AI Exponentializes Your Tech Debt

If you have tech debt, you're now exponentially worse off, because you can't leverage the massive productivity gains that AI offers, while your competitors with clean codebases are moving faster.

As a freelance developer working with various companies on all sorts of projects, I've been using AI coding tools since the very first ones came out. These days, I'm heavily into Cursor, Claude Code, Codex CLI, and Gemini CLI. And I've noticed something really important that I think every developer needs to understand.

Code Quality Matters Most

How well you can leverage AI for coding depends a lot on your code quality. And when I say "a lot," I mean this is literally the most important factor that determines whether AI will be a massive productivity boost or a frustrating waste of time.

When AI Works Awesome

If you follow all the best practices, AI is incredible. I'm talking about:

• Self-explaining code
• DRY principles
• Everything nicely organized into files with classes
• Self-explaining method names
• Each method does one thing only
• Frontend built with reusable components
• Logic properly separated into composables or service files
• Well-documented code

When you have this kind of codebase, AI tools work pretty much perfectly. They understand the structure, they follow your patterns, and they generate code that fits right in.

When AI Becomes Useless

But when you have typical tech debt (things just hacked together, spaghetti code, 5000-line files), AI is barely usable for coding. It will struggle all the way and just cannot get things done. The AI gets confused by the mess, suggests changes that break other parts of the code, and you end up spending more time fixing its mistakes than if you'd just written it yourself.

AI Exponentializes Tech Debt

Here's my main point: AI exponentializes your tech debt.

Tech debt was already bad before AI. It slowed you down, made changes risky, and made onboarding new developers painful. But with AI in the picture, if you have tech debt, you're now exponentially worse off. You can't leverage the massive productivity gains that AI offers, while your competitors with clean codebases are moving 3-5x faster with the same tools.

The gap between teams with good code and teams with bad code has become exponential.

Vibe Coders and Tech Debt

There's an interesting side note to all this: the "vibe coder" problem. These are people who use AI to build stuff but don't actually know how to code. And they almost always end up with a vibe-coded bunch of tech debt.

I'm not exactly sure why this happens, because when I, as an experienced developer, use AI, it produces even better, cleaner, more professional code than I would have written myself. But when a non-expert uses tools like Claude Code or Codex, they always end up with something unmaintainable.

I know this because those people eventually hire me to fix it.

My theory is that experienced developers know how to guide the AI, review its output critically, and structure the project in a way that stays maintainable. Non-developers don't have that skill, so they accept whatever the AI gives them without understanding whether it's good architecture or not.

Bottom Line

If you're serious about using AI to accelerate your development work, your first priority needs to be code quality. Clean up your tech debt. Refactor that spaghetti code. Break up those giant files. Make your code self-explanatory. (Or hire me to do it for you.)