Report · stanforddaily.com
Anthropic trained Claude on illegally obtained Stanford professors' books

stanforddaily.com
Source published October 27, 2026
Summary: 2 min read
69/100
Summary
A federal ruling in Bartz v. Anthropic found that Anthropic used illegally obtained books, including works by more than 10 Stanford professors, to build a permanent central library for training Claude models. Judge William Alsup barred Anthropic from pirating books for that library but held that training large language models on legally acquired books qualifies as fair use, comparing the process to a human reader learning from a work’s themes and style. A $1.5 billion settlement in the case covers authors and publishers of nearly 500,000 registered works, or roughly $3,000 per work. Stanford professor emeritus Tobias Wolff, whose 10 publications were used without permission, and Stanford English professor Chang-rae Lee opted out. In May 2026, they joined Cruz v. Anthropic, a separate lawsuit seeking a jury trial, statutory damages of up to $150,000 per infringed work, and a permanent injunction against training Claude on their works. The plaintiffs allege Anthropic downloaded, reproduced and stored copyrighted books from shadow libraries including LibGen and PiLiMi. More than 70 copyright suits have been filed against AI companies by authors, artists and news organizations, including The New York Times and Getty Images. The Authors Guild is also pursuing litigation and federal licensing requirements for books used in AI training.
Key Takeaways
- Judge William Alsup barred Anthropic from pirating books to create a permanent training library, while finding that training Claude on legally acquired books is fair use.
- The Bartz v. Anthropic settlement totals $1.5 billion for authors and publishers of nearly 500,000 registered works.
- Tobias Wolff and Chang-rae Lee opted out of the settlement and sued Anthropic separately, seeking up to $150,000 in statutory damages for each infringed work.
- The Cruz v. Anthropic plaintiffs allege that Anthropic sourced copyrighted books from shadow libraries including LibGen and PiLiMi.
- The Authors Guild supports litigation and federal legislation requiring AI companies to license books used for model training.
What the discussion said
The thread spent less time on Stanford professors specifically than on the legal and moral split between acquiring books illegally and using books to train a model. Several commenters stressed that the reported ruling treated training on lawfully obtained books as transformative fair use; in their view, Anthropic’s expensive mistake was sourcing material through LibGen rather than buying and scanning it. That distinction mattered because it suggests a route for large-scale model training that avoids treating every learned idea as licensed property. Others called that distinction evasive. They argued that pirating works expressly to build a commercial model cannot be neatly separated from the resulting training process, especially when models can operationalize an author’s accumulated expertise at enormous scale. The most forceful criticism was that AI firms have extracted broadly from creators while reserving tight control over their own models, making objections to model distillation look hypocritical. A smaller group welcomed the erosion of academic and publishing gatekeeping, but opponents replied that broad access does not justify stripping authors of compensation. Commenters also rejected the article’s original framing as legally imprecise: the alleged illegality concerned acquisition, while the fair-use finding on training remains a crucial, contested precedent rather than a blanket moral acquittal.
Where opinion split
The core fight is whether lawful training can be meaningfully separated from pirated acquisition. One side says the judge correctly recognized model training as transformative learning, so the remedy should target LibGen sourcing and leave properly acquired corpora usable. The other says a commercial AI system built from deliberately stolen books is inseparable from that theft, and fair use lets companies capture creators’ expertise without a fair bargain.
Coverage & discussions
Hacker News · stanforddaily.com
Community discussions
Community Sentiment
Positives
- A fair-use path for training on legitimately acquired books could preserve broad, practical model development without requiring every idea a model learns to be individually licensed.
- The ruling’s human-learning analogy persuaded some readers that models can absorb themes and methods to generate new work rather than merely reproduce the books they ingest.
- Buying and scanning large physical-book collections was seen as a workable way to build training datasets without repeating the piracy that triggered the settlement.
- Cheaper access to knowledge and less dependence on elite academic gatekeepers appealed to readers who see AI as a vehicle for spreading expertise widely.
Concerns
- Using shadow-library copies to feed a commercial frontier model looks like an astonishingly avoidable shortcut, one that converted ordinary dataset sourcing into a $1.5 billion liability.
- Critics fear that calling training fair use hands AI companies the ability to industrialize authors’ hard-won knowledge while offering little payment or control in return.
- The claim that training is separate from theft struck opponents as legalistic hair-splitting when the books were allegedly acquired precisely to improve Claude.
- AI firms’ complaints about others distilling their models ring hollow to commenters who see training on creators’ work as a far larger one-way extraction.
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