Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption

TL;DR

This paper develops a macro-financial stress test to analyze how rapid AI adoption can lead to demand deficiency and economic instability due to a mismatch between AI-generated abundance and human demand, highlighting potential systemic risks.

Contribution

It introduces a formal framework for understanding macro-financial impacts of AI, including mechanisms like displacement spirals, Ghost GDP, and intermediation collapse, with testable predictions and calibrated simulations.

Findings

AI adoption can cause demand deficiency despite productivity gains.

Economic instability may arise from displacement spirals and intermediation collapse.

Stable or explosive outcomes depend on AI growth rates and diffusion speed.

Abstract

We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify a distribution-and-contract mismatch: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to human cognitive scarcity. Three mechanisms formalize this channel. First, a displacement spiral with competing reinstatement effects: each firm's rational decision to substitute AI for labor reduces aggregate labor income, which reduces aggregate demand, accelerating further AI adoption. We derive conditions on the AI capability growth rate, diffusion speed, and reinstatement rate under which the net feedback is self-limiting versus explosive. Second, Ghost GDP: when AI-generated output substitutes for labor-generated output, monetary velocity declines monotonically in the labor share absent compensating transfers, creating a wedge between measured output and consumption-relevant income. Third, intermediation collapse: AI agents that reduce information frictions compress intermediary margins toward pure logistics costs, triggering repricing across SaaS, payments, consulting, insurance, and financial advisory. Because top-quintile earners drive 47--65\% of U.S.\ consumption and face the highest AI exposure, the transmission into private credit (\$2.5 trillion globally) and mortgage markets (\$13 trillion) is disproportionate. We derive eleven testable predictions with explicit falsification conditions. Calibrated simulations disciplined by FRED time series and BLS occupation-level data quantify conditions under which stable adjustment transitions to explosive crisis.