Verification Reversal: Cascades and Synthetic Productivity in an AI-Mediated Economy

Generative AI has inverted a relationship that economic measurement systems assumed was stable: the cost of producing an artifact now falls below the cost of verifying it. When verification becomes the binding constraint, rational agents stop checking and start forwarding. This paper formalizes verification reversal as a regime condition and derives its equilibrium consequences. In a sequential forwarding game, we show that when the cost gap between verification and forwarding exceeds the expected benefit of catching errors, a cascade equilibrium emerges: agents propagate artifacts regardless of private beliefs, and information aggregation fails even as throughput metrics soar (Proposition 1). We then show that market selection, operating on observable throughput rather than latent verification stocks, systematically favors low-verification strategies during stable periods—the very periods that dominate expected duration (Proposition 2). The resulting divergence between measured and verification-adjusted productivity constitutes synthetic productivity: conventional TFP rises while utility-relevant output stagnates, because verification effort and remediation burdens are omitted from the measurement frame. We formalize epistemic debt as a stock variable—the accumulated gap between system complexity and cognitive grasp—and show how it compounds when verification capacity erodes faster than artifact volume grows. A distinct contamination channel compounds these dynamics. As model-generated content enters the substrates used for evaluation and decision-support, measurement systems become endogenously self-referential. We derive conditions under which this contamination introduces directional bias and extends recognition lags, allowing genuine degradation to persist undetected. The resulting verification bottleneck also creates an exploitable attack surface, enabling adversarial artifact injection at reduced detection cost. We examine two candidate self-correction mechanisms—recursive AI verification and market selection—and identify structural conditions under which both fail. Recursive verification lacks independent rejection signals when models share training distributions; market selection operates on lagging proxies that favor low-verification strategies until crises force revaluation. Finally, we propose an empirical agenda with instrumentation baselines, anchored by a GitHub pull-request testbed, to measure verification intensity, remediation burden, cascade fragility, substrate contamination, and the accumulation of epistemic debt. The framework yields eight testable hypotheses with explicit falsification conditions.