What is comprehension debt in software engineering?

Comprehension debt is the growing gap between the amount of code a team has in production and the amount of that code its engineers actually understand.

AI coding tools now produce plausible-looking code far faster than developers can evaluate it. As organizations push for more output with these tools, the distance between what teams ship and what they understand widens. Addy Osmani, author and researcher at Anthropic, explains that technical debt creates friction you can feel, such as slow builds or tangled dependencies, while comprehension debt creates a false sense of security. The codebase looks healthy until something critical fails, and then no one can explain why the system was built the way it was.

What causes comprehension debt?

Comprehension debt, also referred to as cognitive debt, builds up when developers merge code they don't fully understand. Drawing on research from Wharton, Osmani describes two ways developers can work with AI:

• Cognitive offloading: the developer hands execution to the AI but keeps the architectural judgment
• Cognitive surrender: the developer accepts AI output without building a mental model of how it works

Osmani calls cognitive surrender the mechanism by which comprehension debt accumulates. Every merge that happens only because the syntax is clean and the tests pass adds to the debt. Across an organization, those shortcuts compound.

In this article, we cover the leading research on how over-reliance on AI coding tools creates comprehension debt, how that debt shows up across the software development lifecycle, and what engineering organizations can do to keep their systems healthy.

What does research say about AI's impact on developer comprehension?

Recent studies point to the same pattern: the more people rely on AI to do the work, the less they understand and remember about what was produced. The studies below move from general cognition to software engineering, and from individual developers to teams.

MIT: Heavy AI use lowers brain engagement and recall

In 2025, researchers at the MIT Media Lab investigated the cognitive impacts of using AI tools like ChatGPT during essay-writing tasks compared with using search engines or working entirely unassisted. By monitoring brain activity with EEG sensors, they found that neural connectivity scaled down as external support increased, with the AI group exhibiting the weakest overall brain engagement. Because the AI handled much of the work, students using ChatGPT reported a significantly lower sense of personal ownership over their finished essays. These students also struggled with recall and comprehension, struggling to quote or recall details from the essays they had just produced. Researchers warned that these short-term productivity gains from heavy AI reliance incur a compounding, long-term mental cost, ultimately reducing active cognitive engagement and overall learning outcomes.

Anthropic: Heavy AI reliance cuts developer comprehension scores by 17%

In January 2026, an Anthropic research study explored how AI assistance affects developers’ ability to learn new coding skills and master the craft. While AI tools can increase efficiency, the study found that participants who relied heavily on automation scored 17% lower on comprehension tests than those who coded by hand. Heavy reliance on AI particularly impacts a developer’s ability to debug and understand complex systems. However, the research suggested that intentional interaction styles, such as asking the AI for conceptual explanations rather than just code generation, can mitigate these learning deficits. These findings emphasize that meaningful oversight of AI systems requires human experts to maintain their own foundational knowledge through effortful practice. 

Ahmad: Four patterns that create comprehension debt in AI-assisted teams

In April 2026, Muhammad Ovais Ahmad, an Associate Professor at Karlstad University Sweden, investigated how GenAI coding tools influence team understanding during software development. Ahmad argues that comprehension debt differs from technical debt because it resides in team cognition, not in code. The study analyzed 621 reflective diaries from 207 undergraduates working on an eight-week software project. The findings identified four distinct patterns that accumulate comprehension debt when AI is used primarily for rapid output: 

1. Black-box code acceptance: Occurs when developers integrate AI-generated code directly into a codebase without understanding its underlying logic, leaving them unable to modify or debug it later when requirements shift
2. Context-mismatch debt: Arises when standalone GenAI tools lack awareness of the project's overall architecture and naming conventions, producing isolated code suggestions that require extensive manual tweaking to function within the broader system
3. Dependency-induced comprehension atrophy: Occurs when sustained over-reliance on AI assistants reduces independent effort in reading documentation or tracing logic, gradually eroding core problem-solving skills and self-directed understanding
4. Verification bypass: Happens when developers lack the domain knowledge required to spot subtle AI inaccuracies or hallucinations, allowing flawed code to be committed unverified until runtime failures occur

Conversely, the paper identified one mitigating pattern where students treat GenAI as a comprehension scaffold. This occurred when AI was used as an interactive tutor—asking for code explanations and rewriting generated snippets before committing them—to actively build and reinforce mental models. Ultimately, the study concluded that AI tools can strengthen existing habits, good or bad. The study recommended teaching engineering students to check AI-generated code by reflecting on their process and walking through how the code works to build essential verification competence.

What happens when AI produces code faster than teams can understand it?

When AI speeds up code production faster than teams can absorb it, the strain shows up at every stage of the SDLC: larger changes in development, skipped reviews, and more incidents in production. Data from Faros’s Speed Trap report shows this pattern across engineering organizations with high AI adoption. It's also the environment in which comprehension debt builds up.

