I still think there is value in external contributors solving problems using LLMs, assuming they do the research and know what they are doing. Getting a well written and tested solution from LLM is not as easy as writing a good prompt, it's a much longer/iterative process.

> assuming they do the research and know what they are doing.

This is the assumption that has almost always failed and thus has lead to the banning of AI code altogether in a lot of projects.

This is the part people keep missing. The bottleneck was never typing code. It's understanding the problem deeply enough to know what the right solution looks like. AI made the cheap part cheaper.

I don't know, I've had good experiences getting LLMs to understand and follow architecture and style guidelines. It may depend on how modular your codebase already is, because that by itself would focus/minimize any changes.

Some months back I would have agreed with you without any "but", but it really does help even if it only takes over "typing code".

Once you do understand the problem deep enough to know exactly what to ask for without ambiguity, the AI will produce the code that exactly solves your problem a heck of a lot quicker than you. And the time you don't spend on figuring out language syntax, you can instead spend on tweaking the code on a higher architecture level. Spend time where you, as a human, are better than the AI.