As long as you're reinventing the wheel (implementing some common pattern because you don't want to pull in an entire dependency), that kind of AI generation works quite well. Especially if you also have the AI generate tests for its code, so you can force it to iterate on itself while it gets things wrong the first couple of tries. It's slow and resource intensive, but it'll generate something mostly complete most of the time.

I'm not sure if you're saving any time there, though. Perhaps if you give an LLM task before ending the work day so it can churn away for a while unattended, it may generate a decent implementation. There's a good chance you need to throw out the work too; you can't rely on it, but it can be a nice bonus if you're lucky.

I've found that this only works on expensive models with large context windows and limited API calls, though. The amount of energy wasted on shit code that gets reverted must be tremendous.

I hope the AI industry makes true on its promise that it'll solve the whole inefficiency problem because the way things are going now, the industry isn't sustainable.