Oct 15, 2026 08:10 PM
http://dx.doi.org/10.1126/science.ael4888
PRESS RELEASE: In a Policy Forum, Pamela Samuelson and colleagues discuss whether generative AI causes “market dilution,” i.e., crowding human-authors works out of markets and weakening incentives for people to create original works. Generative AI holds considerable promise for advancing scientific research, but its future depends in part on unresolved questions about copyright law.
Because foundation models are trained on vast collections of material, including copyrighted books, images, and other human-authored works, more than 100 active US lawsuits are examining whether this practice is lawful. Although two federal courts have ruled that such training qualifies as “fair use,” a legal doctrine that permits certain uses of copyrighted material without permission, the broader legal dispute remains unsettled.
One emerging argument, known as market dilution, proposes that AI-generated content could undermine entire markets for human-created works, even when it does not directly harm any copyrighted work. Here, Samuelson et al. question the conceptual basis of this theory and argue that it oversimplifies the relationship between human and AI creativity. According to Samuelson et al., any assessment of market dilution must account for how AI is used, the extent of human contribution, and the specific creators and markets affected.
Claims of harm must be evaluated through rigorous empirical research focused on particular AI systems and creative markets, rather than broad assumptions about AI’s impact on human authorship. The authors suggest that collaboration between AI developers and researchers, alongside longitudinal data and surveys of creative practices, could help clarify these dynamics.
If evidence eventually establishes that AI harms particular creative markets, they argue that legislatures, not courts resolving individual copyright disputes, are better equipped to develop targeted policies, such as licensing or funding schemes. They warn that court rulings restricting the use of copyrighted works to train AI models could create widespread legal uncertainty and significantly affect scientific researchers and developers who rely on these technologies.
“If courts decide that developers of generative-AI models are likely diluting markets for human-authored works, rulings against fair uses may substantially raise the liability risks for all developers and users of generative AI in ways that are difficult to predict,” write the authors. “This could have considerable impacts on scientist users and builders of generative-AI systems, including open-source and domain-specific models.”
PRESS RELEASE: In a Policy Forum, Pamela Samuelson and colleagues discuss whether generative AI causes “market dilution,” i.e., crowding human-authors works out of markets and weakening incentives for people to create original works. Generative AI holds considerable promise for advancing scientific research, but its future depends in part on unresolved questions about copyright law.
Because foundation models are trained on vast collections of material, including copyrighted books, images, and other human-authored works, more than 100 active US lawsuits are examining whether this practice is lawful. Although two federal courts have ruled that such training qualifies as “fair use,” a legal doctrine that permits certain uses of copyrighted material without permission, the broader legal dispute remains unsettled.
One emerging argument, known as market dilution, proposes that AI-generated content could undermine entire markets for human-created works, even when it does not directly harm any copyrighted work. Here, Samuelson et al. question the conceptual basis of this theory and argue that it oversimplifies the relationship between human and AI creativity. According to Samuelson et al., any assessment of market dilution must account for how AI is used, the extent of human contribution, and the specific creators and markets affected.
Claims of harm must be evaluated through rigorous empirical research focused on particular AI systems and creative markets, rather than broad assumptions about AI’s impact on human authorship. The authors suggest that collaboration between AI developers and researchers, alongside longitudinal data and surveys of creative practices, could help clarify these dynamics.
If evidence eventually establishes that AI harms particular creative markets, they argue that legislatures, not courts resolving individual copyright disputes, are better equipped to develop targeted policies, such as licensing or funding schemes. They warn that court rulings restricting the use of copyrighted works to train AI models could create widespread legal uncertainty and significantly affect scientific researchers and developers who rely on these technologies.
“If courts decide that developers of generative-AI models are likely diluting markets for human-authored works, rulings against fair uses may substantially raise the liability risks for all developers and users of generative AI in ways that are difficult to predict,” write the authors. “This could have considerable impacts on scientist users and builders of generative-AI systems, including open-source and domain-specific models.”
