Meta Wins Kadrey Case, but Judge Opens New AI Copyright Front
A federal judge has ruled that Meta’s use of copyrighted books to train Llama was fair use in the Kadrey case. But the decision warns that AI could still damage the publishing market without reproducing the original works.
Meta has scored a significant victory in the copyright lawsuit brought by several writers over the training of its Llama models. U.S. District Judge Vince Chhabria of the Northern District of California ruled that using the books in this specific case was protected by fair use, the U.S. copyright exception.
The decision, issued on June 25, does not make training language models on copyrighted works automatically legal. On the contrary, the judge delivered a clear warning to AI companies. The authors largely lost because they did not provide enough evidence of how the models could damage the book market.
Llama’s training was deemed transformative
The lawsuit, Kadrey v. Meta, was brought by 13 authors, including novelist Richard Kadrey. They accused Meta of copying their books without permission to train Llama, its family of open-source language models. The texts came from so-called shadow libraries: online repositories that distribute copyrighted works for free without authorization from their owners.
Meta did not deny using copyrighted books. The question was whether that use fit within the four factors U.S. law applies to determine fair use: the purpose of the use, the nature of the work, the amount copied, and the effect on its market.
Chhabria sided with Meta on three of the four factors. He found that training a model has a “highly transformative” purpose: the books were created to be read, while the company used them to develop a system capable of processing and generating language.
The judge did acknowledge that novels, memoirs, and plays are “highly expressive” creations, a circumstance that favors the authors. But he found it reasonable for Meta to copy the works in full for its training objective. He also rejected the plaintiffs’ claim that Llama had been shown to reproduce their books in a way that would substitute for the originals.
The relevant market is not just the licensing market
The most important part of the ruling concerns the fourth factor: market harm. Among other arguments, the writers claimed that Meta had deprived them of a potential market for licensing their works to train AI. The court rejected that theory. The mere existence of a hypothetical market for licensing data is not enough, by itself, to bar fair use.
But Chhabria identified a different, potentially stronger path: market dilution. A model trained on millions of books could enable the rapid production of countless works that compete with the originals for readers, attention, or revenue, even if those new works do not literally infringe the copyright in any specific text.
The judge warned: “No matter how transformative LLM training may be, it’s hard to imagine that it can be fair use to use copyrighted books to develop a tool to make billions or trillions of dollars while enabling the creation of a potentially endless stream of competing works that could significantly harm the market for those books.”
That theory did not save the lawsuit, however. The authors presented a market-dilution theory that was too weak and lacked enough concrete evidence to take the case to trial. That is why the ruling is limited. The judge described the decision as limited: it does not declare the use of copyrighted works to train models generally lawful. Instead, it concludes that these plaintiffs advanced the wrong arguments and failed to develop evidence for the appropriate theory.
A different victory from Anthropic’s
The ruling comes two days after Judge William Alsup’s decision in the case against Anthropic. Alsup also deemed language-model training transformative, but legally separated several actions: training, digitizing printed books, and creating a central library of pirated copies.
Chhabria took a different approach. He analyzed Meta’s downloads according to their ultimate purpose—the training of Llama—rather than treating them as independent uses. That divergence matters because AI companies face not only the question of what a model does with a work, but also how they obtained the copy and what infrastructure they built with it.
For publishers, writers, and AI companies, the Kadrey case shifts the next battleground. The debate is no longer limited to whether a model memorizes or regurgitates protected passages. Future lawsuits will likely try to determine whether generative systems create a substitute supply broad enough to reduce the commercial value of human-made works.
That showing will be difficult: it will require data on sales, user behavior, and actual competition—not merely the abstract possibility that an AI could write a similar book. But the ruling makes clear that if such data reaches a courtroom, the transformative nature of training may not be enough to protect an AI company.
