When AI Helps You Ace a Test, Is That Cheating?

"Except for three students, everyone else seems to have cheated with AI." That's what Paul Graham said after seeing a grade chart from Brown University. A professor there tried something this semester: the midterm was take-home, the final was in-person. The results: nearly everyone aced the take-home. Final scores dropped sharply. Same chart. Three completely different readings in the comments. One: This is proof. High grades at home, low scores in the room — students never really learned anything. Two: Hold on. In most workplaces, managers care about results. If your job allows AI, submitting strong work that used AI should count as doing your job. Three: Both readings are looking at the wrong thing. The real curiosity is S22 — the student who scored about the same regardless. While everyone else apparently flew with AI, this person just stayed level. Three readings, three different questions underneath: Did students cheat? What is the school actually testing? What does it even mean to "know" something now? YC is one of the world's most well-known startup accelerators. CEO Gary Tan recently shared that he now writes AI code himself every day — tens of thousands of lines a month for several months. His reason: AI coding capability is improving so fast that without firsthand experience, he can't tell whether a startup's demo represents real technical depth or something anyone could replicate in a day with AI. To evaluate "how hard is this?" you need to feel where the difficulty actually is. The Brown professor responded to the same problem by changing the format. Gary Tan responded by doing it himself. But not every evaluator has the time or willingness to do either. While that gap hasn't closed yet, those being evaluated and those doing the evaluating may be using entirely different measuring sticks on the same test or work. Ten years from now, will job interviews test what you can do with AI — or what you can do without it?