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A U.S. federal judge has given the green light to Anthropic's $1.5 billion settlement, resolving a class-action copyright lawsuit brought by authors and publishers. The approval allows the artificial intelligence company to start distributing compensation to rights holders. However, the decision does little to definitively answer the larger legal question of whether using copyrighted works to train AI models constitutes lawful fair use.

Judge Araceli Martinez-Olguin finalized the settlement on Monday, taking over from Judge William Alsup, who had previously granted preliminary approval. This settlement is reportedly the most substantial in the history of U.S. copyright law. Rights holders are expected to receive $3,000 per work across an estimated 500,000 works.

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training an AI model on copyrighted text constitutes fair use, a significant finding for the AI industry.

Despite the financial resolution, many authors do not consider this a true victory due to how the underlying legal issue was addressed. Judge Alsup had previously ruled that training an AI model on copyrighted text falls under fair use, a significant finding for the AI industry. Nevertheless, he ruled against Anthropic on a related point: the company had obtained part of its training data from pirate websites like Library Genesis and Pirate Library Mirror, rather than legally acquiring the books.

Anthropic opted to settle the case rather than proceed to a jury trial on damages. Because the settlement avoids a trial and potential appeal, Alsup's fair use ruling will not set a binding legal precedent. Other courts are still free to make different determinations on the matter. Numerous copyright cases against major tech companies, including Google, Meta, Midjourney, and OpenAI, are still active. Most recently, publishers and authors filed a class-action suit against Google concerning the use of their works for training its Gemini AI model.

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the company had sourced part of its training library from pirate websites including Library Genesis and Pirate Library Mirror rather than purchasing the books it used.