AI-Generated Code and Code Review Work

Yes, AI-generated code can create more work for code reviewers, especially when it increases the amount of code submitted or makes changes harder to understand. It can also reduce review effort when it handles routine tasks well; the outcome depends on the change, the developer’s checks, and the team’s review process.

Why AI can change the review workload

Code review is not simply a search for typos. Reviewers assess whether a change meets its requirements, fits the surrounding design, handles unusual inputs, avoids security and reliability problems, and can be maintained. AI coding tools can produce code quickly, but speed of generation does not guarantee that these properties have been checked.

A developer who previously wrote a small function by hand may now generate a larger implementation, tests, documentation, or several alternative files in the same session. If all of that is submitted, reviewers have more material to inspect. Even when the code is correct, they still need to understand its intent and how it interacts with existing systems.

There is a countervailing possibility: a tool may help a developer complete routine changes, add clear tests, or improve documentation, making a patch easier to review. The relevant question is not whether code came from a person or a model, but whether the submitted change is understandable, appropriately scoped, and supported by evidence that it works.

Where extra review effort comes from

Larger changes: Generated code can expand a narrow request into extra abstractions, defensive checks, or files that are not needed. More changed lines generally mean more material to examine, though line count alone does not measure difficulty.
Unclear intent: Code can be syntactically polished while making assumptions that are not explained in the patch. A reviewer then has to reconstruct why a particular approach was chosen.
Subtle defects: Plausible-looking code may mishandle empty inputs, errors, concurrency, permissions, or boundary conditions. These are not always apparent from a quick read.
Weak or misleading tests: Generated tests may repeat the implementation’s assumptions, check only the easy path, or assert behavior without covering the requirement that matters.
Unfamiliar dependencies and patterns: A patch may introduce a library, API call, or design pattern that reviewers must investigate before they can judge its suitability.

These sources of effort are not unique to AI. They also occur in hurried human-written changes, copied code, and large refactors. AI can affect how often they appear if it makes producing more code easier than carefully validating it.

When AI-generated code can save time

AI assistance can make reviews more efficient when it removes mechanical work without obscuring the design. Examples include generating a repetitive data mapping, drafting a straightforward test scaffold, or applying a consistent rename across files. In each case, the reviewer still needs to check that the transformation matches the requested behavior.

A useful patch is usually small enough to review as a coherent unit and explains what changed and why. Tests should demonstrate important behavior, not just increase a coverage number. A developer can also use tools to inspect the resulting diff, run the project’s checks, and remove generated material that is unnecessary.

AI can be useful during review too, for example as an additional way to identify possible edge cases. That output should be treated as a lead to verify, not as proof that a patch is safe or correct. Review responsibility remains with the people and processes accountable for the software.

What evidence says about review time

There is no single reliable multiplier that describes how much extra review work AI-generated code creates. Results depend on the programming task, the tool, the developer’s skill, the codebase, and what a study counts as “work.” The time needed to type or generate code is not the same as the time needed to validate, maintain, or repair it.

Research on coding assistants often measures task completion or developer speed in controlled settings. Those measures do not by themselves establish that code review becomes faster or slower in production teams. To answer that question, a study would need to account for factors such as patch size, reviewer experience, defect discovery, follow-up changes, and the time spent after a change is merged.

It is therefore more accurate to describe the effect as conditional than to claim that AI universally increases or decreases review burden. Teams can learn more from their own review data, while taking care not to treat one metric—such as lines changed or review duration—as a complete measure of quality.

How teams can keep reviews manageable

Good review habits apply whether code was written manually, generated, or produced through a mixture of methods. Teams can reduce avoidable work by setting clear expectations for every contributor:

Keep changes focused on one understandable purpose; split unrelated work into separate patches.
Include a concise description of the requirement, the approach, and any important trade-offs or assumptions.
Run relevant tests, formatters, type checks, and other established project checks before requesting review, and report failures honestly.
Review the diff personally before submitting it. Remove unnecessary files, duplicated logic, speculative features, and comments that do not help explain the code.
Provide tests for meaningful behavior, including relevant failure cases and boundaries, rather than relying on generated tests without inspection.
Follow the project’s ordinary standards for dependencies, security-sensitive changes, and approval. Do not use an AI tool’s confidence as a substitute for evidence.

Reviewers can also ask authors to clarify intent or reduce an overlarge patch rather than trying to reverse-engineer it all at once. For high-impact or difficult changes, a design discussion before implementation may prevent reviewers from spending time on an unsuitable solution.

A practical way to judge the impact

Teams considering AI coding tools can compare similar work before and after adoption, without assuming that every difference was caused by the tool. Useful signals include review turnaround, reviewer time where it can be recorded consistently, size and scope of changes, requests for revision, defects found during review, and follow-up fixes. These indicators need context: a longer review may reflect a more careful review, and a short one does not prove a change was sound.

It can also help to separate routine, low-risk tasks from changes involving sensitive data, permissions, critical business rules, or complex interactions. The latter deserve careful human review regardless of how the code was created. Teams should protect confidential code and data according to their own policies and the tool’s applicable terms.

For an individual contributor, the practical test is straightforward: would a colleague understand the patch and have enough information to verify it? If not, add context, improve the tests, or narrow the change before asking for approval.

Frequently Asked Questions

Does AI-generated code take longer to review?
Sometimes. It can take longer when the patch is larger, its assumptions are unclear, or tests do not establish the intended behavior. A focused, well-explained change may be no harder to review than comparable human-written code.

Can reviewers tell whether code was generated by AI?
Not reliably from code alone. Style and structure are not dependable proof of how a change was written. Reviewers should assess behavior, design, tests, and project fit instead.

Should AI-generated code get a different review process?
Not necessarily. The same standards for correctness, security, maintainability, and testing should apply. A team may add disclosure or documentation expectations, but those do not replace normal review.

Can AI review code automatically?
Tools can flag possible problems or suggest edge cases, but suggestions may be incomplete or incorrect. Automated findings need verification, and passing checks do not establish that a change meets its requirements.

How can developers make AI-assisted changes easier to review?
Keep the patch focused, inspect the generated diff, explain the intent and assumptions, and run meaningful checks. Remove unnecessary output and make tests demonstrate the behavior being changed.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.

More cards in this drawer

The whole Numbers and Powers drawer →