Governance of Technological Transition: A Predator-Prey Analysis of AI Capital in China's Economy and Its Policy Implications

The rapid integration of Artificial Intelligence (AI) into China's economy presents a classic governance challenge: how to harness its growth potential while managing its disruptive effects on traditional capital and labor markets. This study addresses this policy dilemma by modeling the dynamic interactions between AI capital, physical capital, and labor within a Lotka-Volterra predator-prey framework. Using annual Chinese data (2016-2023), we quantify the interaction strengths, identify stable equilibria, and perform a global sensitivity analysis. Our results reveal a consistent pattern where AI capital acts as the'prey', stimulating both physical capital accumulation and labor compensation (wage bill), while facing only weak constraining feedback. The equilibrium points are stable nodes, indicating a policy-mediated convergence path rather than volatile cycles. Critically, the sensitivity analysis shows that the labor market equilibrium is overwhelmingly driven by AI-related parameters, whereas the physical capital equilibrium is also influenced by its own saturation dynamics. These findings provide a systemic, quantitative basis for policymakers: (1) to calibrate AI promotion policies by recognizing the asymmetric leverage points in capital vs. labor markets; (2) to anticipate and mitigate structural rigidities that may arise from current regulatory settings; and (3) to prioritize interventions that foster complementary growth between AI and traditional economic structures while ensuring broad-base distribution of technological gains.

Summary

Main Finding

AI capital behaves like the "prey" in a three-way Lotka–Volterra system for China (2016–2023): it stimulates accumulation of physical capital and increases labor compensation (wage bill) while receiving only weak negative feedback from those sectors. Equilibrium points are stable nodes, implying convergent, policy-mediated adjustment rather than endogenous cyclical instability. Global sensitivity analysis shows labor outcomes are overwhelmingly driven by AI-related parameters; physical-capital outcomes depend both on AI interactions and on its own saturation/self-limitation dynamics.

Key Points

Modeling framework: a Lotka–Volterra predator–prey type model linking AI capital, physical capital, and labor (wage bill).
Empirical result: AI capital positively drives both physical capital accumulation and labor compensation; feedbacks from physical capital and labor onto AI are weak.
Stability: equilibrium points are stable nodes (no persistent cycles), suggesting dynamics converge toward equilibria under current estimated parameters.
Sensitivity: labor-market equilibrium is dominated by AI-related parameters; physical-capital equilibrium is jointly influenced by AI interactions and physical-capital self-dynamics (saturation).
Policy leverage is asymmetric: interventions targeting AI parameters have large effects on labor outcomes and material effects on capital, whereas targeting physical-capital parameters has more limited effects on labor.
Caution: results are based on a short annual panel (2016–2023) and a stylized interaction model—interpretation should account for potential omitted factors and aggregation bias.

Data & Methods

Data: annual Chinese aggregate series covering 2016–2023 for proxies of AI capital, physical capital stock, and labor compensation (wage bill). (Paper uses national-level aggregates; micro/firm heterogeneity not modeled.)
Model: deterministic Lotka–Volterra system adapted to represent mutual interactions and self-limiting (saturation) terms for the three stocks.
Estimation: interaction coefficients and self-limitation parameters were calibrated/estimated on the 2016–2023 data to quantify interaction strengths and locate equilibrium points.
Stability analysis: computed equilibria and evaluated local stability (Jacobian eigenvalues) to classify equilibria as stable nodes.
Global sensitivity analysis: variance-based exploration of parameter space to identify which parameters drive equilibrium outcomes (reported as dominance of AI-related parameters for labor, mixed drivers for physical capital).
Limitations: short time series, annual aggregation, model simplifications (e.g., no explicit heterogeneous firm/sector structure, no explicit policy shock identification), and potential endogeneity/measurement issues.

