From Technical Debt to Cognitive and Intent Debt:
Rethinking Software Health in the Age of AI
Margaret-Anne Storey, University of Victoria, Canada
March 23, 2026

Generative AI is dramatically accelerating the velocity of software development, enabling small teams to ship
features at a pace that would have seemed implausible just a few years ago [Peng et al. 2023]. I saw this
firsthand in an entrepreneurship course I taught recently. Student teams were building software products over the
semester, moving quickly to ship features and meet milestones. By week eight, one team hit a wall. Simple
changes were breaking things in unexpected places, and progress had stalled. When I met with them, they
initially blamed technical debt: messy code, hurried implementations, architectural shortcuts. But as we dug
deeper, a different problem emerged. No one on the team could explain why certain design decisions had been
made, or how different parts of the system were supposed to work together. The code might have been messy,
but the deeper issue was that the team's shared understanding, the theory of the system [Naur 1985], had quietly
fragmented. They had also failed to write down or communicate the rationale behind decisions. They had
accumulated cognitive and intent debt faster than technical debt, and it had paralyzed them.
This is not an isolated story [Willison 2026]. Generative AI does not remove the challenges of software
engineering; it redistributes them. In this article, I propose a triple debt model for reasoning about software
health, built around three interacting debt types: technical debt refers to problems in the code layer, cognitive
debt refers to inadequate understanding across a team, and intent debt refers to a lack of externalized rationale,
information that both humans and AI systems need to work safely and efficiently with the code. Technical debt
makes systems harder to change. Cognitive debt makes systems harder to understand. Intent debt makes it
difficult to know what the system is actually for.