AI CapEx

This week we talk about tech bubbles, building moats, and infrastructure investment.

We also discuss capital expenditure, data centers, and employee compensation.

Recommended Book: The Art of Gathering by Priya Parker

Transcript

Many technology booms have early periods in which innovators have a first-mover advantage, and a lot of what happens in their industry is informed by the decisions those innovators make.

After that—depending on the technology, but this is common enough to be considered a trend—after that there tends to be a period of build-out and consolidation amongst the people and business entities that survived that initial, innovation-focused throw-down.

In the context of personal computers, this moment saw computer-makers like Microsoft and Apple scramble to pivot from figuring out what an operating system should look like and whether or not to use mice to navigate user interfaces, to a period in which they were rushing to scale-up the manufacture of now-essential, but previously comparably rare components: suitable screens for their monitors, chips that could power their increasingly graphical machines, and the magnetic materials necessary to produce floppy disks and spindle-based hard drives.

There’s an initial period in which new ideas and approaches provide these entities with a moat that protects them against competition, in other words, but then the game they’re playing changes, the rules are more fully understood and to some degree locked into place and agreed upon, and instead of competing for the biggest, most brazen new ideas, they lock onto one set of ideas that seemed to be the best of what’s available at that moment and build on those, iterating them at a regular cadence, but focusing especially on scaling them.

So at this second stage, they’re investing in the ability to out-produce their competition in some way, so they can eventually bypass that competition and (they hope) safely increase their prices and make a profit, as opposed to just larger and larger revenues with equal or greater expenses, continuing to be reliant on investor injections of capital, rather than generating their own surplus returns.

By many analysts’ and insiders’ estimates, we’ve just entered that second stage in the generative AI industry. That’s the sort of AI that generates text and images and code and such, and it’s increasingly becoming a sort of commodity, rather than a new, hot things that few companies can offer the market.

What I’d like to talk about today are the increasingly massive financial figures associated with this industry’s shift to that second stage of development, and why some of those insiders and analysts are voicing fresh concerns that this could all lead to a bubble, and possibly an historically large one.

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There are many ways we could measure the growth of the AI industry over the years.

The US market size, for instance, which is a measure of the value of AI-oriented companies based on how much shares of their company cost or would cost on the open market, has ballooned from just over $100 billion in 2022 to an estimated $174 billion in 2025. That figure is expected to grow at a not quite 20% compound annual growth rate through 2034, which, if accurate, would put this market, in the US alone, at more than $850 billion.

Another metric we might use is that of capital expenditure, or capex, in this corner of the tech industry, which refers to the amount of money AI companies are using to buy, upgrade, or maintain their long-term assets, like new computer chips or the data centers they fill with those chips.

The seven most valuable US tech companies—Meta, Alphabet, Microsoft, Amazon, Apple, NVIDIA, and Broadcom (that last spot formerly held by Tesla, which was dropped from this designation in late-2024)—just those seven companies have spent $102.5 billion on capex this last financial quarter (and most of that was from just four of them, Meta, Alphabet, Microsoft, and Amazon, the remainder only spending something like $6.7 billion).

That’s a staggering amount of money, and due to a recent drop in consumer demand—the money individual US citizens spend on things like food and clothes and smartphones and cars and all the other things people buy—AI-related capex, spending by these massive US tech companies, has added more to GDP growth than consumer spending for the past two quarters.

All the things all the people in the US bought over the past two quarters did not cost as much, in aggregate, as what these companies spent during the same period, on new and existing assets. That’s pretty wild.

And it’s the consequence, partly, of the shift in these companies’ focus from providing goods and services that relied heavily on people—salary and stock compensation, basically, which is not a