alt Hacker NewsI was referring to this decision:
"To summarize the analysis that now follows, the use of the books at issue to train Claude and its precursors was exceedingly transformative and was a fair use under Section 107 of the Copyright Act. And, the digitization of the books purchased in print form by Anthropic was also a fair use but not for the same reason as applies to the training copies. Instead, it was a fair use because all Anthropic did was replace the print copies it had purchased for its central library with more convenient space-saving and searchable digital copies for its central library — without adding new copies, creating new works, or redistributing existing copies."
https://fingfx.thomsonreuters.com/gfx/legaldocs/jnvwbgqlzpw/...
IANAL and don't know how significant this decision is, but it is, at the very least, how one judge views it.
Personally, I don't think judges will rule a certain way because of the money involved but because it seems clear that training a ML model is highly transformative.
Not quite the precedent that it may sound like. A district court judge ruled that using copyrighted materials for the training itself was not infringement, but that the materials must be obtained legally.
Anthropic is trying to settle the case with most plaintiffs with respect to obtaining their works in an infringing way, but there are still plaintiffs pursuing the case on both the grounds that the remedy is insufficient (being only about $3000/work, when it has been as high as $250K/work in other copyright infringement cases and via statutory damages) and also on the grounds that the ruling that training is fair use was an error of law on the district court judge’s part.
Notably it doesn’t cover whether the output of the trained LLM continues to attach the training set’s copyright, which is independent from whether the training itself was an infringing activity. And there’s a substantial argument that the judge erred, if it can be shown that the training works are stored in a recoverable manner (even with some loss/defredation) rather than more extensively transformed.