Enterprise AI Analysis

The AI-driven process exhibits an inverted 'U' pattern: initially promoting development, then inhibiting it beyond an optimal threshold (inflection point ~8.95). Excessive AI investment without adequate institutional adaptation or labor retraining can lead to negative consequences, such as productivity paradox and resource misallocation.

AI empowers rural revitalization primarily by enhancing innovation activity and economic dynamism. It transforms traditional rural processes, lowers technology barriers, stimulates new business models (e.g., smart agriculture, rural e-commerce), and improves factor allocation efficiency.

The enabling effect of AI is significantly more pronounced in regions with data trading centers. These centers provide a superior data element foundation by aggregating resources, facilitating circulation, and promoting marketization, thereby strengthening the synergy between technology and rural development.

AI's enabling role is highly heterogeneous based on population density. In densely populated regions (e.g., southeast of Hu Huanyong Line), AI shows a significant positive effect. In contrast, non-densely populated regions (e.g., northwest) show a non-significant, negative coefficient, indicating population density as a key influencing factor.

Policies should focus on building 'digital-intelligent villages' ecosystems, integrating AI strategically, and ensuring technology inclusivity for smallholders. A coordination mechanism between technology governance and institutions is crucial to prevent issues like algorithm bias, data rights imbalance, and resource misallocation. Differentiated strategies, tailored to regional development levels, are necessary for optimal resource allocation.