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The binding constraint in AI at scale is compute infrastructure: training large models and running inference at commercial volume demands specialized processors, purpose-built data centers, and the sustained power delivery to keep them running.
Cumulative capital expenditure by Google, Amazon, Microsoft, and Meta has now crossed $1 trillion since the AI boom began in 2023, a tally that reflects how far each company has moved to control that layer of the stack rather than lease it.
What the spending is actually buying Where this capital lands in the infrastructure stack determines how it should be read.
The specific unit that drives the economics is cost per compute cycle, which depends on whether a company owns or leases its hardware at scale.
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