I have spent enough time building cloud and AI infrastructure to know one thing: Efficiency problems never show up where teams expect them. They tend to sit just beneath the surface, quietly shaping outcomes long before they appear in the metrics anyone is tracking. The per-unit cost of using models has fallen quickly, and organizations want to understand whether AI can scale without pushing budgets out of bounds. The International Energy Agency has warned that AI and data centers are becoming a major source of electricity demand, and Goldman Sachs Research has projected that data center power demand could rise 165% by 2030 compared with 2023 levels. It begins in the data layer.