AI Made Writing Code Almost Free. Understanding It Is Now the Most Expensive Part.

I have spent enough time working on backend services to learn something that took me years to properly understand: clean code can still be dangerously wrong. A controller can look perfect, the method names can be clear, the tests can pass, and the pull request can receive approval. Yet one missing decision around retries, transactions, duplicate requests, timeouts, or event delivery can turn that beautiful code into a production problem.

This is why AI coding tools make me excited and uncomfortable at the same time. I enjoy watching repetitive work disappear. I do not miss writing the same request classes, mapping functions, configuration files, and basic tests again and again. But every time an AI tool produces a complete implementation in a few minutes, I find myself thinking about the part it cannot remove: someone still has to understand what has been created.

That person has to know why the code belongs in the system, which assumptions it makes, what happens when one dependency fails, and who will be responsible when the original prompt has been forgotten. The code may arrive almost instantly, but trust does not. Trust still has to be earned slowly, line by line, decision by decision.