The argument is that converting static text into an LLM is sufficiently transformative to qualify for fair use, while distilling one LLM's output to create another LLM is not. Whether you buy that or not is up to you, but I think that's the fundamental difference.
The whole notion of 'distillation' at a distance is extremely iffy anyway. You're just training on LLM chat logs, but that's nowhere near enough to even loosely copy or replicate the actual model. You need the weights for that.
> The U.S. Court of Appeals for the D.C. Circuit has affirmed a district court ruling that human authorship is a bedrock requirement to register a copyright, and that an artificial intelligence system cannot be deemed the author of a work for copyright purposes
> The court’s decision in Thaler v. Perlmutter,1 on March 18, 2025, supports the position adopted by the United States Copyright Office and is the latest chapter in the long-running saga of an attempt by a computer scientist to challenge that fundamental principle.
I, like many others, believe the only way AI won't immediately get enshittified is by fighting tooth and nail for LLM output to never be copyrightable
Thaler v. Perlmutter is an a weird case because Thaler explicitly disclaimed human authorship and tried to register a machine as the author.
Whereas someone trying to copyright LLM output would likely insist that there is human authorship is via the choice of prompts and careful selection of the best LLM output. I am not sure if claims like that have been tested.
It's a fine line that's been drawn, but this ruling says that AI can't own a copyright itself, not that AI output is inherently ineligible for copyright protection or automatically public domain. A human can still own the output from an LLM.
