A Structural and Bayesian Risk Assessment
Abstract
This paper examines the macro-financial implications of rapid artificial intelligence (AI) adoption in advanced economies. While AI substantially increases productive capacity and corporate efficiency, it also accelerates the substitution of capital for labour across cognitive and professional sectors. Because household consumption in advanced economies is predominantly financed through labour income, persistent labour share compression may weaken aggregate demand relative to productive supply. Financial markets, which capitalise expectations of future cash flows, may therefore experience nonlinear repricing if demand assumptions embedded in asset valuations prove inconsistent with income trends.
Using a Bayesian scenario framework, this paper evaluates four potential equilibrium paths: coordinated adaptation, gradual demand compression, nonlinear financial repricing, and institutional stress. Based on current observable trends in labour share dynamics, capital concentration, and AI investment velocity, we assign probabilistic weights to each scenario. We argue that markets may be underestimating transitional disequilibrium risk arising from the temporal gap between technological acceleration and institutional adaptation. The core constraint is not technological feasibility but income routing: AI-generated productivity gains must be transmitted into household purchasing power to sustain demand-driven market economies.
I. Introduction
Artificial intelligence represents a structural transformation in the organisation of production. Unlike prior automation waves that primarily substituted for routine manual tasks, frontier AI systems increasingly perform complex cognitive functions, including analysis, coding, legal drafting, financial modelling, and administrative coordination.
In advanced economies, labour income remains the dominant source of household purchasing power. Consumption constitutes the majority of aggregate output. Fiscal systems are substantially financed through income and payroll taxation. Consequently, labour income plays a central stabilising role in macroeconomic equilibrium.
The current AI transition introduces a structural tension: productivity growth may outpace the institutional mechanisms that distribute income derived from that productivity. If capital income expands while labour income stagnates or declines, aggregate demand may weaken relative to supply potential. Financial markets, which discount expected future earnings, may then experience repricing if revenue expectations embedded in asset valuations are revised.
This paper investigates whether the speed of AI-driven labour substitution introduces systemic risk through income compression and demand misalignment, and evaluates likely macroeconomic outcomes using a Bayesian scenario framework.
II. The Structural Mechanism
II.i. Labour Substitution and Income Allocation
AI adoption increases the substitutability of capital for labour across a growing set of tasks. Firms facing competitive pressure rationally adopt cost-reducing technologies. When competitors automate, non-adopters risk margin compression. This generates strategic incentives for widespread adoption.
At the firm level, AI investment improves productivity and reduces operating costs. At the aggregate level, however, widespread labour substitution can reduce the proportion of income accruing to households.
In advanced economies, household consumption is largely financed by wages. If labour income declines as a share of total output, consumption growth may decelerate unless offset by transfers or alternative income channels.
This creates a potential decoupling between total output and household purchasing power.
II.ii. Demand Sustainability Constraint
Market economies require effective demand to clear supply. While AI can generate goods and services at large scale, it does not autonomously generate human demand. Final consumption decisions are made by households and institutions acting on behalf of humans.
If income becomes increasingly concentrated among capital owners with lower marginal propensities to consume, aggregate demand growth may weaken relative to productive capacity.
Without redistribution mechanisms, the equilibrium outcome may involve:
Slower consumer revenue growth,
Downward revisions to earnings expectations in demand-sensitive sectors,
Increased financial market volatility.
The constraint is therefore not technological production capacity, but demand distribution.
III. Why AI Can Generate Market Upheaval
Financial markets are forward-looking discount mechanisms. Equity valuations reflect expectations of future cash flows, which depend on both profitability and revenue growth.
AI adoption affects both:
It can increase margins by reducing labour costs.
It may weaken aggregate demand if labour income compresses materially.
If markets initially price AI as margin-expanding but do not fully incorporate potential demand-side effects, valuations may embed overly optimistic long-term revenue trajectories.
Market upheaval can occur if:
Earnings growth expectations remain high,
But household income growth slows,
Leading to systematic downward revisions in revenue forecasts.
Such revisions can produce nonlinear repricing, particularly in sectors heavily exposed to consumer demand.
The risk is not immediate collapse but expectation misalignment.
IV. A Bayesian Scenario Framework
Given uncertainty about institutional adaptation, redistribution policies, and the pace of AI substitution, we model four macroeconomic equilibrium scenarios. Probabilities are illustrative posterior assessments derived from current observable trends in labour share dynamics, AI capital expenditure concentration, fiscal structures, and policy responsiveness.
