thanks. I guess the problem is more acute in larger codebases, where the code AI writes takes "inspiration" from existing human-written code (i.e. conforms to earlier human opinions), so it doesn't go out of its way to write meaningfully verifiable new code - because that would stick out like a sore-thumb in a code-review.

Writing modular, verifiable code was always an engineering tenet - but the use of AI in codebases that ignored those foundations ends up having several gaps.