Developer & Tech
How Should Code Review Change When Most Code Is AI-Generated?
By Jim Vernon, Editor, AI Intelligence International · Published 15 August 2026 · Reviewed against our editorial standards · About the author
Review practices were designed around human error patterns: typos, tired shortcuts, misunderstanding of a ticket. Generated code makes different mistakes, and reviewing it with the old checklist misses them.
This article covers how the failure profile differs, what deserves attention now, and how to stop review becoming the bottleneck when generation is nearly free.
Key takeaways
- Generated code is stylistically clean and semantically confident, which defeats visual review.
- The high-yield checks are interface boundaries, error paths, and whether the code solves the actual problem.
- Author understanding is now part of review: if the submitter cannot explain it, it is not ready.
- Volume is the real risk; cap review batch size rather than reviewing faster.
How does the failure profile differ?
Human code fails in ways that look wrong: inconsistent naming, obvious omissions, an unfinished branch. Reviewers are well-trained to spot these visually.
Generated code is consistent, idiomatic and complete-looking, and fails on things that read fine: a plausible but wrong API signature, an error path that swallows failures, an assumption about input shape that holds in the example and not in production.
It also tends toward over-generality — configurable abstractions for a case with one variant — which passes review easily because it looks thorough.
What deserves the most attention now?
Whether the code solves the problem in the ticket, rather than a nearby problem. This is the single highest-yield check and it requires reading the requirement, not just the diff.
Interface boundaries: what is called, with what arguments, and whether those calls exist and behave as assumed. Hallucinated or outdated API usage is common and compiles surprisingly often in dynamically typed code.
Error and edge paths, which are the least exercised and most confidently written part of generated output. Empty inputs, partial failures, timeouts, and concurrent access.
Data handling: what gets logged, what crosses a trust boundary, what is interpolated into a query or a template.
What can you stop spending time on?
Style, formatting and naming consistency, which are near-universally fine and should be enforced by tooling regardless.
Obvious structural issues that a linter or type checker catches. If review time is going here, the pipeline is misconfigured.
Micro-optimisation commentary. It was rarely valuable and it is now noise against a much more important set of checks.
Why is author understanding now part of review?
Because the traditional guarantee — that someone who wrote the code understands it — no longer holds automatically, and that guarantee was doing a lot of quiet work for maintenance and incident response.
A reasonable norm: the submitter must be able to explain any line on request and describe what happens when each external call fails. If they cannot, the change is not ready regardless of whether it works.
This is not distrust of the tool. It is ensuring someone on the team can debug the thing at 2am.
How do you stop review becoming the bottleneck?
Cap the size of changes. Generation makes it trivial to produce a 900-line pull request, and review quality collapses well before that. Keep changes under a few hundred lines and split aggressively.
Require tests written against the requirement rather than against the implementation, so the tests are independent evidence rather than a restatement of the code.
Automate everything mechanical — types, lint, security scanning, dependency checks — so human attention goes to intent and boundaries.
Track review latency and defect escape rate together. Falling latency with rising escapes means review has become a rubber stamp.
Does AI-assisted review help?
For mechanical checks and for a first pass on obvious issues, yes, and it catches things reviewers skim past.
For the checks that matter most — does this solve the actual problem, is this assumption true in our system — it is weak, because those depend on context the reviewer has and the tool does not.
Use it as a pre-review filter that runs before a human looks, never as the approval.
Worked example: a team that rebuilt its review process
A twelve-engineer platform team saw pull request volume rise about 60% over two quarters as assisted coding spread, while median review time fell from 40 minutes to 12. Defects reaching production rose from roughly 4 a month to 11.
An analysis of the 22 escaped defects over two months showed a clear pattern: 14 were in error-handling paths, 5 involved an incorrect assumption about an internal service's response shape, and 3 were duplicated logic that already existed elsewhere in the codebase.
Only 2 of the 22 were the kinds of issue their existing checklist targeted, which was mostly style and structure.
They rewrote the checklist to four items: does this match the ticket, are all external calls real and correctly used, what happens on each failure path, and does this duplicate something that exists. They added a hard 400-line limit on pull requests, enforced by a bot.
They also added an explain-on-request norm, which was contentious for about three weeks and then uncontroversial. Two engineers reported it changed how they used the tools, in that they read the output more carefully before submitting.
After one quarter: median review time rose to 26 minutes, pull request count rose again as changes were split smaller, and escaped defects fell to 5 a month. Review was slower per change and cheaper overall.
Frequently asked questions
Should the pull request declare AI involvement?
Some teams require it and it does help reviewers calibrate attention. Others find it becomes universal and therefore uninformative. Either is defensible; what matters is that the checklist changed.
What line limit is realistic?
Somewhere between 200 and 400 lines for most teams. The exact number matters less than having one that is enforced, since the failure is unbounded growth rather than any specific threshold.
How do you handle a submitter who cannot explain their code?
Return it without judgment and treat it as a normal review outcome. If it recurs with the same person, it is a coaching conversation about how they are using the tools, not a disciplinary one.
Do generated tests count as tests?
Only if they were written from the requirement rather than from the implementation. Tests generated from the code confirm that the code does what it does, which is not evidence of anything.