The "AI skill erosion" you're describing has a name and evidence behind it. Rossi, Fraccaro, and Manzotti wrote about this: passive reliance on AI output weakens activity-dependent brain plasticity, the same use-it-or-lose-it mechanism behind any skill. Their "3R" framework, results, responses, responsibility, argues active co-creation sustains it, passive consumption erodes it.

@proc0, what you said about needing to develop one's skill involves the brain doing the work, not asking the AI to learn for me, which is what I've felt using AI. It gives me the "feeling" that I've learned/done something, I did not really learn what it did.

> If you can explain how the code works and pinpoint bugs on your own, it’s your codebase. But if you rely on an LLM for every single edit, you aren’t managing software - you’re just letting an AI manipulate random code you no longer understand.

This is true, but then there is "AI skill erosion" or whatever we may call it, where skills diminish in proportion to AI usage. At least that's been my experience. I have to still do manual code on the side to remain relatively sharp, which defeats the purpose of using AI to the extent I'm willing to sacrifice my own skills. If AI was consistent in its output, meaning we knew exactly where it fails and when, that could then become the focus. Without consistency one needs to remain sharp at every level, from the details of syntax to design.