Manage AI-generated code as part of your existing software delivery system, not as a special category that automation can fix on its own. Keep changes small, make a human accountable for every merge, run fast automated checks while code is being written, and judge success by maintainability, delivery stability and user outcomes—not by how many lines an AI tool produces.
AI can help teams find, explain and reduce old debt. It can also make a weak process produce more code, more quickly, while defects and deferred work accumulate out of sight. The practical response is a measurement-and-learning loop: set clear boundaries, verify continuously, review for understanding, record debt as product work and watch quality and flow together.
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What technical debt means when AI writes more of the code
Technical debt is the future cost created when a team accepts a shortcut, an incomplete solution or an insufficiently understood change. The debt may appear as harder maintenance, slower delivery, fragile tests, confusing interfaces, repeated work or production instability. It is not automatically bad: a deliberate shortcut can be sensible when its consequences are visible, owned and repaid. Unmanaged debt is the problem.
AI-generated code does not create a separate species of debt. A generated function can be well designed, tested and documented; a human-written function can be opaque and brittle. The important question is whether the team can explain the change, verify its behavior and maintain it in the context of the product.
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The 2026 Technical Debt in the AI Era manifesto describes the goal as moving from automation-focused debt management to human-driven decisions throughout the lifecycle, with automation supporting those decisions. That principle is useful for teams adopting code assistants: tools can suggest, generate, scan and summarize, but people remain responsible for trade-offs.
Why AI changes the debt equation
AI increases the speed of both good and bad practices
DORA’s 2025 State of AI-assisted Software Development report, based on nearly 5,000 technology professionals and more than 100 hours of qualitative research, says AI’s primary role in software development is that of an amplifier. It magnifies the strengths of organizations with clear priorities, reliable platforms, effective testing and healthy collaboration. It also magnifies weak review, slow feedback, unclear ownership and brittle delivery practices. The report is available at Google’s DORA publication page.
That means a team can see a dramatic increase in accepted code while its ability to understand and support that code stays flat. A large pull request that would once have taken a week to write may now arrive in a day. If tests, review capacity and architectural guidance have not improved, the queue simply moves: effort shifts from typing into review, integration, debugging and incident response.
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Output volume is not value
Lines of code, generated suggestions accepted and pull-request size are activity measures. None tells you whether users received a reliable capability or whether the next change became more expensive. DORA’s 2026 article on using AI effectively in the software development lifecycle cautions against treating faster coding as a complete productivity result. An anonymous participant summarized the tension this way: “AI tools increase my productivity, they write code faster than I could, but the code is (currently) lower quality than I could write myself.”
Use output data only as context. Pair it with delivery throughput, review turnaround, rework, deployment recovery, production incidents and developer experience. A faster authoring step is beneficial only if the whole system remains healthy.
Trust is an operating constraint
DORA reports that 30% of developers had little to no trust in AI-generated code in its time-specific survey finding. In DORA’s 2024 findings, 39% of developers outside Google said they trusted generative-AI output only “a little” or “not at all”; those results are reported in Fostering Trust in AI. These percentages are survey results, not universal measurements of code quality. They do show why a team should not assume that adoption equals confidence.
Trust grows when developers work in familiar languages and codebases, can choose where assistance is useful, and receive rapid evidence from tests and other checks. Forcing a tool into every task can reduce understanding and make reviewers either over-trust plausible output or reject it without examining the actual risk.
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Early evidence on self-admitted AI-related debt is narrow
An exploratory 2026 study, “Fix the Mess Gemini Created”: Towards Understanding GenAI-Induced Self-Admitted Technical Debt, screened 6,540 comments that referenced large language models in public GitHub repositories. It found 81 comments that also self-admitted a technical shortcoming. Among those 81, 33 concerned design debt, 17 requirement debt and 17 test debt.
