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This paper formalizes a macro-financial stress test to analyze the impact of rapid AI adoption, focusing on distribution-and-contract mismatches rather than productivity busts or existential risks. It models three mechanisms: a displacement spiral where AI adoption reduces labor income and demand, Ghost GDP where AI-generated output reduces monetary velocity, and intermediation collapse where AI agents compress intermediary margins. Calibrated simulations using FRED and BLS data quantify conditions leading to stable adjustment versus explosive crisis, generating eleven testable predictions.
AI's abundance could trigger a macro-financial crisis not through productivity collapse, but by creating a distribution-and-contract mismatch where AI displaces labor, reduces demand, and collapses intermediary margins.
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.
