Market Design for AI: Beyond the Copyright Binary
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 failu...
| Authors | Yan Dai, Maryam Farboodi, Negin Golrezaei et al. |
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| Primary category | econ.TH |
| Published | 10 June 2026 |
| Last updated at source | 11 August 2026 |
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