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

2026-01-07

AI Summary

This study investigates the impact of artificial intelligence (AI) development on traditional capital and labor markets and explores corresponding governance pathways. Innovatively adapting the Lotka-Volterra predator–prey model from ecology, the authors construct a dynamic framework capturing the interactions among AI capital, physical capital, and labor. Empirical validation is conducted using Chinese macro-level panel data from 2016 to 2023. The findings reveal that AI capital—acting as “prey”—consistently promotes both physical capital accumulation and labor compensation, with the system converging to a stable equilibrium node. Labor market dynamics are primarily driven by AI-related parameters, whereas physical capital accumulation is additionally constrained by its own saturation effects. These results offer a quantitative foundation for differentiated policy interventions and identify critical leverage points for effective governance.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Other Foundations of Human Computation & AI
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• AI governance
• technological transition
• labor market disruption
• capital dynamics
• policy dilemma

Innovation

• Lotka-Volterra model
• AI capital
• technological transition governance
• economic dynamics
• sensitivity analysis