Technical debt and intellectual debt are concepts that highlight the consequences of prioritizing short-term solutions over sustainable, long-term strategies. Technical debt arises when development processes favor quick fixes or familiar solutions, leading to accumulated rework and maintenance challenges that hinder innovation. Similarly, intellectual debt refers to a gap in understanding how or why solutions work, often emerging in fields like pharmaceuticals where outcomes are prioritized over explanations. This lack of understanding can lead to complacency and an inability to predict or swiftly correct failures. The rise of AI and machine learning in IT emphasizes the urgency of addressing intellectual debt, as these systems often provide answers based on correlation without explaining cause and effect, potentially leading to increased errors in complex environments. The text argues that while machine learning can enhance problem-solving and scale, relying solely on it without understanding the underlying mechanisms can result in accumulating intellectual debt, thus affecting IT efficiency and decision-making.