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

arXiv 2603.09209 ↗First posted 10 Mar 2026cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 46 references that are in the map. First posted on arXiv 10 Mar 2026.

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References · 4

  1. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  2. 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 174
  3. 2024Thousands of AI Authors on the Future of AIcited by 48
  4. 2024International Scientific Report on the Safety of Advanced AI (Interim Report)cited by 18

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