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5 hours ago
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Picture of fishermen, coming out at sunset to start their “workday” — they understand the ocean’s complexities enough to do their jobs
AI is rewriting the rhythm of software development. What used to take days now takes hours. But there’s a quiet tradeoff that most teams don’t notice until it’s too late: something I call understanding debt.
We already talk about code debt (messy code that needs cleanup) and engineering debt (shortcuts in architecture or infrastructure). Now there’s a third kind: understanding debt, the gap between what’s written and what the team actually understands.
It’s not caused by bad code. The code might be clean, correct, and even elegant. But if your team doesn’t understand what it’s doing or why it was written that way, you’ve taken on a liability that compounds quietly over time.
1. What Is Understanding Debt?
Every time you accept an AI suggestion you can’t fully explain, you’re borrowing against your future time and sanity. It’s like swiping a credit card of productivity: you move faster now, but the bill always comes due, usually during a refactor, a bug hunt, or a production incident at 3 a.m.
Understanding debt isn’t caused by sloppy work; it’s caused by opaque code.
I coined the term because this problem feels new. It’s unique to the era of AI-assisted development, where velocity often outruns comprehension.
2. Why It’s Dangerous
Understanding debt compounds. At first, it’s invisible. Then:
- Debugging becomes slow because no one knows the intention behind a function.
- Refactors turn risky because no one understands the side effects.
- Interns copy patterns they don’t grasp, multiplying the problem.
You end up with a system that technically works, but every line has a hidden cost. You start moving slower even though you’re coding faster than ever.
And when a live site issue hits, when users are waiting, errors are flooding logs, and every second counts, understanding becomes the difference between a quick fix and a total scramble.
If no one really knows how that AI-generated system fits together, every decision becomes a guess. That’s when understanding debt reveals its true cost.
3. Balancing Speed and Comprehension
Rejecting AI isn’t the answer. The answer is awareness and control. Here’s how we handle it in our studio:
- Verbalization step: Before merging AI-generated code, the developer must be able to explain what it does and why it’s written that way. If they can’t, it doesn’t go in.
- Reasoning-focused reviews: Code review isn’t just about style or tests; it’s about understanding. The reviewer should ask, Can you walk me through this?
- Rationale logs: For significant AI contributions, we add a one-line comment or note explaining intent. It’s like leaving breadcrumbs for future maintainers.
- Dissection sessions: Once in a while, we take an AI-generated block and break it down together. Everyone learns something. The goal isn’t blame, it’s clarity.
But process alone isn’t enough. We also need structured time to close the gap.
The speed AI gives us should create buffer, not burnout. That means occasionally slowing down on purpose: hosting brown-bag sessions where we explain how key features actually work, running internal “grok days” to explore code paths, or dedicating Fridays to closing understanding debt just like we’d pay down tech debt.
Understanding isn’t a tax on progress. It’s part of sustainable velocity.
4. Training the Next Wave
When onboarding interns, this balance matters even more. They’re encouraged to use AI, but only as a collaborator, never as an oracle. The rule is simple:
“If you don’t understand it, you don’t own it.”
That mindset builds judgment. It turns AI from a crutch into a teaching tool. Over time, they start spotting patterns, questioning decisions, and gaining real intuition, not just output.
5. The Long Game
As AI becomes the default co-pilot for developers, understanding debt will become one of the defining management challenges of modern software teams.
The best studios won’t be the ones that use AI the fastest. They’ll be the ones that move fast while staying in control, maintaining a culture where understanding scales with output.
Because at the end of the day, AI can generate code, but it’s humans who will pay the interest.
In future write-ups, I’ll explore the next layer of this: AI agents whose sole job is to understand, explain, and document code for humans.
If generative AI created understanding debt, these upcoming agents might be the ones who finally pay it down.
