Code Review in the Age of AI: Why It Matters More, Not Less

I used to think of code review mostly as a necessary checkpoint: something we do before code enters the shared codebase, sometimes helpful, sometimes slow, often a little tiring.

After AI coding tools became part of my daily work, I started noticing code review differently.

The first change was not that the code suddenly became better, or that bugs disappeared. It was that first drafts arrived faster. Pull requests became easier to create, but not necessarily easier to understand. The review queue could fill up sooner, and the cognitive load of reviewing did not shrink. Sometimes it grew.

That made me think more carefully about what code review is actually for.

When Code Gets Cheap, Understanding Gets Expensive

For a long time, we have been trying to shorten the distance between idea and first draft. AI makes that distance much smaller. This can be useful: it helps people get unstuck, explore options, and spend less time on repetitive work.

But speed changes what becomes scarce. When code is easier to produce, understanding becomes harder to protect. A change can work today while leaving behind assumptions nobody remembers making.

AI-generated code makes this more subtle because it often looks finished. It can look polished and coherent even when it does not quite fit the system, misses a domain rule, or assumes an error path that will not hold in production.

This is why code review still matters. Not because humans are better typists than AI, and not because every line deserves suspicion. Review is one of the places where individual output becomes shared understanding.

When code becomes cheaper to produce, understanding becomes the scarce resource. Review is one way teams protect it.

What Review Is Actually For

I want to be honest about something. A lot of code review I have seen, including some I have done myself, was mostly a scan for surface problems: naming, formatting, the obvious logical error. Increasingly, those are things tools can catch before a human even opens the PR.

It is natural to wonder whether humans still need to read the code at all. Maybe it is enough to validate the output: run the tests, click through the feature, and check that it behaves correctly.

But output validation only tells us what happened in the cases we thought to test. Review is where we look for the risks our tests did not know to ask about.

The higher value of review is judgment.

Does this change belong in the architecture we agreed on? Does it respect domain rules that live in our heads, not in any document? Does it create coupling we will pay for later? Does it expose data or skip a permission check because the risk is contextual, not structural?

Some might argue that a better agent harness, stronger evals, clearer prompts, and more thoughtful agentic workflows can reduce these risks. Teams should use those tools. But even a strong AI workflow still needs someone accountable for whether the change fits the product, the architecture, the operational reality, and the standard of quality the team is willing to ship.

These are questions of shared ownership. AI can generate code, but it does not own the system. We do.

The shift I have had to make is from thinking of review as a bug hunt to thinking of it as a judgment checkpoint. Bugs matter, but the deeper question is: do we understand why this code works, when it will fail, and what it will cost us later?

The Context Problem

Most slow and painful reviews I have been part of share one root cause: the reviewer has to reverse-engineer the author’s thinking from the code alone.

The PR description is vague. The ticket is incomplete. AI was used, but nobody says which parts are generated, which parts were manually inspected, or what assumptions were left unchecked. The reviewer opens the diff and starts doing detective work.

This is not review. It is reconstruction. And it is exhausting.

The fix is not more process. It is more context from the author before the review begins. A good PR answers: What problem does this solve? What changed intentionally? What is deliberately out of scope? Where was AI used, and what did the author personally verify? What should reviewers focus on? Where does uncertainty remain?

None of that needs to be long. A few sentences often suffice. But without it, every reviewer on the team has to rebuild the same mental model independently. That is wasted time that compounds across every PR.

The fastest review is not the one with the fewest comments. It is the one where the context is already clear before the first comment is written.

What Authors Owe Reviewers

AI makes it easy to generate code. That does not mean the first generated version should go directly to a pull request.

There is a discipline I have come to think of as the author’s obligation: before you ask another human to spend their attention on your work, you should have spent serious attention on it yourself. Read the code. Simplify where you can. Remove changes unrelated to the goal. Make sure you can explain every file that changed and why.

The test I use is simple: if I cannot explain this implementation, I am not ready to ask for review.