Scenario A: Coordinated Adaptation (Probability ≈ 30%)
Labour share declines moderately.
Governments implement redistribution mechanisms (e.g., targeted transfers, tax reforms, public participation in AI returns).
Household demand remains stable.
Markets experience sectoral reallocation but avoid systemic instability.
This represents a successful institutional adjustment.
Scenario B: Gradual Demand Compression (Probability ≈ 35%)
Labour income growth lags productivity growth.
Redistribution policies are partial or delayed.
Consumer-facing sectors underperform relative to expectations.
Equity valuations compress gradually over several years.
This scenario involves structural but orderly repricing.
Scenario C: Nonlinear Financial Repricing (Probability ≈ 20%)
Markets overestimate durability of AI-driven earnings growth.
Labour income weakens more than anticipated.
Consumer revenue growth disappoints.
Credit spreads widen.
Equity markets undergo sharp repricing once demand assumptions adjust.
This resembles a demand-driven Minsky-type dynamic.
Scenario D: Institutional Stress and Fiscal Fragmentation (Probability ≈ 15%)
Labour-tax-dependent fiscal systems experience sustained revenue pressure.
Political polarisation intensifies.
Sovereign credit spreads widen in vulnerable jurisdictions.
International coordination deteriorates.
This represents broader macro-financial instability beyond equity markets.
V. The General Equilibrium Constraint
In the limit case of near-complete automation, production can theoretically expand substantially. However, if income accrues primarily to capital owners and capital ownership is concentrated, aggregate consumption may be insufficient to absorb total output.
Market economies require distributed purchasing power. Without income transmission mechanisms, supply expansion does not guarantee stable demand.
Therefore, sustained AI-driven productivity growth must be accompanied by institutional redesign of income distribution mechanisms. Otherwise, demand-side fragility may emerge.
The risk arises from the temporal gap between capital deepening and redistribution adaptation.
VI. Empirical Trends
Several observable indicators support cautious monitoring:
Persistent long-term decline in labour’s share of income in advanced economies.
Increasing concentration of equity market capitalisation in AI-intensive firms.
Rapid growth in compute-related capital expenditure.
Divergence between productivity growth in AI-intensive sectors and median wage growth.
Rising fiscal sensitivity in jurisdictions reliant on labour income taxation.
These trends do not indicate imminent crisis. They suggest increasing structural sensitivity to redistribution timing.
VII. Financial Stability Implications
If labour income growth slows materially while asset valuations assume sustained broad-based demand expansion, markets may experience:
Revisions to revenue growth forecasts,
Valuation compression in consumer-dependent sectors,
Increased volatility in credit markets,
Portfolio rotation toward capital-intensive infrastructure firms.
The adjustment process may be gradual or nonlinear depending on expectations and leverage conditions.
The central risk is misalignment between asset pricing assumptions and underlying demand dynamics.
VIII. Policy Implications for G7 Economies
Stability requires mechanisms that route AI-generated productivity gains into household income in a systematic manner.
Potential approaches include:
Coordinated taxation of AI-related capital returns.
Public equity participation in core AI infrastructure.
Targeted transfer mechanisms funded by productivity gains.
International coordination to prevent regulatory arbitrage.
Given the global nature of AI infrastructure, unilateral measures may be less effective without coordination among major advanced economies.
Policy design must balance innovation incentives with demand stability.
IX. Conclusion
Artificial intelligence represents a significant productivity advance with the potential to expand global output. However, macroeconomic stability in advanced economies depends not solely on production capacity, but on the distribution of income that sustains aggregate demand.
If labour substitution proceeds faster than institutional adaptation, labour income compression may weaken demand relative to supply potential. Financial markets, which capitalise expectations of future cash flows, may then experience repricing as revenue assumptions are revised.
Using a Bayesian scenario framework, we find that while coordinated adaptation remains the most desirable outcome, alternative scenarios involving gradual demand compression or nonlinear financial repricing carry non-trivial probabilities under current trends.
The central constraint is temporal. Technological acceleration is proceeding rapidly; institutional redesign is inherently slower. The stability of advanced economies during this transition will depend on whether coordinated policy mechanisms can align AI-generated productivity gains with sustainable household purchasing power.
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