Centralisation AI Compute and Data Sources

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

This centralisation results from earlier, overlapping technological and institutional choices. Five moments stand out:

• 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

Several mechanisms drive this centralisation, but path-dependence ones dominate. Because AI compute is a capital-intensive industry, new entrants struggle to compete with hyperscalers that have already sunk huge sums into infrastructure, a barrier that produces monopoly-like situations. This dynamic, known as increasing returns, generates system-scale economies that harden into a near-irreversibility of investment (David, "Clio and the Economics of QWERTY," 1985). It overlaps with a second mechanism, standardisation, itself a form of technical interrelatedness: researchers' investment in Nvidia's CUDA-based tooling locks the field into that ecosystem regardless of whether better alternatives exist. A further mechanism is legal and contractual entrenchment. For example, Microsoft's stake in OpenAI binds developers to a specific provider by agreement rather than technical necessity.

Politics and Distribution

Technology does not have a history of distributing power equally. Rather, its development has strengthened the interests and authority of powerful groups that already possessed economic and political resources, and it is yet to be determined whether AI compute and data will follow suit.

Author Langdon Winner illustrates the historical constraints of technological development with the case of New York from the 1920s to the 1970s. He argued that the city’s roads, bridges, and parks’ physical arrangement contained “explicit and implicit political purpose.”[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]

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.

Therefore, the bearer of the initial cost of technology is the person putting in the capital to erect it, but ultimately, the person bearing the long-term cost of technology, whose choices diminish, is not the rich and privileged, but the everyday person subject to technologies. Forced to adapt, to reshape their professional skills to adapt to an environment trying to make them obsolete.

AI compute and data are at risk of creating the same effect. In their critique of dotcom neoliberalism, authors Barbrook and Cameron exposed a similar socio-economic tension. The Californian Ideology claims digital technology empowers individuals; however, perpetual unequal access to it creates an apartheid between “information-rich” and “information-poor” populations.[4]

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.

Contingency and Alternatives

If a different technological trajectory was plausible, the most apt time for it to have occurred would be during the transition from the Industrial Revolution to the “Control Revolution”. When industrial production accelerated, our society experienced a “crisis of control”, creating opportunities to organize information and communication differently.[6]

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.

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.

Governance Implication

History always threatens to repeat itself. As Barbrook discussed, digital societies are shaped by the political context in which they develop. It is a valid concern, then, that the development of information technology, through AI computing, would follow the same trajectory- that is, be constrained to benefit the wealthy who funded its development, and ultimately harm the middle and lower classes enabling its functioning.

However, AI’s place in our society has not yet crystallized. Technology governance does not have to adhere to this system. Such a seemingly sustainable approach to AI compute and data governance has already been broached. In 2022, the Notre Dame Journal on Emerging Technologies published an article in its issue on Artificial Intelligence where the authors proposed an AI compute system based on a “dual investment strategy”[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”.

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.

By implementing these frameworks, never neglecting the human element, and the economic and intellectual impact AI has on our society, we move from a historical determinist perspective to one acknowledging historical constraints and learning from them.

Bibliography

[1] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)

[2] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 124, (1980)

[3] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 450 (1986)

[4] Richard Barbrook, Andy Cameron, The Californian Ideology, Science as Culture, 6(1), 44–72, 61 (1996)

[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)

[6] James R. Beniger, The Control Revolution: Technological and Economic Origins of the Information Society, Harvard University Press, p. 443, 442 (1986)

[7] Richard Barbrook, Andy Cameron, The Californian Ideology, Science as Culture, 6(1), 44–72 (1996)

[8] Langdon Winner, Do Artifacts Have Politics, Daedalus, Winter, 1980, Vol. 109, No. 1, Modern Technology: Problem or Opportunity? pp. 121-136, 130 (1980)

[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)

[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)

PAGE AUTHORS: Juliette Chamot and Sisi Hammoudi