I think that academia has not absorbed the fact that AI agents are now good enough to independently reconstruct complex papers without access to code or the papers themselves; just the methods & data. They aren’t perfect but the errors are often in the human paper, not the AI.

The most prominent academic society in my field (the Academy of Management) prohibits AI use in reviews, when the evidence suggests it should be mandatory to run AI reviews (with human discretion, of course). elliottash.com/wp-content/upl…

There is also too much focus on two issues in discussions of paper reviewing: hallucinations & privacy. Hallucinations are not gone, but the latest models rarely hallucinate sources (& it is relatively easy to make the human responsible) And you can get IP compliance easily now

Gemini 3.1 gets citations wrong frequently. Even if it doesn’t hallucinate the title of the article, it hallucinates the other metadata of the citation or inserts the citation for something that the citation doesn’t support. The GPT and Claude models are, admittedly, much less likely to hallucinate. Given the highly variable expertise of authors, it might make sense to still have them note what models were used. I agree that AI review should be at least allowed (many journals strictly prohibit it). It would be particularly useful in medicine where most authors are subject matter experts and relatively poor with methods. Though it is worth mentioning that AI reviews often bring up lots of relatively meaningless pedantic (non-)issues. So reviewers with subject expertise are still needed to strictly prioritize or eliminate concerns raised by AI. Models also tend to lack nuanced subject matter intuition. Since most academic papers are being written by subject matter experts, the main value of AI reviews is probably in methods verification and replication rather than discussion of implications.