AI Will Kill Technical Debt as We Know It

I've spent countless hours negotiating team capacity to pay "tech debt". Sprint after sprint, I'd make the same case: we need to go back and clean up code that works but is messy, because we needed to ship it fast. And sprint after sprint, I'd lose. The business needs new features, and the business takes priority.

And let's be honest about something we rarely say: is it fair to ask for time for code we chose to write badly? Is that a debt we owe to the code?

We serve a business. Not a codebase. If that's the case, why would we owe the code anything at all?

Or have we spent the last two decades misusing this term?

Debt Was Never About Bad Code

This metaphor was first introduced by Ward Cunningham in 1992. At that time, Ward was developing a financial application and needed to justify why they needed to do a refactor. For that, he used the language a finance person could understand: a debt analogy:

"Shipping first-time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite. The danger occurs when the debt is not repaid. Every minute spent on not-quite-right code counts as interest on that debt."

He later clarified what he meant in a 2009 video. Contrary to how the term has been used, he didn't mean that it was ok to write poor code with the objective of shipping faster. In fact, he was saying almost the opposite. That it is justifiable to write code and ship a product with your current understanding of a problem, even if that understanding is still partial.

The "debt" is not meant to be messy code. It is the gap that opens as you learn more about the problem you are trying to solve, while the code stays behind. You repay it by refactoring it to match what you now understand.

And here's the thing: the field never even agreed on this definition.

"A mess is not a technical debt. A mess is just a mess. Technical debt does not mean bugs and defects. It does not mean messy code. It means intentionally suboptimal structure."

Robert 'Uncle Bob' Martin

Notice that his own definition isn't Cunningham's either. Two of the most-cited voices in software, two different definitions, and neither is the one most teams actually use. We never really agreed on what the word meant.

But there is one thing that holds: you don't owe clean code to your codebase. You owe the business a product that reflects your best understanding of the problem.

Why We Collapsed 'Debt' Into 'Messy Code'

For most of software's history, writing code was the expensive part of building software. You couldn't do it without highly specialized professionals who knew how to translate business requirements into instructions that a machine could execute. Want to build more? Hire more people. Want it faster? Trade off quality.

When we took that speed-for-quality trade, it got a name: debt. Code we knew wasn't right and that we promised to clean up later. And 'later,' more often than not, never came.

The reason for the definition to drift was that the code was visible and expensive to fix, so it looked like debt. The gap in understanding, the knowledge whether you were building the right thing or not was invisible. This caused the industry to only address what had a price tag: the code.

The cause for this wasn't incompetence. It was the incentive. The business asks, the engineering team finds a way to deliver, and the cost gets pushed to a future that never comes.

When Refactoring Costs Nothing

AI is redefining the economics of building software. Writing code has never been cheaper. Refactoring is almost free. Which means the thing we've spent 20 years calling 'tech debt,' the messy code, is now the cheap part to pay down. And the old reason we wrote messy code in the first place, not enough time to do it right, is disappearing. When an agent rewrites an implementation in seconds, 'we had to cut corners to ship' stops being an excuse.

What remains expensive is something that AI can't do for you: knowing what you actually want.

An AI agent is only as good as your understanding of the problem. Give it a clear one, with real acceptance criteria and guardrails, and it's a force multiplier. Give it a vague one, and it multiplies the vagueness, just faster.

So take Cunningham at his word. The debt was never in the code. It was in the gap between what you understood and what you built. AI doesn't close that gap. It just makes it cheaper than ever to throw code into it before you've thought it through.

AI won't kill technical debt. It will kill the version of it we've been chasing for 20 years, and replace it with a worse one.

Yes, Debt Is Exploding. But Look at Which Kind

A CodeRabbit study of 470 real-world pull requests found that AI-coauthored PRs carry about 1.7x more issues than human-only ones. Meanwhile, Forrester reports that most technology leaders expect their technical debt burden to get worse before getting better.

Good. Let's take a closer look at what is actually happening.

The biggest jump in the study was not in style or syntax. It reads like a competent engineer wrote it. It was in the logic and correctness, which rose 75%.

The problem lies in the dozens of assumptions baked into every feature we ship, in code that implements the wrong thing or skips a validation nobody thought to specify. Individually small. But together they compound into a product that doesn't actually solve the problem.

The mess isn't in how the code looks. It's in the gap between what the team understood and what the agent shipped.

That is the Cunningham definition of tech debt, at machine speed. The debt didn't explode because AI writes worse code. It exploded because AI let us skip the thinking and ship right away.

Implementation Debt vs Understanding Debt

The need to separate messy code from messy understanding is becoming hard to ignore. So let's name the two.

What we've been calling technical debt is essentially an implementation debt: code that works but isn't right, written to ship something on time. This is the debt AI is making cheap. As agents get more capable, rewriting a clumsy implementation costs less and less.

What we need to start addressing is the understanding debt: the clarity that we didn't have when we were feeding our agents. The questions you never asked the business. The edge cases nobody defined because it was faster to generate something and see. It's the distance between the problem you think you're solving and the one you actually are.

Some people have started calling this "intent debt" or "cognitive debt". I think it's simpler, and older: it's what Cunningham meant all along.

And here's what should worry every engineering leader: while implementation debt is doomed to disappear, understanding debt is set to explode as companies are pushing AI-native engineering at all speed, while spending less time than ever deciding what they actually want built.

Your Job Was Always to Provide Clarity

This is where it stops being an engineering problem and becomes a management one.

If the expensive debt is now understanding, then the scarcest thing on your team is no longer the ability to write code. It's the clarity of thought, product sense, the courage to ask the uncomfortable questions early. Those used to be nice-to-haves layered on top of strong engineering. Now, they're becoming the main event.

That changes four concrete things.

1. You hire differently. Coding ability is being commoditized. What you can't commoditize is judgment: the person who reads a vague request and knows which questions need to be answered before even opening Claude.
2. Seniority means something different. It used to track how well someone was able to code. Now, it's closer to how well someone removes ambiguity, for themselves, for the agent, and for the rest of the team.
3. Planning changes. The main question is no longer "can we build this?" It's "do we actually understand what we're building, and do we know what impact we need to have?" Planning that only estimates effort is measuring the wrong thing. The real question is if we have enough clarity on what we want to build, before we hand it to an agent that will build it.
4. Your own job changes. Managing technical debt used to be a negotiation about code you'd clean up later. Now it's about catching understanding debt in a conversation, a spec, a diagram, before it becomes ten thousand lines nobody meant to write, to build a product that doesn't solve what we need.

You've probably seen a version of this already. A team moves fast. Then a feature lands and something feels off. Not because the code is broken, or even badly written, but because it solves a different problem than the one the business actually had. The requirements drifted, and nobody caught it, because it was faster to deliver something than to stop and ask the question of what we really needed. That's understanding debt. And no agent is going to catch it for you.

The Metaphor Is Coming Home

For over twenty years we've been paying interest on the code we wrote. From now on, we'll pay it on how well we understood the problem before we started building. And the irony is that this was exactly what Cunningham meant all along: if we don't sync the solution with the knowledge we have about the problem, there is a price to pay.

The teams that will shine in the AI era will be the ones that understand that tech debt has never been, truly, about code.