Abstract

As Artificial Intelligence (AI) evolves from passive tools into proactive actors within socio-technical systems, traditional social network theories face fundamental limitations in explaining AI’s structural power. Drawing on the Network Capabilities framework, this study investigates the mechanism of AI power generation within homogeneous communities from a structural hole perspective. This study analyzes a COVID-19 vaccine interaction network (N = 9314) on X via social network analysis, Propensity Score Matching (PSM), counterfactual simulations, and weighted Independent Cascade Model (ICM) dynamics. The results reveal that bot-like agents do not rely on traditional brokerage to acquire power; instead, they execute a Tight Integration strategy by filling micro-structural holes. After isolating the confounding effects of connection scale via rigorous Propensity Score Matching, it creates an anomalous high-density, high-constraint configuration, with these algorithmic agents exhibiting significantly higher network constraint (0.514) than comparable human users (0.453). Counterfactual removal experiments demonstrate a profound structural dependence of the social system on AI: their removal triggers a systemic cascade collapse, decreasing the largest connected component (LCC) size by a factor of 82.9 and topologically isolating 79.7% of human users. Furthermore, transitioning from static structural analysis to dynamic simulations, ICM simulations confirm AI’s topological redundancy translates into substantial information diffusion dominance (Cohen’s d = 1.081). Revealing AI’s power generation mechanism provides essential governance insights and strategic approaches for mitigating AI-driven information cocoons and group polarization.

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