The numbers describe selected comments, not the prevalence of debt in software. The sample covers public Python and JavaScript repositories from November 2022 through July 2025. Keyword selection can miss relevant cases or include comments that do not represent substantial debt, and the findings do not establish what happens in private projects, other languages or later workflows. Treat comments such as “the model’s behavior is not fully understood” or “tests were deferred” as prompts for investigation, not as an automated debt counter.
Build a workflow that keeps AI-generated changes understandable
1. Publish expectations before scaling use
Write a concise acceptable-use policy before broad deployment. It should answer practical questions rather than attempt to regulate every prompt:
- Which tasks may use an approved AI tool, and which require additional authorization?
- What source code, customer information, credentials, proprietary data and regulated information may be entered?
- Who is accountable for reviewing and maintaining the resulting change?
- Which tests, linters, type checks, security scans and repository-specific validations must pass before merge?
- Which coding conventions, architectural rules and documentation standards apply?
- How should a developer record uncertainty, generated dependencies or deferred work?
DORA’s trust guidance links transparent policy with greater developer trust and argues that even lightweight guidance can signal sanctioned, responsible use. Publish the policy where developers work, keep it current and explain the reason for each boundary. A rule that nobody can find will not shape behavior.
2. Put verification in the authoring path
Do not wait for a reviewer to discover basic failures in a large AI-assisted pull request. Run automated checks continuously or at every meaningful checkpoint:
- Unit and integration tests for changed behavior.
- Linters and formatters that enforce repository conventions.
- Static type checks where the language and project use them.
- Dependency, secret and security scans.
- Build, migration and performance checks that are specific to the service.
- Documentation or generated-file checks when interfaces change.
Deliver useful feedback to the author while the change is being written. Author-side feedback reduces the amount of detective work that reviewers must perform later. It does not transfer accountability to the tool: a human still decides whether the behavior is correct, appropriately scoped and maintainable.
3. Keep changes small enough to review
Require AI-assisted work to be divided into reviewable, testable units. A small change makes a defect easier to localize, a test failure easier to interpret and a rollback less disruptive. It also gives the author repeated opportunities to compare generated code with the project’s conventions.
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Useful controls include a pull-request size guideline, separate commits for mechanical refactoring and behavior changes, and a rule that a generated migration or API change ships with its tests and documentation. The exact line count is less important than whether a reviewer can understand the purpose, affected paths and failure modes in one sitting. If a task is inherently broad, split it into a sequence with a safe intermediate state.
4. Make human ownership explicit
Assign a responsible engineer to every accepted change, regardless of who or what produced the first draft. That person should be able to explain:
- What user or operational problem the change solves.
- Which assumptions the implementation makes.
- How the tests demonstrate expected and failure behavior.
- What dependencies, data handling or security implications were introduced.
- What remains intentionally incomplete.
Reviewers should focus on correctness, fit, maintainability and risk rather than trying to detect whether a line was machine-generated. If the author cannot explain a section, pause the merge: ask for a rewrite, add an explanatory test or involve someone with the necessary domain knowledge.
Should AI-generated code get a different code review?
It should receive an evidence-based review, not a ceremonial second approval or a blanket presumption that it is unsafe. The review depth should reflect change risk, uncertainty and the strength of automated evidence. The following comparison turns the main safeguards into review questions.
| Review axis | Questions for any change | Additional attention for AI-assisted work |
|---|---|---|
| Reviewability | Is the purpose clear, and is the change small enough to inspect? | Look for bulk-generated code, repeated patterns that hide different edge cases and unrelated edits that arrived with the requested feature. |
| Feedback speed | Do automated checks run close to the time the code is written? | Require the author to resolve failed or contradictory tool feedback instead of treating a green subset of checks as proof. |
| Delivery quality | What could affect stability, recovery, rework or incidents? | Ask whether plausible-looking output has been tested against boundary conditions, failure paths and production constraints. |
| Human understanding and ownership | Can the responsible engineer explain and maintain the code? | Probe unfamiliar abstractions, generated dependencies and comments that signal uncertainty or deferred testing. |
| Governance fit | Does the change follow data, security and coding policies? | Confirm that the tool used was approved and that no restricted data or unreviewed dependency entered the workflow. |
| Developer experience | Does the process help the team learn and make sound decisions? | Use review findings as training examples; avoid policies that remove appropriate developer choice without reducing risk. |
High-risk changes—authentication, authorization, payments, data migrations, safety-critical behavior or public interfaces—may warrant a domain specialist, additional tests or staged rollout whether the first draft came from a person or an AI tool. The reason for the extra scrutiny is the consequence of failure, not the label attached to the authoring method.
