AI's compute demands are driving a precarious $7TN infrastructure buildout. In Part 1, I examine the context and the soaring capital expenditures for training. Tomorrow: Operating costs for inference.
I'm still having a hard time comprehending the staggering amounts of money and the scope. But this just made my jaw drop:
"The recent revelation that Microsoft has committed $100 billion to AI infrastructure through 2027 includes a single data center in Wisconsin, which is supposed to be the 'world’s most powerful AI datacenter,' per Microsoft. It is one of two data centers Microsoft is building in the state that will require a projected total of 3.9 gigawatts of power, which would be enough for 4.3 million homes. In a state with 2.82 million homes, that is extraordinary. And it illustrates the capital moats emerging."
I'm still having a hard time comprehending the staggering amounts of money and the scope. But this just made my jaw drop:
"The recent revelation that Microsoft has committed $100 billion to AI infrastructure through 2027 includes a single data center in Wisconsin, which is supposed to be the 'world’s most powerful AI datacenter,' per Microsoft. It is one of two data centers Microsoft is building in the state that will require a projected total of 3.9 gigawatts of power, which would be enough for 4.3 million homes. In a state with 2.82 million homes, that is extraordinary. And it illustrates the capital moats emerging."