ARTICLE
Comprehension Debt: How misusing AI can make you worse at your job
Using AI to move faster is useful. Losing the reasoning behind what it produces is a risk most teams underestimate. Our Software Architect, Russ, explores comprehension debt, what it is, why it applies across every role, and how to avoid it.
Russ Painter
Software Architect, 8 West Consulting
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June 08, 2026
In a recent presentation from one of my colleagues, Victoria Zuro, she walked through how AI assistance had helped her accelerate technical planning on a project. There were parts of it that worked really well. The plan came together faster. The structure looked solid. The artifacts were neat and professional.
But at the end, she said something that stuck with me: she felt less confident in her own deep knowledge of the plan than she normally would. If she hadn't thought through and written the details of the test cases, then it was difficult to answer questions about these. She'd taken herself somewhat out of the loop by delegating tasks to a bot. That’s an example of comprehension debt.
Being mostly code fucused personally, I'm well aware of this danger in AI generated code. But I found it interesting to see it manifest itself in other roles.
Delegation Isn’t the Problem. Losing the Reasoning Is
Delegating work has always come with trade offs. If I hand a task to another human, some of the thinking happens in their head instead of mine. That’s fine if they’re still on the team. I can lean on them later if I need that detail. The reasoning lives somewhere durable.
But when an AI generates a plan, a document, a set of test cases, or a chunk of code, it does build a mental model. It seems to "understand" dependencies. It reasons about structure. It makes choices. Then the task ends, and unless you deliberately extract and persist that reasoning, it evaporates.
The gap between what exists and what you can confidently explain is comprehension debt.
Comprehension Debt and Coders
Research has shown that developers using AI assistance often complete tasks a bit faster than those coding manually, but retain less understanding of what they just built. In one controlled study, participants scored noticeably lower on comprehension tests immediately after completing AI-assisted tasks.
The problem wasn’t using AI. It was how it was used. Passive delegation (“just make it work”) reduced understanding. Active engagement, asking why choices were made, reading and critiquing the code, and considering alternatives preserved understanding.
When we're in a hurry and ask a bot to write some code, it can look reasonable, do everything we wanted it to do, and pass all of the tests. It may even give us great documentation about the feature set and the technical details of the solution.
But then later on someone points out a critical flaw in our initial design and asks "Why did we do it this way?" or "What would be involved in changing this feature?", or worse "Why are we being sued for this obvious security flaw?". Now you're on the spot and don't have a clue.
Comprehension Debt and Other Roles
⚠️Warning: AI generated content ahead 🤖
Project Managers
AI-generated plans, timelines, and delivery updates are fast and polished. But if the PM can’t defend sequencing, explain dependencies, or reason about test coverage, they’re carrying comprehension debt.
Business Analysts and Product
AI-generated requirements and acceptance criteria look complete, until edge cases appear. If the person who owns the requirement can’t explain why a rule exists or how it behaves under stress, the understanding has already leaked.
HR
AI written policies, job descriptions, and internal comms often sound authoritative. But if no one can explain why wording was chosen or how it plays out in real situations, that’s organizational risk, not just wording risk.
Finance
AI can produce beautiful spreadsheets and summaries. But when assumptions aren’t internalized, numbers become fragile. The math works, until someone asks where it came from.
Creative and Marketing
Campaign copy and concepts arrive quickly. But brand coherence erodes if teams can’t articulate the strategy behind them: why this message, why this framing, the intent behind the art.
Same pattern. Different artifacts.
Spaghetti code → Plans that don't flow in a simple understandable and modifyable way.
Excessive‑complexity → Over‑engineered processes that waste time and resources
Lack of DRY → Output that has the same material duplicated in multiple places, resulting in a future maintenance issue where you make a change in one place and cause an inconsistency.
Incomplete Testing → Superficial checks passing. Looks correct, behaves oddly under real pressure or unconsidered edge cases.
Some simple rules help across all roles:
- Treat AI output as a draft proposal, not a conclusion.
- Capture reasoning while it’s fresh, and retain it along with the generated artifact.
- If you can’t explain it in plain language, don’t ship it.
The moment you say “it looks right” instead of “I understand why,” comprehension debt starts compounding. When you later need to work on something you don't understand, you're going to call in the AI again, and the problem is going to get even worse.
Comprehension debt isn’t just a developer problem. It’s what happens in all roles we let AI produce work faster than humans rebuild the reasoning behind it.
If you’re accountable for something — a plan, a policy, a budget, a delivery — but can’t explain it without reopening the chat history, you’ve already outsourced too much.
REFERENCES
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Addy Osmani: Comprehension Debt — the hidden cost of AI generated code.
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Anthropic - How AI assistance impacts the formation of coding skills
#aibetter
