI model heavily relies on hardware in order to perform complex data processing for deep learning. The data centre models followed require larger model sizes (which require higher resource, information and capital investment) to be able to yield better results. Due to the limitation of resources and a rush to establish the best working model of an AI agent, data centres are built on a rudimentary model heavily dependent on massive amounts of electricity and fresh water (2).

 
==== Regulation and Decentralization of Information ====
Regulation and influence of sovereigns and institutions has resulted in data sharing and information exchange structurally through open source and cloud computing. But, while AI models claims their selling point to be accessibility for all, their information and derivations remain in a closed server, stored for the purpose of training their AI agents to be the best (3). Big AI contenders attract higher consumer and information flows overall resulting in a better developed AI model, proving disadvantageous in any market where early contenders have an advantage and accumulation of power.

=== Mechanisms of Path Dependence ===
Early on in the industry there is an expectation of human and capital investment from all stakeholders involved, this high cost for entry into the market and expected capital to sustain and develop a model by both consumer and enterprise makes for a high switching cost. For the AI model to run and scale there is a presumption that the rate of growth is dependent on the size of data fed, resulting in a better model and hence, more consumer usage. A closed centralised data loop is created but the requirement for storage and processing far surpasses current infrastructures capabilities - heavily relying on early market entry advantage and sustained capital investments taking away from smaller aspirants and hoarding of power within closed circles. Despite its physical dependency, AI data processing has unique requirements beyond legacy infrastructures (4) - larger demand and production of GPUs and designed to handle immense thermal heat generated while consuming electricity beyond current capabilities. 

 The AI space largely remains unregulated due to its undefined form and fluid nature, however, this has given way for unethical and morality issues regarding data privacy, exploitation of labour and environmental harms.

=== Politics and Distribution ===
Concentration strengthens the values, interests, capacities, and authority of the “Big Tech” over other actors of public and private nature. In this context, Barbrook and Cameron (5) illuminate the ethos behind AI technology development while Winner (6) makes observable the power relations surrounding it: concentration is the consequence of an AI development anchored in the belief that it must be free from all institutional and normative restriction since it is inherently liberating and non-political which, in turn, provides a political/moral justification for the concentration (if technology is inherently liberating and non-political then there is no power relations to observe or question) that translates itself into power dynamics where the AI developer not only owns the AI technology but also decides how it is trained and deployed and which models the user can or cannot access.

=== Contingency and Alternatives ===
Concentration is due, first and foremost, to the fact that AI computing capacity requires massive and cutting-edge physical infrastructure - such as data centers and microprocessors - which, in turn, requires upfront investments that are unaffordable to most companies. In David’s (7) language, companies that could finance the infrastructure and position themselves first obtained self-reinforcing advantages and a “lock-in” effect in the market. In other words, concentration is the consequence of the development of AI physical infrastructure privately with no mixed or public alternatives as a public service. Therefore, there was a space for an alternative future at the end of the 1980s or the beginning of the 1990s when the privatization model of the technological infrastructure was consolidated in line with the “Californian Ideology”. In that moment governments had the chance to treat technological infrastructure as a mixed or public good rendering it universally accessible as it was the case of the Internet; this would have permitted companies of all sizes to profit from economies of scale, reducing entry barriers and fostering competition. However, this alternative path did not prevail since the Californian Ideology provided the moral/political justification to exclude governments from the development of technological infrastructure in the exact moment in which such a decision would produce structural consequences. If that path had been followed it could have contributed to collective access to economies of scale, lower entry barriers, and a more competitive AI market.

