Code review was built around an idea that has quietly stopped being true for a lot of teams: that the person who wrote the code thought about it while writing it. A human author, even a junior one, usually has some sense of why they made each choice, and a reviewer can ask them about it. AI-generated code does not come with that sense built in. It comes with output that looks considered, whether or not any real thought happened behind it.
That changes what review needs to do, even though the process looks the same on the surface.
Looking clean is not proof of being correct anymore
The oldest habit in code review is that messy-looking code gets more scrutiny. Inconsistent naming, awkward structure, missing edge cases used to line up fairly well with actual bugs, because a rushed or careless author tends to produce both.
AI-generated code breaks that link. It is often clean, consistently formatted, and confidently written, whether or not the logic underneath is correct. That means the visual cues reviewers have relied on for years stop being a useful shortcut for “does this actually work.” The code can look like it came from your most careful engineer and still be built on a wrong assumption about the requirement.
What to check instead
Does it solve the problem you actually have, or a reasonable-sounding version of it? AI tools are very good at producing code that would be correct for a sensible reading of a prompt, which is not the same as being correct for your specific requirement, your specific edge cases, your specific data. The question is not “does this code work” in general. It is “does this code work given the actual constraints of this system,” which the tool often does not fully know.
What is missing, not just what is there? Human authors tend to miss edge cases they did not think of. AI-generated code has a different weak spot. It often handles the cases that are common in general, and quietly misses the ones that are specific to your product or your system’s history: the odd legacy input format, the retry behavior your infrastructure actually needs, the failure your team has been burned by before. Review with that specific blind spot in mind, not just a general “did they cover the edge cases” pass.
Can someone explain why, not just what? In a review conversation, ask the author, whether human or AI-assisted, to explain the reasoning behind a choice that is not obvious. If the answer is “that is what it suggested,” that is a sign to slow down, not speed up. A choice that nobody on the team actually understands is a risk regardless of whether it works today, because nobody will be able to safely change it later.
Is the test coverage really testing the risk, or just the easy path? AI-generated tests have the same problem as AI-generated code: they look thorough. They often are thorough for the obvious cases and thin on the cases that would have actually caught a real bug. Read the tests as carefully as the code, not as something you can take for granted.
The review conversation has to change too
Review used to work partly as a way to pass on context, a way for a second engineer to understand a change by reading it and asking questions. When code is AI-assisted, that context might not fully exist in the author’s head to pass on, because they may not have deeply reasoned through every line themselves. That means reviewers cannot assume the author has a ready answer for every “why” question, and it means the author has a real responsibility to actually understand the output before sending it for review, not just accept whatever looked reasonable.
A useful team habit: if you cannot explain a choice in your own words, you are not ready to put it up for review, no matter how it was written. That standard is not new. It just became something worth saying out loud, because it used to be assumed.
What has not changed
The basics of good review are the same as always: understand the problem being solved, check that the solution actually solves it, look for what is missing, make sure someone can maintain this six months from now. What has changed is that you can no longer use clean-looking code as a shortcut for any of that. Code looking neat was never real proof it was correct, it just used to line up well enough that reviewers could relax a little. That link is gone now, and review has to be more deliberate to make up for it.