LLMs likened to a cognitive virus
bias claude openai
| Source: Mastodon | Original article
A new arXiv paper argues that large-language models function like a cognitive virus, influencing human thought through offloading, meme propagation, and automation bias.
A new pre‑print posted on arXiv on 3 September 2026 argues that large‑language models (LLMs) are behaving like a “cognitive virus.” The paper, authored by researchers from Harvard and the Santa Fe Institute—including Ricard Solé, David C. Krakauer, Michael Levin and Santiago F. Elena—applies concepts from evolutionary biology, complex systems and epidemiology to map how LLMs spread through everyday cognitive practices. By treating adoption as an infection, the authors identify tipping points, bistable regimes and a risk of technological lock‑in that could trigger abrupt, large‑scale shifts in how information is produced, transmitted and relied upon.
The authors argue that LLMs increasingly serve as external memory and reasoning aids, a form of cognitive off‑loading that reshapes the “extended mind.” This dependence can amplify automation bias, embed meme‑like patterns, and create feedback loops that reinforce further reliance—dynamics the paper likens to viral propagation. Their epidemiological model suggests that once a critical mass of users integrates LLMs into routine thought processes, reversal becomes difficult, raising concerns about societal resilience and agency.
Why it matters is twofold. First, the framing highlights a systemic risk that goes beyond isolated technical failures, echoing recent debates about AI governance and corporate responsibility, such as the OpenAI‑related incident we covered on 6 September. Second, the work provides a quantitative lens for policymakers to anticipate lock‑in effects and design interventions before the tipping point is reached.
The next steps will likely involve scholarly critique of the model’s assumptions, empirical validation of the proposed dynamics, and policy discussions on mitigating cognitive dependency. Watch for responses from AI developers, ethicists and regulators, as well as any follow‑up studies that test mitigation strategies or propose alternative diffusion frameworks.
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