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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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    Cited by:

    1. is not listed on IDEAS
    2. Pierre Azoulay & Joshua Krieger & Abhishek Nagaraj, 2024. "Old Moats for New Models: Openness, Control, and Competition in Generative Artificial Intelligence," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, pages 7-46, National Bureau of Economic Research, Inc.
    3. Christian Peukert & Florian Abeillon & Jérémie Haese & Franziska Kaiser & Alexander Staub, 2024. "Strategic Behavior and AI Training Data," CESifo Working Paper Series 11099, CESifo.
    4. Christian Peukert & Florian Abeillon & J'er'emie Haese & Franziska Kaiser & Alexander Staub, 2024. "AI and the Dynamic Supply of Training Data," Papers 2404.18445, arXiv.org, revised Jun 2025.
    5. Gaétan de Rassenfosse & Adam B. Jaffe & Joel Waldfogel, 2025. "Intellectual Property and Creative Machines," Entrepreneurship and Innovation Policy and the Economy, University of Chicago Press, vol. 4(1), pages 47-79.
    6. Fasheng Xu & Xiaoyu Wang & Wei Chen & Karen Xie, 2025. "The Economics of AI Foundation Models: Openness, Competition, and Governance," Papers 2510.15200, arXiv.org.
    7. Hangcheng Zhao & Ron Berman, 2025. "Strategic Response of News Publishers to Generative AI," Papers 2512.24968, arXiv.org, revised Apr 2026.
    8. Annie Liang & Jay Lu, 2026. "Creative Ownership in the Age of AI," Papers 2602.12270, arXiv.org.
    9. Bouchra Al MAWLA & George M. El KAZZI & Hiba S. OTHMAN, 2025. "Artificial intelligence as a disruptive force in economics: transformations, challenges, and future prospects," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(643), S), pages 87-106, Summer.

    More about this item

    JEL classification:

    • K20 - Law and Economics - - Regulation and Business Law - - - General
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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