Most people and businesses believe that implementing AI is a simple process: you input your data, and an AI magically generates value. However, this episode breaks down this misconception, highlighting the significant work that exists in the "missing middle." This middle piece is a deep dive into data science, a non-glamorous, but vital step that is often overlooked. AI, while it may seem intuitive, is fundamentally driven by mathematics and requires clean, organized, and properly formatted data to function efficiently. This is the main reason why many AI projects fail before they even start—because they skip this critical, time-consuming phase of data preparation and normalization.Lee and Rich introduce three foundational "laws" of AI to help listeners reframe their understanding of its true nature. The first law is simple: what you put in is what you get out. An AI agent is only as good as the data it is trained on. This means that a project with an ambiguous scope and low-quality data will inevitably produce poor results. The second law is that what is being used is getting better. They stress that AI is an iterative tool that learns through use, and continuous reinforcement learning is necessary for it to become truly effective. The final law is a reminder not to judge AI by human standards. AI does not think, it mimics; expecting it to behave like a human will only lead to frustration, as its purpose is to process information and complete tasks in a mathematically efficient way, not to replicate human common sense or intuition.