This episode argues that the current AI infrastructure buildout — data centers, GPUs, chips, and power — is unprecedented in scale and already materially affecting U.S. GDP and capital allocation. Paul Kedroski explains that a majority of data-center costs are GPUs (short-lived assets that must be refreshed every few years), which makes this boom fundamentally different from long-lived infrastructure bubbles like railroads or fiber. The conversation outlines how hyperscalers, private credit and PE firms, REITs, and even insurance vehicles are using opaque financing (SPVs and similar structures) to spread and hide risk, concentrating exposure across the financial system. They discuss second-order effects: capital being sucked away from manufacturing and other sectors, local NIMBY and energy-grid tensions, and a plausible two- to three-year window when returns on AI CapEx could come under severe stress. Still, Kedroski is bullish on practical, “under‑the‑hood” AI uses — e.g., automating messy business-language and supplier-integration tasks — even as he warns of a likely bubble phase.
DEREK THOMPSON (01:39 - 05:00): Today, the AI bubble. This year, American tech companies will spend about 300 to $400,000,000,000 on artificial intelligence. That's more in nominal dollars than any group of companies has ever spent to do just about anything. And notably, these companies are not anywhere close to earning back that 400,000,000,000 that they're about to spend. This is why you're starting to hear some people wonder if the AI build out is turning into the mother of all economic bubbles.
Sometimes you'll hear this case from critics of the technology. Critics will sometimes point out that we're on track to spend trillions of dollars this decade building something that might be all smoke and mirrors. I'm more interested though in the boosters of artificial intelligence. They'll sometimes argue that we are living through a transformative tech akin to the creation of the Internet or the railroads or the telegraph. I think they might be right.
I also think they don't realize what being right would imply. The infrastructure build out of the Internet created an enormous bubble in the late nineteen nineties and early two thousands. The infrastructure build out of the telegraph created another bubble in the nineteenth century. The construction of the transcontinental railroad system, as we explained in a previous episode, created several bubbles ending in the Panic of eighteen fifty seven, the Panic of eighteen seventy three, and the Panic of eighteen ninety three, a half century of panics. In the twentieth century, radio was a bubble.
The dawn of automobiles and aviation companies, also quite bubbleicious. In short, if AI's boosters are right with their comparison of AI to the greatest technology of the last one hundred fifty years, their own analogy anticipates that their product too will pass through a calamitous crash on the way to changing the world. This should absolutely scare you if you care about The US economy. Half of GDP growth comes from infrastructure spending on AI, on data centers, chips, and energy. More than half of stock market appreciation in the last few years comes from companies associated with AI.
If you open up the hood of these biggest companies, Meta, Microsoft, Alphabet, Amazon, AI infrastructure spending or CAPEX accounts for, you guessed it, nearly half of their revenue. If the AI spending project blows up in the next few years, as our next guest says it might, the implications for technology, the economy, and politics would be immense. Paul Kedroski is an investor and writer. Today we talk about the AI boom: how it works, who's paying for it, and how they're financing it. We put the AI build out in historical context.
And then we spend a great deal of time walking through what could go wrong and when it might go wrong. I'm Derek Thompson. This is Plain English. Paul Kedroski, welcome to the show.
PAUL KEDROSKI (05:01 - 05:02): Hey, Derek. Good to be here.
DEREK THOMPSON (05:02 - 05:04): Before we start, who are you and what do you do?
PAUL KEDROSKI (05:06 - 05:56): Yeah. That's a good question. So my I have a couple of day jobs. One day job is I'm a partner with a venture capital firm called SK Ventures where we're mostly doing early stage investing, which is to say high failure rate, low capital, most things break. And then I also sit in as a as a fellow at the MIT Center for the Digital Economy.
So this is sort of where it's closer to the the spirit of some of the things we're working on. And then I also have a newsletter that goes out to a bunch of hedge funds and generally to hedge funds and buy side firms and things like that. Just because my background way back when was I was on the sell side, I worked for a brokerage firm and I've just never been able to shake that, so I can't help myself. Sometimes I just provide I wanna give them advice and whether they like it or not and so I still do a lot of work with a bunch of hedge funds and buy side firms, which is takes us back to data centers and AI and blah blah blah.