Large-Language Models as a Cognitive Virus

Key points

• In a population dynamics model (epidemic-style diffusion) that divides people into three states (non-use U, autonomous use C, and persistent dependence D), crossing a critical range of transmission rates produced bistability (a range in which two stable states coexist), with a sharp shift from coexistence between autonomous and dependent groups toward dependence.
• With illustrative parameters (ρ=0.10, κ=0.40), hysteresis occurs between the thresholds λSN=0.40 and λTC=0.50. Once adoption runs away, reducing transmission pressure to its previous level does not automatically restore the earlier state (technological lock-in).
• The persistent-dependence fraction is determined by D*=(µ/σ)C*. Reducing the rate of entering dependence µ or increasing the recovery rate σ can reduce dependence (through training, verification habits, and deliberate cognitive friction).
• Using illustrative cognitive-capacity values (autonomy 1.0, autonomous use 0.5, and persistent dependence 0.1), average capacity follows the same hysteresis. The key point is that population-wide capacity can drop sharply even when individual adoption is gradual.
• It defines 'cognitive immunization' as maintaining habits of independent verification and unaided problem solving, along with educational designs that support them, rather than blocking exposure to AI. The conclusion is that prevention is much easier than recovery.
• Experimental evidence cited by the paper: a 'think first, ChatGPT later' protocol improved subsequent independent creativity, while unrestricted AI use improved performance while using the tool but reduced unaided performance, understanding, and memory.

Paper links

External research summaries. These are not HDATF publications or measured product results.