[2603.26707] The Cognitive Divergence: AI Context Windows, Human Attention Decline, and the Delegation Feedback Loop

Computer Science > Computation and Language arXiv:2603.26707 (cs) [Submitted on 17 Mar 2026] Title:The Cognitive Divergence: AI Context Windows, Human Attention Decline, and the Delegation Feedback Loop Authors:Netanel Eliav (Machine Human Intelligence Lab) View a PDF of the paper titled The Cognitive Divergence: AI Context Windows, Human Attention Decline, and the Delegation Feedback Loop, by Netanel Eliav (Machine Human Intelligence Lab) View PDF HTML (experimental) Abstract:This paper documents and theorises a self-reinforcing dynamic between two measurable trends: the exponential expansion of large language model (LLM) context windows and the secular contraction of human sustained-attention capacity. We term the resulting asymmetry the Cognitive Divergence. AI context windows have grown from 512 tokens in 2017 to 2,000,000 tokens by 2026 (factor ~3,906; fitted lambda = 0.59/yr; doubling time ~14 months). Over the same period, human Effective Context Span (ECS) -- a token-equivalent measure derived from validated reading-rate meta-analysis (Brysbaert, 2019) and an empirically motivated Comprehension Scaling Factor -- has declined from approximately 16,000 tokens (2004 baseline) to an estimated 1,800 tokens (2026, extrapolated from longitudinal behavioural data ending 2020 (Mark, 2023); see Section 9 for uncertainty discussion). The AI-to-human ratio grew from near parity at the ChatGPT launch (November 2022) to 556--1,111x raw and 56--111x quality-adjusted, after accoun...