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Copyright Policy Options for Generative Artificial Intelligence

Joshua S. Gans

The Journal of Law and Economics2026https://doi.org/10.1086/733761article
AJG 3ABDC A*
Weight
0.50

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.

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https://doi.org/https://doi.org/10.1086/733761

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@article{joshua2026,
  title        = {{Copyright Policy Options for Generative Artificial Intelligence}},
  author       = {Joshua S. Gans},
  journal      = {The Journal of Law and Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1086/733761},
}

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Copyright Policy Options for Generative Artificial Intelligence

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.