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Generative AI, machine learning, and other computational uses of copyrighted works pose profound conceptual questions for copyright law. This Article surveys multiple countries with different legal traditions and local conditions to explore how they have responded to these questions in relation to the use of copyrighted works for AI training without express permission from the relevant rightsholders. Our survey suggests an emerging international equilibrium in which jurisdictions from around the world have found ways to reconcile copyright law and AI training. In this equilibrium, countries recognize that text and data mining, computational data analysis, and AI training can be socially valuable and may not inherently prejudice the copyright holders' legitimate interests. Such uses should therefore be allowed without express authorization in some, but not all, circumstances. We identify three forces driving toward this equilibrium: (1) the centrality of the idea-expression distinction in copyright law; (2) global competition in AI; and (3) the race to the middle among countries undertaking copyright law reforms. However, we also address factors that may upset this emerging equilibrium, including ongoing copyright litigation, partnerships, and licensing deals in the United States, as well as legislative and regulatory efforts in both the United States and the European Union, most notably the adoption of the EU Artificial Intelligence Act. A key lesson of our multi-country survey is that, globally, the binary policy debate that assumes that text and data mining and AI training must be categorically condemned or applauded has been eclipsed by a more granular debate about the specific circumstances in which the unlicensed use of copyrighted works for AI training should be allowed or prohibited. Countries that have hesitated until now to modernize their copyright laws in the area of AI training have several templates open to them and little reason for hesitation.
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1 Jonas Robitscher Professor of Law in Artificial Intelligence, Machine Learning, and Data Science, Emory University School of Law.
2 University Distinguished Professor, Regents Professor of Law and Communication, and Director, Center for Law and Intellectual Property, Texas A&M University.