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Short abstract:

This paper examines the theory and practice of state-funded AI infrastructure amidst Big Tech dominance. It advocates for viewing large-scale AI computing as a public utility, proposing an "infrastructural state" framework for AI governance.

Long abstract:

Cutting-edge AI systems like large language models demand extensive computational resources. However, the global cloud computing industry is extremely concentrated, with Amazon, Google and Microsoft accounting for two-thirds of this market. EU policymakers aim to foster the development of public alternatives to Big Tech’s computing facilities, exemplified by state-funded supercomputers. MareNostrum 5, for instance, the most capable machine of the Barcelona Supercomputing Cluster cost a total of 202 million euros of EU taxpayer money. This paper examines the political-economic context in which publicly funded AI infrastructure projects operate and compete with Big Tech. How can state-owned large-scale AI computing be theorized, and under which conditions is it feasible?

To answer this question, the key argument of this paper is to consider large-scale AI computing as a public utility – an institution that provides essential services to the public. This key argument unfolds in three steps. First, the paper dissects the relationship between applications, models, and infrastructure in the context of large language models. Second, it uses this groundwork to probe the applicability of public utility thinking on AI infrastructure, resorting to three analytical registers of public utility thinking: stifled competition, downstream effects, and state-supported innovation. Analyzing those drivers is paramount to distinguishing large-scale AI computing from other public utilities, such as electricity, water, and sewage systems. Third, the paper stresses the importance of articulating the “infrastructural state” as a normative horizon in AI governance debates, going beyond a narrow regulatory focus on surface-level problems such as misinformation and deep fakes.