AI Coding Risks Producing Developers Who Can Patch Systems Without Understanding Them

I agree with the central point, but iteratively asking AI to fix the next error can teach a real skill: getting useful results from AI. That only counts if the results are actually good, rather than just increasing the token bill.

I use AI daily for limited coding and substantial debugging. The problem starts when I stop reviewing output and click accept for the feeling of productivity, but I can still study a diagnosis after AI correlates logs across cluster nodes that I could not inspect quickly on my own.

I want a practical account of staying in control while gaining productivity. If I need to work through a full curriculum for every AI-written change, I may be better off writing it myself.

The hardest problem is knowing what I have forgotten. I cannot audit gaps that I do not know exist.

LLMs have separated software design from implementation enough that I now write designs and ask models to synthesize code, then review conformance. It resembles older engineering practice, but I am still figuring out which parts of coding, design, and delivery I enjoy.