Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption
arXiv:2603.09209v1 Announce Type: new 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-r
arXiv:2603.09209v1 Announce Type: new 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.
Executive Summary
This article proposes a macro-financial stress test for rapid AI adoption, identifying a distribution-and-contract mismatch as the primary concern. The authors argue that AI-generated abundance coexists with demand deficiency due to economic institutions anchored to human cognitive scarcity. They formalize three mechanisms: displacement spiral, Ghost GDP, and intermediation collapse, and derive conditions under which the feedback is self-limiting versus explosive. The authors also provide testable predictions and calibrated simulations to quantify conditions under which stable adjustment transitions to explosive crisis. Their work highlights the potential for AI to exacerbate income inequality and create systemic risks in financial markets.
Key Points
- ▸ The distribution-and-contract mismatch is a result of economic institutions being anchored to human cognitive scarcity.
- ▸ AI-generated abundance coexists with demand deficiency due to the displacement of labor-generated output.
- ▸ The intermediation collapse mechanism highlights the risk of AI agents compressing intermediary margins and triggering repricing in various industries.
Merits
Strength in Theoretical Framework
The authors develop a comprehensive theoretical framework that incorporates multiple mechanisms to understand the macro-financial implications of rapid AI adoption.
Quantitative Analysis
The use of calibrated simulations and testable predictions provides a rigorous and quantifiable approach to understanding the potential risks and consequences of AI adoption.
Relevance to Policy Debate
The article highlights the need for policymakers to consider the potential systemic risks of AI adoption and develop strategies to mitigate these risks.
Demerits
Simplifying Assumptions
The article relies on simplifying assumptions, such as the labor share and AI capability growth rate, which may not accurately reflect real-world complexities.
Limited Empirical Evidence
The article relies on calibrated simulations and testable predictions, but the empirical evidence supporting these predictions is limited.
Unclear Policy Implications
The article highlights the need for policymakers to consider the potential systemic risks of AI adoption, but the policy implications and recommendations are unclear.
Expert Commentary
This article provides a comprehensive and rigorous analysis of the macro-financial implications of rapid AI adoption. While the article highlights the potential risks and consequences of AI adoption, it also provides valuable insights into the need for policymakers to develop strategies to mitigate these risks. The article's use of calibrated simulations and testable predictions provides a rigorous and quantifiable approach to understanding the potential risks and consequences of AI adoption. However, the article relies on simplifying assumptions and limited empirical evidence, which may not accurately reflect real-world complexities. Furthermore, the policy implications and recommendations are unclear, highlighting the need for further research and discussion on this topic.
Recommendations
- ✓ Researchers should conduct further empirical research to test the predictions and assumptions made in this article.
- ✓ Policymakers should develop and implement strategies to mitigate the potential systemic risks of AI adoption, such as tax policies and social safety nets to address income inequality.