As generative AI tools race ahead, a growing debate is unfolding over who should benefit when machines learn from human creativity, a question explored in a recent Cornell Chronicle story by Laura Reiley.
The article examines how today’s copyright system struggles to keep up as AI systems train on massive collections of books, articles, images, music, and other creative works—often without permission or payment to the people who made them.
At the center of the discussion is a new proposal called “learnright,” outlined in a paper published in the Journal of Technology and Intellectual Property. The idea would give creators a specific legal right to license their work for use in AI training, creating a new pathway for compensation without banning the technology itself.
Supporters of t…
As generative AI tools race ahead, a growing debate is unfolding over who should benefit when machines learn from human creativity, a question explored in a recent Cornell Chronicle story by Laura Reiley.
The article examines how today’s copyright system struggles to keep up as AI systems train on massive collections of books, articles, images, music, and other creative works—often without permission or payment to the people who made them.
At the center of the discussion is a new proposal called “learnright,” outlined in a paper published in the Journal of Technology and Intellectual Property. The idea would give creators a specific legal right to license their work for use in AI training, creating a new pathway for compensation without banning the technology itself.
Supporters of the idea argue that the current system leaves both creators and tech companies in limbo. Artists, writers, and journalists say their work fuels AI systems that now compete with them for attention and income. At the same time, AI developers face ongoing lawsuits and uncertain court rulings over whether training on copyrighted material counts as fair use.
The issue goes beyond law and economics. Creators worry about losing incentives to produce new work if their output can be absorbed and replicated by machines at scale. Journalists point to declining traffic as chatbots summarize reporting without directing readers back to publishers. Workers in fields like design, marketing, and coding increasingly fear that AI systems trained on their past work could replace parts of their jobs.
The authors behind the learnright proposal frame the problem as a moral one as well as a legal one. They argue that a healthy creative ecosystem depends on fairness, attribution, and respect for the labor that underpins innovation. Without those norms, they warn, the supply of high-quality human-created content could shrink.
They also push back against the idea that compensating creators would slow progress. Instead, they suggest that unchecked AI training could ultimately harm the technology itself. Research cited in the paper points to “model collapse,” where systems trained too heavily on AI-generated content begin to lose quality over time.
Under a learnright system, companies would license the right to train on specific datasets, much like how music or photography licensing works today. Market negotiations or collective licensing organizations could help set fair rates, offering clarity for developers and income for creators.
As lawmakers signal increased interest in regulating generative AI, the proposal presents a possible middle ground. It does not shut down AI development, but it challenges the idea that creative work should be treated as a free raw material.
The question now facing policymakers, technologists, and artists alike is whether society is willing to redraw the rules to ensure that the people who create the content powering AI systems share in the benefits of their success.
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