Fair Use and AI Training Data

Section 107 of the Copyright Act lists four factors. Factor one asks whether the use is transformative and commercial. The Supreme Court's 2023 Warhol v. Goldsmith decision emphasized that a different purpose matters more than adding new meaning. Factor two considers whether the work is creative or factual. Factor three looks at how much was taken. Factor four asks whether the use harms the market for the original, and courts often treat it as the most important. The first rulings came in 2025. In February, Judge Stephanos Bibas held in Thomson Reuters v. Ross that Ross's copying of Westlaw headnotes to train a legal search tool was not fair use. Ross was building a direct competitor, and the AI was not generative. The case went to the Third Circuit on interlocutory appeal. In June, Judge William Alsup ruled in Bartz v. Anthropic that training on books was 'exceedingly transformative' and fair use, and that scanning purchased print books was also fair. He held that building a central library from pirate sites was a separate use that fair use did not cover. After class certification, Anthropic agreed to a settlement reported at $1.5 billion, about $3,000 per covered work. Days later, Judge Vince Chhabria ruled for Meta in Kadrey v. Meta, but only because the authors did not develop evidence of market harm. He stressed that 'market dilution' from floods of AI-generated competing works could weigh heavily against fair use in a better-argued case. A common misconception is that courts have declared AI training fair use across the board. These are district court rulings on specific facts. Appeals, other cases and the method of acquiring data all matter.