Plain English with Derek Thompson
Plain English BEST OF: This Is How the AI Bubble Could Burst
27 Jan 2026
Chapter 1: What is the main topic discussed in this episode?
If you're a fan of the inner workings of Hollywood, then check out my podcast, The Town, on the Ringer Podcast Network. My name's Matt Bellany. I'm founding partner at Puck and the writer of the What I'm Hearing newsletter. And with my show, The Town, I bring you the inside conversation about money and power in Hollywood.
Every week, we've got three short episodes featuring real Hollywood insiders to tell you what people in town are actually talking about. We'll cover everything from why your favorite show was canceled overnight, which streamer is on the brink of collapse, and which executive is on the hot seat. Disney, Netflix, who's up, down, and who will never eat lunch in this town again.
Chapter 2: How much are American tech companies spending on AI?
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Hi everybody, Derek here. In December, my wife and I welcomed our second baby girl into the world. I'm gonna be taking some time off, but we wanted to keep the pod going through the holidays. So we're gonna be re-airing some of our favorite episodes from the last 12 months, a kind of best of compendium.
And this list includes interviews that really stuck with me and others that really stuck with you and you had lots of feedback and thoughts on, including this one. I'll be back in the new year with fresh content, but until then, happy holidays and happy new year. Today, the AI bubble.
Chapter 3: What historical parallels exist for AI investment and economic bubbles?
This year, American tech companies will spend about $300 to $400 billion 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 billion 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 railroad or the telegraph. I think they might be right. I also think they don't realize what being right would imply.
Chapter 4: What implications does AI spending have on the US economy?
The infrastructure build out of the internet created an enormous bubble in the late 1990s and early 2000s. The infrastructure build out of the telegraph created another bubble in the 19th century.
The construction of the transcontinental railroad system, as we explained in a previous episode, created several bubbles, ending in the Panic of 1857, the Panic of 1873, and the Panic of 1893, a half century of panics. In the 20th century, radio was a bubble. The dawn of automobiles and aviation companies, also quite bubblicious.
Chapter 5: Who is Paul Kedrosky and what insights does he provide?
In short, if AI's boosters are right with their comparison of AI to the greatest technology of the last 150 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 Kudrowski 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 Kudrowski, welcome to the show.
Hey Derek, good to be here. Before we start, who are you and what do you do? Yeah, that's a good question. So 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 fellow at the MIT Center for the Digital Economy. So this is sort of closer to the spirit of some of the things we're working on. And then I also have a newsletter that goes out to a
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Chapter 6: How does AI infrastructure spending affect job markets?
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 want to 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 takes us back to data centers and AI and blah, blah, blah.
Well, you should know your newsletter doesn't just go out to hedge funds. It also goes out to podcast hosts, which is one reason why you're on this show. Yes. One thing I find so interesting about your analysis is that artificial intelligence is sometimes talked about as being the technology of the future.
And I'm trying to ring the bell very loudly that AI is the most important economic phenomenon of the present. It is here. It's happening right now. And you've been sounding the alarm maybe more than just about anybody. or more effectively than just about anybody, on just how massive U.S. investment in artificial intelligence is by historical standards.
So why don't you just start with your thesis statement? How big is this?
So, yeah, let's maybe go, can I go back and tell a quick backstory here first?
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Chapter 7: What are the risks of the current AI investment strategy?
Just because what got me interested was sort of what you're describing, which is there's a huge amount of money being deployed. It's going to a very narrow set of recipients, some of these chip firms and others, and it's going to some really small geographies like Northern Virginia. So it's an incredibly concentrated pool of capital, and yet it's so large that
that when you do the aggregating and do the math, it seems to be large enough to affect GDP. So I was saying, okay, fine, this is crazy. I should do the math. So I did the math and I found out that in the first half of this year,
data center-related spending, so spending on these giant buildings full of GPUs and racks and servers and what have you, that are then used in turn by the large AI firms to generate responses and train models, that that probably accounted for something like half of GDP growth in the first half of the year, which was absolutely bananas.
And I was like, I did the math four or five different ways trying to prove myself wrong. And then I said, okay, fine. This feels like something I should mention. And so I said it, and...
I think it's a startling figure for a whole bunch of reasons, one of which you alluded to, which is that even compared to historical spending, whether you pick the telecom bubble or railroads or whatever else, and we can dive into those, it's unprecedented.
It's also unprecedented because of the nature of the spending, which I think is incredibly important because railroads are very different from GPUs, not just in the trivial sense, but in some very deep and important ways. And all of this gets missed, but the upshot is Spending is huge. It's driving the economy. People are very confused about this.
And as a result, you end up making bad policy decisions because you think policy decision A is driving the economy when it's this wacky stuff over here on the left.
So we're talking about infrastructure boom that is on par with the broadband build out of the 1990s, early 2000s.
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Chapter 8: How might the AI bubble impact future economic conditions?
Still behind, it seems like the railroad boom of the 19th century, but we're talking essentially about an amount of spending on one emerging technology that is without precedent in at least 60 to 100 years. How does AI CapEx break down, right? We're talking about capital expenditures. So money that's being spent on essentially machines rather than people.
How much of this is chips versus energy versus building the actual data centers themselves? Is there a good way to think about where all this money is going?
A little more than half the cost of a data center are the chips that are going in. So say 60%, it varies depending on the model of the data center, because there's a whole bunch of different styles of data centers, if you will. There are some that are built almost on spec. Think of companies like CoreWeave, where they're buying it, and it's almost like they're hoping to tend into building.
Think about it as commercial real estate. And I'm hoping to get people to move in. I'm building a shell and people are moving in and I'm hoping to get tenants and then the tenants pay rent. Right. So think of it in those terms. And then there's the Metas and the Googles and the Amazons where they're using a huge amount of what they're building, which, again, roughly.
50% to 60% of it is the GPU cost. The rest is a combination of cooling and energy. And then a relatively small component is the actual construction. So think about the frame of the building, the concrete pad, and purchasing the real estate. So you can break it out that way. So it depends a little bit on what you're planning to use it for.
If I'm trying to build something that's for training, well, I'm going to buy more expensive GPUs, I need the latest products from NVIDIA. If I'm building something that's more for inference, meaning that I'm just going to have using it largely for people that are trying to generate responses, I'm hoping, well, then I don't need the latest GPUs and I can cut costs and cut some corners in there.
So you can think about it as that continuum.
You've compared the infrastructure boom several times to the railroads, the fiber build-out. You also indicated that there's ways in which that analogy does not hold up. So I want to get into that, right? What the analogy misses. Like the rail that we laid in, say, the 1860s was still around in the 1890s. Maybe some of it's still around the 1950s, right?
The fiber optic cable that was laid still works for years. But I keep seeing news headlines about GPUs getting better all the time, right? So I wonder, like, are companies buying something they're going to have to replace in like three years?
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