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

Research into AI regulatory frameworks indicates that regulators cannot simultaneously guarantee a fixed risk ceiling and a development deadline when safety research productivity is uncertain, unless low-productivity outcomes are ruled out. A calibrated model demonstrates that maintaining risk ceilings demands a minimum threshold of safety knowledge, and regulations that mandate fixed compute allocations fix delivery timelines rather than mitigating actual risk.

This analysis demonstrates the mathematical incompatibility of simultaneously enforcing hard safety ceilings and rigid deployment timelines under uncertain technical progress. It matters because it reveals that standard regulatory interventions based on fixed compute thresholds fail to control risk, shifting systemic costs onto operational and downstream services.

Simultaneously promising an artificial intelligence risk ceiling and a development deadline is only possible if weak safety research productivity can be ruled out.

Forward consideration, not a verified fact.