Every model launch comes with a chart. Bars, usually, or a spider diagram, showing the new model edging past its rivals on a row of benchmarks with acronyms most people cannot expand. The bar is taller. The press writes it up as a leap. And within a week, users report that the new state-of-the-art model is, for their actual work, about the same as the last one or occasionally worse. The benchmark said one thing. Reality said another. This happens so reliably that it is worth understanding the mechanics of the gap.
The test is public, which ruins the test
The most fundamental problem is contamination. Many popular benchmarks are published, discussed, and sitting on the open web — which is exactly where models get their training data. When the questions and answers to your exam are in the study material, a high score measures memorisation as much as ability. Nobody needs to cheat deliberately; the leak is structural. A model can score brilliantly on a benchmark it has effectively already seen and then flounder on a genuinely novel version of the same task.
A benchmark stops measuring intelligence the moment it becomes famous enough to end up in the training data. Fame is the thing that breaks it.
