1 min readKnowledge Resource

Knowledge Resource

Large-Language Models as a Cognitive Virus

Author
Aziz Shuaib Ausi
Published
7 September 2026
Reading time
1 min
Publication type
Knowledge Resource
Availability
Open access
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Recent research from arXiv posits that Large Language Models (LLMs) are disseminating through human culture akin to a 'cognitive virus,' fundamentally altering information generation, dissemination, and utilization. The study models user transitions and identifies potential tipping points where LLM adoption could lead to rapid, population-level shifts towards persistent dependence, driven by social transmission, recovery dynamics, and collective reinforcement.

Why it matters

This research highlights the potential for rapid, transformative societal shifts driven by widespread LLM adoption, underscoring the need for proactive strategy development. Organizations must understand the dynamics of technological lock-in and potential 'runaway dynamics' to manage risks and opportunities effectively as these models become pervasive.

Key insights

  • LLMs are rapidly integrating into human culture, influencing how information is produced, transmitted, and consumed.
  • The diffusion of LLM use can be understood through a viral analogy, spreading through populations and embedding in cognitive and cultural practices.
  • A model outlines transitions among uncoupled, coupled, and persistently dependent users of LLMs.
  • The interplay of social transmission, user recovery, and collective reinforcement can lead to critical tipping points and technological lock-in.
  • Crossing a critical threshold in LLM adoption can trigger runaway dynamics, resulting in rapid population-level shifts towards persistent dependence.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.03344

Citation

Cite this publication (APA 7)

Aziz Shuaib Ausi (2026). Large-Language Models as a Cognitive Virus. Knowledge Resource. Aziz Shuaib Ausi. https://www.azizshuaib.com/verify/ASA-EXE-2026-00177

Verification

This is an authenticated institutional record.

Verification ID
ASA-EXE-2026-00177
Version
v1.0 · r0
Issued
7 September 2026
Publisher
Aziz Shuaib Ausi
Licence
All rights reserved. Reproduction requires written permission.

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