Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 29 min ago
PreprintCC BY 4.0arXiv

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 industry profit and market welfare. 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.

Related

  1. Climate Cooperation and Spatial Industrial Relocation: Stability, Emissions, and Regional Policy Incentives
    Angela Martiradonna
    arXivPreprintarXiv licenceFull textAnalysis & PDEsarXiv
  2. Efficiency of cooperation incentives in evolutionary population games under payoff-observation errors
    Shengxian Wang, Chengyu Yin, Xiaojie Chen +1
    arXivPreprintarXiv licenceFull textOptimisation & controlarXiv
  3. Combining optimal low emission zones scheduling and electric vehicle purchase incentives for transport policy backcasting
    Asmae Alami, Vinith Lakshmanan, Antonio Sciarretta
    HALPreprintCC BY 4.0Full textOptimisation & controlHAL
  4. What drives generation Z's appetite for crypto investment? The role of financial literacy, gender, and channels of influence
    Enareta Kurtbegu, Caroline Marie-Jeanne, Bruno Séjourné
    HALPreprintPublisher termsFull textStatisticsHAL
  5. Diversity, Efficiency, and Incentives in Political Prediction Markets
    Harry Crane, Koleman Strumpf
    arXivPreprintarXiv licenceFull textStatisticsarXiv
BibTeX
@misc{tsoy2026market,
  title = {{Market Incentives for AI Safety Investment}},
  author = {Nikita Tsoy},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.21607},
  eprint = {2610.21607},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.21607},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Market Incentives for AI Safety Investment
AU  - Nikita Tsoy
PY  - 2026
DA  - 2026/10/29
PB  - arXiv
DO  - 10.48550/arxiv.2610.21607
UR  - https://arxiv.org/abs/2610.21607
AB  - 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 industry profit and market welfare. 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.
ER  -