Public AI Infrastructure

In The You On AI Field Guide

The proposal responds to a structural feature of the AI economy that distinguishes it from previous technology cycles: the capital intensity of frontier AI development creates barriers to entry that effectively preclude competitive entry without institutional support. Training a competitive large language model requires hundreds of millions to billions of dollars in computational infrastructure alone, before counting the data, talent, and operational costs of deployment. These capital requirements concentrate AI capability among firms with access to enormous capital — a handful of US and Chinese companies and their affiliates.

Public AI infrastructure provides an alternative to the market-concentration dynamic without replacing it. The public infrastructure need not dominate the market; it needs only to exist at sufficient scale to provide a meaningful alternative. The precedent is the BBC, PBS, public universities, national libraries, and public postal services — institutions that coexist with private alternatives and provide functions the private alternatives either cannot or will not provide at accessible terms.

The functions public AI infrastructure would serve include: providing researchers with access to frontier models without commercial lock-in, supporting mission-oriented applications (health, education, climate) that commercial platforms underinvest in, training builders and students on platforms whose governance prioritizes public rather than commercial interests, maintaining data commons with appropriate privacy and consent frameworks, and providing competitive discipline on private platforms whose market power is currently unchecked.

Several national initiatives have begun to implement versions of this framework. The EU AI Factories initiative provides public computational infrastructure to researchers and SMEs. The UK AI Research Resource provides similar capability. The US National AI Research Resource pilot is a smaller-scale experiment in the same direction. These initiatives are currently inadequate in scale relative to the private AI economy, but they establish institutional precedent for public AI capability that could be expanded.

Origin

The public AI infrastructure proposal emerged from Mazzucato's engagement with UK policy debates in 2024 and was elaborated in her Project Syndicate essays with Tommaso Valletti and Fausto Gernone. The proposal drew on the precedent of publicly funded computational infrastructure in scientific research — CERN, national supercomputing centers, publicly funded genomic databases — and applied it to the specific circumstances of the AI platform economy.

The framework has subsequently been adopted in various forms by EU initiatives, UK Labour Party policy proposals, and multiple Latin American national strategies on AI sovereignty.

Key Ideas

Not state-run AI, but public alternative. Infrastructure that coexists with private platforms while providing alternative governance.

Competitive discipline. The existence of a public option constrains the market power of private platforms.

Mission-oriented capability. Public infrastructure can support applications commercial platforms underinvest in.

Capital intensity as barrier. AI concentration dynamics are more severe than previous platform economies because of hardware requirements.

Precedent exists. Public universities, public broadcasting, national libraries — institutional models for coexisting with private alternatives.