I don't disagree necessarily on the fundamentals; I am slowly catching up on what LLMs do and don't do but that sounds right to me.
But what I would observe is that the healing brush does not have a random seed. It will always do the same thing if applied at the same pixel.
(I am actually less sure if content-aware fill randomises; I always got the impression it did not)
This makes it both incredibly powerful and occasionally frustrating.
Because on the one hand, you can learn to apply your judgement to precisely control what it will do, and change the radius or position if you learn it is likely to fail, which becomes instinctive. I absolutely love using it to fix scratches in film scans; it's a quick, precise, controllable tool that can be used in a way that is amazingly convincing, and it ends up quite a "zen gardening" thing as a result. It'll sell you on the cheapest wacom pen once you know how efficient it can be.
On the other hand there are situations where it simply cannot work the way you want because it will always find a pattern you don't want it to.
(You can sometimes use the clone brush tool first, to manually break up the pattern that patchmatch will find)
> But what I would observe is that the healing brush does not have a random seed. It will always do the same thing if applied at the same pixel.
Given a model architecture that supports it, greedy decoding + the same inputs + prompts, that's true for most LLMs today too, I don't think people consider them less/more AI because of that.