Why Readable Code Matters More Than Ever in the Age of AI
There’s a common train of thought that I see quite often now that I think is dangerous, and it goes like this: AI can write the code now, so the craft underneath doesn't really matter. Just describe what you want, generate it, ship it.
That's wrong, and the businesses betting on it are going to find out the hard way.
Anyone can vibe code. Almost nobody can maintain it.
We're living through quite a strange moment. Someone with no formal engineering background can sit down with Claude or ChatGPT, describe an app in plain English, and have something working by lunchtime. That's genuinely remarkable. It's also created a wave of codebases that look fine on the surface, but utterly fall apart the second anyone tries to change them.
It’s a bit of a double-edged sword. You prompt your way to a working prototype. It feels like magic. So you keep going, adding features, patching bugs, layering on more prompts. Six weeks in, the thing kind of works, but no one fully understands it. Not the person who built it. Not the AI that helped. Not the next developer told to "just add a small feature."
What started as a productivity miracle has become a maintenance nightmare. And because it was generated rather than written, there's often no mental model behind it at all, just code that “happened”.
AI reads your code the same way a new hire does
Ask an AI assistant to extend or fix an existing system, and it doesn't magically understand it, it has to read what's there. If your variables are called tmp2 and doStuff, if functions sprawl over hundreds of lines, if logic is scattered across files nobody can locate, it will guess. And more often than not the guesses will be wrong in ways that look right.
Clean code gives AI the context to be genuinely useful, whereas sloppy code gives it “permission” to invent.
This is the part that catches teams out. They assume AI gets better as you give it more code to work with. The opposite is often true. More bad code means more confused output.
The "ship it and worry later" trap
I've seen people treat AI as a shortcut around discipline rather than a force multiplier on top of it. The first few weeks look brilliant, with velocity up, tickets closing, everyone happy. Then the bugs arrive, and they're strange ones. Subtle, hard to reproduce, sitting in code that nobody fully wrote and nobody fully owns.
Readable code is what keeps a codebase maintainable. Maintainability is what decides whether your AI investment compounds over years or rots into a massive backlog expensive technical debt.
Five habits worth doubling down on
- Name things properly. calculateTax tells everyone what to expect. processData tells nobody anything.
- Keep functions small and single-purpose. Easier to review, easier for AI to reason about, easier to change without fear.
- Write comments that explain why. AI is fine at describing what code does. It can't read your mind on why you chose this approach over the obvious one.
- Use boring, conventional structure. AI tools have seen ten thousand standard layouts. They've seen zero of your clever bespoke one.
- Treat AI output like a junior developer's pull request. Read it. Challenge it. Refactor it. Never merge anything you wouldn't have written yourself.
What this means for the business
The vibe code era has lowered the bar for getting started and raised the bar for doing it properly. Prototypes have never been cheaper; however production systems have never been more punishing of shortcuts.
This is where we spend a lot of our time at Dax. We prioritise creating solutions that are maintainable, modular, and easy to work with. By creating codebases that are well maintained, structured and clean, we give clients the confidence that AI is accelerating our progress, not hampering it.