Cactus: Technically Sound, Personally Useless: Fixing the Last Mile of Peer Review

Twenty years ago, megajournals revolutionized publishing by separating two questions: Is this research technically sound? And is it important? We achieved scalable objective peer review, but never built tools for the personalized assessment readers actually need. The result: researchers drowning in technically validated papers with no practical way to determine personal relevance.

Artificial intelligence finally makes this possible. This session introduces "personal peer review"—AI-enabled assessment that gives every reader a customized evaluation based on their specific research context, methods, career stage, and questions. We'll show how the same paper generates distinctly different assessments for a clinical researcher, PhD student, and journal editor, all building on shared objective quality verification.

We'll explore implications for publisher value propositions, early-career researcher support, and emerging business models, alongside governance questions about who should provide these tools. We can finally finish the revolution megajournals started.