A new Forecasting Research Institute survey asked experts, superforecasters, AI risk specialists and members of the U.S. public how specific policies would change the odds of an AI-caused catastrophe by 2050. Despite wide disagreement about how risky AI is, the groups largely agreed on what would help most, and agreed that those measures are the least likely to be adopted.

In this episode

• Who was surveyed, and how their baseline risk estimates differ (2% for experts, 5.2% for AI risk specialists)
• Why an international body that approves frontier models before release was the most popular policy across groups
• Why forecasters rate U.S.–China measures above U.S.-only ones, but expect them less
• Federal preemption of state AI laws: rated the only risk-increasing policy, yet seen as the most likely to pass
• Caveats: these are opinions, not measurements; possible priming; small superforecaster sample; a mixed forecasting track record

Bottom line: The survey doesn’t show which policies would work. It shows that informed people who disagree about AI risk largely agree that coordinated international measures would help most, and doubt those measures will happen before 2030.

Sources and further reading

• FRI: AI Experts Largely Agree on Which Policies Most Reduce Catastrophic AI Risk (Oct 5, 2026)
• LEAP Wave 12 full report
• FRI: How Accurate Have AI Progress Forecasts Been So Far? (Sep 23, 2026)
• Broadband Breakfast on the draft federal preemption bill (Jun 4, 2026)

This post was written by Claude and fact checked by Nathan Nguyen.