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AI can help a team produce code faster, but faster output does not guarantee faster delivery or lower maintenance costs. When generated changes arrive faster than people can understand, review, test, and maintain them, technical debt can accumulate. That is a risk—not an inevitable result: the outcome depends on the team’s practices and capacity.
How AI-assisted coding can make debt accumulate
Technical debt is the future cost of choices that make software harder to change, operate, or understand. AI does not need to produce obviously broken code to add to that cost. A plausible change can still be difficult to verify, duplicate an existing pattern, omit tests, or leave maintenance work behind.
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The bottleneck can shift from writing code to understanding, reviewing, testing, and maintaining it. This is a synthesis of DORA’s reported workflow findings, not a measured rule for every team.
More generated code can mean a longer review queue
Faster generation helps the person drafting a change, but reviewers still need to determine what it does and whether it fits the system. If large changes arrive faster than a team can inspect them, review becomes a queue and defects or inconsistencies may be harder to spot. The time saved during authoring does not automatically translate into an equal reduction in end-to-end delivery time.
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Feature delivery can crowd out maintenance
GitHub’s technical-debt guidance names duplicated code, missing tests, outdated dependencies, and inconsistent patterns as examples of debt. It also notes that feature work often takes priority over cleanup. A team can therefore ship more quickly while leaving a larger backlog of work that makes future changes riskier or slower.
Code that is hard to explain is hard to own
Generated code still needs a human owner who can explain its assumptions, dependencies, and failure modes. If the team accepts a change without building that understanding, later debugging or modification may become more expensive. DORA’s findings discuss knowledge gaps and verification effort, but do not establish how often this happens across all developers.
Debt in AI-enabled products is a related, different issue
A 2024 Journal of Systems and Software study surveyed 53 AI practitioners about debt in systems that embed AI components. Respondents described quality concerns, particularly around understandability and security, and reported relying largely on manual review and ad-hoc refactoring to address them. This research concerns debt in AI-enabled systems; it is not a direct measurement of debt caused by using coding assistants.
What the evidence says—and does not say
DORA’s 2025 research included more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals around the world. DORA’s central interpretation is that AI acts as an amplifier of an organization’s existing strengths and dysfunctions. This is broad practitioner research, not a controlled experiment demonstrating that AI inevitably creates debt faster than teams can repay it.
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DORA’s 2026 insight page, discussing the 2025 report, says 30% of developers report little to no trust in AI-generated code. That is a reported trust measure, not a defect rate or proof that the code is poor.
There is also evidence against the blanket claim that AI-generated code is 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 task with unit tests and expert review. Participants with Copilot access were 53.2% more likely to pass all 10 unit tests in that task. Blind reviewers rated their submissions higher for readability (3.62%), reliability (2.94%), maintainability (2.47%), and conciseness (4.16%). These are outcomes from that study’s exercise and review design, not a forecast of long-term maintenance results in production codebases.
Together, these findings support a conditional conclusion: a team with useful standards, dependable tests, review capacity, and time for maintenance may be positioned to gain from AI assistance. A team with weak controls may amplify its existing problems. DORA presents this as a system-level framing, not a guaranteed prediction for any one organization.
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How teams can keep the risk manageable
Review changes in small batches
Keep AI-assisted changes small enough that a reviewer can understand their purpose and inspect the relevant tests. Smaller batches make it easier to identify the source of a problem and reduce the burden of evaluating a large volume of generated code at once. DORA recommends small batches as a countermeasure to risks in AI-assisted development.
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Verify suggestions before accepting them
GitHub’s guidance for Copilot workflows tells users to review suggestions and run tests before accepting or refining them. Treat an AI-generated change like a proposed change from any contributor: check its behavior, fit with team conventions, and test coverage rather than relying on fluency or plausibility.
Track debt as work, not as a vague concern
Record concrete items such as missing tests, duplication, outdated dependencies, or inconsistent patterns. Prioritize them by impact and risk: a gap affecting a critical path or making safe changes difficult may deserve attention sooner than a low-impact cleanup. A visible backlog makes maintenance trade-offs easier to discuss alongside feature work.
Measure delivery outcomes, not generated volume
Counting lines or suggestions generated says little about whether the team is delivering reliable, maintainable software. DORA recommends looking at downstream signals such as review turnaround, deployment rework, recovery time, and incidents. Read these alongside delivery speed: a faster first draft is not a net gain if it creates more rework or operational risk.
Choose a workflow that fits the work
Broader code generation, bounded autocomplete or refactoring, AI-assisted review, and limiting AI use on hard-to-validate changes are different ways to apply assistance. No source here establishes one universally best configuration. Teams can judge options against the factors that shape their own risk:
- How much review and verification work remains for people.
- Whether tests provide adequate coverage for the changed behavior.
- How well the workflow fits existing version control, CI, and team standards.
- Whether generated changes and downstream maintenance outcomes remain visible.
AI can accelerate code production, but it cannot by itself provide the understanding, review capacity, or maintenance time that safe ownership requires. Whether debt grows faster than a team can fix it depends on how well those responsibilities keep pace.
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