Thats pretty silly. you can just put a gatekeeper to prevent it from spitting out anything too similar, or prevent a user from forcing it to. it is not an intractable or pervasive problem.
it is a fringe case that rarely occurs, and only with a lot of user prompting.
I don't buy the argument that the models are not sufficiently transformative. Nobody would look at a weight table and confuse it for the original work, and it is different in basically every way.
If there is a case to be made, I think it has to be around the original use of the works, the transcription process. Not the weights, or the output
No one would look at a veracrypt archive containing a copyrighted work and confuse it either. They look very different to the original files but both the encrypted file and the learning model's weights allow one to reproduce the copyrighted work.
Not falling back to the argument that the output is infringing, not the archive.
If the archive can't produce the original work, it's not infringing.
If you printed the binary of Harry Potter and sold it as a painting, that would be fair use. It doesn't matter if the data is encoded in it, if it is not used for extraction.
Think of Andy warhol's Campbell Soup. Nobody is going to confuse the art for a can of soup and try to eat it. That's not being sold as a label for other soups. However, the original Campbell Soup data is absolutely encoded there.
