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

2026-03-10

AI Summary

This study addresses the macrofinancial risks precipitated by the rapid diffusion of artificial intelligence, rooted in a structural mismatch between technological abundance and insufficient institutional demand. The authors develop a macroprudential stress-testing framework that integrates a dynamic general equilibrium model with FRED macroeconomic time series and BLS occupational data to quantify the systemic implications of AI-driven labor displacement. They introduce three novel mechanisms—“mismatch spirals,” “phantom GDP,” and “intermediary collapse”—to elucidate the fundamental tension between AI abundance and human-centric institutional scarcity, offering eleven falsifiable predictions. Simulations reveal that without effective income redistribution in high-AI-exposure sectors, nonlinear risk amplification could destabilize $2.5 trillion in credit markets and $13 trillion in mortgage markets, potentially triggering a systemic crisis.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Other Foundations of Human Computation & AI
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• AI adoption
• demand deficiency
• macro-financial stress
• labor displacement
• economic institutions

Innovation

• macro-financial stress test
• AI-driven displacement spiral
• Ghost GDP
• intermediation collapse
• distribution-contract mismatch