The piece highlights a common misconception about AI output: the belief that issues arise from poor prompts or inadequate tools. In reality, the problem often stems from an underlying instability in the information fed into AI systems. The author describes how drift manifests subtly, with AI producing fluent language that feels correct but lacks consistency over time. When users adjust minor details, they find the message never settles, indicating a deeper issue with the structure of the input rather than the quality of generation. A significant point made is that understanding buyer needs isn't enough if that understanding isn't structured properly for the AI to process. Unstructured context, such as long business descriptions or extensive lists of niche problems, may seem helpful but often leads to confusion. AI averages the inputs, resulting in generic outputs that fail to resonate. The author emphasizes that without a clear goal or narrative, problems remain just noise, making it difficult for the AI to generate meaningful content. The piece also critiques the vagueness of typical AI instructions, which can lead to unsatisfactory results. The AI's default behavior is to fill gaps with probabilistic outputs instead of creating coherent narratives. As AI generation improves in fluency, the issues can become even harder to detect, creating an illusion of understanding. The article suggests that a focus on stability and context is essential for effective system design. High-quality output can mask underlying instability, making it crucial to build in structures that guide AI reasoning for better long-term results.