风险上限与发展截止期：在安全生产力不确定下为AI发展定节奏
Risk Ceilings and Development Deadlines: Pacing AI under Uncertain Safety Productivity
Li Gan
AI 速读 基于摘要
研究在安全生产力未知时，监管者能否同时承诺风险上限与发展截止期，并探讨先学习后重放开发、预留安全算力的策略。
econ.GN · cs.CY
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Can a regulator promise both a risk ceiling and a development deadline when safety productivity is unknown? A ceiling below the final model's unprotected hazard requires a minimum stock of safety knowledge, so both promises hold only if weak research can be ruled out. Learning first and then replaying development, with spare compute in safety, comes close to that minimum. In a calibration with only state risk, constant safety yield, and full knowledge transfer, it needs only 3.3 percent more productivity than the necessary bound. Customer services pay for the guarantee. Rules that fix compute allocation fix dates, not risk.