SPAR FA25 - Market Incentives for Responsible AI: Final report

Safety progress in AI is ultimately an economic problem: current markets do not generate strong enough incentives for firms to invest in safety across the relevant domains or time horizons. This led us to ask whether “market-shaping” tools could pull more safety into existence rather than relying on unconvincing voluntary commitments, limited grants, or slow and rigid regulation. We examined three families of mechanisms: advance market commitments (AMCs), prize competitions, and liability/insurance, using precedent review, desk research, light mechanism design, and a small number of expert conversations. The core finding is that AMCs are a poor fit for most software safety problems but may be viable for verifiable hardware and assurance mechanisms, particularly given ongoing UK/EU policy on sovereign compute. Prizes look more promising for many technical domains (e.g. evaluation, verification, interpretable tooling), where results can be tested and iterated more quickly than through conventional grants. An empirical scan of recent AI safety prizes shows that (i) prizes are culturally normalised but financially insufficient to attract top teams on purely economic grounds; (ii) they cluster in commercially aligned domains (cybersecurity, robustness, evaluation); and (iii) they do not scale at the pace of capability improvements. This suggests that prize design should target neglected, socially valuable safety problems rather than simply amplifying existing market incentives. Taken together, the work points to a practical niche for AMCs in hardware and assurance, and a broader role for prizes in safety-adjacent research.