Technology Governance Wiki - User contributions [en]

Governance by markets

2026-10-02T14:34:10Z

Hammoudi Sisi:

= Glossary =
= Data as an input to market power (Sergey) =
Draft <p>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.  </p><p>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. </p><p>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.</p><p>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.</p>

....

= 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 ===
<p>The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. </p><p>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’.</p><p>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. </p><p>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.<br></p>
=== AI Training and the (non-)excludability of information. ===
<p>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. </p>

= Interoperability (Sisi) =

#What is interoperability?[[https://www.ibm.com/think/topics/interoperability 1]]<p>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]]</p><p>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.</p>

#Concrete example from a digital technology or platform<p>Interoperable 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]]</p><p>For example, a health care professional may access lab results and patient history data from various platforms to enable them to make a diagnosis.</p>

#One governance implication: what does this concept explain about how platforms exercise power?<p>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.</p><p>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]]</p><p>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.”</p><p>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]]</p>

#Where does the concept break down or fail to explain something<p>While 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]]</p><p>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.</p><p>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]]</p>
= "Economies of Scale (Jihae)" =

Governance by markets

2026-10-02T14:30:51Z

Hammoudi Sisi: /* Interoperability (Sisi) */

= Glossary =
= Data as an input to market power (Sergey) =
Draft <p>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.  </p><p>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. </p><p>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.</p><p>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.</p>

....

= 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>
<p>The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. </p>
<p>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’.</p>
<p>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. </p>
<p>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.<br></p>
<h3 class="mwt-heading" >AI Training and the (non-)excludability of information.</h3>
<p>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. </p>

= Interoperability (Sisi) =

#What is interoperability?[[https://www.ibm.com/think/topics/interoperability 1]]
<p>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]]</p><p>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.</p>
#Concrete example from a digital technology or platform
<p>Interoperable 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]]</p><p>For example, a health care professional may access lab results and patient history data from various platforms to enable them to make a diagnosis.</p>
#One governance implication: what does this concept explain about how platforms exercise power?
<p>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.</p><p>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]]</p><p>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.”</p><p>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]]</p>
#Where does the concept break down or fail to explain something
<p>While 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]]</p><p>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.</p><p>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]]</p>

Governance by markets

2026-10-02T13:02:06Z

Hammoudi Sisi:

Governance by markets

2026-10-02T13:01:55Z

Hammoudi Sisi:

Talk:Submarine Internet Cables - By Pablo Latorre

2026-09-23T09:19:58Z

Hammoudi Sisi:

this way very enlightening! With the task of suggesting an improvement, I might question whether UNCLOS may be construed as an institution (although I agree it is a governance tool). - Ceren

This was a very interesting topic. I found the difference between the ICPC and the US FCC particularly interesting because, where ICPC relies on voluntary industry guidelines, the FCC can use its own regulatory power to deny cable licenses based on national security concerns and whether the are US 'adversaries'. I think it was good to highlight that the US never ratified the UNCLOS treaty, which allows it to use "national interest" concerns more broadly to create differences in submarine cable regulations based on the country it is contracting with. Great entry! - Sisi

Talk:Cloud service (Ceren)

2026-09-23T09:11:53Z

Hammoudi Sisi:

From a governance perspective, Cloud Computing presents serious challenges for the European Union and its users that usually revolve around the question of infrastructure and data control. However, there is another aspect that receives less attention but that is also interesting: interoperability control. Who decides if services and data can be freely transferred from one provider to another? Can users change provider without prohibitive costs? Technical limitations to interoperability can operate as regulatory restrictions in the sense of Lessig's "code as law", and it seems that European regulation does not really address the issue (beyond some portability principles in the Data Act). This makes me think that independency from the hyperscalers is not only about having Europeans options but also about being able to change providers at will. Thanks for the entrance, much appreciated. Pablo

