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

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 accounting for retrieval degradation (Liu et al., 2024; Chroma, 2025). Beyond documenting this divergence, the paper introduces the Delegation Feedback Loop hypothesis: as AI capability grows, the cognitive threshold at which humans delegate to AI falls, extending to tasks of negligible demand; the resulting reduction in cognitive practice may further attenuate the capacities already documented as declining (Gerlich, 2025; Kim et al., 2026; Kosmyna et al., 2025). Neither trend reverses spontaneously. The paper characterises the divergence statistically, reviews neurobiological mechanisms across eight peer-reviewed neuroimaging studies, presents empirical evidence bearing on the delegation threshold, and proposes a research agenda centred on a validated ECS psychometric instrument and longitudinal study of AI-mediated cognitive change.

Figures (2)

• Figure 1: The Cognitive Divergence, 2017--2026. AI context window capacity (blue, upper curve) versus human Effective Context Span (ECS; red, lower curve) expressed in tokens on a logarithmic scale. The crossover inflection occurs ca. 2022 at the ChatGPT launch. By 2026, raw AI context exceeds human ECS by 556--1,111$\times$. Quality-adjusted gap (accounting for retrieval degradation, Section 6) is 56--111$\times$. Human ECS derived via Equation (\ref{['eq:ecs']}): $\text{ECS}(t) = S(t) \times R_{\text{tok}} \times \text{CSF}(t)$, where $S(t)$ is session reading duration; see Table \ref{['tab:ecs_params']}; AI data from Appendix A. Note: Human 2026 value ($\approx$1,800 tokens) is an extrapolated estimate; see Section 9 for uncertainty discussion. Summary statistics below the chart use 2017 as the AI baseline (512 tokens) and the corresponding 2017 human ECS ($\approx$13,500 tokens); the paper's primary narrative uses the 2004 human ECS baseline ($\approx$16,000 tokens).
• Figure 2: The Delegation Feedback Loop. Each arrow represents a theorised causal relationship. Evidence bearing on each relationship is reviewed in Section 8.2--8.4. The loop is self-reinforcing: no node spontaneously reverses under current technological and behavioural trajectories.