Strategic Content Creation in the Age of GenAI: To Share or Not to Share?
Gur Keinan, Omer Ben-Porat
TL;DR
This work tackles how GenAI-enabled platforms should allocate revenue to incentivize creators to share their data and maintain high-quality content. It introduces a Stackelberg framework where a platform’s revenue-sharing rule $ ho$ guides creators who simultaneously decide content quality $x_i$ and data-sharing level $s_i$, with GenAI quality $Q_{ ext{AI}} = oldsymbol{x}^ op oldsymbol{s}$ and traffic $T(oldsymbol{x}) = ext{mu} (\|oldsymbol{x}oldsymbol ight ext{)}^{oldsymbol{ extgamma}}$. The authors define and analyze Full Sharing Equilibria (FSE) and Enforced Sharing Equilibria (ESE), establishing thresholds and conditions under which FSE exist, including a Prisoner’s Dilemma dynamic in sharing behavior. They develop an efficient approximation algorithm to solve the platform’s bi-level optimization, prove convergence guarantees under strongly convex costs, and demonstrate through simulations how revenue-sharing and GenAI strength shape equilibrium outcomes, content production, and platform revenue. The results offer design guidance for real platforms on monetization, data consent, and sustaining high-quality, user-relevant AI-generated content at scale. The work advances understanding of strategic data sharing and revenue allocation in GenAI ecosystems and provides practical algorithms with performance guarantees.
Abstract
We introduce a game-theoretic framework examining strategic interactions between a platform and its content creators in the presence of AI-generated content. Our model's main novelty is in capturing creators' dual strategic decisions: The investment in content quality and their (possible) consent to share their content with the platform's GenAI, both of which significantly impact their utility. To incentivize creators, the platform strategically allocates a portion of its GenAI-driven revenue to creators who share their content. We focus on the class of full-sharing equilibrium profiles, in which all creators willingly share their content with the platform's GenAI system. Such equilibria are highly desirable both theoretically and practically. Our main technical contribution is formulating and efficiently solving a novel optimization problem that approximates the platform's optimal revenue subject to inducing a full-sharing equilibrium. A key aspect of our approach is identifying conditions under which full-sharing equilibria exist and a surprising connection to the Prisoner's Dilemma. Finally, our simulations demonstrate how revenue-allocation mechanisms affect creator utility and the platform's revenue.