Code Quality Still Matters

The Temptation to Skip the Basics

Just because a model wrote it doesn’t mean we can skip the basics. There’s a temptation — when code appears instantly and looks reasonable — to trust it and move on. The speed creates a false sense of confidence. But AI-generated code has the same failure modes as human-written code: it can be unclear, overly complex, poorly structured, and hard to maintain.

The difference is volume. AI generates code faster, which means it can generate bad code faster too. Without quality checks, you accumulate technical debt at machine speed.

First Principles Haven’t Changed

We still need to care about clarity, simplicity, and design. These aren’t nostalgic preferences — they’re engineering requirements that exist for practical reasons:

• Clarity because someone (or some agent) will need to understand this code later
• Simplicity because every unnecessary abstraction is a future maintenance burden
• Design because the structure of your code determines how easily it can change

Bad code — AI or human — still rots the same way. Unclear naming leads to misunderstandings. Tight coupling makes changes risky. Missing tests mean bugs hide longer. The source of the code doesn’t change any of these dynamics.

What Quality Looks Like Now

Code review matters more, not less. When AI generates a hundred lines in seconds, the human reviewing those lines is the quality gate. That review needs to be thorough:

• Does this code actually solve the right problem?
• Is it simpler than it needs to be, or more complex?
• Does it follow the patterns established in this codebase?
• Are the edge cases handled?
• Will someone understand this in six months?

The medium changed, not the craft. Write it fast, review it carefully, and maintain it thoughtfully. That’s always been the job.

This article was originally posted on LinkedIn.