Use case mapping matters. I use AI tools at work (have for a few years now, first Copilot from GitHub, now I use Gemini and Claude tools primarily). When the use case maps well, it is great. You can typically assume anything with a large corpus of fairly standard problems will map well in a popular language. JavaScript, HTML, CSS, these have huge training datasets from open source alone.

The combination of which, deep training dataset + maps well to how AI "understands" code, it can be a real enabler. I've done it myself. All I've done with some projects is write tests, point Claude at the tests and ask it to write code till those tests pass, then audit said code, make adjustments as required, and ship.

That has worked well and sped up development of straightforward (sometimes I'd argue trivial) situations.

Where it falls down is complex problem sets, major refactors that cross cut multiple interdependent pieces of code, its less robust with less popular languages (we have a particular set of business logic in Rust due to its sensitive nature and need for speed, it does a not great job with that) and a host of other areas I have hit limitations with it.

Granted, I work in a fairly specialized way and deal with alot of business logic / rules rather than boiler plate CRUD, but I have hit walls on things like massive refactors in large codebases (50K is small to me, for reference)