As AI tools become capable of generating entire features, a new problem emerges: code that works initially but becomes unmaintainable. This post shares a common scenario where a student's AI-generated project breaks after minor changes, revealing a lack of test coverage and code review discipline. The author argues that fundamentals like evaluation sets, bug tracking, and regular code deletion are not obsolete—they are more critical than ever. AI accelerates code production, but it also amplifies the consequences of poor engineering practices. Teams adopting AI coding must double down on quality gates, not abandon them. The lesson is clear: AI is a tool, not a replacement for engineering judgment.
A reflection on why testing and maintainability matter more than ever when using AI to write code.