From Technical Debt to Cognitive and Intent Debt: Rethinking Software Health in the Age of AI
This work addresses software health challenges for developers and teams in the AI era, offering a conceptual framework that is incremental in extending existing debt models.
The paper tackles the problem of hidden risks in software development accelerated by generative AI, proposing a Triple Debt Model that includes cognitive and intent debt alongside technical debt to reason about software health.
Generative AI is accelerating software development, but may quietly shift where the real risks lie. As AI generates code faster than teams can understand it, two under appreciated forms of debt accumulate: cognitive debt, the erosion of shared understanding across a team, and intent debt, the absence of externalized rationale that both developers and AI agents need to work safely with code. This article proposes a Triple Debt Model for reasoning about software health built around three interacting debt types: technical debt in code, cognitive debt in people, and intent debt in externalized knowledge. Cognitive debt concerns what people understand; intent debt concerns what is explicitly captured for humans and machines to use. We discuss how generative AI changes the relative importance of these debt types, how each can be diagnosed and mitigated, and surfaced points of debate for practitioners.