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I'm trying to build a framework for thinking about AI CapEx spend relative to the return, i.e., what revenue and profitability are required to generate a reasonable return.
1. CapEx Spending
For Microsoft, Amazon, Alphabet, and Meta, combined CapEx has gone roughly:
- 2023: ~$151B
- 2024: ~$246B
- 2025: ~$410B
- 2026 (guidance): ~$725B
- 2027 (consensus): ~$920B
That's almost a 5x increase in three years.
Historically, this group spent in the low teens as a percent of revenue. Today it's around 45%, with individual companies ranging from about 25% (Amazon) to 50%+.
2. Defining the Return
The way I'm thinking about it is:
«Required incremental profit = Incremental invested capital × Target ROIC»
«Required incremental revenue = Required incremental profit ÷ Operating margin»
My assumptions:
- AI infrastructure earns around a 25% operating margin.
- GPUs probably have closer to a three-year economic life than people assume. I'm not able to explicitly model this assumption other than referencing the telecom bubble where Cisco still managed to generate 60% margins on the equipment, no matter how much additional functionality they added. Basically the hyperscalers are the new dump pipes.
- There's also an NPV issue since the CapEx comes years before much of the revenue which would require more math than I'm currently able to do at this point.
3. Backing into the Required Breakeven Revenue
I've found two independent approaches:
Sequoia's "$600B Question" and Bain & Company both arrive at roughly the same answer: AI ultimately needs something like 4–5x annual revenue relative to annual infrastructure spending.
4. Implied Growth
One assumption I'm making is that these companies still require a baseline level of CapEx to support their existing businesses. Prior to the AI build-out, combined CapEx was roughly $250–300B annually, so I'll use that as a rough proxy for baseline investment.
If total CapEx reaches roughly $900B annually, that implies about $600–650B of incremental AI CapEx.
Applying the 4–5x framework to that incremental investment implies the AI business ultimately needs to generate roughly $2.5–3.0T of annual revenue.
As a generous starting point, call today's AI-related revenue roughly $400B by including essentially all of AWS, Microsoft Intelligent Cloud, and Google Cloud—even though that almost certainly overstates true AI revenue.
Going from roughly $400B to $2.5–3.0T over four years implies roughly a 60–70% annual revenue CAGR at a minimum. Additionally, you probably need to multiply the 2030 revenue estimate by at least 15% to account for the time difference between the spend and the return, assuming modest inflation.
One additional assumption I'm trying to think through is customer concentration. Today, a meaningful portion of AI infrastructure demand appears to come from a handful of companies like OpenAI and Anthropic. If demand broadens to thousands of enterprises over time, these growth assumptions become much more believable. If demand remains concentrated in just a few frontier model companies, the hurdle becomes significantly higher.
I'm curious how others are evaluating the spend in terms of ROI.