AI Has No Wisdom and Neither Will You

In the past month, the industry has undergone significant transformation, but those who rely solely on AI code generation are in for a rude awakening. Vibe-coded projects often degenerate into unmanageable messes due to the lack of measurable metrics for code maintainability and architecture. Bad code is characterized by its difficulty in reading, understanding, and evolving, as well as its tendency to produce unpredictable results when changes are made.

Inconsistencies arise when adding features, leading to a significant undertaking of patching multiple areas of code. Invariants of the design become unclear, and the lack of experienced developers to guide against violations compounds the issue. Testing becomes fragile, requiring mocks and exposing implementation details, which hinders meaningful refactoring.

Experienced developers rely on intuition, developed through years of debugging and fixing production issues, which cannot be reduced to rigid rules. AI models are not trained to understand maintainable code, as they learn from beginner rulebooks and patterns in code in the wild. They lack a fitness function for maintainable code and struggle to simplify code into reusable functions.

As developers become increasingly reliant on AI, they relinquish responsibility for their coding mistakes, failing to learn from them. While LLMs are valuable tools, over-reliance on them will ultimately hinder mastery and lead to a decline in the quality of code. The future may bring a trend of companies proudly advertising their "NO-AI" policy as a competitive advantage, and they will be right.

However, the software industry has always embraced automation, and LLMs are just one tool among many. The act of coding is far from solved, and there are still numerous opportunities for innovation and development. It is crucial that people and companies take responsibility for their use of AI to avoid potential consequences.