Risk Ceilings and Development Deadlines: Pacing AI under Uncertain Safety Productivity

arXiv:2610.11093v1 Announce Type: cross Abstract: 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.

The text discusses pacing AI development under uncertain safety productivity, implying potential risks and opportunities.

This is a theoretical modeling paper with a narrow, idealized calibration, not an empirical finding about deployed systems. It implies current simplistic regulatory rules may be inadequate but also offers a near-feasible fix, making the net societal implication mildly uncertain rather than strongly negative or positive.

The paper explores the challenge of simultaneously guaranteeing a risk ceiling and a development deadline for AI when safety productivity is uncertain. It suggests that achieving both requires a minimum stock of safety knowledge and the ability to rule out weak research. This implies that managing AI development will be complex and costly to ensure safety.

The paper proposes a method for regulators to ensure AI safety. It suggests that by learning first and then replaying development with spare compute in safety, regulators can promise both a risk ceiling and a development deadline. This implies a structured and safe approach to AI development, which is optimistic for human flourishing.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   eca67e80-ccd2-4bfc-b363-ce89f50c5c4a
           https://arxiv.org/abs/2610.11093
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EVIDENCE   1 extracted excerpt
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -26  (Adverse)
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CONFIDENCE 74%