AI Reviews Your Code. But Who Reviews the AI?

We are starting to trust AI with one of the most important parts of software development: code review.

At first, this feels like a natural step. AI writes code, so it can also review it. It is faster, it removes bottlenecks, and it feels consistent.

But there is a deeper change happening.

Code review is not only about finding bugs. It is also about understanding intent, checking assumptions, and taking responsibility for what we ship to production.

When AI joins this process, it does not just add another reviewer. It changes how developers think about code quality.

Not because AI is always wrong, but because it sounds right even when it is not.

This leads to a simple but important question:

Are we still reviewing code, or are we just trusting that it has already been reviewed?

A Concrete Example: Review at the Point of Commit

Tools like git-lrc are a good example of this change.

Instead of waiting for pull requests, they run code review at commit time. Every change is checked automatically before it even enters the repository.

In theory, this is very useful.

AI review becomes fast, continuous, and always available. It fits modern workflows with small commits and fast changes.

But this also makes the question more important.

If review happens earlier and more often, what exactly is being reviewed?

Moving review closer to the commit does not automatically make it better.

It only makes it easier to trust.

The Nature of AI Review

AI code review does not usually fail in obvious ways. It does not produce random or useless feedback. Most of the time, it finds real issues like style problems, edge cases, or refactoring suggestions.

This is what makes it useful—and also risky.

The problem is not if the suggestions are correct.

The problem is that they are correct only in a limited context.

AI does not know the full history of the code. It does not know business rules or past decisions. It only sees the current code, not the reasons behind it.

A suggestion can be technically correct, but still wrong for the system.

For example, removing a “redundant” check might break a rare case in production. Simplifying logic might remove an important rule that is not obvious in the code.

Each change may look like an improvement on its own.

But together, they can slowly change the design of the system.

AI focuses on local correctness. Real systems need contextual correctness.

And code review exists to protect that context.

The Hidden Shift

The effect of AI is not only in what it suggests, but also in how developers react.

When feedback is usually good, we start to trust it. Not on purpose, but in practice.

We accept suggestions faster. We question them less. Review becomes something we read, not something we do.

This is where we start relying on automation without thinking.

When something looks reviewed, we assume it is correct.

Over time, this can lead to a simple problem:

more code is reviewed, but less code is truly understood.

And in software, understanding is what matters most.

Responsibility Does Not Disappear

AI does not remove responsibility. It changes how responsibility feels.

With AI in the review process, there is a quiet shift:

• from thinking about code to accepting suggestions
• from understanding decisions to trusting outputs

This does not happen because developers stop caring. It happens because the system pushes in that direction.

This is the real risk.

Not wrong suggestions, but the slow loss of critical thinking.

The Question That Remains

So the question is not if AI can review code.

The question is if we are still fully doing code review ourselves, or if we are slowly giving that responsibility away without noticing.

Because when something goes wrong, the answer will not be:

“the AI approved it.”

It will still be:

Who understood the code well enough to take responsibility for it?