AI Is Flooding Repos—Who’s Watching What It Writes?

Nearly half of all code generated by AI contains security flaws—even when it’s coming from top-tier tools like GitHub Copilot or Amazon CodeWhisperer. That’s not a typo, and it’s not just theoretical, either: researchers pitted code generation tools against real-world security tests, and most didn’t pass.

AI has crossed a threshold: it’s not just helping developers write code—it’s writing much of it. And while that’s good for speed, it’s also making codebases harder to trust, harder to debug, and harder to understand.

You’d think more teams would have their eyes on tools that can monitor, review, and flag issues across AI-generated code, but they don’t. And that’s where a modern code analysis tool alternative comes in.

The AI Code Boom: Just How Much Is Being Written?

We’re not talking about a niche workflow here. Generative AI is now central to how software is getting built. GitHub Copilot alone is reportedly writing up to 46% of code in repos where it’s installed, according to GitHub’s enterprise collaboration with Accenture. A separate study from the University of Groningen found that developers using Copilot were 2.5 times more likely to submit pull requests in a single work session compared to those not using it. Meanwhile, Stack Overflow’s 2024 Developer Survey shows that 70% of developers have adopted AI tools to assist with code generation. As you can see, this isn’t a gradual shift… it’s a flood. And it’s transforming the shape of codebases behind the scenes.

What We Gain (and What We Miss) with Productivity

There are some real upsides that shouldn’t be ignored.

Developers who use GitHub Copilot often finish tasks in half the time, according to GitHub’s own studies and third-party validation. A separate study published by Microsoft and GitHub engineers on arXiv found that developers using Copilot completed a series of benchmark tasks 55% quicker than those without it.

It’s no surprise then that satisfaction metrics are up too. A recent McKinsey report confirms that developers using generative AI tools like Copilot report higher productivity, improved focus, and less frustration in their workflows.

But let’s not sugarcoat it: there’s plenty of risk hiding underneath the speed.

The Bugs Are Crawling In…

Speed isn’t the same as quality. And when AI is writing the code, the quality can be wildly inconsistent. A new MIT Technology Review piece highlights research showing that even state-of-the-art models frequently produce buggy, unreliable code—especially in edge cases, uncommon frameworks, or older libraries.

And GitClear’s recent findings point to doubling code churn—meaning a ton of AI-written code is being thrown out or rewritten shortly after being committed.

Why Traditional Tools Aren’t Built for This

Most legacy code review tools are built on the assumption that humans wrote the code.

They look for anti-patterns, check for stylistic violations, and maybe run static analysis. But none of that is enough when almost half of your commits are coming from a machine that doesn’t care about architecture, naming conventions, or even whether a function is necessary.

What developers need now is a code analysis tool alternative—something purpose-built for a world where AI is a co-author. These tools don’t just lint your files, they tell you how much of your repo is machine-written, where AI-generated code is introducing complexity or redundancy, and which areas are silently accumulating debt.

Engineering Leaders Are Feeling the Pressure

A 2023 analysis from OECD examined how AI is reshaping skilled labor, with a particular focus on software engineering roles. One takeaway? AI isn’t just replacing repetitive work—it’s creating new forms of oversight burden. That’s especially true for engineering managers.

The explosion in AI-written code has opened a new kind of visibility gap: leaders can no longer assume their teams fully understand the code they’re shipping. PRs move faster, but post-release bugs are keeping up the pace too.

Without visibility into the source and quality of AI-generated contributions, leaders are flying blind. Traditional dashboards can’t help here. They weren’t built for this kind of codebase.

A Better Code Analysis Tool Alternative

A better code analysis tool doesn’t just scan your files for syntax issues. It tracks where AI code enters your system. It maps churn, detects duplication, and monitors complexity in AI-written code vs. human-written code. It flags problem areas early—before bugs hit production or debt spirals out of control.

This is exactly what Flux was built for. Flux is a code analysis platform designed for the realities of AI-assisted development. It shows you what percentage of your codebase is AI-written, how that code behaves over time, and where risk is quietly accumulating. Instead of static rules, Flux gives you living visibility: into churn, duplication, complexity, and unreadable code clusters—so you know where to step in, and where to let things run.

Because when you can see what’s been touched, rewritten, or blindly accepted from a suggestion tool, you can finally make sense of what’s inside your own repo again.

Final Thoughts

The stats are loud and clear: AI is writing more of your code than you realize. And it’s doing so at a pace that’s hard to keep up with… unless you have the right tools helping you out.

A modern code analysis tool alternative like Flux helps teams catch what traditional tools miss. It gives you visibility into the chaos, and a chance to clean it up before it bites you. 

The code flood isn’t slowing down. But your team doesn’t have to be caught off guard.