This preprint formalizes Cognitive Cache Misses (CCMs) as a systems-level inefficiency in human–AI interaction and provides the first empirically calibrated quantification of its global impact. Moving beyond the conceptual assumptions of prior versions, v5.0 derives the baseline CCM rate (R) from a meta-analysis of 2.16 million real-world conversations (LMSYS-Chat-1M, WildChat-1M, and OpenAssistant), yielding an adjusted empirical rate of R = 15.1%. The paper introduces a data-derived Cognitive Input Quality Index (CIQ), constructed from PISA reading scores, social media usage, and digital skills indices, to parameterize interaction quality across 12 countries. A large-scale Monte Carlo simulation (50,000 iterations) reveals that while the empirical calibration narrows uncertainty, the structural impact remains significant: modest Minimal Cognitive Friction (MCF) interventions can yield median annual energy savings of 299 GWh and an increase of 4.3% in effective compute capacity without additional hardware. A key theoretical contribution of this version is the identification of the "China Paradox," where sensitivity analysis reveals divergent causal pathways—comprehension vs. fragmentation—necessitating distinct intervention designs. This work positions human attention as a first-order systems variable and establishes a data-driven empirical agenda for AI sustainability and alignment.
Alexis Arellano Urquiaga (Tue,) studied this question.