Public Compute Commons: AI Research Infrastructure Without Hyperscaler Dependency

The strongest objection is that public compute can become a subsidy for well-connected universities, large firms, or risky dual-use research. A critic could argue that scarce public resources will go to institutions that already know how to win grants, while security review lags behind capability.

That objection is strong because allocation capture is a recurring public-program failure. The answer is not to abandon public compute; it is to publish allocation criteria, reserve capacity for new entrants, separate sensitive and dual-use tiers, audit outcomes, and use competitive provider access rather than a single incumbent. The position holds because a private-only compute market already has its own capture pattern: access follows money.

Taxpayers bear the cost of compute credits, operations, user support, and secure enclaves. Cloud providers and labs bear compliance and reporting burdens. Researchers bear review and security requirements. Communities near data centers bear energy, water, and land impacts if expansion is unmanaged. Safety and security reviewers bear the cost of deciding which projects need stricter access.

This issue accepts those costs because compute access now shapes national innovation capacity. The mitigation is competitive procurement, transparent utilization, energy accounting, allocation audits, and tiered security review. Public compute should be scarce enough to govern carefully and broad enough to change who can participate.