# Data Sharing with a Generative AI Competitor

arXiv: https://arxiv.org/abs/2505.12386 · HTML: https://mlsanity.com/paper/2505.12386/

## 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.

## Builds on (most cited first, 30 of 30)

- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Performative Power](https://mlsanity.com/paper/2203.17232/) (2022)
- [Generative Social Choice](https://mlsanity.com/paper/2309.01291/) (2023)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Competition, Alignment, and Equilibria in Digital Marketplaces](https://mlsanity.com/paper/2208.14423/) (2022)
- [Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models](https://mlsanity.com/paper/2308.04399/) (2023)
- [Truthful Data Acquisition via Peer Prediction](https://mlsanity.com/paper/2006.03992/) (2020)
- [Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?](https://mlsanity.com/paper/2402.15467/) (2024)
- [Competition and Diversity in Generative AI](https://mlsanity.com/paper/2412.08610/) (2024)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?](https://mlsanity.com/paper/2306.07893/) (2023)
- [Incentives in Private Collaborative Machine Learning](https://mlsanity.com/paper/2404.01676/) (2024)
- [User Welfare Optimization in Recommender Systems with Competing Content Creators](https://mlsanity.com/paper/2404.18319/) (2024)
- [Braess's Paradox of Generative AI](https://mlsanity.com/paper/2409.05506/) (2024)
- [A Survey on Data Markets](https://mlsanity.com/paper/2411.07267/) (2024)
- [Coopetition Against an Amazon](https://mlsanity.com/paper/2005.10038/) (2020)
- [Strategic Data Sharing between Competitors](https://mlsanity.com/paper/2305.16052/) (2023)
- [Selective Response Strategies for GenAI](https://mlsanity.com/paper/2502.00729/) (2025)
- [Data Distribution Valuation](https://mlsanity.com/paper/2410.04386/) (2024)
- [The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents](https://mlsanity.com/paper/2305.16695/) (2023)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Pareto-Improving Data-Sharing](https://mlsanity.com/paper/2205.11295/) (2022)
- [Equilibrium of Data Markets with Externality](https://mlsanity.com/paper/2302.08012/) (2023)
- [On the Convergence of No-Regret Dynamics in Information Retrieval Games with Proportional Ranking Functions](https://mlsanity.com/paper/2405.11517/) (2024)
- [Selling Data to a Competitor (Extended Abstract)](https://mlsanity.com/paper/2307.05078/) (2023)
- [Prediction-sharing During Training and Inference](https://mlsanity.com/paper/2403.17515/) (2024)
- [Selling Information in Games with Externalities](https://mlsanity.com/paper/2505.00405/) (2025)

## 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.