True, but at least IME, once you get beyond fixing the 'obvious bugs' (how many there are depends on the initial quality of the code base - but I agree that AI is really useful to find those), you'll get into a murky grey area of "maybe false positives" which require a lot of time to validate. As a result you sink a lot of time creating and tweaking code-base-specific rules to try filtering out false positives, and IMHO this is exactly the tipping point where the whole thing becomes pointless because it becomes a bureaucratic monster.

E.g. a good code analysis tool needs to work predictably at button press on any code base.

Still better than nothing of course, e.g. I actually think bug scanning / code analsysis is indeed the one area where LLMs are actually useful, but it suffers from the same 'diminishing returns' problem as traditional approaches, if not worse (e.g. still no silver bullet, but a mostly useful additional tool in the toolbox).