And if the expectations aren't met by the reality, then we have allegations of benchmaxing. Benchmaxing, of course, being when labs are training too hard on benchmarks in a way that deviates from what people actually care about. So the existence of that term indicates that we have a sense that benchmarks don't always equal reality. And so in this talk we're going to figure out why does benchmaxing happen? Why are traditional benchmarks not always accurate reflections of real world value? Is this intrinsic to all benchmarks? And will we ever know which models are best? And the answers are incentives, poor methodologies, no and yes. All right,