According to @godofprompt citing @TheChiefNerd’s video post, Sam Altman highlighted that while AI model training consumes substantial compute energy, human expertise also requires decades of biological energy investment, reframing debates on AI energy intensity (source: X post by @TheChiefNerd, Feb 2026). According to @TheChiefNerd, this comparison underscores a business imperative to measure AI lifecycle energy alongside productivity gains, informing TCO models, data center siting, and power procurement. As reported by @TheChiefNerd, enterprises building frontier models should evaluate energy per token trained and inferred, prioritize high PUE efficiency, and explore long-term PPAs with renewables and nuclear to stabilize costs. According to @godofprompt, Altman’s framing supports corporate strategies around energy-aware model architecture, sparsity, quantization, and inference offloading, enabling lower carbon intensity while maintaining capability.