No one knows if this is AI related, but it's easy to imagine that where changes written by AI fail in production, if AI can't find the solution (which will be common as AI access to production systems is typically reduced), then the devs won't either, or at least it will take them some time.

Perhaps the velocity is the problem, not that AI inherently produces worse code (you just need one bad or just inexperienced engineer have their PR slip through to have bad human written code in you code base). When the velocity is high, there's less time to battle test each change and new code piles up on the old one. When the velocity is low, shitty code fails in production, but people are aware of what was recently changed and can find and fix the problems.

The bigger issue with having your systems written by AI, is that nobody knows how the system works anymore and can't pinpoint where the likely failure points are.

Maybe the error itself isn't event AI related, but the engineers that knew how to fix it were let go

The status updates have a pretty strong "LLM-driven agent failing to debug a complex system" feel to them. I hope it's not the case, but I could imagine a scenario in which inexperienced engineers - or experienced ones who have lost touch with the codebase - are deferring to LLMs to drive the troubleshooting and fix here.