Why AI Code Gets Less Scrutiny Than a npm Package, and Why That's a Problem

A software engineer argues that development teams apply rigorous checks — lockfiles, checksums, signed commits, and code review — to human-written code but routinely skip equivalent scrutiny for AI-generated output. The core concern is not that models hallucinate obvious errors, which existing tooling tends to catch, but that they produce fluent, well-cited responses even when the underlying logic is wrong. The author illustrates this with a real case where a model correctly cited a documented Odoo rule yet still implemented it incorrectly, showing that citation accuracy does not equal rule compliance. The piece frames AI as a 'fluency machine' — one that mimics the surface signals of confidence and correctness without providing a genuine uncertainty indicator. The author concludes that the real challenge is redefining what counts as 'checked' when the author of the code cannot be questioned, held accountable, or expected to hedge when unsure.