TBH I'm seeing the opposite, I have a legacy codebase where the people originally writing it were incompetent and inexperienced, but the project took off. It's scaling up but the foundation is shit and has to be gradually rebuilt. Every AI slop commit is still better than the underlying crap.

And this is the pattern I've seen on most (semi)successful projects I've worked on in the past - I'd say correlation between financial success and code quality is 0 (up to a point where the whole thing doesn't fall apart). Once scale (both in load and in code size/features) starts mattering you're stuck building on a foundation of shit. LLMs are very good at identifying and cleaning up said shit layers, as long as you're steering them towards a desireable outcome.