The purpose of a reviewer is to provide the reviewer's feedback on a paper. If the editor wants to get feedback from an LLM, they are perfectly capable of doing

The purpose of a reviewer is to provide the reviewer's feedback on a paper. If the editor wants to get feedback from an LLM, they are perfectly capable of doing so themselves. There is an attribution chain here that may not be terribly relevant in the short term but in the long term is a big deal. Historically, we speak of "plagiarism" as being something you do against human text, because human text is all there was. But I would suggest that most of the issues with plagiarism are actually around misattribution, which means that it is perfectly sensible to speak of "plagiarizing" an AI. The AI may not be victimized, but victimization is not the only issue with plagiarism and most or all of the rest of them apply here. It matters over time where the text comes from. Even if the text of the review is high quality, in order to tune the editor's own tracking of reputation they need to know if it is from a human reviewer, GPT-1, GPT-7.5, or NotGPTAtAllSciAI-2026. This is especially true in this case, because the entire point of a reviewer's review is that they are doing something the editor is not supposed to be doing! If the editor has to do a deep due diligence on all reviews, the reviewer are failing to provide any value as the editor might as well directly review the paper in question. So reputation is not something we can just wave away with "well if it was a good review it doesn't matter"; trust is a huge deal here. The editors need reviews to be properly attributed. Even if they are fine with AI reviews they need to know they are from AIs, and as I said, which AIs.