Generative AI as a Non-Convex Supply Shock: Market Bifurcation and Welfare Analysis

TL;DR

This paper models Generative AI as a non-convex supply shock that causes market bifurcation, welfare changes, and complex transition dynamics, emphasizing the importance of congestion management and skill reconfiguration.

Contribution

It introduces a three-layer equilibrium framework to analyze how GenAI's cost shock affects market structure, transition paths, and welfare, highlighting the role of congestion externalities.

Findings

Market bifurcation into exit, AI, and human segments

Non-monotonic transition with ecological collapse and recovery

Welfare effects highly sensitive to pollution levels

Abstract

The diffusion of Generative AI (GenAI) constitutes a supply shock of a fundamentally different nature: while marginal production costs approach zero, content generation creates congestion externalities through information pollution. We develop a three-layer general equilibrium framework to study how this non-convex technology reshapes market structure, transition dynamics, and social welfare. In a static vertical differentiation model, we show that the GenAI cost shock induces a kinked production frontier that bifurcates the market into exit, AI, and human segments, generating a ``middle-class hollow'' in the quality distribution. To analyze adjustment paths, we embed this structure in a mean-field evolutionary system and a calibrated agent-based model with bounded rationality. The transition to the AI-integrated equilibrium is non-monotonic: rather than smooth diffusion, the economy experiences a temporary ecological collapse driven by search frictions and delayed skill adaptation, followed by selective recovery. Survival depends on asymmetric skill reconfiguration, whereby humans retreat from technical execution toward semantic creativity. Finally, we show that the welfare impact of AI adoption is highly sensitive to pollution intensity: low congestion yields monotonic welfare gains, whereas high pollution produces an inverted-U relationship in which further AI expansion reduces total welfare. These results imply that laissez-faire adoption can be inefficient and that optimal governance must shift from input regulation toward output-side congestion management.