AI Contagion in Social Networks

[Submitted on 13 Jun 2026 (v1), last revised 18 Jul 2026 (this version, v2)]

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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.

Submission history

From: Olivier Bos [view email]
[v1] Sat, 13 Jun 2026 09:02:22 UTC (51 KB)
[v2] Sat, 18 Jul 2026 10:10:17 UTC (53 KB)