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.

翻译：人工智能（AI）在中国经济中的快速融合提出了一个经典的治理挑战：如何利用其增长潜力，同时管理其对传统资本和劳动力市场的颠覆性影响。本研究通过将AI资本、物质资本与劳动力之间的动态互动建模为Lotka-Volterra捕食者-猎物框架，以应对这一政策困境。利用中国年度数据（2016-2023年），我们量化了互动强度，识别了稳定均衡点，并进行了全局敏感性分析。我们的研究结果揭示了一个一致的模式：AI资本扮演“猎物”角色，刺激物质资本积累和劳动力报酬（工资总额），而仅面临微弱的约束性反馈。均衡点为稳定节点，表明存在一条政策介导的收敛路径而非剧烈波动周期。关键的是，敏感性分析显示劳动力市场均衡主要受AI相关参数驱动，而物质资本均衡也受其自身饱和动态的影响。这些发现为政策制定者提供了系统性的量化依据：（1）通过识别资本与劳动力市场中不对称的杠杆点来校准AI促进政策；（2）预见并缓解当前监管环境下可能出现的结构性僵化；（3）优先采取干预措施，以促进AI与传统经济结构之间的互补性增长，同时确保技术收益的广泛基础分配。