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

Xupeng Chen

TL;DR

A macro-financial stress test for rapid AI adoption is formalized and a distribution-and-contract mismatch is identified: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to human cognitive scarcity.

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.

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

TL;DR

A macro-financial stress test for rapid AI adoption is formalized and a distribution-and-contract mismatch is identified: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to human cognitive scarcity.

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 (\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.
Paper Structure (33 sections, 7 theorems, 17 equations, 4 figures, 6 tables)

This paper contains 33 sections, 7 theorems, 17 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Contribution.
  3. Model
  4. Production Environment and AI Capability
  5. The Displacement Spiral with Task Reinstatement
  6. Ghost GDP: The Velocity Collapse
  7. Intermediation Collapse
  8. Applications.
  9. Macro-Financial Transmission
  10. Quantifying credit exposure: a DSCR sensitivity exercise.
  11. Private credit exposure.
  12. Mortgage market exposure.
  13. Empirical Analysis
  14. Macro Preconditions: Labor Share and Velocity
  15. Occupation-Level Evidence: AI Exposure and Wage Growth
  16. ...and 18 more sections

Key Result

Proposition 1

Under Assumptions ass:capability and ass:reinstatement, the labor share dynamics in eq:displacement exhibit three regimes: The threshold $g_A^*(\rho)$ is increasing in $\rho$: stronger reinstatement raises the bar for explosive displacement.

Figures (4)

  • Figure 1: Macro preconditions for the displacement spiral. (a) Labor share (BLS, FRED series PRS85006173) has declined $\sim$19% from its 1960 peak. (b) M2 velocity (FRED series M2V) has declined 36% from its 1997 peak, consistent with Proposition \ref{['prop:velocity']}. (c) Ghost GDP proxy: divergence between real GDP and real personal income growth, widening post-2020. (d) Productivity--compensation gap since the 1970s demonstrates that technological progress need not translate into wage growth. Shaded regions indicate NBER recession dates.
  • Figure 2: Occupation-level evidence: AI exposure and wage outcomes. (a) Nominal wage growth (2019--2023) vs. AI exposure ($\beta = -6.53$, $p < 0.01$). (b) Real wage growth after adjusting for 18% CPI inflation. (c) Pre-trend placebo (2015--2019): the significant negative slope ($\beta = -3.86$, $p < 0.01$) indicates pre-existing trends, likely reflecting the denominator effect (higher-wage occupations have lower percentage growth). (d) Wage acceleration (DiD): the post-minus-pre difference ($\beta = -2.68$, $p = 0.14$) isolates the AI-specific shift. Bubble size proportional to 2019 employment. Source: BLS OEWS; AI exposure based on felten2021occupational and eloundou2023gpts.
  • Figure 3: Calibrated displacement dynamics with diffusion and reinstatement ($\rho_0 = 0.002$, $\bar{d} = 0.80$, $\kappa = 2.0$, $\tau = 0$). (a) Labor share under three AI adoption rates. (b) Monetary velocity. (c) Reinstatement comparison under the rapid scenario. (d) Consumption-to-GDP ratio.
  • Figure 4: Policy response under rapid AI adoption ($g_A = 0.20$). (a) Labor share trajectories under four policy scenarios. (b) Crisis depth as a function of policy lag $\ell$ for three transfer magnitudes.

Theorems & Definitions (23)

  • Definition 1: AI-Automatable Task Set
  • Remark 1: Heterogeneous Effects Within Occupations
  • Proposition 1: Self-Reinforcing Displacement with Reinstatement
  • proof
  • Remark 2: Displacement versus Reinstatement: When Does Each Dominate?
  • Remark 3: Complementarity and Sectors Where AI Increases Labor Demand
  • Remark 4: Three Modes of Displacement
  • Definition 2: Ghost GDP
  • Proposition 2: Monetary Velocity Decline
  • proof
  • ...and 13 more