Organizational Knowledge Drift: How AI Assistants Create Textual Echo Chambers

Recent studies show that generative models can experience model collapse when they are repeatedly trained on or rely on their own outputs. Over time, the share of synthetic content increases relative to human-authored material, reinforcing existing patterns and reducing informational diversity. This dynamic is not confined to model training processes; it can also emerge organically within organizations as employees increasingly rely on AI assistants to generate documents, summaries, reports, and responses. In such environments, organizational knowledge systems may drift toward textual echo chambers, in which AI-generated content circulates, is repeatedly retrieved as contextual input, and subsequently amplifies itself with diminishing novelty. While echo chambers are well documented in political communication and social media, they have not yet been theorized or empirically examined in AI-mediated organizational knowledge systems. In this study, we first demonstrate – through controlled simulations across multiple real-world text domains – how recursive AI assistance gives rise to echo-chamber effects in organizational corpora. We then introduce a diagnostic framework that continuously monitors an organization’s knowledge base and detects when it is drifting toward echo-chamber formation and collapse. This study contributes to the theory of AI-driven knowledge ecosystems and offers practical guidance for sustaining healthy, diverse, and resilient organizational information environments.