Implications for AI Economics

Policy targeting: because labor outcomes are highly sensitive to AI parameters, policymakers can meaningfully influence employment and wage distribution via AI-policy levers (training, diffusion incentives, wage support, regulation of AI deployment).
Asymmetric leverage and sequencing: promoting AI without complementary policies for physical capital and labor may produce uneven outcomes; policies should be sequenced to build complementary investments (e.g., capital modernization, workforce upskilling).
Avoiding structural rigidities: weak negative feedbacks from capital/labor onto AI suggest AI can grow rapidly; regulators should monitor lock-in, concentration, and distributional effects and consider redistributive or competition policies to avoid long-run rigidities.
Prioritize complementarities: interventions that strengthen AI–physical-capital complementarities (tax incentives for capital upgrades linked to AI adoption, co-investment programs) and broaden access to AI benefits (training, portable skills, wage support) will produce more inclusive growth.
Monitoring & further research: extend the analysis with higher-frequency and firm-level data, causal identification of policy shocks, and richer models (heterogeneous agents, sectoral linkages) to refine policy prescriptions and detect non-linear transition risks.

Assessment

Paper Typetheoretical Evidence Strengthlow — Results are based on calibration of a stylized Lotka–Volterra system to an 8-year aggregate (2016–2023) Chinese time series without exogenous variation or causal identification; short sample, aggregate proxies, and model structure drive conclusions, so empirical support for causal claims is weak. Methods Rigormedium — The paper uses internally consistent dynamical-systems methods (equilibrium computation, Jacobian/stability analysis, and variance-based global sensitivity analysis), but estimation/calibration on a very short national aggregate panel, limited robustness checks to alternative model specifications, and absence of micro-level validation reduce overall methodological rigor. SampleAnnual national-level Chinese aggregate series (2016–2023) for three stock-like variables: a proxy for AI capital, physical capital stock, and labor compensation (wage bill); no firm- or sector-level heterogeneity, short 8-year panel. Themeslabor_markets productivity GeneralizabilityChina-only national aggregates (may not hold for other countries), Very short time span (2016–2023) limits inference about longer-run dynamics, Annual frequency may miss higher-frequency adjustment or transitional cycles, Aggregate/national-level analysis masks firm-, sector-, and regional heterogeneity, Stylized Lotka–Volterra structure may not capture important economic mechanisms or omitted variables, AI capital is proxied and may suffer measurement error or definition sensitivity, No identification of exogenous policy or technology shocks limits causal generalization

Claims (14)

