Why AI-Generated Code Is Always Good Enough — And Never Great

This is a dev essay, not a tool, but it articulates something a lot of builders are experiencing without quite naming it: AI code passes tests and handles the obvious edge cases, and then leaves you with something subtly wrong that only shows up three months later. The argument is that AI optimizes for local correctness — the function does what the prompt says — while consistently missing systemic qualities like naming that communicates intent, abstractions that survive changing requirements, and the kind of restraint that keeps a codebase from accreting complexity. For a SaaS team deciding how much of the codebase to hand to an AI assistant, this framing is useful. It is not an anti-AI take; it is a calibration take. Reservation: the essay is opinion rather than data, and the examples are illustrative rather than rigorous. Read it as a prompt for your own audit, not as a study. -> Best for: technical PM or SaaS team of 2-5 setting internal policy on AI-assisted code review.