The Verification Bottleneck in AI-Accelerated Knowledge Work: Queueing Epistemics, Verification Capacity and Adaptive Human Oversight
TL;DR
The verification bottleneck is developed as a distinct socio-technical mechanism and queueing epistemics is introduced, a framework for analysing knowledge reliability when verification-demanding outputs arrive faster than bounded review capacity can process them, to establish a general design principle: AI productivity should be governed by verification capacity, not generation capacity alone.
Abstract
Generative artificial intelligence can increase the rate at which knowledge work is produced, but it does not proportionally increase the human capacity to verify claims, assumptions, sources, calculations, and consequential recommendations. This paper develops the verification bottleneck as a distinct socio-technical mechanism and introduces queueing epistemics, a framework for analysing knowledge reliability when verification-demanding outputs arrive faster than bounded review capacity can process them. The model defines the Epistemic Load Ratio (ELR), derives a Verification Capacity Frontier that jointly constrains delay and review quality, and formalizes Marginal Verification Value for allocating scarce human attention across heterogeneous tasks. Four propositions show that verification delay is convex in load, review quality can deteriorate under overload, raw productivity and reliable throughput can diverge, and risk-ranked adaptive review can dominate volume-based oversight under heterogeneous expected loss. A synthetic Monte Carlo stress test compares blanket verification, a production-first 20% sample, a static risk threshold, and Queue-Aware Adaptive Verification (QAV) across five workload acceleration levels. At the highest acceleration level, QAV maintained ELR near 0.80 with no verification backlog, achieved mean net value of 5.732 synthetic units per task, and reduced severe escaped errors by 56.7% relative to production-first sampling and by 37.5% relative to static thresholding. Sensitivity analysis across 27 combinations of risk-estimation noise, capacity reserve, and overload severity preserved a positive QAV net-value advantage over the best comparator in every tested condition. These results are model-conditional rather than empirical estimates. They nevertheless establish a general design principle: AI productivity should be governed by verification capacity, not generation capacity alone. The paper concludes with an operational control architecture and auditable metrics for organisations seeking to scale AI-assisted knowledge work without converting speed into epistemic fragility.
