GDP-moving amounts of money on GPUs and such, it is not, by definition, being spent on something else.

Some examples:

• Non- life science venture capitalists are mostly only doing AI right now. Have something else needing funding? Good luck with that.

• Cloud compute companies are diverting spending from cloud offering to GPU-centric data centers. Am

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2. Where is the Money Coming From?

Private capital expenditures don't, for the most part, come from nowhere. While governments can print money to spend, companies generally can't. Where is all this capital coming from?

For the most part, six sources:

1. Internal Cash Flows (Primary for Microsoft, Google, Amazon, Meta , etc. )


2. Debt Issuance (Rising role)


3. Eq

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Compare this to prior capex frenzies , like railroads or telecom. Peak railroad spending came in 19th century, and peak telecom spending was around the 5G/fiber frenzy. It's not clear whether we're at peak yet or not, but ... we're up there. Capital expenditures on AI data centers is likely around 20% of the peak spending on railroads , as a percen

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Here are some basic numbers to get things started:

• US 2025 GDP: $25 trillion (Source: Consensus forecasts and extrapolations from BEA data)

• Nvidia sales to datacenters (Q1 FY2026): **$39.1B (**Annualized: $156.4B)

• % of Nvidia datacenter sales that is AI related: ~99% (Primarily H100/GH200 sales to hyperscalers and enterprises)

• Nvidia share of

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Conclusion

We are in a historically anomalous moment . Regardless of what one thinks about the merits of AI or explosive datacenter expansion , the scale and pace of capital deployment into a rapidly depreciating technology is remarkable. These are not railroads—we aren’t building century-long infrastructure . AI datacenters are short-lived, asset-i

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