Language models are increasingly used not just as generators but as evaluators : they grade outputs, filter candidates, assess arguments. But what happens when these AI judges systematically ride the wrong signals? In a new study ("Pseudo‑Literary Quality Inflation Across the GPT‑5 Family: Replication and Downstream Evaluator Vulnerabilities"), I investigated whether a specific kind of blind spot — a preference for pseudo-literary surface cues — distorts not only the aestheti

Lots of things have gone wrong with the rollout of GPT-5. However, most of the things that have been noted so far have to do with how the...

Last week, together with Nina Beguš from UC Berkeley, I had the privilege of organizing a small conference on "AI and Human...