Technical Debt from AI Code: What the Data Shows

Technical debt is the extra work a quick solution imposes on later changes. In AI-assisted development it grows measurably faster: according to GitClear's analysis, the share of duplicated code blocks rose from an index value of 40.3 in 2023 to 73.0 in 2026, while the share of moved code, the best available proxy for genuine cleanup, fell from 21 percent (2022) to 3.8 percent (2026).

What is technical debt?

The term comes from Ward Cunningham and describes a deliberate trade-off: you ship faster by choosing an unclean solution, and you pay interest for that in the form of extra effort on every future change. The key word in the original image is deliberate: a debt you take on knowingly is one you know about.

That is exactly what has shifted. When code appears that nobody has read, the debt is not taken on, it is overlooked. It only surfaces once someone wants to change something.

How fast does technical debt from AI code grow?

Faster than before, and the evidence is now solid. For its report "The Maintainability Gap," GitClear analyzes hundreds of millions of code changes. Three metrics are relevant to this question.

Metric	Earlier measurement	2026 level	Meaning
Duplicated blocks (index)	40.3 (2023)	73.0	up 81 percent, the highest level ever measured
Moved code	21% (2022), 13% (2023)	3.8%	genuine restructuring has nearly disappeared
Copied code	9.4% (2022)	15.7%	duplicating instead of generalizing

The two movements belong together. Duplicating is the obvious path for a language model: it sees the excerpt it is supposed to change, not the three other places where the same pattern already exists. And refactoring requires understanding the bigger picture, exactly what is missing when nobody has read the existing code.

Why is duplicated code expensive?

Because every copy is a place where a fix can be forgotten. That is not a matter of style, it is arithmetic.

If a check exists four times instead of once, a bug has to be fixed four times. If one spot gets missed, you end up with behavior that is correct in three places and wrong in one, the most expensive kind of bug, because it only shows up under certain conditions and cannot be reproduced in a test.

On top of that comes the search effort: anyone who needs to change a rule first has to find out how often it occurs in the codebase. As the duplication rate grows, this effort grows with every change.

How do I notice that my project has accumulated technical debt?

Five everyday observations that need no measurement at all: they are warning signs, not proof, though they rarely occur without a reason.

• Estimates become unreliable. If a "small change" regularly takes three days, the problem is not the estimate, it is that nobody knows in advance what else depends on it.
• Bug fixes create new bugs. The classic sign of duplicates: one spot got fixed, three did not.
• Certain files get avoided. If the team knows which file "nobody touches," that is an unpaid bill with a name on it.
• New colleagues take unusually long to ramp up. Onboarding time is a good proxy for how understandable the code is.
• Tests get skipped because they are "flaky anyway." An unreliable test is worse than none: it burns trust that is missing later.

These five points work better as an opener for a conversation with management than any metric, because everyone has already experienced them.

How do I measure technical debt in my project?

With four metrics you can pull from your repository and existing tools, no new product required.

• Duplication share. Every common static analysis tool delivers it. What matters is not the absolute value but the direction over months.
• Change frequency versus complexity. Files that get touched often and are complex are your most expensive spots. Together they produce a priority list without any debate.
• Share of untested changed lines. Not overall coverage, but coverage of what is new. Details in the article on test coverage.
• Age and number of dependencies. If the list grows faster than the team, nobody keeps an eye on it anymore.

Which metrics carry weight beyond this and which only offer reassurance is covered in the article measuring code quality.

What helps against technical debt from AI code?

Three measures, in this order, the third is the only one that stops the inflow.

• Make it visible. Duplication share and test coverage of new code in the pipeline, with a history. What is not measured gets disputed.
• Pay it down deliberately. Not a blanket cleanup, but wherever change frequency and complexity coincide. Approach described in the article refactoring AI-generated code.
• Limit the inflow. A threshold in the pipeline that rejects new duplicates and untested new code. Without this step you are shoveling against an open tap. See quality gates in the pipeline.

Frequently asked questions about technical debt

Is technical debt always bad?

No. Debt taken on deliberately can be the right call, for instance when a market deadline matters more than the clean solution. What is problematic is debt nobody took on, which therefore shows up in no planning at all.

Isn't the drop in refactoring simply a measurement artifact?

GitClear measures moved code as a proxy, not refactoring directly, that is a real limitation. But the scale of the drop, from 21 to 3.8 percent within four years, cannot be explained by that alone, and the simultaneous rise in duplicates points in the same direction.

Can AI help pay down technical debt?

Yes, and it is one of its stronger use cases: finding duplicates, carrying out renames consistently, adding missing tests. The precondition is test coverage that notices a mistake, otherwise you are replacing visible debt with invisible bugs.

How do I convince management to budget time for this?

With change frequency rather than duplication share. "These five files were changed 200 times last quarter and are our most complex" is a cost argument. "The duplication share has increased" is a technical debate.

Paying down debt helps little as long as the inflow stays open. Code Guardian therefore checks every change for redundancy and duplicates right after it is created, before it lands in the codebase.

Sources

• GitClear, The Maintainability Gap: 2026 AI Code Quality Research, source of all metrics in the table above.
• Ward Cunningham, original coining of the debt metaphor (OOPSLA 1992).

As of July 27, 2026. General professional context, not an assessment of any specific project.