Governance of Technological Transition: A Predator-Prey Analysis of AI Capital in China's Economy and Its Policy Implications
Kunpeng Wang, Jiahui Hu
TL;DR
The paper addresses how to govern AI-driven transformation in China by treating AI capital, physical capital, and labor as interacting ecological agents within a generalized Lotka–Volterra framework. It estimates six interaction parameters from 2016–2023 data, analyzes equilibrium stability, and conducts a Sobol global sensitivity analysis to reveal leverage points. The results show AI capital acts as prey that stimulates both physical capital and labor, yet experiences only weak feedback, with all equilibria being stable nodes; labor outcomes are predominantly driven by AI-related parameters, while physical capital equilibria depend more on their own saturation dynamics. The findings inform policy by suggesting selective AI promotion, cautious relaxation of rigidities, and labor-market strategies that align with AI diffusion to achieve stable, inclusive growth during the technological transition.
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.