=== Governance Implication ===
Considering the above, public or mixed investment on AI physical infrastructure is a necessary governance measure for market decentralization and collective access to AI computing capacity and data. From an historical perspective, the proposed measure is not merely a corrective action for a market failure but for a contingent and ideological decision made between the late 1980s and early 1990s that produced a structural “path dependence” that now is necessary to change.
==== References ====
(1) The Quiet Re-Centralization of Tech: Why AI Is Reversing 20 Years of Platform Decentralization. (2026, January 10). Retrieved on 8 September 2026, from Medium website : [https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13 https://medium.com/@sumitbhardwaj1357/the-quiet-re-centralization-of-tech-why-ai-is-reversing-20-years-of-platform-decentralization-ce15962b6a13]

(2) [https://www.mdpi.com/1996-1073/19/1/137 Ginzburg-Ganz, E., Lifshits, P., Machlev, R., Belikov, J., Krieger, Z., & Levron, Y. (2025). Technical challenges of AI data center integration into power grids—A survey. Energies, 19(1), 137.]

(3) [https://decai-resources.s3.us-east-1.amazonaws.com/decentralized_ML_perspective-16.pdf Singh, A., Lu, C., Gupta, G., Chopra, A., Blanc, J., Klinghoffer, T., ... & Raskar, R. (2024). A perspective on decentralizing AI.]

(4) The Rise of AI Data Center Models and the Decline of the General-Purpose Data Center. (Circa 2026). Retrieved on 8 September 2026, from SubZero Engineering website : [https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/ https://www.subzeroeng.com/the-decline-of-the-general-purpose-data-center/]

(5) [https://git.xpub.nl/onebigear/SI_09_reference_library/raw/commit/22bf61e1dcfed35d58fba23808212bd00584be47/Richard_Barbrook_Andy_Cameron_Californian_ideology.pdf Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72].

(6) [https://d1wqtxts1xzle7.cloudfront.net/87708502/langdon_winner_do_artifacts_have_politics-libre.pdf?1655588533=&response-content-disposition=inline%3B+filename%3DDo_Artifacts_Have_Politics.pdf&Expires=1788890463&Signature=UBBiMqkip5MfYZbkbLNAN6aE2hyZdKkER-HFcmyxXkwIZuTnMKdOGoao8ddFr-rQ5T55A8NWUOlnZkGYmi66e7XR9h99DgTELMCrh48sXjOb8KYgA48r~SUUymt1IW~R~aOKJzitMIUHcrc6yEGKK1OuaA~mrEIVOXpUjQMYjqOd26Xmk9YJEglTkqaxeR47VCCth5FvozNQIowdfjzeu8aNHnI8V7Yur1IiXY~PGvYRcN6wBtcgpVMHZUtNaI8NjmYbp2p5TBYrPMF9MCLtvpGhPlsCfsvA8Bdm6XulGwRlksZBKWvVUQ8D~M8oLu12V39YaiETIObyJqbE7ctIcQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge].

(7) [https://sjbae.pbworks.com/f/David+1985.pdf David, P. A. (1985). Clio and the Economics of QWERTY. The American economic review, 75(2), 332-337].

== Centralization of AI compute and Data - by Sergey Abramov and Paul Stein ==
<div>
=== The current governance issue ===
Advanced AI currently depends on a narrow group of tech giants1 and semiconductor oligopolies controlling microchips and software, hyperscale cloud infrastructure, large datasets, frontier models, and distribution platforms. This presents a governance problem where this narrow group exercises control over foundational digital infrastructure. This directly affects consumers, businesses, and academia out of AI. Legally and institutionally, this matters because it creates a profound democratic deficit: this neutralizes antitrust frameworks designed for traditional markets, often bypassing conventional copyright and data privacy protections through unchecked data harvesting thereby complicating enforcement.This ultimately might affect national sovereignty as the countries that host the buildout will shape the future of AI: who controls it, what values it contains, how it is used2.
 
