I broadly agree, in the sense of providing the vision, direction, and design choices for the LLM to do a lot of the grunt work of implementation.

The comprehension problem isn't really so much about software, per se, though it can apply there too. LLMs do not think, they compute statistically likely tokens from their training corpus and context window, so if I can't understand the thing any more and I'm just asking the LLM to figure it out, do a solution, and tell me I did a good job sitting there doomscrolling while it worked, I'm adding zero value to the situation and may as well not even be there.

If I lose the ability to comprehend a project, I lose the ability to contribute to it.

Is it harmful to me if I ask an LLM to explain a function whose workings are a bit opaque to me? Maybe not. It doesn't really feel harmful. But that's the parallel to the ChatGPT social thing: it doesn't really feel harmful in each small step, it's only harmful when you look back and realise you lost something important.

I think comprehension might just be that something important I don't want to lose.

I don't think, by the way, that LLM-style prose is the new Esperanto. Having one AI write some slop that another AI reads and coarsely translates back into something closer to the original prompt like some kind of telephone game feels like a step backwards in collaboration to me.