AI-mediated production changes the relationship between observable productivity and the evidence through which institutions recognize intellectual contributions. This paper investigates the conditional possibility that accelerated output may reduce substantive evaluative attention or weaken competence attributions even where underlying ability remains unchanged. The proposed explanation distinguishes output expansion, epistemic quality, attributional uncertainty, institutional recognition, and resource return. It examines evaluative congestion, possible recognition inversion, and the tragedy of productive diligence, in which acceleration, restraint, and additional verification can each impose costs. The project develops a bounded theoretical account, empirically discriminable hypotheses, a limited formal model, and candidate institutional responses. The empirical implications remain to be tested; no original causal estimates are reported.
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Wanhong HUANG (2026) studied this question.
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