Benchmark Mineability and the Financing of AI Innovation

Alex Chan

No 35641, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D40 D47 D82 D83 D86 O3 O32 (search for similar items in EconPapers)
Date: 2026-08
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