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Copyright Policy Options for Generative Artificial Intelligence

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  • Joshua S. Gans

Abstract

New generative artificial intelligence (AI) models have created new challenges for copyright policy as such models may be trained on data that include copy-protected content. This paper examines this issue from an economic perspective and analyzes how different copyright regimes for generative AI will impact the quality of content generated and AI training. Because of transaction costs (for example, because of the large amount of content being used to train generative AI models), it is not possible for copyright holders and AI providers to engage in negotiations. The result is a characterization of the factors that would favor full copyright and no copyright protections, balancing the level of potential harm to original content providers and the importance of content for AI training quality. However, it is demonstrated that an ex post mechanism like fair use can lead to higher expected social welfare than traditional rights regimes.

Suggested Citation

  • Joshua S. Gans, 2026. "Copyright Policy Options for Generative Artificial Intelligence," Journal of Law and Economics, University of Chicago Press, vol. 69(1), pages 1-19.
    • Handle: RePEc:ucp:jlawec:doi:10.1086/733761
    DOI: 10.1086/733761
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