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

Xupeng Chen|arXiv (Cornell University)|Mar 10, 2026

Complex Systems and Time Series Analysis0 citations

TL;DR

The paper formalizes a macro-financial stress test showing how rapid AI adoption can coexist with, or trigger, a macroeconomic contraction via distribution-and-contract mismatch, displacement-reinstatement dynamics, velocity effects, and intermediation compression.

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.

Motivation & Objective

• Formalize how rapid AI adoption interacts with aggregate demand and financial markets through a distribution-and-contract mismatch.
• Develop three mechanisms—displacement spiral with task reinstatement, Ghost GDP/velocity collapse, and intermediation collapse—to assess fragility.
• Provide falsifiable predictions, empirical grounding, and calibrated simulations to identify stability versus explosive crisis regimes.
• Connect micro-level AI productivity effects to macro-financial fragility using a formal framework and testable propositions.

Proposed method

• Adopts a task-based production framework where AI automates a measurable set of tasks with a CES aggregation across tasks.
• Defines AI capability growth, diffusion speed, and substitution costs with a logistic diffusion function for adoption dynamics.
• Introduces three core mechanisms: displacement spiral with reinstatement effects, Ghost GDP (velocity-driven demand mis-measurement), and intermediation margin compression.
• Derives propositions identifying conditions for stability, reinforcement, or explosive decline in the labor share and demand.
• Calibrates the model using FRED time series and BLS occupation data to simulate scenarios and generate testable predictions.

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 2 . (c) Ghost GDP proxy: divergence between real G

Experimental results

Research questions

• RQ1 Under what conditions do AI-driven displacement and reinstatement lead to a self-limiting versus explosive labor-share dynamic?
• RQ2 How does AI-driven output substitution affect monetary velocity and the gap between measured GDP and consumption-relevant income (Ghost GDP)?
• RQ3 To what extent does intermediation margin compression occur across sectors, and how does this interact with macro-financial transmission?
• RQ4 What are the exponential risk channels through which consumption concentration in top income quintiles amplifies macro-financial shocks?
• RQ5 What policy or institutional responses can shift the system from crisis to stable adjustment?

Key findings

• Three mechanisms generate macro distress: a displacement spiral with reinstitutionalization potential, a Ghost GDP velocity collapse, and intermediation-margin compression across services.
• The model nests regimes where reinstatement offsets displacement, a stable displacement path, or an explosive displacement path depending on AI capability growth and reinstatement rate.
• Ghost GDP implies monetary velocity declines as labor income share falls, making standard demand indicators misleading during rapid AI transitions.
• Intermediation margins compress toward a fixed floor due to AI reducing information friction, with higher exposure in friction-driven sectors like SaaS, payments, and consulting.
• Consumption concentration amplifies macro shocks because top-quintile wage earners drive a large share of consumption and face higher AI exposure, intensifying transmission to credit and mortgage markets.
• Calibrated simulations indicate conditions under which rapid AI adoption can transition from stable adjustment to explosive crisis, depending on policy response and adaptation speed.
• The framework yields eleven testable predictions and falsifiable conditions grounded in empirical moments from FRED and BLS data.

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.