A while ago, I wrote about a concept that I consider fundamental to our profession: The importance of prioritizing code optimization for humans rather than machines.

The premise was simple: code is read far more often than it is written. Writing cryptic code to save a few nanoseconds of execution time is usually a bad investment if it prevents your team (or your future self) from understanding and maintaining it.

However, in recent months, the landscape has shifted drastically. Tools like Claude Code, Cursor, GitHub Copilot etc have become indispensable copilots in our daily workflows. And with them, a new and unexpected truth has emerged:

Writing clean code for humans is, now more than ever, the most effective way to optimize your AI's performance.

Understanding Your New Partner: Context is King

Tools like Claude Code don't operate in a vacuum; they treat your entire codebase as a living prompt. This means code readability is now a functional requirement for AI performance. If your code is ambiguous, the model is forced to guess. By writing clean, semantic code, you provide the unambiguous context these tools need to stop hallucinating and start understanding your actual business intent.

The ROI of Clean Code Has Multiplied

There is a fallacy that with AI, we can afford to be sloppier or faster by writing "draft" code. It is exactly the opposite.

A clean codebase generates a Virtuous Cycle:

  1. You write clean, semantic (Human-Readable) code.
  2. The AI processes that code as high-quality context.
  3. The AI's suggestions become more precise, intelligent, and aligned with your architecture.
  4. You develop faster and with fewer bugs.

Maintaining clean code today is not just a matter of hygiene or empathy for your human colleagues; it is preventive maintenance for your most powerful productivity tool.

Conclusion

Returning to the thesis of my previous article: writing for humans was always the right decision. The irony is that, in the end, machines have become "human" enough in how they process language to benefit from the exact same rules.

Don't treat AI like a magic black box. Treat it like a new team member who needs clear context to shine. If you want smart answers, make sure your code is telling the right story.

Thanks for reading me 😊