Governance by history
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 (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" (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 (3) or Meta employees having their every click tracked (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 (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 (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 to decrease walking distances, eliminating unnecessary movements and preventing deliberate slow-walking among workers. (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 (8).
2.3 PC in the workplace: John Blankenbaker designed the first commercially available personal computer in 1971 (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 (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 (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" (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 (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 (14), and the AI Act Article 5 (15). It’s also worth noting that in December 2026, the Platform Work Directive (16) will come into effect which will ban automated termination.
8 September 2026 - Platform Dependence in Public Communication - Ceren and Rodrique (work in progress)
I. PLATFORM DEPENDENCE IN PUBLIC COMMUNICATION – WHAT IS IT , WHO IS AFFECTED BY IT AND WHY IT MATTERS LEGALLY AND INSTITUTIONALLY – C
A. 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.
B. 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 (such as on social media platforms) 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,i members of the public who do not have the resources to access such platforms and digital services remain “information poor”. Digital rich, the virtual class of tech-professional and entrepreneurs benefit from the dependence to their platforms.
C. 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 heteregenious 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.
II. HISTORICAL LINEAGE – HISTORICAL MOMENTS/DECISIONS/INSTITUTIONAL ARRANGEMENTS - R
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.
]
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. There is and has always been an obvious tension between the rarity that gives information its worth and the act of transmission that makes it less rare. Modern form of this can be traced to Renaissance Europe,
]
where a single innovation not only leaned on but also displaced established pattern of trusted networks collecting, processing and sharing news by way of the church, merchants, students, diplomats and so on.
]
This innovation is the invention of printing in the 15th century. It had consequences beyond those imagined by its inventor, as it offered new possibilities for the dissemination of news. It must be reiterated that printing benefitted from the existing networks and not the other way around. This is evident by Johann Carolus' Relation aller Fürnemmen und gedenckwürdigen Historien handwritten weekly newspaper for an elite audience fruitfully running prior to the advent of printing. However, the printing press coupled with existing network and infrastructure of the postal system brought industrialisation and scale unknown to the trade and the world at the time. One of the first paying customer for press was the Church, selling indulgences. Beniger's stands: control depends on information processing and communication working together, and the this innovation at that current point time supplied both at once.
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Nonetheless, a strong parallel between the press and later computing revolutions it's stated relationship with state.
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With the former seeming to lean more towards the state and the other in the opposite direction.
The next pivotal historical point which disturbs the way in which news is transmitted is by way of the radio. The radio is huge leap frog as it permitted the decoupling of reach from cost, reliance on aging network such as the postal system. Where the method of control under print was through the content printed, the radio moved closer to physics on licensing broadcast rights. This changed the trajectory of new's as it birth the state owned monopoly broadcaster, responsible for broadcasting news to anyone with a receiver on the other end within appropriate frequency. News outlet now had more than one method for distributing news and for the first time had a way of engaging in dialogue on the news of the day as it makes it way via print and sound-wave. Diversity of dependency is an important aspect of this stage in history.
III. MECHANISM OF PATH DEPENDENCE - C
A. The public is generally partaking in consuming 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.
IV. POLITICS AND DISTRIBUTION – WHOSE INTERESTS ARE STRENGTHENED AND WHO HAS FEWER CHOICES - C
A. 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.
V. AT WHAT POINT IN HISTORY WAS A DIFFERENT TRAJECTORY POSSIBLE? - R
A. News not subscribing to / presenting news on social media as platform (?)
VI. GOVERNANCE IMPLICATION – ONE PROPOSITION FOR CURRENT DAY GOVERNANCE - R
OPEN PLATFORMS (ALTERNATIVE FACEBOOK A)
<section class="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.34accessed 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)
↩︎
</section>
Centralization of AI Compute and Data
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. 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.
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. 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 - 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 (1) illuminate the ethos behind AI technology development while Winner (2) 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 (3) 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) Barbrook, R., & Cameron, A. (1996). The Californian Ideology. Science as Culture, 6(1), 44-72.
(2) Winner, L. (2017). Do artifacts have politics?. In Computer ethics(pp. 177-192). Routledge.
(3) 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
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.13
In 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
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.