The Four Criteria for a Tech Bubble

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0:49This show is supported by OutShift, Cisco's incubation engine. Today's AI agents operate in silos, limiting their true potential. We've been focused on building bigger, smarter models, but scaling up is just one approach. To reach superintelligence together, we need to do more. We need to scale out. And we actually have a blueprint from 70 ,000 years ago. Humans didn't just get smarter individually. The cognitive revolution transformed society because we began sharing knowledge, goals, and innovation. Agents are now at that same inflection point. They can connect, but they can't think together.

1:27That's why Outshift by Cisco is building the Internet of Cognition, transforming AI from isolated systems into orchestrated superintelligence. By creating an open, interoperable infrastructure, Outshift by Cisco is enabling agents and humans to share intent, context, and reasoning. The cognitive evolution for agents is here. Explore the Internet of Cognition at outshift.com. That's outshift.com. Hey, Lauren. How are you doing? I'm okay, Mike. It's earnings season. So a lot of us in the business desk here at Wired have been tuning into tech companies' earnings reports and their earnings calls. And I guess that basically means it's capex season.

2:11CapEx? Capital expenditures. You say capex? Yeah, now that I'm a business desk reporter, I say capex. You're one of those. I throw it around. at parties. No, I really don't. But we are seeing a trend in how tech companies are sleeping on piles of money, but they aren't just sleeping on it. They are sharing big plans to spend on it, and especially to spend on AI infrastructure. Right. Data centers. Yeah, more data centers. Not just data centers, but yes, that's a big part of it. And this is all partly what is fueling all of this talk about a bubble, which we touched on a little bit a couple of weeks ago with our colleague, Molly Taft.

2:44But Today, we're going to be talking about AI bubble mania. Yes, we are. And our guest today has a lot to say about it. The writer Brian Merchant is joining us. Brian, welcome to Wired's Uncanny Valley. We're really thrilled to have you here, although we're not super thrilled about the topic we're going to be talking about. How are you doing today? Oh, I am as well as can be, I think. Yeah, thanks for having me. I am thrilled to get into the ins and outs of bubbledom with you both. This is Wired's Uncanny Valley, a show about the people, power, and influence of Silicon Valley. It feels like suddenly everyone started talking about the AI bubble and whether we're in one right now.

3:27Since this summer, when OpenAI's Sam Altman casually mentioned the possibility of an AI bubble, the debate has only grown louder. Just last week, Bill Gates told CNBC that yes, we are in a bubble, but added that just like in the dot-com era, quote, something profound, unquote, will come out of it. Perhaps he means an insane amount of money for the founders of chipmaker NVIDIA, which just became the world's first$5 trillion company. For context, that is 2.5 times the entire Canadian economy. Investments in AI are still flowing at full blast, and there are no signs of the money slowing down. Big tech is already on track to spend$400 billion on artificial intelligence this year, and that is still not enough.

4:12Google, Meta, Microsoft, and Amazon are pledging even more money in 2026. And yet, according to some reports, 95 % of businesses currently using AI say that they're seeing little to no return. So are we in a bubble or not? And if we are, how do we even assess that? And what is the potential fallout if the bubble bursts? To make sense of this, we have Brian on the show today. Recently, Brian used a historical framework to analyze whether AI fits the classic signs of an economic bubble, and if so, what that means for all of us. I'm Michael Kalori, Director of Consumer Tech and Culture. I'm Lauren Good.

4:51I'm a senior correspondent. And I am Brian Merchant, author of the newsletter and book, Blood in the Machine, and a contributor to Wired.

5:10So, Brian, you have set out to understand if AI is really causing a bubble, but I think we have to start by asking you to define what a bubble even is, because some purist economists would say that bubbles of any kind don't really exist. Right. I think that's why it was so important for me to try to actually sink my teeth into something a little more analytical, something a little more methodical. Because so far, it's been a lot of vibes-based commentary, right, when we're looking at what's going on with AI. And we see NVIDIA cross these, you know, thresholds. I mean,$5 trillion is just the most recent.

5:51It was pretty astonishing when it crossed$3 and$4 trillion. And it's happening so quickly. I mean, folks might not remember that it's only been really a handful of years since we even had a$1 trillion company. And so the rate of capital accumulation and concentration is really accelerating and has accelerated over the last, you know, five to 10 years. And so we look at what's going on and we look at the data center build out and it's like, it feels like a bubble. It seems like a bubble. It's got to be a bubble, right? But yeah, but what does that mean? And the most basic way to understand what a bubble is, is basically to think about the level of investment in a technology or a company or a commodity.

6:38And if more is being invested in that technology, then it will ever return in its revenues and its profits down the line. You know, you think of Pets.com or some of those early.com companies as good examples where there's way more money invested in these companies. Toys.com was worth at one point way more than Toys R Us was ever worth, despite it doing far smaller revenue figures. When it tanked and those investors never got paid out, we can say in hindsight that this was a bubble, that dot-com was a bubble because people invested more than was ever returned. And now the fear with AI is that this will happen on a truly gargantuan scale with all the money that's pouring into NVIDIA and the like.

7:26So you ended up turning to two scholars, Brent Goldfarb and David Kirsch, who wrote a book about bubbles, and you applied their framework to AI. Talk a little bit about this framework. Yeah, so, you know, honestly, I'm kind of surprised that this book hasn't figured into more discussion about the AI bubbles, given the enormity of said bubble. But in 2019, Goldfarb and Kirsch published this book, Bubbles and Crashes, The Boom and Bust of Technological Innovation. And what they did is they attempted to sort of go back and identify as many historical tech bubbles as they could and then identify exactly what made those bubbles bubbles and what sort of things they had in common, what kind of factors played into them, you know, what prevented one sort of boom from becoming a bubble and what pushed other investment booms into bubble territory.

