I'd add it depends also on your field. If you spend a lot of time assembling technically bespoke solutions, but they are broadly consistent with a lot of popular projects, then it can cut through a lot in short order. When I come to a segment like that, LLM tends to go a lot further.

But if you are doing something because you can't find anything vaguely like what you want to do, it tends to only be able to hit like 3 or so lines of useful material a minority of the time. And the bad suggestions can be annoying. Less outright dangerous after you get used to being skeptical by default, but still annoying as it insists on re emphasizing a bad suggestion.

So I can see where it can be super useful, and also how it can seem more trouble than it is worth.

Claude and GPT have been my current experience. The best improvement I've seen is for the suggestions getting shorter. Used to have like 3 maybe useful lines bundled with a further dozen lines of not what I wanted. Now the first three lines might be similar, but it's less likely to suggest a big chunk of code.

Was helping someone the other day and the comic felt pretty accurate. It did exactly the opposite of what the user prompted for. Even after coaxing it to be in the general ballpark, it has about half the generated code being unrelated to the requested task, with side effects that would have seemed functional unless you paid attention and noticed that throughout would have been about 70% lower than you should expect. Was a significant risk as the user was in over their head and unable to understand the suggestions they needed to review, as they were working in a pretty jargon heavy ecosystem (not the AI fault, they had to invoke standard libraries that had incomprehensible jargon heavy syntax)