The Hidden Cost of AI-Generated Code: Why PR Reviews Matter More Than Ever

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In today’s development landscape, AI-assisted coding has become the norm rather than the exception. Tools like Copilot and ChatGPT can generate code in seconds, accelerating development in ways we couldn’t have imagined a few years ago. But as with most things that boost speed, there’s a trade-off — and it’s showing up clearly in pull request (PR) reviews.

The New Reality of PR Reviews

As software engineers, reviewing PRs has always been part of our responsibility. But recently, many of us have noticed something: PR reviews are taking significantly longer than before.

Why?

Because a growing portion of code is AI-generated.

At first glance, AI-generated code often looks clean and correct. It compiles, follows syntax rules, and may even adhere to best practices. But once you dig deeper, you start to see the cracks:

• Unnecessary logic branches
• Redundant conditions
• Unused variables or functions
• Over-engineered solutions for simple problems

This creates a new kind of review burden — not just checking if the code works, but why it exists at all.

AI Doesn’t Understand Your System

AI tools are excellent at pattern recognition. They can suggest syntactically correct code and even catch certain security issues. However, they lack a critical component: context.

They don’t fully understand:

• The actual business logic
• The flow across multiple services or repositories
• The real-world use case behind the feature

This becomes even more challenging when a single PR spans multiple repositories. Understanding the flow across 3–4 repos requires deep system knowledge — something AI simply doesn’t have.

The Problem of “Unnecessary Code”

In my experience, one of the biggest issues with AI-generated contributions is the amount of unused or irrelevant code.

You might find:

• Functions that are never called
• Conditions that never trigger
• Extra layers of abstraction that add no value

While none of these may break the system, they increase complexity, reduce readability, and make maintenance harder over time.

Ironically, what was meant to speed up development can slow down teams during review and debugging phases.

The Role of Engineers Has Shifted

This shift means that our role as software engineers is evolving.

We’re no longer just reviewing for:

• Syntax correctness
• Code style
• Basic functionality

We now need to focus more on:

• Logical correctness
• Relevance to the actual use case
• System-wide flow and integration
• Code minimalism (is this really needed?)

Back to Fundamentals

With AI in the loop, strong fundamentals matter more than ever.

Engineers need a solid understanding of:

• Core programming concepts
• Logical thinking and problem-solving
• System design and flow
• Real-world use cases

Without this foundation, it becomes easy to approve code that “looks right” but doesn’t truly belong.

Final Thoughts

AI is an incredible tool, but it remains just that, a tool.

It can assist, accelerate, and even inspire development. However, it cannot replace a human’s understanding of context, intent, and overall system behavior. Because of this, PR reviews have become more critical and more demanding than ever before.

The challenge today goes beyond simply writing code. It is about ensuring that every piece of code we accept truly makes sense within the bigger picture.

This responsibility ultimately lies in the hands of thoughtful engineers.