Generative models attempt to model their training data. Essentially, they try to be a model of the underlying data distribution from which all samples in the training data were drawn from. A model of that distribution which cannot reproduce all samples from the original training data given the right prompting/query/seed is an incomplete model by definition. If it can't reproduce all samples drawn from the original distribution then it clearly does not model the same distribution those samples were drawn from.

That said, this is very very different from just copying at a conceptual level. This is going to end up being a an interesting legal question going forward. I'm curious to see how it turns out.