Cloud services are rather interesting taken from the perspective of governance. I think it's interesting that the EU promotes innovating frameworks such as the Gaia project and yet continues to depend so heavily on non-EU cloud services. I think you did really well drawing each weakness from each institution and authority. Great entrance! Sisi

Governance by institutions

2026-09-21T19:43:38Z

Hammoudi Sisi:

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

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

Decentralized digital currency: BITCOIN (Sisi)

2026-09-21T19:42:42Z

Hammoudi Sisi: Created page with "= '''I. Source of Authority and Governing Instrument''' = <p>Bitcoin is a crypto-asset in the form of a digital token which is exchanged electronically, rather than physically. The most notable characteristic of bitcoin is that is not issued by a central public authority,[1] it is a decentralized cryptocurrency.  “Decentralized” means that there is not a single source of authority, neither a single person, or corporation oversee bitcoin.[2] Bitcoin is a system o..."

= '''I. Source of Authority and Governing Instrument''' =
<p>Bitcoin is a crypto-asset in the form of a digital token which is exchanged electronically, rather than physically. The most notable characteristic of bitcoin is that is not issued by a central public authority,[1] it is a decentralized cryptocurrency.  “Decentralized” means that there is not a single source of authority, neither a single person, or corporation oversee bitcoin.[2] Bitcoin is a system of computer nodes that form a network guided by a set of “unspoken rules” and decided upon by the consensus of users through each node.[3]</p><p>Users are attributed different roles in the networks. The ''minors'' produce new blocks on the electronic chain. The ''node operators'' develop software to verify individual transactions on each block. ''Developers'' propose changes to the open-source software Which only take effect if nod users voluntarily update the software. Finally, the ''nodes'' actually enforce the rules.[4]</p><p>Therefore, no single authority can unilaterally make decisions, or “change the rules” for the rest of blocks, instead, bitcoin relies on “a community of open-source developers with many regular contributors.[5] As such, while no government instrument controls Bitcoin, it purports to be a democratic form of financial exchange.</p><p><br></p>
= '''II. Interest and Value it Purports to Protect''' =
<p>Bitcoin is enabled through blockchain technology. This means that Bitcoin transactions are recorded on a transparent, permanent ledger that can be verified by any user by eliminating the need for a bank or government institution.[6] This is an attractive feature because users have complete control over their currency. Moreover, Bitcoin is not subject to inflation. Inflation occurs from the increase in price of goods due to a given currency losing value. On the contrary, Bitcoin was specifically designed to never have above 21 million bitcoins “in circulation”, therefore inflation may not occur.[7]</p><p>No single individual or entity can manipulate the decentralized system. Blockchain security is ensured by “thousands of minors across the world” That individually verify transactions and ensure security on the network. Bitcoin’s accountability fosters trust and increases its reliability for users. Indeed, to successfully attack the network, a hacker would require 51% of the entire system’s mining power.[8]</p><p><br></p>
