Copyright Policy Options for Generative Artificial Intelligence

Joshua Gans

Journal of Law and Economics, 2026, vol. 69, issue 1, 1 - 19

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.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1086/733761 (application/pdf)
http://dx.doi.org/10.1086/733761 (text/html)
Access to the online full text or PDF requires a subscription.

Related works:
Working Paper: Copyright Policy Options for Generative Artificial Intelligence (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ucp:jlawec:doi:10.1086/733761

Access Statistics for this article

More articles in Journal of Law and Economics from University of Chicago Press
Bibliographic data for series maintained by Journals Division ().