The author expresses frustration with the trend of relying on AI-generated code, specifically through tools like Copilot and Claude. While these tools can assist with basic tasks, they often fail when asked to perform more complex functions. The author finds it difficult to justify the time spent learning to prompt these AI tools effectively when they can write code faster themselves. There's a growing concern that engineers are outsourcing critical thinking to AI, which can lead to poorly reasoned software development. Drawing parallels to the Industrial Revolution, the author highlights issues such as environmental impact and the decline of skilled labor. Mechanization produced consistent results, but AI-generated code is unpredictable and often flawed. Citing the tragic Horizon scandal, where software errors led to wrongful accusations and even suicide, the author emphasizes the need for accountability in software development. The reliance on LLMs has resulted in a cycle of poor coding practices. They learn from existing subpar code, perpetuating mistakes rather than improving quality. The author argues that relying on AI for coding diminishes human skill and judgment. There's a stark contrast between reviewing code written by a colleague versus that generated by an AI, as the former comes with a level of trust and reasoning. As AI-generated code floods repositories, developers risk losing their ability to critically engage with their work.