Abstract
Public AI benchmarks steer research and allocate investments. They are therefore market designs. Public examples can reveal the process behind a private final test, while a finite public score cannot cover a broad task space inherent to general intelligence. I show how both gaps become profitable when scores move capital and how targeted effort erodes the signal used by later investors. The market design lesson is to separate development from certification: publish practice tasks, but choose the investment-consequential generator after the submitted system's evaluation policy is fixed.