Pre-trained models are added late in projects, accumulate rather than get replaced, and change three times less often than libraries, with distinct documentation driven by capability needs and testing uncertainty.
From Technical Debt to Cognitive and Intent Debt: Rethinking Software Health in the Age of AI
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
abstract
Generative AI is accelerating software development, but may quietly shift where the most significant 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 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 is a team-level, project-level property reflecting the erosion of shared understanding across a software system over time, leading to increasingly inadequate shared mental models for reasoning about and safely changing the system. Intent debt refers to the absence or erosion of explicit rationale, goals, and constraints that guide how humans and agents evolve the system. We discuss how generative AI changes the relative importance of these debt types, how each can be diagnosed and mitigated, and surface points of debate for practitioners.
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2026 8roles
background 2representative citing papers
Longitudinal panel study of 802 developers shows an enterprise AI coding mandate doubled per-capita merged pull requests to 2.09x baseline, with gains associated with AI adoption and accumulated use while review processes automated.
Developers mostly accept AI-produced work under approval; accountability blocks AI action, while identity blocks AI decision-making and demand encourages it.
Study of 930k+ agent PRs shows repository explains ~50% of integration friction variance, with agents concentrating it twice as much as humans (ICC 0.30 vs 0.16) after controls.
A nine-dimension risk framework for institutional DeFi adds three new dimensions to prior taxonomies and shows that five of twelve 2024-2026 incidents, including the two most systemic, require at least one of the new dimensions for full explanation.
Aporia makes design decisions explicit and interactive in AI-assisted programming, leading to higher engagement and 5x fewer mental model disagreements with code in a 14-person user study compared to a baseline agent.
AI-native software ecosystems exhibit emergent behaviors best explained by complex adaptive systems theory, requiring new ecosystem-level monitoring and seven testable propositions that may extend or replace Lehman's laws.
Aleena is an open-source AI agent that ingests multi-modal research software collaboration artifacts and transforms them into structured GitHub records to maintain continuous stakeholder alignment across the project lifecycle.
citing papers explorer
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When AI Models Become Dependencies: Studying the Evolution of Pre-Trained Model Reuse in Downstream Software Systems
Pre-trained models are added late in projects, accumulate rather than get replaced, and change three times less often than libraries, with distinct documentation driven by capability needs and testing uncertainty.
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AI Writes Faster Than Humans Can Review: A Longitudinal Study of an Enterprise 2x Mandate
Longitudinal panel study of 802 developers shows an enterprise AI coding mandate doubled per-capita merged pull requests to 2.09x baseline, with gains associated with AI adoption and accumulated use while review processes automated.
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You Shall Not Pass! Where and Why Developers Draw The Line on AI Autonomy
Developers mostly accept AI-produced work under approval; accountability blocks AI action, while identity blocks AI decision-making and demand encourages it.
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Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software
Study of 930k+ agent PRs shows repository explains ~50% of integration friction variance, with agents concentrating it twice as much as humans (ICC 0.30 vs 0.16) after controls.
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Toward a Risk Assessment Framework for Institutional DeFi: A Nine-Dimension Approach
A nine-dimension risk framework for institutional DeFi adds three new dimensions to prior taxonomies and shows that five of twelve 2024-2026 incidents, including the two most systemic, require at least one of the new dimensions for full explanation.
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Decision-Oriented Programming with Aporia
Aporia makes design decisions explicit and interactive in AI-assisted programming, leading to higher engagement and 5x fewer mental model disagreements with code in a 14-person user study compared to a baseline agent.
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More Is Different: Toward a Theory of Emergence in AI-Native Software Ecosystems
AI-native software ecosystems exhibit emergent behaviors best explained by complex adaptive systems theory, requiring new ecosystem-level monitoring and seven testable propositions that may extend or replace Lehman's laws.
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Aleena: Alignment Agent for Research Software Engineering Collaborations
Aleena is an open-source AI agent that ingests multi-modal research software collaboration artifacts and transforms them into structured GitHub records to maintain continuous stakeholder alignment across the project lifecycle.