My model is about gaps - all ideas start with gaps. LLMs can fill in these gaps quickly - but some ideas are just bad - so the filling in does not improve them. Maybe it would be useful to treat that filling in as a kind of hallucination - the LLM trying to meed impossible requirements. But on other cases the ideas are good and the LLMs fill in the gaps in the right way. It is a kind of skill to see when LLMs are hallucinating in the gaps. I use LLMs very extensively and I am adding checks to that should catch when the filling in is weak - but it is still not fully autonomous.

I also try to keep the llm-enhanced (or even fully llm-generated) content separate from my human writing - because it is true that the failure modes are different. But think LLM writing might be useful even if it is not consumed directly - it can enhance LLM answering your questions.