Signalling Inflation and Rational Adaptation: Why the Market for Cognitive Depth Collapses Gradually, Then All at Once

We construct a game-theoretic account of AI-mediated cognitive decoupling in the production and consumption of knowledge content. Extending Spence’s costly signalling framework to environments where production costs collapse asymmetrically, we prove that AI-mediated decoupling is a strictly dominant strategy under a broad class of utility functions (Dominant Decoupling Theorem). This dominance holds not because agents are deceived, but because the observable signal — a lengthy, wellstructured document — is decoupled from its previously costly production process, rendering the signal cheap for all types. We then model the resulting market for cognitive depth as a dynamic signalling game. We show that as AI adoption increases, the market passes through three distinct regimes: a separating equilibrium (high-depth agents are distinguishable), a pooling equilibrium (all agents produce identical signals), and finally a Lemons collapse in which no credible signal remains and the market for deep content unravels (Signalling Inflation Theorem). We characterise the speed of this collapse as a function of AI adoption rate and derive the critical adoption threshold q∗ beyond which the separating equilibrium is irreversibly destroyed. Finally, we identify two equilibrium escape routes: (i) certified costliness — institutional mechanisms that artificially re-introduce production cost (peer review, Turing-style verification) — and (ii) market stratification — the emergence of a high-trust, low-volume premium market for “AI-free” content. We characterise conditions under which each escape route is stable, and show that without external intervention, the system converges to a pooling equilibrium with socially sub-optimal information production.