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Benchmark Mineability and the Financing of AI Innovation

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  • Alex Chan

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.

Suggested Citation

  • Alex Chan, 2026. "Benchmark Mineability and the Financing of AI Innovation," NBER Working Papers 35641, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35641
    Note: CF IO PR
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    More about this item

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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