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AI-assisted coding can create technical debt faster than a team can fix it when code generation outpaces review, testing, and maintenance. That is a risk, not an inevitability: DORA’s 2025 research describes AI as an amplifier of an organization’s existing strengths and weaknesses, rather than a universal cause of debt. The practical question is whether your team can understand, verify, and maintain what it produces.
How AI-assisted coding can add to technical debt
Technical debt is the future work or risk created when a software team takes shortcuts or leaves quality problems unresolved. It can be a deliberate trade-off, such as postponing a refactor to meet a deadline, or an accidental result of rushed or poorly understood changes. AI does not change the definition; it can change the pace and shape of the work that creates or pays down debt.
DORA’s 2025 report draws on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals around the world. Its central finding is that AI primarily amplifies existing organizational strengths and dysfunctions. This broad practitioner research is not a controlled experiment proving that AI causes technical debt. DORA’s 2025 report
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AI can help an author produce code faster, but faster generation does not necessarily shorten the whole delivery process. Someone still needs to understand the change, review it, test it, and take responsibility for it. DORA’s qualitative findings describe verification overhead and heavier review burdens when large changes arrive. The bottleneck can shift from writing code to understanding, reviewing, testing, and maintaining it; that is a synthesis of reported workflow findings, not a measured universal law. DORA’s AI insights
Unfunded maintenance turns shortcuts into debt
Feature work often gets priority over maintenance, leaving known issues in place. GitHub’s technical-debt guidance names duplicated code, missing tests, outdated dependencies, and inconsistent patterns as examples of debt. AI can contribute to these conditions if a team accepts changes without adequate checks or never budgets time to address what it knows is fragile. GitHub’s guide to reducing technical debt with Copilot
Generated code can be harder to own if it is poorly understood
A change is costly to maintain when the people responsible for it cannot readily explain its behavior, assumptions, or fit with the rest of the system. DORA’s findings describe knowledge gaps and the need to verify generated output. That supports a plausible debt mechanism: accepting code without understanding it can make later debugging and modification harder. It does not establish that every developer using AI will understand less or that every generated change is difficult to maintain. DORA’s AI insights
What the evidence does—and does not—show
DORA points to amplification, not inevitable damage
DORA’s framing cuts both ways. Teams with sound standards, dependable tests, manageable review sizes, and capacity for maintenance may be better positioned to benefit from AI. Teams with weak controls may magnify existing problems. This is a system-level interpretation of DORA’s findings, not a guaranteed forecast for a particular team.
DORA’s 2026 insight page reports that 30% of developers say they have little to no trust in AI-generated code. That is a reported trust measure—not a defect rate or a measure of how much code is wrong. DORA’s AI insights
A bounded Copilot study found positive task outcomes
Evidence also complicates claims that AI-generated code is inherently worse. In a randomized GitHub study, 202 valid participants—all with at least five years of experience—completed a bounded Python web-server API endpoint exercise with unit tests and expert review. Participants with access to Copilot had a 53.2% greater likelihood of passing all 10 unit tests in that task. Blind reviewers also rated their submissions higher by 3.62% for readability, 2.94% for reliability, 2.47% for maintainability, and 4.16% for conciseness. These are results from that study’s task and reviewer design, conducted by the product vendor; they do not measure long-term maintenance costs in production codebases. GitHub’s Copilot study
Debt in AI-enabled systems is a related but different issue
A 2024 peer-reviewed survey in the Journal of Systems and Software asked 53 AI practitioners about technical debt in systems that embed AI components. Respondents described risks to quality, especially understandability and security, and said remediation relied largely on manual review and ad-hoc refactoring. This concerns debt in AI-enabled systems and their code or architecture; it does not directly measure debt caused by using coding assistants. The 2024 study in the Journal of Systems and Software
How to tell whether debt is accumulating faster than your team can address it
Generated-code volume is not enough to answer this. Look for evidence that the work is improving delivery without leaving a growing queue of unreviewed changes, rework, incidents, or known maintenance risks. DORA recommends moving beyond output-only measures toward downstream indicators. DORA’s AI insights
- Review turnaround: Are pull requests waiting longer for review, or are reviewers repeatedly asking for changes they cannot confidently verify?
- Rework: Are changes causing more follow-up fixes, reversions, or deployment rework?
- Reliability: Are incidents or recovery time worsening as AI-assisted changes increase?
- Known debt: Are missing tests, duplicated code, outdated dependencies, or inconsistent patterns being recorded and resolved—or simply accumulating?
- Comprehension: Can the people who will maintain a change explain what it does and how its behavior is tested?
These checks are most useful as trends over time and in context. A change in one indicator alone does not establish that AI caused it; compare the workload and practices involved before drawing conclusions.
How teams can keep AI output maintainable
1. Keep changes small enough to review
Small batches make it easier to understand what changed, test the relevant behavior, and spot mismatches with team conventions. DORA explicitly recommends small batches as a way to counter risks in AI-assisted development. Avoid treating a large generated diff as one reviewable unit just because it was quick to produce. DORA’s AI insights
2. Review suggestions and run tests before accepting them
Treat generated code as a proposed change, not a verified one. GitHub’s guidance says to review Copilot suggestions and run tests before accepting or refining them. Check behavior, edge cases, dependencies, and consistency with the surrounding code—not just whether the change looks plausible. GitHub’s guide to reducing technical debt with Copilot
3. Track debt as specific, prioritized work
Record concrete items such as a missing test, duplicated logic, a dependency update, or a pattern that diverges from the team’s standards. Prioritize by impact and risk; not every cleanup has equal urgency. Make ownership and follow-up visible so feature pressure does not silently turn known issues into permanent maintenance costs. GitHub’s guide to reducing technical debt with Copilot
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4. Measure downstream outcomes, not just code production
Pair any measure of output with indicators that show what happens after code is written: review turnaround, deployment rework, recovery time, and incidents. If the amount of code produced rises while review queues, corrections, or reliability problems worsen, the team may be shifting work rather than removing it. DORA recommends focusing on downstream outcomes rather than relying on output-only measures. DORA’s AI insights
5. Match the amount of assistance to the work’s verifiability
Teams can use broader generation, bounded autocomplete or refactoring, AI-assisted review, or less AI assistance for work that is difficult to validate. There is no universally established best configuration. Compare options by the review and verification load they leave behind, their fit with existing standards and version-control and CI workflows, the tests available for the change, and whether the team can track maintenance outcomes. GitHub’s study and DORA’s AI insights
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AI assistance is a better fit when a team can check the work it produces and absorb the resulting changes. Warning signs are not proof of harm, but they indicate where to strengthen the workflow:
- Changes are too large for reviewers to understand in a reasonable pass.
- Tests do not cover the behavior being changed, or are skipped to save time.
- Developers accept suggestions they cannot explain or validate.
- Known debt has no visible owner or time allocated for remediation.
- Teams celebrate generation speed while review queues, rework, or incidents are left unmeasured.
The right response is not necessarily to stop using AI. It is to align the scope of assistance with the team’s ability to verify and maintain the result, then adjust based on observed downstream outcomes.
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