PR reviews are a way of learning from each other, keeping up with how the codebase evolves, sharing progress and ideas, giving feedback and asking questions. For example at $job we 90% approve PRs with various levels of pleas, suggestions, nitpicks and questions. We approve because of trust (each PR contains a demo video of a working feature or fix) and not to block each other, but there might be important feedback or suggestions given among the comments. A "rubber stamp bot" would be hard to train in such a review system and simply misses the point of what reviews are about.
What happens if there is a mistake (hidden y2k bomb, deployment issue, incident, regression, security bug, bad database migration, wrong config) in a PR that passes a human review? At a toxic company you get finger pointing, but with a healthy team, people can learn a lot when something bad passes a review. But you can't discuss anything with an indeterministic review bot. There's no responsibility there.
Another question is the review culture. If this app is trained on some repo (whether PRs were approved or not), past reviews reflect the review culture of the company. What happens when a blackbox AI takes that over? Is it going to train itself on its own reviews? People and review culture can be changed, but a black box AI is hard to change in a predictable way.
I'd rather set up code conventions, automated linters (i.e. deterministic checks) etc. than have a review bot allow code into production. Or just let go of PR reviews altogether, there were some articles shared on HN about that recently. :)