= '''III. Weaknesses in its Capacity, Legitimacy, or Accountability''' =
<p>It may be said that bitcoins biggest weakness in capacity is its own nonphysical nature. Bitcoin users can send and receive payments directly through digital wallets which contain both public and private keys necessary to secure the transactions.[9] Bitcoin is not backed by physical assets such as silver and gold. This makes highlights the volatility of bitcoins price which makes it difficult for everyday use.[10]</p><p>Bitcoin’s open-source system also creates new opportunity for complex forms of crimes. Perpetrators of crypto-crime operate anonymously with their identities disguised through “mixing services or privacy coins” to make it difficult to track them.</p><p>Furthermore, because Bitcoin is a global network and does not recognize national “jurisdictional boundaries”, these crypto criminals operate in a new system of less effective law enforcement. The challenge of capturing a new form of crime in a system without traditional jurisdictional boundaries highlights the “urgent need” to develop “criminal regulatory reform” to adapt to our traditional legal environment.[11]</p><p>Finally, other disadvantages include manufacture lack of governmental regulation which leave it open to being “co-opted by the existing financial system”, and a larger environmental concern as Bitcoin networks consumer a lot of energy.[12]</p><div><br><div>
= Sources =
<p>[1] European Central Bank, ''What is bitcoing? ''13 February 2018, updated 14 July 2021, https://www.ecb.europa.eu/ecb-and-you/explainers/tell-me/html/what-is-bitcoin.en.html</p></div><div><p>[2] The Smarter Web Company, ''Who Controls Bitcoin, https://www.smarterwebcompany.co.uk/knowledge-base/who-controls-bitcoin/ ''</p></div><div><p>[3] Bitstack, ''Who controls Bitcoin, https://www.bitstack-app.com/en/learn-bitcoin/who-controls-bitcoin?c=EUR ''</p></div><div><p>[4] The Smarter Web Company, ''Who Controls Bitcoin, https://www.smarterwebcompany.co.uk/knowledge-base/who-controls-bitcoin/''</p></div><div><p>[5] Bitstack, ''Who controls Bitcoin, https://www.bitstack-app.com/en/learn-bitcoin/who-controls-bitcoin?c=EUR''</p></div><div><p>[6] Dhiraj Nallapaneni, ''How Does Bitcoin Have Value If It’s Backed by Nothing'', CoinLedger, https://coinledger.io/learn/what-is-bitcoin-backed-by</p></div><div><p>[7] Mazzei, M. ''Bitcoin'', EBSCO, 2023, https://www.ebsco.com/research-starters/computer-science/bitcoin</p></div><div><p>[8] Dhiraj Nallapaneni, ''How Does Bitcoin Have Value If It’s Backed by Nothing'', CoinLedger, https://coinledger.io/learn/what-is-bitcoin-backed-by</p></div><div><p>[9] Mazzei, M. ''Bitcoin'', EBSCO, 2023, https://www.ebsco.com/research-starters/computer-science/bitcoin</p></div><div><p>[10] Dhiraj Nallapaneni, ''How Does Bitcoin Have Value If It’s Backed by Nothing'', CoinLedger, https://coinledger.io/learn/what-is-bitcoin-backed-by</p></div><div><p>[11] Pambudi1* , Zudan Arief Fakrulloh, ''Criminal Liability of Perpetrators in Crypto Ecosystem, the Regulatory Challenges, And Legal Voids in the Criminal Law System in Indonesia'', Majelis: Jurnal Hukum Indonesia Volume 2, 2025</p></div><div><p>[12] Dhiraj Nallapaneni, ''How Does Bitcoin Have Value If It’s Backed by Nothing'', CoinLedger, https://coinledger.io/learn/what-is-bitcoin-backed-by</p></div></div>

