Carlo CocuzziAI Expert, Pod Cast Host, Real Estate Investor and Funding Specialist

Power Is Cheap. GPUs Are the Bottleneck.

20 Aug 2026 · 1 min

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Building a gigawatt-scale AI data center costs $50 billion, with $35 billion going to chips and only a small fraction to electricity. The real bottleneck isn't power generation or GPU supply, but the physical infrastructure to deliver it: poles and wires. AI companies would happily pay double for power because the revenue per unit of electricity justifies it.

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Power is cheap compared to GPUs. So if you look at building a gigawatt data center, you're gonna spend $50 billion, $35 billion of that for chips. When you compare the ratio of like the all-up CapEx of your data center to your 5-year energy cost, it is amazing how little energy costs. So when you say AI is power hungry, it's not really a cost issue. It is that energy is the bottleneck for AI. And this has a lot of ramifications because these numbers are in billions or tens of billions of dollars. Every hyperscaler has whole teams devoted to optimizing the cost of energy. But if you tell OpenAI or Anthropic today, look, we can give you power at twice the cost That's on tomorrow, they'll take it. They won't tell you that, of course, but they will take it because the revenue you can generate from a unit of electricity to the cost of it is basically the same ratio as this. So what's the challenge? The challenge is we stopped being able to build out the grid fast. And I'm not talking about power generation. We can still do that. Pretty fast, at least for solar, wind, batteries, and natural gas, but the poles and wires are a huge problem.