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AI Contagion in Social Networks

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  • Olivier Bos
  • Stefano Bosi

Abstract

We study how artificial intelligence (AI) interacts with social communication networks to shape the stability of collective knowledge. Agents exchange information through a network while AI systems generate content and retrain on the aggregate informational environment they influence. This interaction creates a recursive feedback loop in which informational distortions diffuse through society and subsequently feed back into future AI outputs. Despite the high dimensionality of the environment, we show that the long-run dynamics admit a two-dimensional representation whose spectral radius completely characterizes the stability of AI-mediated information systems. We derive a sharp regulatory frontier identifying the minimum filtering required for stability and show how homophily and core-periphery network structures shape systemic informational risk.

Suggested Citation

  • Olivier Bos & Stefano Bosi, 2026. "AI Contagion in Social Networks," Papers 2606.15206, arXiv.org, revised Jul 2026.
    • Handle: RePEc:arx:papers:2606.15206
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    File URL: https://arxiv.org/pdf/2606.15206
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    References listed on IDEAS

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    1. Mohammad Akbarpour & Suraj Malladi & Amin Saberi, 2025. "Just a Few Seeds More: The Value of Network Data for Diffusion," American Economic Review, American Economic Association, vol. 115(11), pages 3713-3748, November.
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