Why the Line Between Legal and Infringing AI Won’t Be a Line at All
By Matthew James Sag
Everyone wants to know whether training AI on copyrighted works is legal. The real answer is: it depends—and the boundary between what’s permissible and what isn’t will be far messier than anyone expects.
In my forthcoming article in the Duke Law Journal, I argue that the copyright boundary for generative AI will be jagged rather than smooth. Not a clean bright line, but an irregular, context-dependent frontier shaped by the interaction of varying memorization rates across different AI models, divergent legal standards of similarity across different creative media, and the interplay of three distinct bodies of copyright doctrine (substantial similarity, fair use and secondary liability).
Understanding that jaggedness turns out to be essential—not just for predicting litigation outcomes, but for seeing the opportunities that lie on the other side.
The phrase “jagged frontier” will be familiar to many. It comes from the influential 2023 study by Fabrizio Dell’Acqua, Ethan Mollick, and colleagues, who used it to describe the uneven capability landscape of AI itself. It’s a useful concept because it captures the way that AI can be astonishingly good at some tasks while failing at others that seem equally difficult.
I borrow the metaphor deliberately, because copyright law presents generative AI with an analogous problem. The legal boundary between permissible and infringing AI conduct is similarly jagged: not because AI’s capabilities are uneven (though they are), but because the legal standards that determine infringement are themselves uneven across different creative domains. It seems likely that an AI system can cross the line into copyright infringement far more easily when generating music or images of recognizable characters than when generating prose—even when the underlying technology is essentially the same.
Explaining how and why the intersection of copyright and AI leads to a jagged frontier accounts for the first third of the article.
That jagged frontier is only the beginning of the story. Drawing on Ronald Coase’s insight that legal rules are starting points for adaptation and negotiation rather than final allocations, the Article argues that the extensive literature on AI and copyright has focused almost exclusively on fair use while ignoring what comes next. I might have something to say about that in a future post.
Matthew James Sag is the Jonas Robitscher Professor of Law in Artificial Intelligence, Machine Learning, and Data Science at Emory University School of Law. His article “Copyright’s Jagged Frontier” is forthcoming in the Duke Law Journal.