This article analyzes the profound shift in programming paradigms brought about by AI, focusing on changes in the development process itself rather than specific AI features. It highlights the crucial role of coding patterns and static analyzers in maintaining code quality for long-term system maintainability.
The article discusses a silent problem with AI-written code: the intent behind an AI agent's decisions vanishes after the session ends. Unlike humans who leave a trail of context, this lack of an evidence trail makes understanding and maintaining the code difficult in the future, rendering `git blame` useless.
The author reflects on an AI-assisted workflow that, while fast, leads to a loss of understanding and maintainability, as AI excels at implementation but struggles with grasping intentions and correcting mental models. They propose treating every feature as a thinking problem first and an implementation problem second, asserting that critical human thinking remains the essential task.