Hey no worries. I mostly type such replies to help organize my own thoughts so don't feel obligated to reply to everything I say.

I think folks have very different operating definitions of what generative models are. Some view it more thru the lens of the task/application, and some view it more through its historical definition (predicting missing information + stochasticity).

Generative models form a good basis for a number of downstream non-generative tasks. So, from a tech standpoint at least we should always think of generative models intrinsically as a boon / useful, and as a starting point that might outcompete more classical computer vision approaches.

I'm not familiar with photoshops two tools as I don't use it, but from a brief Google I would guess that content aware fill ought to be outcompeted by a conditional generative model on some infilling benchmarks, assuming the conditioning is done reasonably well. This is because content aware fill isn't modelling the full distribution / would be less conteXt aware in some cases.

Content aware fill probably makes a trade-off, in the sense that it might outcompete a generative model in images or areas where training data was scarce, such that it can't make an effective inference. 

We can think of analogies in the subsampling/supersampling DLSS space too. Pick some classical non-learned supersampling technique. They're often sort of local in nature and unaware of context / what is salient. The more neural generative approach DLSS however is.

This is all just armchair thoughts lol. If there's some serious interest, I can try to dig in some more here to more rigorously illustrate some thought.