Sounds like everyone have different use cases for LLMs, which makes sense, we need help with different things.

Personally, I try to get rid of writing boilerplate (which it sounds like you're talking about?) fully, no one should write that.

Instead I get help from LLMs to deal with areas where I'm not super great, like math. So I know what results I want, but I'm not sure how to get them. So setup a bunch of tests and the interface I want, then let the LLM figure out how to do the implementation itself.

I find it is good at writing about 90% of tests, either from requirements or existing code.

It is terrible once you get 3 or 4 tweaks into main parts of the code base. Just like autocorrect degrades over time.

> Where I find coding assistants the most useful _is_ in writing code that I already want to write.

... for a very specific definition of "code that I already want to write". :-)

The code that I want to write often involves very novel (and sometimes quite mathematical) concepts. Or code that is "low-level wizardry". Thus the AI nearly by definition will commonly fail with the tasks.

Also, this kind of code often (though not always) has the property that "if you do something wrong, everything falls apart", i.e. it is the kind of problem that has a low tolerance concerning any "AI hallucinations", meaning the code is either (nearly) perfectly right, or not helpful. Debugging any wrong code (even if you have testcases available) will commonly take a very long time.