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A California federal judge has issued a groundbreaking decision in the copyright lawsuit brought by authors against Anthropic, the AI company behind Claude. This ruling is one of the most detailed and consequential to date on how U.S. copyright law applies to training large language models (LLMs).
The plaintiffs—authors including Andrea Bartz, Charles Graeber, and Kirk Wallace Johnson—sued Anthropic for copying their books to build a central library and to train Claude. They argued this was copyright infringement.
Training AI models on copyrighted books is fair use. The court held that training an LLM is “spectacularly transformative.” Even though the models effectively “memorized” the works, the purpose—developing an AI that can generate new text—was fundamentally different from simply reproducing or distributing the books themselves.
✅ Digitizing legally purchased print books is also fair use. When Anthropic bought print copies and destroyed them to create searchable digital files for its own internal library, that format change was deemed permissible.
❌ Creating a permanent library of pirated books is not fair use. The court was unequivocal: downloading millions of unauthorized copies from pirate sites crossed the line. The judge emphasized that no AI company has a special right to stockpile infringing copies simply because it plans to train a model.
In other words, while the transformative nature of training LLMs shields some copying, the source of the data still matters.
This decision is the first major U.S. ruling to clearly separate:
It acknowledges the enormous value that high-quality books provide to training AI but affirms that creators have enforceable rights when their works are pirated.
The decision also points to a path forward for AI companies: if you acquire content legally—whether by purchase or license—then training on it is likely protected as fair use. But shortcuts like mass downloads of pirated material remain an infringement risk.
Here are some likely next steps and broader implications:
Potential Damages Phase or Settlement: Because the court found that the pirated copies were infringing, Anthropic could be liable for statutory damages. The case could proceed to determine how much they owe, unless the parties settle first.
Appeal: Anthropic or the authors may appeal aspects of this decision. The line between transformative fair use and infringement of source copies will be tested in higher courts.
Class Certification: A separate motion to certify a class of authors is pending. If granted, this could multiply the scale of liability.
Industry Impact: This ruling clarifies that AI companies cannot ignore copyright ownership in their data pipelines. It also provides some reassurance that training itself—when conducted on lawfully obtained materials—will often be protected as fair use.
Policy and Legislation: This case highlights the need for clearer rules around AI training data. Legislators may eventually address whether AI companies should pay for ingesting copyrighted works, even for transformative purposes.
This decision is a landmark step in defining the legal boundaries of AI training. While the court embraced the transformative potential of AI, it firmly rejected the idea that everything is fair game. Going forward, companies training models will need to be scrupulous about where their data comes from—even as courts recognize that using that data to build AI is often protected.
