I've used them for unit tests and it still makes some really weird decisions sometimes. Like building an array of json objects that it feeds into one super long test with a bunch of switch conditions. When I saw that one I scratched my head for a little bit.

Isn't writing tests with AI like a really bad idea? I mean, the whole point of writing separate tests is hoping that you won't make the same mistakes twice, and therefore catch any behavior in the code that does not match your intent. But If you use an LLM to write a test using said code as context (instead of the original intent you would use yourself), there's a risk that it'll just write a test case that makes sure the code contains the wrong behavior.

Okay, it might still be okay for regression testing, but you're still missing most of the benefit you'd get by writing the tests manually. Unless you only care about closing tickets, that is.

It's great for verbose log statements

But nobody who uses it treats it like "just a tool."

I do. I use it to tighten up some lazy code that I wrote, or to help me figure out a potential flaw in my logic, or to suggest a "better" way to do something if I'm not happy with what I originally wrote.

It's always small snippets of code and I don't always accept the answer. In fact, I'd say less than 50% of the time I get a result I can use as-is, but I will say that most of the time it gives me an idea or puts me on the right track.