This is not anti-AI. It is pro-accountability. The moment the author stops being the person who understands the code, the reviewer becomes the first real engineer to think carefully about it. That is not collaboration. It is outsourcing responsibility upward, to someone whose job is to protect the codebase, not to do your design work for you.

There is also a strong argument for smaller PRs, which AI makes tempting to ignore. A generated diff can be enormous. But humans cannot review enormous diffs well. We skim. We approve things we do not fully understand because blocking feels costly and the PR is already three days old. This is how technical debt enters the system: not because nobody cared, but because the review was too large to be meaningful.

If your team is using AI to ship code faster, you should be stricter about PR size, not looser.

When a large change is unavoidable, the author should separate the mechanical parts from the judgment-heavy parts: isolate generated changes, explain the strategy, and make the review surface smaller than the diff.

What Reviewers Should Focus On

If we accept that tools should handle formatting, linting, and basic test execution, then human review time is too valuable to spend on what tools can already do. The question is what reviewers should focus on instead.

Architecture and fit: Does this change belong in the structure we have built? Does it introduce unnecessary coupling or duplication?

Domain correctness: Does it follow the rules that live in our heads: the ones about how data flows, what fields are sensitive, and what user actions are allowed under which conditions?

Operational risk: What happens when this fails? Is the error handling honest? Does a retry loop risk overloading something downstream?

Security assumptions: Scanners find vulnerable patterns. Humans find dangerous assumptions. A scanner may not know that an endpoint bypasses the team’s standard permission flow, or that a field being returned in an API response should never be exposed to this caller. Those are context problems, not pattern problems.

Maintainability: Will another developer understand this six months from now? Will the author understand it?

That last one matters more than it sounds. One of the hidden costs of heavy AI usage is that developers can ship code they do not really own. Review should be the checkpoint where we ensure ownership transfers genuinely: that the author understands what they are merging, not just that it passed tests.

Review as Learning, Not Just Gatekeeping

The case for code review I have not made yet is the one I think is most important for the long run: review is how teams build shared judgment.

A junior engineer learns why an abstraction is risky by seeing a senior engineer name the risk. A backend developer learns how the frontend depends on an API contract by reading a comment that explains it. A new team member absorbs hidden domain rules by seeing them surface in review, not in a wiki nobody reads.

In the AI age, this learning function becomes more important, not less. If AI writes more first drafts, humans have fewer organic opportunities to encounter the full range of design problems and tradeoffs. Review becomes one of the primary places where engineering judgment actually develops and spreads.

AI can explain general concepts, and that is useful. But code review teaches the local judgment of a specific team: which tradeoffs this codebase accepts, which abstractions have failed before, which risks matter in this product, and what quality means here.

The Practical Shape of This

None of this means review should be slow. It means it should be purposeful.

Authors: self-review before you open a PR. Write a clear description. Explain where AI was used and what you verified. Keep the PR small enough that a reviewer can actually think about it. Mark what needs attention and what is safe to skim.

Reviewers: start with the requirement, not the diff. Check whether the solution fits the architecture. Look for hidden assumptions. Treat tests as evidence of intent, not decoration. Separate things that must be fixed from things that could be better. Ask questions before making accusations.

Both: separate the code from the person. Review is not a performance. It is not about proving expertise or defending decisions. It is two or more engineers trying to make the system better than any one of them would alone.

AI can generate code. It can summarize diffs, suggest edge cases, and flag suspicious patterns. Those are genuinely useful contributions.

Even when AI has access to a large amount of context, access is not the same as accountability. It can retrieve architecture notes, summarize incidents, and infer patterns from the repository. But it does not carry the consequences of a bad abstraction, a fragile rollout, or a decision that makes the next year of maintenance harder.

The goal is not to distrust AI. The goal is to design workflows where AI increases leverage without weakening ownership.

Code review is where that ownership gets exercised. In the AI age, that function is not obsolete. It is more necessary than it has ever been, because the code is coming faster, and the judgment still has to come from us.