Does Clean Code Make AI a Better Coder? A Controlled Study Puts the Gospel to the Test

Every developer has heard the sermon: write clean code. Meaningful variable names. Short functions. Tidy structure. But here’s the thing — code isn’t just for humans anymore. AI coding agents now crawl through our repositories, reading and rewriting as they go. Which raises a question nobody has really answered with data: does clean code actually make AI perform better?

Why This Question Matters Now

The past few years have buried us in advice on “how to prompt AI well.” YouTube alone tells the story. Matt Pocock’s video on AI coding workflows cleared 1.13 million views. A video on “how AI coding changed our dev team” pulled in 400,000. Builder.io’s Steve shared tips on using an AGENTS.md file to squeeze better output out of agents.

But almost all of this is anecdote. “I did it this way and it worked.” What’s been missing is rigorous evidence on how much the cleanliness of the code itself moves the needle on AI performance. That’s where a controlled study comes in — and it’s a very different beast from a YouTube tip.

What a Controlled Study Actually Buys You

Quick refresher, because this is the whole point. A controlled study changes exactly one variable, holds everything else constant, and compares the results.

Picture two versions of the same program. One is clean and well-organized. The other is a tangle. The logic is byte-for-byte identical — only the readability and structure differ. Now hand an AI agent the same task on each: fix a bug, add a feature, whatever.

Do that, and you can isolate whether any performance gap came from “the code was cleaner” or from random luck. It converts the developer’s vague hunch — “it just feels like it helps” — into a number. That’s the difference between a hot take and an actual finding.

Does Human Intuition Even Transfer to Machines

Here’s the crux. The reasons messy code trips up a human may have nothing to do with why it might trip up an AI.

A person reading a cryptic variable name burns cognitive load reconstructing what it means. Deeply nested, sprawling functions make it easy to lose the thread. But AI works differently. It processes code as tokens and predicts its next move based on patterns it saw in training.

And that’s where two competing hypotheses split apart. One camp argues that since AI learned from human-written code, whatever reads well to a human is also the more familiar pattern to the model — so clean code should boost AI performance too. The other camp pushes back: AI doesn’t get tired, and it can swallow enormous context in one gulp, so a little mess might barely dent its accuracy. A controlled study is the referee that decides which camp is right. The gap between hype and reality is real — one skeptical “I actually tried AI coding and it was underwhelming” video racked up 160,000 views for a reason.

What the Answer Means for Your Workflow

If the experiment confirms that clean code lifts AI performance, this stops being a matter of taste. Cleaning up your codebase becomes an investment in AI collaboration.

For years, refactoring was justified by “future humans will have an easier time maintaining this.” Now there’s a second, more immediate justification: tidy the code so your AI agent fixes it more accurately today. It means the quality of the code body itself — not just a guidance file like AGENTS.md — governs the quality of what the AI produces.

And if the answer swings the other way — “AI doesn’t much care about messy code” — that’s useful too. It signals you don’t have to rewrite your entire legacy stack before pointing AI tools at it. Either result changes how you plan the work.

The Takeaway

Honestly, this topic isn’t a hot debate in the community yet. Discussion over the last 30 days has been thin, and most practical content is still stuck on “how to prompt AI better.” Which is exactly why this arXiv study is asking a question that’s ahead of the curve.

At bottom, it’s an attempt to test — with data — whether the “clean code” gospel we’ve believed for decades still holds when the reader is a machine instead of a human. So where do you land? Do you tidy the code before handing the job to an AI? Or is neatness strictly a human concern?