Paper Models LLM Adoption as Viral Spread With Lock-In Risk — SkimNews
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- Luis F Seoane and co-authors submitted "Large-Language Models as a Cognitive Virus" to arXiv on September 3, 2026, categorizing the work under physics.soc-ph (physics and society).
- The paper frames LLM diffusion through a viral analogy, modeling transitions among uncoupled, coupled, and persistently dependent users as adoption spreads through populations.
- Researchers found that the interplay between social transmission, recovery, and collective reinforcement can generate tipping points and technological lock-in once a critical threshold is crossed.
- The model shows that small increases in adoption beyond that threshold can trigger rapid population-level shifts toward persistent dependence, accompanied by what the authors describe as abrupt losses in cognitive competence.
- The same framework identifies conditions for "cognitive immunization" — strategies that reduce transmission and facilitate reversibility of LLM reliance.
- Coverage of the paper across other outlets frames it as a warning about runaway LLM adoption dynamics, with the dominant angle emphasizing the tipping-point threat to cognitive autonomy.
Why it matters: By importing epidemic-modeling tools into LLM-adoption debates, the authors offer policymakers and platform designers a quantitative case for interventions that lower transmission rates or boost reversibility — a concrete alternative to purely qualitative concerns about AI dependency.
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