I don't think they are clueless, but rather, struggling. Being an AI provider is a money burner, and they probably don't have enough fuel for the fire anymore, so they are trying things to squeeze more $$$ and limit usage at the same time.
Yes, it's related to the limits of the subscription plans. 2ish months ago the same sub started hitting limits earlier and earlier on very similar tasks/codebases. Then they said, Oh, sorry, it was a caching bug, now it's fixed. It wasn't. Then a couple more bugs. Still no fix. Recently they announced "doubling the limits" (made possible by the Grok deal) - still hitting limits super fast compared to how it used to be 2+ months ago. Moreover, the models got somewhat dumber and slower, too.
This discussion feels analogous to me to the age-old "we can do it fast now, and do it right later" tradeoff discussion, except now it's about knowledge, maintainability etc.. There are stages of companies and types of products where solution quality has a bigger buffer, some are more critical. We also know that in many places, once you shipped a crappy solution in a week, you've now set an expectation, and management will expect you do repeat that forever, not getting the time to actually fix/rearchitect when needed.
We used to brush that off with the "we'll fix all of that once we've shipped", now we brush it off with "doesn't matter, AI can fix it later easily". This applies to knowledge about the code, domains and quality itself, too.
To me, this is a reasonable tradeoff to discuss, and sticking to either extreme ("AI is cancer" and "AI is the silver bullet") feels silly.
There's real risk here, and I do see a lot of seniors act like kids in the candy store. That speaks volumes to what AI really unlocks (cheap experimentation, for one!), but also warrants caution.
Based on the data (not great quality tbh...) we have, the net speedup is more realistically around 5-10% of the total time of software engineers, and we're yet to see the cost of that speedup.