Cognitive Tribology: Friction, Wear and the Long-Term Effects of AI Assistance on Human Cognitive Capability

Version 1.1—May 2026 This version incorporates additions published after the original submission date. Section 5.1 adds neuroplasticity evidence from Rossi et al. (2026) establishing that passive AI reliance weakens activity-dependent brain plasticity while active co-creation sustains it, providing a neurobiological substrate for the atrophy mechanism. Section 5.5 adds empirical evidence from Chakrabarty and Dhillon (2026) documenting that expert writers experienced an identity crisis and eroded aesthetic confidence when confronted with convincing AI-generated writing, confirming the cognitive surface texture erosion argument in the creative domain. Section 5.6 adds findings from DosSantos et al. (2026) demonstrating that LLMs adhere strictly to policies even when counterproductive, and that aligning AI with human judgment requires training on how decisions are made rather than just which decisions are made, providing the mechanical basis for the artificial certainty construct and reframing epistemic friction as an alignment requirement. Section 5.7 adds Uzzi (2026) arguing that whether AI assists or substitutes for human creativity depends on whether it is asked how to think rather than what to think, mapping directly onto the productive friction construct. Section 5.7 also adds insight research from Becker et al. (2025) demonstrating that insight almost doubles memory for solutions and requires conditions of productive struggle that frictionless AI removes, together with a companion review by Becker and Cabeza (2025) providing the mechanistic account: insight functions as a prediction error signal triggering hippocampal and dopaminergic memory consolidation, a cascade frictionless AI prevents by delivering answers before the brain has formed a prediction. Section 5.10 acknowledges the Cognitive Atrophy Paradox framework (Kabashkin, 2025) as a parallel independent contribution addressing the same problem space, while distinguishing cognitive tribology's theoretical and design contributions. This version also includes minor formatting standardizations to in-text citations and typographical corrections in the reference list.No substantive changes to the argument, framework, figures or conclusions. Abstract The drive toward frictionless human-AI interaction has produced interfaces of unprecedented smoothness and capability. We argue this achievement carries an unexamined cost: the systematic erosion of human cognitive capability through the removal of the productive resistance that maintains it. We introduce cognitive tribology—a framework drawing on the science of friction, wear, and lubrication between interacting surfaces—to name, explain, and propose design responses to this problem. The framework makes three core contributions. First, it develops a tribological model of human-AI interaction in which cognitive surfaces are maintained by friction and degraded by its absence, extending Cooper's foundational cognitive friction concept by distinguishing between friction types. Second, it introduces a taxonomy of cognitive friction types—productive, parasitic, and phantom—with particular attention to phantom friction: the substitution of the subjective experience of cognitive engagement for its substance. Third, it proposes seven design principles for friction-conscious human-AI interaction, evaluated against a reversibility standard that asks not how well humans perform with AI assistance but whether they retain the capacity to perform without it. The framework identifies three consequences of over-lubrication at distinct time horizons: phantom friction within individual interactions, cognitive atrophy across months and years, and serendipity loss across careers and fields. The framework is grounded in converging empirical evidence: randomized experimental demonstrations that AI assistance impairs skill formation and reduces persistence on unassisted tasks; neurological evidence of reduced brain connectivity accumulating across months of LLM use; large-scale documentation of metacognitive laziness and cognitive surrender; organizational research on artificial certainty; and formal economic modelling establishing that rational AI adoption can produce a steady state in which workers end up less capable than before. Design implications are developed for AI product design, professional education, organizational policy, and regulatory frameworks. Cognitive tribology does not argue against AI assistance. It argues that AI assistance without friction design progressively diminishes the human partner and that the design philosophy and evaluative frameworks currently governing human-AI interaction are not equipped to detect or prevent this outcome.