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Create a useful debt record
Put technical-debt items in the same planning system as features and defects. A useful record names the affected component, the observed evidence, the consequence and the next actionable step. “Clean up AI code” is too vague to prioritize. “Replace duplicated authorization checks in the invoice service; three endpoints diverge and a policy change requires edits in each; add a shared policy module and contract tests” gives a team something it can schedule and verify.
Record the trigger when it matters: deferred tests, a workaround for a deadline, an unexplained generated section, an incident, repeated review comments or a change that took much longer than expected. Link the debt to the relevant code, test, incident or design decision so a future maintainer can see why it exists.
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Prioritize by consequence, not by embarrassment
There is no universal debt score. A practical ranking can consider:
- Risk: potential security, privacy, correctness or availability impact.
- User impact: who is affected and how severe the failure would be.
- Recurrence: whether the same defect or workaround keeps returning.
- Change frequency: how often the component is modified and therefore how often debt taxes delivery.
- Operational instability: incidents, rollbacks, alerts or manual interventions associated with the area.
- Cost of delay: the likely additional rework or constraint if remediation waits.
These factors are decision aids, not a claim that every organization should use one formula. Revisit rankings when product priorities, exposure or evidence changes.
Repay debt in the flow of delivery
Repayment does not always require a standalone rewrite. Add a missing test when changing a function, remove duplicated generated code while touching an endpoint, document an interface before another team adopts it or replace a fragile workaround when its surrounding feature is next scheduled. Larger items deserve explicit capacity and an owner. Close the record only when the agreed evidence—tests, reduced incidents, simpler change paths or measured recovery—shows that the consequence has been addressed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure whether AI assistance improves the whole system
Establish a baseline before changing the workflow. Compare periods with similar product work, staffing and release conditions where possible. Interpret measures together; an improvement in one can conceal deterioration elsewhere.
| Outcome area | Measures to watch | What a concerning pattern may mean |
|---|---|---|
| Delivery flow | Change throughput, lead time, time waiting for review and time from merge to deployment | Generated output may be increasing while review or integration becomes a bottleneck. |
| Stability | Deployment failure, rollback and recovery measures | Faster coding may be shifting cost into release failures or longer recovery. |
| Quality and rework | Defect escape, reopened work, follow-up fixes and repeat review findings | Output that looks complete may be creating hidden corrective work. |
| Operations | Production incidents, alert volume and manual interventions | Unfamiliar behavior or missing failure-path tests may be reaching production. |
| Maintainability | Time to understand a component, change failure points and debt items created or retired | Code volume may be growing faster than the team’s ability to explain and modify it. |
| Developer experience | Confidence in reviewing output, perceived control, learning needs and time spent fighting tools | A mandated tool may reduce trust or move work into unmeasured verification. |
Do not use lines of code, prompt count or AI acceptance volume as standalone productivity targets. If a dashboard rewards those numbers, it can encourage unnecessary generation and discourage deletion or simplification. A smaller change that prevents an incident may create more value than a large accepted suggestion.
When comparing teams, account for differences in service criticality, language, architecture and work mix. DORA’s findings are organization-level research and survey results; they do not prove that adopting a particular tool causes a particular outcome for every team. Use the measures as a learning loop: establish a baseline, change one or more practices, observe quality and flow together, then adjust.
