AI-generated code is often close to correct. That is exactly what makes it dangerous. Obviously broken code is easy to reject. Code that compiles, looks reasonable and passes the happy path is much harder to distrust. "Almost right" is not enough In software, small gaps matter: one missing null check one unhandled timeout one weak authorization condition one unsafe default one test that only covers the obvious path AI tools can produce useful code quickly, but they can also create these gaps quickly. The common failure pattern AI coding tools tend to optimize for a plausible solution. That is not the same as a production-ready solution. You often get: clean-looking structure reasonable names a working example incomplete edge cases shallow tests error handling that looks present but does not help much This is why reviewing AI code can take a different kind of attention. You are not only looking for syntax problems. You are looking for missing thought.…