Search papers, labs, and topics across Lattice.
This paper analyzes the diffusion of large-language models (LLMs) through a viral analogy, illustrating how their adoption can lead to significant shifts in cognitive and cultural practices. By modeling user interactions and transitions among different states of dependence, the authors reveal that social transmission and collective reinforcement can create tipping points that result in rapid population-level changes and potential cognitive decline. Importantly, the framework also identifies strategies for cognitive immunization, suggesting ways to mitigate the risks associated with LLM adoption while promoting reversibility in usage patterns.
Once a critical threshold of LLM adoption is crossed, even minor increases can trigger rapid cognitive decline across populations, underscoring the need for strategies to maintain cognitive autonomy.
Large-language models (LLMs) are rapidly becoming part of human culture, reshaping how information is produced, transmitted, and used. Here we propose that their diffusion can be understood through a viral analogy, with LLM use spreading through populations, becoming embedded in cognitive and cultural practices. We model transitions among uncoupled, coupled, and persistently dependent users, and show that the interplay between social transmission, recovery, and collective reinforcement can generate tipping points and technological lock-in. A central consequence is the possibility of runaway dynamics: once a critical threshold is crossed, small increases in adoption can trigger rapid population-level shifts toward persistent dependence, with abrupt losses in cognitive competence. The same framework, however, identifies conditions for cognitive immunization, based on reducing transmission and facilitating reversibility. Our results highlight how LLM adoption may involve nonlinear collective transitions with important consequences for cognitive autonomy.