=== Historical lineage ===
In 1987, Taiwan Semiconductor Manufacturing Company (TSMC) was founded, establishing the world’s first semiconductor fab. Instead of designing and making its own chips, TSMC only manufactured chips designed by other companies. Today, nearly 100% of the world's most advanced AI processors are manufactured by TSMC, leaving the entire global AI compute supply chain centralized under one company.
 In 2006, Nvidia launched CUDA (Compute Unified Device Architecture), a free software layer that allowed developers to use Nvidia's graphics chips (GPUs) which later became perfect for training deep neural networks. Today, this creates a near-monopoly on AI compute hardware.
 Soon after Amazon launched AWS, Microsoft and Google launched their own cloud platforms marking the structural pivot to the cloud service provider model, shifting computing from a distributed architecture (where companies bought and managed their own physical servers) to a centralized infrastructure. As training modern AI models requires tens of thousands of GPUs, today’s frontier AI companies are entirely dependent on the extremely expensive physical infrastructure of a handful of cloud hyper-scalers (Microsoft, Google, AWS, and Meta).
 In 2009, Fei-Fei Li launched ImageNet, a massive database of over 14 million images, which proved that AI model performance scales directly with data volume. This created an immediate institutional advantage for consumer internet platforms (like Google and Meta), because they were already architected to collect, centralize, and store masses of user data through search engines and social networks, they possessed the unique data stack required to train high-performing AI.
 
=== Mechanism of path dependence ===
The centralization of AI compute and data is a classic case of technological lock-in. When structural choices were made decades ago, they triggered powerful, self-reinforcing loops. One such loop is the switching costs and software standardisation. As developers have spent more than a decade working with CUDA, moving to a rival hardware provider (or a decentralized compute network) requires an enterprise to completely rewrite its foundational software stack. The cost of code migration, developer retraining, and potential hardware downtime force companies to stay tied to dominant hardware monopolies such as Nvidia. Commitments, licensing conditions, and exclusivity provisions add contractual costs. Fewer than 1% of cloud customers switch provider annually, identifying technical differentiation and data-transfer charges as important barriers4.
 Network effects also matter for computing power. More customers generate more revenue, usage information, and opportunities to improve infrastructure and models. Better services attract additional users and developers, whose applications make the platform more valuable. In cloud–AI partnerships, providers may additionally obtain information about model performance and future infrastructure demand. These feedback loops strengthen firms already operating across cloud, models, software, and distribution.
 
=== Politics and distribution ===
According to Langdon Winner's definition, modern AI computing infrastructure may qualify as an "inherently political technology" because its large scale and significant financial requirements not only lend themselves to, but necessitate,5 centralized, hierarchical control. The physical siting of this infrastructure shows evidence of Winner’s second category of politicization of technologies, the ‘political arrangement’:6 Of the five largest U.S. AI firms, four are headquartered in California,7 while just one is (partially) domiciled in Texas. Texas, however, hosts twice the GPU clusters.8 This placement geographically insulates beneficiaries of the data infrastructure boom from its negative externalities. The specific choice of location may itself be a deliberate preference on account of Texas’ unique approach to water usage rights.9 Juxtaposition of this unequal and even exploitative balance of interests with the pontificating rhetoric of the urban, information rich, beneficiaries is evocative of the internal contradictions of the Californian Ideology - the virtual artisans existence and privileged position is still based on the exploitation of a vulnerable out-group, the "information-rich" consuming the output, the "information-poor" hosting the extraction.10
 
=== Contingency and Alternatives ===
Profit motive and economies of scale now concentrate investment among a few hyperscalers,11 but this reflects policy choice, not government incapacity: Compute infrastructure may be inherently political, but the technology is agnostic as to where the organizing authority originates, and states have historically funded infrastructure of comparable or greater scale, while facing less pressure to show short-term returns. The clearest turning point was 1995, when the National Science Foundation retired NSFNET, the publicly funded backbone that had connected U.S. research and supercomputing centers since the mid-1980s,12 handing internet infrastructure to commercial providers with no conditions attached. This was a deliberate policy decision made amid the deregulatory push of the Clinton era. The privatization that followed ushered in the private industrial dominance that persists today in the form of hyperscalers. Had a publicly governed backbone instead been scaled up, today's compute infrastructure might rest on a different foundation entirely.
 
