Benchmarks are the primary instruments through which AI capability is measured, compared, and governed. This paper argues that the validity of frontier AI benchmarks is a function of the quality of human judgment embedded in their construction, and that this quality is structurally scarce in ways that standard scaling narratives obscure. As foundation models approach ceiling performance on existing evaluation suites, discriminating signal concentrates in the hardest benchmark items, precisely those requiring elite expert judgment to design. We term this the benchmark ceiling problem: the progressive exhaustion of evaluation signal as models saturate the easy majority of items while the difficult tail, authored by a thin stratum of highly expert evaluators, remains the only source of genuine discrimination. The paper develops this argument in three steps. First, we present a formal model of benchmark signal depreciation. Benchmark scores are public signals of latent model quality, but their precision depends endogenously on benchmark validity. As frontier capability rises and as contamination or strategic optimization increases, fixed benchmarks depreciate as measurement instruments. The model shows that valid signal concentrates in hard-tail items, that the replacement cost of such items rises convexly with frontier capability, and that private benchmark producers underinvest in validity relative to the social optimum. Second, drawing on platform data from micro1 covering over one thousand credentialed professionals, we document the scarcity premium associated with high-judgment, low-codifiability evaluation labor. Third, we develop the political economy and governance implications.

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