Code review now requires a different posture. When most code in a pull request was written by the person submitting it, the reviewer's job was to catch mistakes and share knowledge. When much of it was generated by a model, the reviewer must also verify that the author actually understands what they are merging.
Why the nature of the problem changed
AI models produce code that is syntactically clean, well-formatted and often plausible at a glance. The surface looks finished. Bugs, when they exist, tend to sit in logic, edge cases or integration assumptions rather than in obvious syntax errors. That means the visual signals reviewers used to rely on, messy formatting, unusual patterns, obvious copy-paste, are less useful. Something can look correct and still be wrong in ways that matter.
There is also a volume problem. Engineers using AI tools can produce more code faster. Pull requests are getting larger and arriving more frequently. Reviewing 800 lines of code that one person wrote over two days is different from reviewing 800 lines that a model produced in twenty minutes, because in the second case there is less guarantee that the author has traced through all of it.
What reviewers now check for
The practical change is that reviewers are increasingly checking for author comprehension, not just code correctness. Useful questions during review now include:
- Can the author explain a specific block if asked?
- Does the test coverage reflect understanding of the edge cases, or just happy paths?
- Are there patterns that look like they were accepted from the model without modification?
- Does the error handling make sense for this system, or is it generic boilerplate?
This is not a claim that AI-generated code is worse on average. It is a claim that the risk profile is different, and that review processes built for human-written code do not automatically catch the failure modes of model-generated code.
For more on what to look for when working with code you did not write, see how to review AI-generated code and testing software you didn't design.
Technical debt accumulates differently
With human-written code, technical debt usually builds gradually. Shortcuts are taken under time pressure; the person who took them generally knows where the bodies are buried. With AI-generated code that was accepted without full comprehension, the debt can appear structurally sound while the underlying assumptions are wrong. Refactoring it later is harder because no one on the team has a clear model of why it was written that way.
This is particularly relevant in codebases where multiple engineers are using AI tools independently, each merging code the others have not read closely. The aggregate effect can be a codebase that is difficult to reason about: no single file need be bad, yet the mental model of how it fits together was never properly built.
What has not changed
The goal of review is still the same: shared understanding of what is in production and confidence that it behaves correctly. The mechanisms for achieving that goal have shifted. Asking authors to walk through logic verbally, requiring explanations of non-obvious design decisions, and being more deliberate about test quality are all practices that were good before and are more important now.
Some teams have responded by adding AI tooling to the review side as well, using models to flag potential issues before human review. That can reduce noise, but it does not remove the need for a human reviewer who understands the system.
What we test for
Both sessions in how we vet bear directly on this. Session 1 establishes whether an engineer can find real problems without assistance, through structured debugging and genuine technical understanding of the stack. Session 2 tests the same instincts under different conditions: whether they catch hallucinated APIs or subtly wrong logic in generated output before it ships, and whether they can explain a change they didn't type. Engineers who treat AI output verification as optional tend to make review ceremonial rather than useful.
Short answers
Is AI-generated code harder to review than human-written code?
It is different to review, not necessarily harder in volume terms. The surface is often clean, which can mask logic errors or incorrect assumptions. The main challenge is that reviewers can no longer assume the author fully understands every line they submitted.
Does faster code generation mean more technical debt?
It can, if review practices do not keep pace. When code is generated and accepted without the author tracing through it, the team accumulates assumptions no one has verified. That creates debt that is harder to find and fix than the kind built up consciously under time pressure.
Should teams add AI tools to the review process itself?
It can help with surface-level checks, but it does not replace a reviewer who understands the system. Automated review catches some issues; it does not verify that the author understood what they merged or that the design fits the broader codebase.