Centralisation AI Compute and Data Sources

2026-09-08T15:10:10Z

Hammoudi Sisi:

== 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 ==
<p>This centralisation results from earlier, overlapping technological and institutional choices. Five moments stand out:</p>
*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: Hyperscale cloud computing turned massive fixed capital investment in data centres into a rentable service, favouring firms able to invest at scale.
*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.
*2022–present: Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.

== Mechanism of Path Dependence ==
<p>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.</p>

== Politics and Distribution ==
<p>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.</p>
<p>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.”[1] 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.[2]</p>
<p>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.[3] This was heavily shaped by the political context of post-World War II warfare.</p>
<p>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.</p>
<p>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.[4]</p>
<p>Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain.[5] 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.</p>
<div><div>
== Contingency and Alternatives ==
<p>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.[6]</p>
<p>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.[7] 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.</p>
<p>Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements.[8] Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.</p>
<div><div>
== Governance Implication ==
<p>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.</p>
<p>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”[9]. 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”.</p>
<p>The pilot program would have a “base level of compute access”[10] 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.</p>
<p>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.</p>

<h2 class="mwt-heading" >Bibliography</h2>
<div></div>
<div></div>
<div><div><p>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</p></div>
<div><p>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</p></div>
<div><p>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</p></div>
<div><p>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</p></div>
<div><p>[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)</p></div></div>
<div><div><p>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</p></div>
<div><p>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</p></div>
<div><p>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</p></div>
<div><p>[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)</p></div>
<div><p>[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)</p>

<p>'''PAGE AUTHORS''': Juliette Chamot and Sisi Hammoudi</p></div></div></div></div></div></div>

Centralisation AI Compute and Data Sources

2026-09-08T15:07:11Z

Hammoudi Sisi:

== 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 ==
<p>This centralisation results from earlier, overlapping technological and institutional choices. Five moments stand out:</p>
*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: Hyperscale cloud computing turned massive fixed capital investment in data centres into a rentable service, favouring firms able to invest at scale.
*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.
*2022–present: Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.

== Mechanism of Path Dependence ==
<p>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.</p>

== Politics and Distribution ==
<p>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.</p>
<p>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.”[1] 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.[2]</p>
<p>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.[3] This was heavily shaped by the political context of post-World War II warfare.</p>
<p>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.</p>
<p>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.[4]</p>
<p>Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain.[5] 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.</p>
<div><div>

== Contingency and Alternatives ==
<p>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.[6]</p>
<p>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.[7] 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.</p>
<p>Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements.[8] Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.</p>
<div><div>

== Governance Implication ==
<p>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.</p>
<p>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”[9]. 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”.</p>
<p>The pilot program would have a “base level of compute access”[10] 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.</p>
<p>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.</p>
<div></div>
<div></div>
<div><div><p>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</p></div>
<div><p>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</p></div>
<div><p>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</p></div>
<div><p>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</p></div>
<div><p>[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)</p></div></div>
<div><div><p>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</p></div>
<div><p>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</p></div>
<div><p>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</p></div>
<div><p>[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)</p></div>
<div><p>[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)</p></div></div></div></div></div></div>

Centralisation AI Compute and Data Sources

2026-09-08T15:05:38Z

Hammoudi Sisi: Created page with "== 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..."

== 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 ==
<p>This centralisation results from earlier, overlapping technological and institutional choices. Five moments stand out:</p>
*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: Hyperscale cloud computing turned massive fixed capital investment in data centres into a rentable service, favouring firms able to invest at scale.
*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.
*2022–present: Semiconductor export controls and the CHIPS Act turned compute into an explicit instrument of geopolitical strategy.

== Mechanism of Path Dependence ==
<p>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.</p>
== Politics and Distribution ==
<p>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.</p><p>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.”[1] 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.[2]</p><p>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.[3] This was heavily shaped by the political context of post-World War II warfare.</p><p>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.</p><p>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.[4]</p><p>Finally, the development of AI compute and data depends on expensive and scarce computing infrastructure and a highly concentrated semiconductor supply chain.[5] 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.</p><div><div>
== Contingency and Alternatives ==
<p>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.[6]</p><p>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.[7] 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.</p><p>Inherently, technological development is not predetermined; it can support either authoritarian or democratic arrangements.[8] Today, monopolies control chip production needed for AI compute and data, but other technologies like blockchain permit democratic participation.</p><div><div>
== Governance Implication ==
<p>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.</p><p>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”[9]. 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”.</p><p>The pilot program would have a “base level of compute access”[10] 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.</p><p>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.</p><div></div><div></div><div><div><p>[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</p></div><div><p>[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)</p></div><div><p>[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)</p></div><div><p>[4] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72, 61 (1996)</p></div><div><p>[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)</p></div></div><div><div><p>[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)</p></div><div><p>[7] Richard Barbrook, Andy Cameron, The Californian Ideology, ''Science as Culture'', ''6''(1), 44–72 (1996)</p></div><div><p>[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)</p></div><div><p>[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)</p></div><div><p>[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)</p></div></div></div></div></div></div>