I'm not sure that "AI generates..." articles are going to age well.

My immediate question would be what kind of adversarial reviews and deterministic gates did the author write to capture the dimensions of maintainability (lack of DRY, cyclomatic complexity, unclear naming, poorly architected flows, etc) that they cared about.

Don't get me wrong. When I was speeding along at 200 PRs a day (personal projects - I would not have inflicted them on others) I pretty quickly slopped together a big ball of mud, but as I'm re-generating my estate (a) the models are a little better but (b) I'm adding a lot more rigor around verification (correctness, performance, security, operational costs and maintainability). It's not an easy problem to solve, but it feels to me like an engineering endeavor, not speculative rocket science.

I fully understand that many AI generated code bases do indeed suffer from more incidents and less maintainability, but I think it's important to distinguish between what is common and what is possible as the fix for one is improving the rigor of your verification whereas the fix for the other would be either accepting crappy code or giving up on LLMs.