Claim	Direction	Confidence	Outcome	Details
The paper models interactions among AI capital, physical capital, and labor using a Lotka–Volterra (predator–prey type) system adapted to include self-limiting (saturation) terms. Other	null_result	high	model structure / interaction specification (no single dependent variable)	Model structure: Lotka–Volterra–type interactions among AI capital, physical capital, and labor (methodological claim) 0.06
The empirical analysis uses annual, national-level aggregate Chinese series for 2016–2023 as proxies for AI capital, physical capital stock, and labor compensation (wage bill). Other	null_result	high	AI capital proxy; physical capital stock; labor compensation (wage bill)	n=8 National annual aggregates for China 2016–2023 used as proxies for AI capital, physical capital, and labor compensation (data description) 0.06
Estimated interaction coefficients indicate AI capital positively drives physical capital accumulation (AI → physical capital positive effect). Firm Productivity	positive	medium	physical capital stock / accumulation	n=8 Estimated AI → physical capital interaction coefficient is positive 0.04
Estimated interaction coefficients indicate AI capital increases labor compensation (AI → wage bill positive effect). Wages	positive	medium	labor compensation (wage bill)	n=8 Estimated AI → labor compensation (wage bill) coefficient is positive 0.04
Feedback effects from physical capital and labor onto AI capital are weak, with only weak negative feedback observed (physical capital → AI and labor → AI small/weakly negative coefficients). Other	negative	medium	AI capital growth/stock (feedback strength)	n=8 Feedbacks from physical capital and labor onto AI are small and weakly negative in the estimated system 0.04
Equilibrium points of the estimated three-stock system are classified as stable nodes (no persistent endogenous cycles under the estimated parameters). Other	null_result	medium	equilibrium stability classification (eigenvalues of Jacobian)	n=8 Estimated system equilibria classified as stable nodes (local stability via Jacobian eigenvalues) 0.04
Under the current estimated parameters, dynamics converge toward equilibria—implying convergent, policy-mediated adjustment rather than endogenous cyclical instability. Other	null_result	medium	convergence behavior of model trajectories (toward equilibrium)	n=8 Model dynamics converge toward equilibria under estimated parameters (implied convergent adjustment) 0.04
Global sensitivity (variance-based) analysis shows labor-market equilibrium outcomes are overwhelmingly driven by AI-related parameters. Wages	positive	medium	labor-market equilibrium (wage bill / labor stock)	n=8 Global sensitivity: labor-market equilibrium outcomes are predominantly driven by AI-related parameters (majority of variance attributed to AI parameters) 0.04
Global sensitivity analysis shows physical-capital equilibrium outcomes are jointly influenced by AI–physical interactions and by physical-capital self-limitation (saturation) dynamics. Firm Productivity	mixed	medium	physical capital equilibrium (physical capital stock)	n=8 Global sensitivity: physical-capital equilibrium influenced jointly by AI–physical interactions and physical-capital saturation dynamics 0.04
Policy leverage is asymmetric: interventions targeting AI-related parameters have large effects on labor outcomes and nontrivial effects on capital, whereas interventions targeting physical-capital parameters have more limited effects on labor. Wages	mixed	low	labor compensation (wage bill) and physical capital stock responses to parameter changes	n=8 Policy leverage asymmetric: interventions on AI parameters have large effects on labor outcomes; interventions on physical-capital parameters have limited labor effects (model-based) 0.02
Promoting AI without complementary policies for physical capital and labor may produce uneven outcomes; policy sequencing and complementarity (capital modernization, workforce upskilling) are recommended to produce more inclusive growth. Governance And Regulation	mixed	speculative	distributional and growth outcomes (qualitative policy impacts inferred from model)	n=8 Promoting AI without complementary physical-capital and labor policies may produce uneven outcomes; sequencing/complementarity recommended (policy implication) 0.01
Because feedbacks from capital and labor onto AI are weak, AI can grow rapidly and may lead to lock-in, concentration, and distributional risks that warrant monitoring and possible redistributive or competition policies. Market Structure	negative	low	AI capital growth dynamics and potential long-run concentration/lock-in risks (qualitative)	n=8 Weak feedbacks to AI imply potential for rapid AI growth, lock-in, concentration and distributional risks (qualitative risk implication) 0.02
The main empirical conclusions are based on a short annual panel (2016–2023) and a stylized aggregate interaction model; results should be interpreted with caution due to potential omitted variables, aggregation bias, and limited sample size. Other	null_result	high	validity/robustness of empirical conclusions (limitations)	n=8 Main empirical conclusions hinge on a short annual panel (2016–2023) and stylized aggregate model; results subject to omitted variables, aggregation bias, and small sample caveats 0.06
Estimation/calibration, stability assessment, and global sensitivity methods used: parameters calibrated/estimated on 2016–2023 data; equilibrium located; Jacobian eigenvalues computed for local stability; variance-based global sensitivity analysis performed over parameter space. Other	null_result	high	methodological procedures applied (estimation, stability analysis, GSA)	n=8 Methods used: parameter estimation/calibration on 2016–2023 data, equilibrium location, Jacobian eigenvalue stability assessment, variance-based global sensitivity analysis 0.06

Entities

AI capital (ai_tool) Physical capital stock (outcome) Labor compensation (wage bill) (outcome) Annual Chinese national aggregates (2016–2023) (dataset) Lotka-Volterra (predator–prey) model (deterministic, three-way) (method) Interaction coefficients (Lotka-Volterra parameters) (method) Self-limitation (saturation) parameters (method) Stability analysis (Jacobian eigenvalues) (method) Global sensitivity analysis (variance-based) (method) Equilibrium points (steady states) (outcome) Labor-market equilibrium (outcome) Physical-capital equilibrium (outcome) Employment and wage distribution (outcome) AI–physical-capital complementarities (outcome) Policymakers (population)

Calibrated dynamics for China suggest AI capital spurs both investment and wages: AI drives physical-capital accumulation and boosts the wage bill, while feedback from capital and labor onto AI is weak; equilibria are stable rather than cyclic.