Development: larger PRs and more rework

With heavy AI adoption, pull request (PR) sizes have surged by 71.8%, and the number of files touched per PR has risen by 52.0%. This rapid expansion creates the conditions for comprehension debt. When developers don't write every line of a massive change themselves, they must spend considerably more time decoding unfamiliar, AI-generated logic before they can safely explain or revise it. This difficulty is compounded by a 66.7% spike in work restarts—an expensive context switch that forces developers to constantly reconstruct their previous problem-solving steps and decisions.

Review and QA: skipped checks and a heavier verification burden

The influx of larger, AI-generated changes is overwhelming the later stages of the pipeline. Faced with ballooning PRs in the review stage, teams are increasingly bypassing checks altogether, leading to a 76.3% increase in PRs merged without any review. Consequently, the verification burden shifts to QA, where testing is slower and more expensive. This dynamic illustrates the core of the Speed Trap: the top of the funnel accelerates code production, while the heavy burden of proving that the code actually works accumulates downstream, leaving more code in production that fewer people have examined closely.

Production: more incidents and rising operational costs

The operational cost is steep. Monthly incidents have spiked by 125.4%, remediation times are slowing down, and development backlogs are growing once again. While organizations may be moving faster at producing and shipping individual code changes, the overarching risk and total financial cost of operating the system continue to rise.

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How can engineering teams reduce comprehension debt?

Reducing comprehension debt entails managing three things together: the code, the team’s understanding of it, and the documented goals and reasoning behind it. That framework comes from Margaret-Anne Storey’s March 2026 paper, From Technical Debt to Cognitive and Intent Debt. Note: Storey uses “cognitive debt” for the team-level gap in understanding. This is synonymous with comprehension debt.

Storey argues that software health depends on three layers staying aligned: what the system is meant to do, how the code implements it, and what the team understands about both. Each layer can decay on its own. For example, an AI agent might build a feature that works and passes its tests. If no one on the team understands its design, the team has cognitive debt. If the goal or constraint that shaped the feature was never recorded, it has intent debt. If the implementation is tangled and hard to modify, it has technical debt.

These debts reinforce one another. Missing intent makes shared understanding harder to build, and weak understanding leads to poor code decisions. AI may reduce technical debt through automated refactoring and testing while cognitive and intent debt grow faster.

Storey recommends the following set of practices to maintain alignment across code, understanding, and rationale:

1. Capture intent (rationale and artifacts)

These practices should happen early and continuously to ensure the system's goals and constraints are explicitly documented.

• Implement intent-first workflows: Capture intent early using Architectural Decision Records (ADRs), Domain-Driven Design (DDD), well-written specifications, decision rationales, plans, and clear user acceptance criteria.
• Create executable intent: Use Behavior-Driven Development (BDD) specifications and tests designed to capture the system's purpose rather than just verifying behavior.
• Develop context artifacts for AI-assisted development: Engage in context engineering (creating skills, agent instructions, and playbooks) and utilize AI-assisted intent capture from meetings and conversations.

2. Build shared understanding (cognitive and people)

Once intent is established, these practices ensure the team actively builds and retains a mental model of the system.

• Treat understanding as a first-class deliverable: Explicitly invest in building shared understanding by allocating time for system walkthroughs, knowledge transfer during onboarding and offoarding, and at critical project milestones.
• Resist the automation of understanding: Avoid using AI to generate explanations of the system if it substitutes the appearance of understanding for the hard cognitive work required to build genuine mental models.
• Utilize human code review and pair programming: Maintain these practices not just for catching defects, but for actively spreading understanding across the team.
• Conduct retrospectives and post-mortems: Hold these sessions when things break or challenges emerge to collectively rebuild frayed mental models.
• Reimplement features to repair cognitive debt: If AI token budget permits, instruct agents to reimplement features using alternate tests or design elements to help developers actively rebuild their understanding of the system.

3. Conduct system-wide management

This overarching practice ensures the ongoing health of the codebase, the artifacts, and the team's knowledge.

• Monitor the three layers in tandem: Track cognitive and intent debt—alongside technical debt—using methods like onboarding time tracking, knowledge concentration metrics, requirements coverage analysis, and regular audits of the gap between documented intent and actual behavior.

AI changes how code is written, not the need to understand it

As AI takes on more of the code writing, building and protecting shared understanding becomes one of the most important responsibilities an engineering organization has. Osmani explains that “what changes with AI is cost (dramatically lower), speed (dramatically higher), and interpersonal management overhead (essentially zero). What doesn't change is the need for someone with deep system context to maintain coherent understanding of what the codebase is actually doing and why.”