Market Incentives for AI Safety Investment

Abstract:Despite their practical significance, modern AI systems pose significant societal risks, including toxicity, malicious use, and misinformation. Their mitigation depends not only on technical feasibility but also on the incentives of firms that develop and deploy these systems. We show how profit maximization shapes safety investment in an AI supply chain with an upstream LLM provider and several competing downstream firms. In our game-theoretic model, we derive the market-driven level of safety investment and compare it with the levels that maximize market welfare and industry profit. We find that downstream competition generally induces downstream firms to invest more in safety than the industry-profit-optimal level. By contrast, the upstream provider may underinvest relative to these benchmarks because it is not fully compensated for the surplus from safety investments. Finally, we analyze how different regulatory interventions affect the participants' incentives and profits, and the resulting levels of model safety.

Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML)

arXiv:2610.21607 [cs.LG]

(or arXiv:2610.21607v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.21607 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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