Market Design for AI: Beyond the Copyright Binary
For AI developers and content creators, the paper addresses the fundamental market design problem of balancing AI progress with creator incentives, revealing novel failures in existing approaches.
The paper identifies market failures in both free-for-all and strong IP rights models for human-generated content used in AI training, showing that strong IP rights underpower creative incentives (especially for innovative creators) and that dynamic feedback from AI-assisted creation degrades model performance. It proposes a market design with a data intermediary that subsidizes innovative contributions to restore efficiency.
How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation? Existing approaches take polar positions: a "free-for-all" model based on fair use and a "strong intellectual property rights" model. We show that both fail: Free-for-all does not compensate creators, and -- by modeling as a static Stackelberg game -- strong intellectual property rights also underpower creative incentives. We find this especially true for more innovative creators, a phenomenon we term the "originality penalty." Extending this insight to a dynamic model, we find another market failure undermining AI model performance, even for an initially good model: Such a model induces greater reliance by humans on AI-assisted creation, resulting in homogenized content feeding back into training, which degrades the model performance -- a "curse of precision." We further propose a market design with a data intermediary internalizing cross-creator externalities and subsidizing innovative contributions, thereby restoring efficiency.