It’s impossible to ignore how much AI has started shaping the way we write and review code.
A couple of years ago, code reviews meant long threads, manual checks on minor details, and a lot of back-and-forth on formatting, naming, and logic.

Now? AI assistants are stepping in to review pull requests, spotting bugs, suggesting improvements, and even predicting potential edge cases before they happen.
It’s exciting, but it’s also… complicated.

The Good Part: Why We Actually Like It

AI tools have made code reviews faster, cleaner, and more consistent.
They catch the obvious stuff instantly, unused imports, inconsistent naming, forgotten null checks, and leave humans free to focus on architecture, readability, and logic.

Some things we’ve noticed AI does really well:

Highlights security risks or performance issues we might miss at first glance.
Suggests more efficient patterns or language-specific best practices.
Keeps code styling consistent across teams, even when multiple developers are working on the same project.

Honestly, it’s like having a junior dev who never gets tired

The Not-So-Great Part

But let’s be real, AI still isn’t replacing human judgment.
There’s nuance that machines don’t get (at least not yet).

Sometimes AI flags things that don’t actually need fixing, or it suggests changes that look “cleaner” but break the intent of the code.
And when that happens, you spend more time arguing with the AI than actually reviewing the code.

There’s also a risk that devs might stop thinking critically if they rely too much on AI suggestions.
The best developers don’t just fix what’s broken; they understand why it broke.

What We’ve Learned to Do

We’ve reached a good balance:
AI handles the mechanical side of reviews, and humans handle the creative side.

Here’s what works for us:

Use AI for first-pass reviews — to clean up syntax, detect minor issues, and suggest quick fixes.
Keep final reviews human — focusing on design, maintainability, and business logic.
Treat AI suggestions as exactly that — suggestions, not orders.

We also realized that using AI effectively means training it with our own coding standards and real-world context. Out-of-the-box tools help, but fine-tuned feedback loops make a big difference.

Where It’s Heading

AI won’t replace developers, but it’s definitely reshaping how we develop.
It’s making code reviews faster, pushing teams toward better quality, and reducing the mental load on repetitive tasks.

The key is learning to work with it, not against it, and not depend on it blindly.

At the end of the day, the best code reviews happen when AI efficiency meets human experience.