=== Governance implication ===
Despite private hyperscaler dominance, the U.S. does have a nominal public alternative: the National AI Research Resource (NAIRR), established following the National AI Initiative Act of 2020 and piloted from January 2024, which explicitly aims to democratize access to the computational resources essential to AI research.13In Europe, a comparable program is the EuroHPCJU,14 an initiative intended to pool resources to build supercomputing capacity. The undertaking’s legislative basis has since been extended to include networked ‘AI factories', as well as the so-called “Gigafactories”.15 The framing in language of sovereignty indicates intent to at least compete with private infrastructure initiatives. Though government-led measures cannot reverse the centralization of compute, they can democratize access - provided they are intensified. The European Commission has “set a goal of investing more than €1 billion per year in AI research and innovation”.16 While laudable in its own right, it pales in comparison to the investments of industry leaders, which are approaching, if not exceeding, trillions of Dollars.

=== References & Notes ===
1.Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 71% of the world’s cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power. Luke Emberson, Josh You and Venkat Somala (2026), "Five hyperscalers now own over two-thirds of global AI compute". Published online at epoch.ai. Retrieved from 'https://epoch.ai/data-insights/hyperscalers-control-most-compute' [online resource]. Accessed 7 Sept 2026.2. Alasdair Phillips-Robins, Teddy Tawil and Sam Winter-Levy, ‘The Compute Coalition: How to Build the Future of AI in the Free World’ (Carnegie Endowment for International Peace, 8 June 2026) https://carnegieendowment.org/research/2026/06/the-compute-coalition-how-to-build-the-future-of-ai-in-the-free-world accessed 7 September 2026.

3. Amazon Web Services (AWS), a cloud computing platform

4. Competition and Markets Authority, Cloud Infrastructure Services: Final Decision Report (31 July 2025) https://assets.publishing.service.gov.uk/media/688b8891fdde2b8f73469544/final_decision_report.pdf accessed 7 September 2026.

5. Langdon Winner, 'Do Artifacts Have Politics?' (1980) 109(1) ''Daedalus'' 121, 128.

6. Ibid. 123.

7. OpenAI, Anthropic, Google DeepMind, Meta AI. xAI is an exception to the rule, domiciled between Palo Alto, CA and San Antonio, TX.

8. Texas’ 17 to California’s 8, cf. Texas Royalty Brokers, 'AI Clusters by State' (''Texas Royalty Brokers'', 2025) https://texasroyaltybrokers.com/ai-clusters-by-state/ accessed 7 September 2026.

9. The ‘rule of capture’, established in Houston & TC Railway Co v East (Tex 1904), permits landowners to extract groundwater beneath their land without liability for depleting adjacent wells or aquifers, a doctrine now exploited by hyperscale AI data centres to draw large cooling-water volumes with minimal regulatory constraint.

10. Richard Barbrook and Andy Cameron, 'The Californian Ideology' (1996) 6(1) ''Science as Culture'' 44.

11. Goldman Sachs, 'Global AI Investment Is Forecast to Exceed $1 Trillion in 2026' (''Goldman Sachs Insights'', 7 August 2026) https://www.goldmansachs.com/insights/articles/global-investment-is-forecast-to-exceed-1-trillion-in-2026 accessed 7 September 2026.

12.  National Science Foundation, 'Birth of the Commercial Internet' (''NSF Impacts'') https://www.nsf.gov/impacts/internet accessed 7 September 2026.

13. CASRAI, 'NAIRR: National AI Research Resource, Explained' (''CASRAI'') https://casrai.org/dictionary/term/national-ai-research-resource-nairr accessed 7 September 2026.

14. Council Regulation (EU) 2021/1173 of 13 July 2021 on establishing the European High Performance Computing Joint Undertaking and repealing Regulation (EU) 2018/1488 [2021] OJ L256/1.

15. Council Regulation (EU) 2026/150 of 16 January 2026 amending Regulation (EU) 2021/1173 on establishing the European High Performance Computing Joint Undertaking [2026] OJ L 2026/150.

16. European Commission, 'Realising Europe's Digital Decade' (''European Commission'', 11 October 2024) https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/story-von-der-leyen-commission/realising-europes-digital-decade_en accessed 7 September 2026.</div>