Research map / Large language models / AI and society

Market Incentives for AI Safety Investment

NeurIPS 2026 · Large language models › AI and society · approach: Human-centered trust and agency

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Game-theoretic model of safety investment in an AI supply chain (upstream LLM provider, Cournot downstream firms, Nash-in-Nash fee bargaining) deriving market-driven safety levels and regulatory effects.

Problem

How profit maximization shapes safety investment by an upstream LLM provider and competing downstream firms, and how that compares with welfare- and industry-profit-optimal levels, and how regulation changes incentives.

Approach

Combines Cournot competition with quasilinear consumer utility, Nash-in-Nash bargaining over access fees, and neural-scaling-law-based safety costs to derive closed-form safety investment levels; finds downstream competition induces more firm safety investment than industry-profit optimum, while the provider may underinvest due to incomplete capture of surplus.

Key results

• Downstream competition generally induces firms to invest more in safety than the industry-profit-optimal level (Proposition 5.3 comparison with market-driven levels).
• Upstream provider may underinvest relative to industry-profit and welfare benchmarks because it is not fully compensated for the surplus its safety investments create.
• Strict downstream or upstream regulation raises safety investment levels but reduces participants' profits and may cause provider exit.

Surprising findings

• Overly fierce downstream competition can lower cumulative safety, since the equilibrium investment levels decrease in competition intensity for large gamma and firm count.
• Under the model, the provider may invest enough that firms make zero investment when the number of firms is large, due to positive externality.

Limitations

• Assumes symmetric firms, which may not hold with horizontal differentiation.
• Excludes catastrophic risks; focuses on lower-impact errors described by scaling laws.
• Relies on absence-of-disruption assumption and perfect regulatory enforcement.

Experimental setup

Metrics: safety investment level industry profit market welfare provider profit

Baselines: Laufer et al. (2025) model industry-profit-optimal investment market-welfare-optimal investment

Builds on: Cournot competition (Dixit, 1979) Nash-in-Nash bargaining (Collard-Wexler et al., 2019) Tsoy and Konstantinov (2023) Laufer et al. (2025) Gordon and Loeb (2002)

Closest papers by meaning

• Modeling the Economic Impacts of AI Openness Regulation (NeurIPS 2025 · similarity 0.83)
• Position: Require Frontier AI Labs To Release Small "Analog" Models (NeurIPS 2025 · similarity 0.83)
• GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory (NeurIPS 2026 · similarity 0.82)
• AI Risk Profiles: A Standards Proposal for Pre-deployment AI Risk Disclosures (AAAI 2024 · similarity 0.82)
• SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior (ICML 2025 · similarity 0.81)
• Position: AI Safety Must Embrace an Antifragile Perspective (ICML 2025 · similarity 0.81)