Market-Based Compute Permits for Frontier AI Safety

Market-based compute permits are a potential governance mechanism for frontier AI training, but a permit regime may appear strict on paper while failing to constrain real compute use if its timing and enforcement rules are poorly designed. Using two stylized simulation tracks, this paper studies banking rules under improving compute efficiency and enforcement dynamics in a two-lab regulator model. The results show that unrestricted banking produces the largest late-period concentration of compute, while decay rules and banking caps reduce this effect only partially. The enforcement simulation shows that compliance depends on whether the expected penalty for evasion is at least as large as the permit price. Once permit scarcity pushes the price above that threshold, tighter caps reduce legal permit holdings, but actual compute remains almost flat as evasion rises. The paper recommends avoiding unrestricted banking as a default, constraining any banking that is allowed, and designing cap stringency and enforcement capacity jointly.