It is clear to anyone that understands this tech that it is not simply "memorizing" or "copying" the training data, even if they can be coaxed into doing this for certain inputs (in the current iteration of the tools).
Ultimately, I think the problem of reproducing certain popular works or code snippets will be solved. One interesting direction here are the tools of information theory and differential privacy. e.g) proving that certain training inputs cannot be recovered from the weights, or that there is a threshold of how much information can be gleaned from a single input.
It is easy to imagine (because its nearly there already) a future version of StableDiffusion (or CoPilot) which provably compresses all training data beyond any possibility of recovery, and yet still produces extremely convincing results which disrupt the creative professions of art and programming.
Until we get to that point, it feels like the only consistent and sensible place to apply regulations is with the model end user. When I use CoPilot, I accept the small (honestly overblown) risk that _maybe_ I won't have the license to use some small snippet of code it spits out. But I'm happy to wear that responsibility, because the boost to productivity is so great that I dread a return to pre-CoPilot world. That is, a world where everyone keeps reinventing the same solution to simple problems over and over and over again.