We still do code review in my team, because we build systems that handle customers core business data and infrastructure. Other teams are generating and reviewing entirely by AI and their codebases are incredibly sloppy and verbose with little understanding, and these problems compound over time.

Our code review step achieves 3 things - check that it does what's intended, make sure it fits into the broader picture, and build human understanding of the codebase.

The question is: Do you require human understand what they're working on?

I'm watching dark factories and all-AI projects being developed elsewhere to see whats working, and thus far I don't think the evidence is there that for our constraints fully AI-developed and checked solutions pass the quality bar and have the traits we need. We have customers, they depend on us. From what I can see, unconstrained AI-based development of PoCs and implementations works for demos, startups, throwaway projects, but not for products you need to work and keep working.

Mostly check "is this actually solve the problem it's trying to solve", skim it, then and use AI to review it.

I download the change into my local context and use AI to give me a high level overview of the change because people always sucked at describing their changes anyway

Then I go through a review it as normal and flag issues

Then I do a pass asking my own local agent to find any issues, I sanity check/confirm those and comment them if necessary