Part 1/2: Is AI Quietly Lowering Your Code Quality?

By the end of 2023, nearly 97% of developers were using some form of AI coding assistant. GitHub Copilot, ChatGPT, Amazon CodeWhisperer, Replit Ghostwriter... what was once a novelty is now standard issue.

The promise is seductive: faster output, fewer grunt tasks, more time to focus on high-leverage work. But there’s a growing concern buried beneath the hype. One that engineering leaders are beginning to voice more urgently: is the code we’re generating actually any good?

A wave of recent studies suggests we may be sprinting into a swamp. AI can write code...but it often writes it badly. Code that compiles. Code that works. But also code that’s duplicated, convoluted, insecure, or misunderstood.

The Rise of AI (and Technical Debt)

GitClear analyzed over 150 million lines of code and found that since 2023, as AI assistants went mainstream, code churn...code that’s modified or thrown out shortly after being added...has doubled. Developers are generating more new code, much of it copy-pasted from AI suggestions, and less time is spent refactoring or improving existing code.

This isn’t just inefficiency...it’s compounding debt. Code is added quickly, but with less context. One engineering leader called it "a brand-new credit card for technical debt."

Another study from Uplevel (cited by CIO.com) looked at pre- and post-Copilot performance. The results? No significant gains in throughput. But a 41% increase in bugs. Teams weren’t delivering more...they were just spending more time debugging AI’s mistakes.

AI-generated code might pass tests. It might even get merged. But it often hides subtle bugs, logic errors, and patterns that violate long-standing engineering principles. The rise in duplicate code (violating the DRY principle) is just one red flag.

Security, Maintainability, and the Ghost in the Machine

In a 2023 academic study, researchers tested hundreds of Copilot-generated snippets pulled from GitHub. The results were unsettling:

• 32.8% of Python snippets contained security vulnerabilities.

• 24.5% of JavaScript samples failed common security checks.

• Many issues mapped to the CWE Top 25—things like command injection, hard-coded credentials, and unsanitized inputs.

Even as Copilot improves...some metrics have trended better since 2021...a non-trivial amount of AI-suggested code is still unsafe.

Security leaders have sounded the alarm. AI isn’t just reflecting best practices...it’s reflecting everything in its training data, including bad habits and outdated techniques. Unless a developer explicitly intervenes, the tool will happily generate insecure or non-compliant code.

Code That Works, but for Whom?

The problem isn’t just correctness...it’s clarity. Engineering teams have reported maintainability issues across the board:

• Code that’s technically correct, but hard to follow.

• Functions that don’t match naming conventions or design patterns.

• Components that pass tests but behave strangely in edge cases.

In one report, a team said parts of their codebase "felt like they were written by an alien." The logic was sound, but unfamiliar. Other teams say they’ve had to refactor entire AI-written modules because they couldn’t make sense of how they worked.

The risk isn’t just bugs...it’s future friction. Code that’s hard to read is hard to improve. And when that code was never truly understood by the person who committed it? That’s a maintenance nightmare waiting to happen.

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Developers Speak: Friction, Fatigue, and the Lost Craft

On forums like Reddit and Hacker News, developers are sharing similar concerns. One summed it up this way:

"Copilot minimized the part of programming I enjoy…writing code…and maximized the part we all hate: reviewing messy code."

Others worry that developers are becoming AI babysitters...spending more time scanning and tweaking AI-generated code than they would have writing it from scratch.

And for newer engineers, there’s a danger of not learning the fundamentals at all. They accept AI suggestions without fully grasping what the code does, trusting the syntax over the substance.

In short: AI makes it easy to ship code you don’t understand.

A Tool, Not a Crutch

Not all feedback is negative. Used carefully, AI assistants offer real value. Developers say they love using AI to write unit tests, boilerplate, and docstrings. Some even use AI to generate multiple implementations of a function just to compare styles.

But the best teams treat AI as a draft writer, not a decision-maker. They inspect the code, rewrite as needed, and hold it to the same standards as human code.

And the best leaders? They’re not banning AI—but they are raising the bar on how it gets used.

In Part 2 (out Thursday), we’ll dig into how engineering teams are responding: the policies, practices, and tools they’re using to keep quality high in an AI-saturated codebase.

Recommended Reads

✔️ "AI in Software Development: Productivity at the Cost of Code Quality?" (DevOps.com)

✔️ "Did GitHub Copilot really increase my productivity?" (Reddit r/programming)

✔️ "Research: Quantifying GitHub Copilot’s impact in the enterprise with Accenture" (GitHub & Accenture)