But they have been training on copyrighted data since GPT-2 at least. 2019, and that's when it came out, so before that of course.

This is very true. As someone with quite the experience with materials published under Penguin, Scholastic, etc. you effectively have a "dictionary attack" on the matter, rather than true "brute force," but that still leaves quite a list to compile to send to each and is easier for larger titles than smaller ones. I wonder how that leads to a bias in what materials get used for training. You are not getting many local self-published books this way.

It is almost like we need a "for use for training" agreement across the board. This would not fix the current issues (at least without substantial work), but going forward would allow for creators (or publishers/rights holders) such as this to designate a work as crawl-able for AI. A robots.txt just for Claude.

You are kind of right, but you also did not look very hard. They deleted huge datasets in anticipation of lawsuits, at least that much is known. Of course plaintiffs were unable to depose their internal lawyers (who apparently know why they were frantically deleted) due to 'attourney client priviledge' further refusing to provide any kind of transparency. But yeah, I guess they are they are better at covering their tracks and destroying evidence.

Also, as one more example, I find it hard to believe that their models could generate 'Studio Ghibli' style images without training on the movies. There is no licensing deal between them.

I think the real issues here are two-fold:

Firstly, Copyright is very ill equipped to handle these cases. Just because the model is tuned not to output the exact training data does not mean that compressing mostly-copyrighted datasets into a proprietary model is ethical, fair or /should/ be allowed, simply because they might destroy entire livelihoods. If you take those copyrighted works away you are left with, in OpenAIs own words, a cute little experiment.

Secondly, there is absolutely no transparency. Datasets are easily deleted and its impossible to tell what the models have been trained on, especially after fine tuning. Moreover, only the biggest most successfull works would be easily identifiable without the fine tuned model. Once again, sticking it to the little man.

Pretty sure (citation needed) predated wikipedia.