AI safety prizes

LessWrong is exploring whether AI safety research should be funded differently: through prizes paid after the fact for valuable work, rather than upfront grants. The idea rests on a simple principle—if you only pay for results that actually mattered, you avoid wasting money on failed bets. Prizes work well when the eventual winner is hard to predict, a solution is easy to verify, and you want to attract diverse talent; the risks are that they can distort incentives if poorly designed, discourage collaboration between researchers, and require innovators to self-fund their work upfront. For AI safety specifically, the post suggests promising prize categories: compute verification advances like inference-only data center retrofits, where technical requirements are clear; alignment research, though harder to verify, might include annual best-paper competitions at top conferences; control protocols, which could use benchmarks similar to ARC challenges; and interpretability problems, some of which could reward discovering hidden triggers in models. The economics might favor prizes for AI funders with high expected returns, since paying after the fact beats betting on grant recipients early.

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