Data Sharing with a Generative AI Competitor

2025-05-18

AI Summary

This paper examines the strategic data-sharing interaction between content providers and generative AI (GenAI) platforms: content providers decide how much proprietary data to share—and pay for—while GenAI platforms determine how much expert third-party data to procure. We model this interaction as a Stackelberg game and derive its subgame-perfect equilibrium. We identify, for the first time, a counterintuitive equilibrium in which content providers voluntarily pay to share their own data. We propose a Pareto-improving data pricing set that jointly optimizes incentives for data sharing, procurement of high-quality external data, and ecosystem balance. We prove that this pricing mechanism enhances welfare for both parties and establish implementable pricing criteria that reconcile data sovereignty with platform ecosystem health—thereby providing theoretical foundations for regulatory design and platform governance. (149 words)

Technology Category

• Game Theory and Economic Paradigms: Equilibrium
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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.

Problem

• Modeling data sharing between content firms and GenAI platforms
• Analyzing Stackelberg game equilibrium in costly data-sharing scenarios
• Exploring Pareto-improving pricing for optimal data exchange design

Innovation

• Model data sharing as Stackelberg game
• Firm may pay GenAI for data sharing
• Characterize Pareto improving data prices