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Data Sharing with a Generative AI Competitor

arXiv 2505.12386 ↗First posted 18 May 2025cs.GT cs.AIcited by 0

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Abstract (arXiv)

As GenAI platforms grow, their dependence on content from competing providers, combined with access to alternative data sources, creates new challenges for data-sharing decisions. In this paper, we provide a model of data sharing between a content creation firm and a GenAI platform that can also acquire content from third-party experts. The interaction is modeled as a Stackelberg game: the firm first decides how much of its proprietary dataset to share with GenAI, and GenAI subsequently determines how much additional data to acquire from external experts. Their utilities depend on user traffic, monetary transfers, and the cost of acquiring additional data from external experts. We characterize the unique subgame perfect equilibrium of the game and uncover a surprising phenomenon: The firm may be willing to pay GenAI to share the firm's own data, leading to a costly data-sharing equilibrium. We further characterize the set of Pareto improving data prices, and show that such improvements occur only when the firm pays to share data. Finally, we study how the price can be set to optimize different design objectives, such as promoting firm data sharing, expert data acquisition, or a balance of both. Our results shed light on the economic forces shaping data-sharing partnerships in the age of GenAI, and provide guidance for platforms, regulators and policymakers seeking to design effective data exchange mechanisms.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 18 May 2025.

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30 of 45 references.

References · 30

  1. 2025Multi-Agent Risks from Advanced AIcited by 163
  2. 2022Performative Powercited by 43
  3. 2023Generative Social Choicecited by 43
  4. 2022Modeling Content Creator Incentives on Algorithm-Curated Platformscited by 35
  5. 2018A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providerscited by 33
  6. 2022Supply-Side Equilibria in Recommender Systemscited by 33
  7. 2023How Bad is Top-K Recommendation under Competing Content Creators?cited by 27
  8. 2022Competition, Alignment, and Equilibria in Digital Marketplacescited by 17
  9. 2023Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Modelscited by 17
  10. 2020Truthful Data Acquisition via Peer Predictioncited by 14
  11. 2024Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?cited by 13
  12. 2024Competition and Diversity in Generative AIcited by 13
18 more
  1. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
  2. 2023Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?cited by 10
  3. 2024Incentives in Private Collaborative Machine Learningcited by 10
  4. 2024User Welfare Optimization in Recommender Systems with Competing Content Creatorscited by 10
  5. 2024Braess's Paradox of Generative AIcited by 7
  6. 2024A Survey on Data Marketscited by 7
  7. 2020Coopetition Against an Amazoncited by 6
  8. 2023Strategic Data Sharing between Competitorscited by 6
  9. 2025Selective Response Strategies for GenAIcited by 6
  10. 2024Data Distribution Valuationcited by 5
  11. 2023The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documentscited by 4
  12. 2024How to Strategize Human Content Creation in the Era of GenAI?cited by 4
  13. 2022Pareto-Improving Data-Sharingcited by 3
  14. 2023Equilibrium of Data Markets with Externalitycited by 3
  15. 2024On the Convergence of No-Regret Dynamics in Information Retrieval Games with Proportional Ranking Functionscited by 3
  16. 2023Selling Data to a Competitor (Extended Abstract)cited by 1
  17. 2024Prediction-sharing During Training and Inferencecited by 1
  18. 2025Selling Information in Games with Externalitiescited by 1

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