June 26, 2025
In a significant ruling issued on June 23, 2025, the U.S. District Court for the Northern District of California held that using copyrighted works to train large language models (LLMs) is fair use under U.S. copyright law. The court found such use is “exceedingly transformative” and does not displace demand for or harm the market for the original works.
The court granted summary judgment for defendant Anthropic PBC, finding both that using copyrighted works for training LLMs was a fair use, and that Anthropic’s creation of a digital library by purchasing and digitizing millions of print books was a fair use. The court, however, found the use of downloaded pirated copies to build Anthropic’s central library was not justified by fair use, and thus denied summary judgment for Anthropic that the pirated library copies must be treated as training copies.
Defendant Anthropic is an AI software firm that offers an AI software service named Claude. When a user prompts Claude with text, Claude quickly responds with text — mimicking human reading and writing. To enable such functionality, Anthropic trained Claude — or rather trained large language models (LLMs) underlying various versions of Claude — using books and other texts selected from a central library Anthropic assembled.
Plaintiffs are the authors of multiple copyrighted books. The plaintiffs filed a class action complaint against Anthropic, alleging that Anthropic had infringed their copyrights by, among other things, using their copyrighted books to train its Claude LLMs. The plaintiffs did not, however, allege that any infringing copy of their works was or would ever be provided to users by the Claude service.
The court’s order characterized Anthropic’s use of plaintiffs’ books as follows:
Anthropic filed a motion for summary judgment, asserting that its uses of the plaintiffs’ books were fair use and thus noninfringing.
The court evaluated Anthropic’s use of the copyrighted works for training and to create a central library (from pirated and purchased sources) under each of the four fair use factors. At a high level, the court concluded that:
Copies Used to Train the LLMs
Use of Purchased Copies to Build a Central Library
Use of Pirated Copies to Build a Central Library
Copies Used to Train the LLMs
Use of Purchased Copies to Build a Central Library
Use of Pirated Copies to Build a Central Library
Copies Used to Train the LLMs
Use of Purchased Copies to Build a Central Library
Use of Pirated Copies to Build a Central Library
After weighing the four factors for each type of use, the court granted summary judgment for Anthropic that the training use was a fair use and that the print-to-digital format change was a fair use. The court found the downloaded pirated copies used to build a central library were not justified by a fair use, and denied summary judgment for Anthropic that the pirated library copies must be treated as training copies.
The case will proceed to trial on the pirated copies used to create Anthropic’s central library and the resulting damages, actual or statutory (including for willfulness).
This is the first substantive decision issued by a U.S. court concerning fair use and generative AI model training. The court’s determination that using copyrighted works to train large language models is “exceedingly transformative” and fair use is likely to influence how other courts confronting this issue — and there are numerous such pending cases — may view this issue.
The case is Bartz v. Anthropic PBC, Case No. 24-cv-5417 (N.D. Cal. June 23, 2025)
Copyright © Finnegan, Henderson, Farabow, Garrett & Dunner, LLP. This article is for informational purposes, is not intended to constitute legal advice, and may be considered advertising under applicable state laws. This article is only the opinion of the authors and is not attributable to Finnegan, Henderson, Farabow, Garrett & Dunner, LLP, or the firm’s clients.
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