I don't see why we can't have AI powered reviews as a verification of truth and trust score modifier. Let me explain.
1. You layout policy stating that all code, especially AI code has to be written to a high quality level and have been reviewed for issues prior to submission.
2. Given that even the fastest AI models do a great job of code reviews, you setup an agent using Codex-Spark or Sonnnet, etc to scan submissions for a few different dimensions (maintainability, security, etc).
3. If a submission comes through that fails review, that's a strong indication that the submitter hasn't put even the lowest effort into reviewing their own code. Especially since most AI models will flag similar issues. Knock their trust score down and supply feedback.
3a. If the submitter never acts on the feedback - close the submission and knock the trust score down even more.
3b. If the submitter acts on the feedback - boost trust score slightly. We now have a self-reinforcing loop that pushes thoughtful submitters to screen their own code. (Or ai models to iterate and improve their own code)
4. Submission passes and trust score of submitter meets some minimal threshold. Queued for human review pending prioritization.
I haven't put much thought into this but it seems like you could design a system such that "clout chasing" or "bot submissions" would be forced to either deliver something useful or give up _and_ lose enough trust score that you can safely shadowban them.
The immediate problem is just cost. Open Source has no money, so any fancy AI solution is off the table immediately.
In terms of your plan though, you're just building a generative adversarial network here. Automated review is relatively easy to "attack".
Yet human contributors don't put up with having to game an arbitrary score system. StackOverflow imploded in no small part because of it.