Cognitive debt: The hidden risk in AI-driven software development

Dr. Margaret-Anne Storey introduces and expands on the concept of 'cognitive debt' — the erosion of shared understanding across development teams as AI and agentic tools accelerate code production. Unlike technical debt, which lives in the code, cognitive debt lives in people's minds and manifests as lost shared theory of what a system does and why. Drawing on Peter Naur's idea that a program is a theory held by its developers, the post argues that AI-driven velocity can outpace human understanding, leading to paralysis, debugging friction, slower onboarding, and developer burnout. Warning signs include fear of making changes, over-reliance on tribal knowledge, and systems becoming black boxes. Mitigation strategies include requiring human understanding of AI-generated changes before shipping, documenting intent not just changes, regular knowledge-sharing checkpoints, rigorous reviews, and tests that capture intent. A Triple Debt Model is proposed adding 'intent debt' — the erosion of externalized rationale needed by both humans and AI agents — alongside technical and cognitive debt.