Private Compute Permit Markets for AI Assurance: An Incentive-Compatible Architecture That Links Evaluations, Insurance, and Procurement

Joel Christoph

Keywords: private AI governance, market design, compute permits, assurance, evaluations, insurance, procurement, accountability, risk management

TL;DR: We propose a practical compute permit market that ties model evaluations to insurance pricing and procurement clauses, creating aligned private incentives for safer frontier AI development.

Abstract: Private governance can complement regulation by shaping incentives for responsible AI development. We present a concrete market design for risk-calibrated compute permits that integrates three existing private governance tools: independent evaluations, insurance with capital at risk, and procurement clauses. The design defines a permit unit indexed by capability-adjusted compute, an issuance and clearing mechanism with supply caps set against ex ante risk budgets, and a settlement workflow where evaluation providers act as oracles whose scores parameterize insurer premiums and performance bonds. Developers must hold permits equal to capability-adjusted compute used in training or deployment, and purchasers can require proof of permits and insurance at contract award. We provide a simple model that shows how premiums increasing in measured risk induce safer effort when developers face permit constraints and assurance pricing. We detail a minimal interoperable schema for attestations and audit logs that can be implemented by clouds, insurers, and registries without revealing proprietary artifacts. We map the design to existing assurance and oversight ecosystems and outline a staged pilot with one cloud, two evaluation providers, and an insurer. The proposal is intended to be testable in 12 weeks with off-the-shelf components and to inform scalable private oversight that complements public regulation.

Submission Number: 27