Originally published on robatdasorvi.com
The bug was there for three weeks. Every time someone ran the test suite, it passed. Every time someone reviewed the PR, the logic looked right. The function did exactly what the comment said it did — and the comment had been written by the same model that wrote the code. It was only when the production environment diverged slightly from the test setup that anything surfaced at all, and by then the damage was already downstream.
I want to talk about that gap. Not the failure itself — the silence before it.
Why Does AI Code Feel So Convincing Even When It Is Wrong?
Generated code presents itself with unearned confidence: perfect syntax, clean structure, and articulate comments that mask subtle logical flaws. The issue is that language models learned to mimic the surface features of correct code—consistency, naming conventions, structure—but the deeper semantics of correctness require understanding the problem domain, which is much harder to learn from text alone.