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Use uncertainty as a signal for investigation
AI-assisted changes often leave recognizable warning signs. None is proof of debt, but each deserves a deliberate check:
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TODOs or comments that admit a workaround or missing test. - Generated code that the author cannot explain in the project’s domain terms.
- Tests that cover only the happy path after a broad behavior change.
- Dependencies added without a clear maintenance, license or security decision.
- Large sections copied across modules instead of a deliberate abstraction.
- Repeated reviewer requests to clarify the same behavior or convention.
- Production fixes that repeatedly touch the same unfamiliar area.
Searches for phrases such as “generated,” “temporary,” “fix later” or “not sure” can help locate candidates, but keyword scans are not a debt detector. Inspect the surrounding code, ask the owner for evidence and create a specific work item only when there is a real maintenance or delivery consequence.
What an AI-use policy should require before merge
A policy can be short and still enforce the controls that matter. Before a developer merges AI-assisted code, require the following checklist:
- Approved context: The task used an approved tool and respected the organization’s data boundaries.
- Named owner: A responsible engineer has reviewed the complete diff and will maintain it.
- Understandable behavior: The owner can explain the implementation, assumptions and important failure paths.
- Automated evidence: Required tests, lint, type, security and repository checks have run and their failures are resolved or explicitly accepted.
- Appropriate scope: The pull request contains a coherent, reviewable change rather than unrelated generated edits.
- Dependency decision: New packages, models, services or generated assets have been checked against security, licensing and support expectations.
- Debt disclosure: Deferred work, uncertainty or known limitations are recorded as actionable items with an owner.
- Documentation: Public interfaces, migrations, operational behavior and non-obvious decisions are documented to the project standard.
Policies should also state what happens when a check cannot run, when a high-risk area needs specialist review and how developers can ask for help. Training should use examples from the team’s own languages and repositories so that guidance transfers to daily work.
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A practical debt-management cycle
The following cycle connects governance, authoring, review and planning without creating a separate AI bureaucracy.
- Define the boundary. Publish allowed tasks, data rules, coding conventions and required checks.
- Set a baseline. Capture current delivery, stability, rework, review and developer-experience measures before changing habits.
- Generate in small units. Ask for narrowly scoped code, tests or explanations that can be checked independently.
- Verify immediately. Run automated checks and investigate failures while context is fresh.
- Review for understanding. Have the owner and appropriate reviewers evaluate correctness, fit, maintainability and risk.
- Record residual debt. Turn deferred tests, workarounds and uncertainty into specific planning items.
- Observe outcomes. Compare quality, flow, incidents and confidence with the baseline rather than counting generated lines.
- Adjust the system. Improve prompts, tests, conventions, training, platform support or policy when the evidence shows a recurring problem.
This cycle keeps automation in a supporting role. It also prevents a common failure mode: adding an AI tool to a process whose review, testing and ownership gaps were already known but never addressed.
What the current evidence can—and cannot—tell you
The available findings support cautious management, not a blanket claim that every AI-generated change creates technical debt. DORA’s reports describe patterns at the organization and survey level. They are valuable for understanding amplification, trust, policy transparency and system outcomes, but they do not predict the result for every team.
The 2026 technical-debt study is even more specific: it examined self-admitted comments in public Python and JavaScript repositories, not all generated code. Its 81 selected comments can illustrate warning signs and categories, but they cannot estimate the percentage of AI-written code that is debt, prove causation or represent private repositories and other languages.
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Bottom line
AI-generated code becomes technical debt when a team accepts behavior it cannot adequately verify, explain or maintain—not simply because a machine produced the first draft. Keep changes small, put tests and policy checks in the authoring path, assign human ownership, record debt as normal product work and measure delivery quality alongside speed. AI can then reduce toil and help expose old debt without allowing higher output to outrun the organization’s ability to understand its software.
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