A developer spent a month letting AI write most of their code, starting with a simple Golang CLI tool. The initial results were impressive—polished, error-handled code that saved hours of work. But this experiment revealed a troubling pattern: as AI handled more complex business logic, the developer realized they were becoming a skilled prompter rather than a better programmer.

The core problem emerged when a bug surfaced in an AI-generated project. The author discovered they couldn't debug it because they didn't understand the underlying logic. In complex systems, AI struggles with context and history, leaving developers helpless during production issues. The experiment showed that outsourcing coding often means outsourcing thinking, leading to dependency and stunted growth.

Today, the developer uses a balanced approach: AI handles boilerplate, research, and unblocking tasks, while personally tackling architecture, security, and critical logic. The golden rule is never shipping code you couldn't debug yourself. This pragmatic strategy maintains speed gains while preserving the deep problem-solving satisfaction that drives real developer growth.