It shouldn't matter. If your model touched X during training, it should be seen as producing derivative work. This is the reason humans use clean room implementation techniques.

> If your model touched X during training, it should be seen as producing derivative work.

I as a programmer touched X during training (learning how to code). Is all my work now derivative because of that?

If you examined the source code of a library for a specific purpose Y, then shortly afterwards went implementing another library for purpose Y, there's a high probability of your code being infringing. That's the entire premise and purpose of clean room design (https://en.wikipedia.org/wiki/Clean_room_design).

Now factor in that machine models don't have a fallible or degrading memory and I think the answer is quite clear.

The opposite of clean room is probably much more common.

My point was more about the possible negative effects than about the legality.

An important difference is that AIs are much cheaper than a human. Having a library reimplemented by a human usually isn’t cost-effective, but having it done by an AI may become viable. That could cause a major change in the open-source dynamics, while possibly also reducing average software quality (because less code is publicly scrutinized).