From Software to Watts: Why the Next AI Boom Is an Energy Story
For a decade, AI’s story was written in software releases and benchmark scores. Now, it is being rewritten in gigawatts, transformer capacity, and land rights for new data centers. Training and running frontier models demands so much electricity, cooling, and water that “physical AI” infrastructure—power-hungry data centers, substations, transmission lines, and advanced cooling systems—has become the true bottleneck. This guide explains why energy is now the limiting factor, how that reshapes the economics of AI, and which parts of the power and infrastructure stack are poised to benefit as capital rotates from pure software to the hardware, grids, and generation needed to keep the models running.
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