8:25And so they come up with four main factors that determine what makes a tech bubble tick. And those are the presence of uncertainty in innovation. So whether or not this technology or innovation is going to make money, how it's going to make money, what's the business model? Is it clear that people can turn a profit from this new technology that's emerging? Number two, the number of pure play investments. That is the number of companies that are inextricably tied to the business success of a innovation or technology. So you think of something like CoreWeave in modern terms, a company that is completely dependent on the AI boom playing out because its entire business model revolves around chips, around renting out space for a cloud compute.

9:15So if the AI bubble bursts, then a company like CoreWeave is gone. So that's a pure play. Number three, novice investors, retail investors, sort of non expert investors need to have access to an investment vehicle that allows them to sink money into a new innovation. And so in this case today, it's anybody who has a Robinhood app and is investing in Nvidia is an example of retail investors taking to this innovation. Historically, we talked about the dot-com boom, and that's a really famous example of retail investors kind of like reading an article or two, maybe on Wired, and saying, oh, the internet is the future.

9:58And then there's all these companies that were going public that they could just plow their money into without recognizing, you know, maybe what the fundamentals of a given company are. Finally, you need what Goldfarb and Kirsch call a coordinating belief or an alignment of belief among investors that a particular innovation is going to be the future. And typically, this is demonstrated by a real-world use case of a technology. So you think about ChatGPT going viral and sort of organically demonstrating its consumer interest levels. In the past, you've had things like Charles Lindbergh's transatlantic flight when the aviation industry was starting to take off and he successfully flies a plane across the Atlantic.

10:47And that's, you know, at the time, that was the biggest tech demo, you know, probably in history. And investors all wanted to get into the aviation game. And then that became a bubble. So those are the four. Uncertainty, peer plays, novice investors, and alignment or coordination of beliefs. So Brian, that historical example of Lindbergh's cross-Atlantic flight is a good one because there are a lot of historical examples in the book and in your story. And I think it would help us understand uncertainty if you told us the anecdote about how we came to understand the possible uses of electricity in the 19th century.

11:27So Goldfarb and Kirsch talk a lot about the advent of electricity and electric light. And when it first comes on the scene, it's clear that this thing is a game changer, right? Electric light. Suddenly you can have streets that are illuminated. You have these giant towers that cities are buying up that are sort of sending sparks everywhere. And it's really sort of chaotic, but it's a powerful demonstration of this technology. But, you know, it's not immediately clear how that technology is going to make money, what the key market for it is going to be. Is it going to be light bulbs in the home?

12:09Is it going to be municipalities buying these giant tower lighting fixtures? You know, what is going to be cost effective? And it really takes decades and decades for all that to get sorted out. You know, we kind of think of, you know, Edison inventing the light bulb and then the world changed. It's decades. It's decades between this technology sort of getting figured out in the lab and transitioning into something that can actually make companies money. Right. So there's no obvious application right away. There's the raw technology and it's awe-inspiring, but translating that into real business takes a while.

12:44And that is certainly what it seems like we're looking at with artificial intelligence right now. Yeah, exactly. And another great example is radio. Like radio was broadcast radio. When that first started being demonstrated publicly, it was a really big deal. And everybody kind of knew that, well, this has to be something. But it wasn't clear whether it was going to be used, you know, as a marketing tool or to broadcast plays or what exactly. So it was another kind of famous bubble because it attracted a ton of investment throughout the 1920s around the same time as aviation. And there were no clear vectors for that business model right away.

13:27And again, just like sort of we are looking at with AI right now, we still don't know, right? We still don't know. We know that the chatbot is popular. We know that there are a lot of people using AI. We still don't know. Okay, so we've touched on uncertainty. The next principle of a bubble you talked about is pure play, which is, as I understand it from your story, is a company's fate rises and falls entirely on a single innovation. So I'm wondering how relevant that is for AI, which has so many moving parts to it. You know, like there's the NVIDIAs of the world, which is, okay, that's a pretty impressive single innovation.

14:07Although I'm guessing Jensen Huang and others at NVIDIA would say it's not just a single innovation, but let's just say for the sake it is. Okay. But then like you look at these models and there are so many different parts of them and possible ways that they could go and be commercialized. So how is this pure play? Yeah. So NVIDIA has effectively become a pure play company. We all know, or those of us that follow the space know that before the AI boom, NVIDIA made chips for graphical processing units for gaming. Like that was one of its big business segments. Oh, Brian, we remember the days well when at CES, NVIDIA would have a press conference every year talking about their gaming inventions.

14:47And we would be like, who's going to go cover the NVIDIA press conference? Cards. We're still talking about cards. And now it's right there on top of the mountain. Right. Yeah, because it has essentially foregrounded all of its business into supplying the chips that make the AI boom possible, right? Like, it's the classic case of selling shovels during the gold rush, right? It's the most certain bet you can probably make is that even if the AI boom, you know, starts going bust, you can imagine that we're still going to need all these chips and the chips are being bought. And so it's the most tangible sort of aspect for a lot of investors.

15:29And it's also, you know, a public company. So you can invest directly into it. And so it has become what we would consider a pure play investment where its fate is tied to the AI boom, more or less. It is a little bit interesting because there have not been a ton of IPOs in the AI era. There have been some, you know, famously CoreWeave. And usually that's one of the sort of ways that people can invest in pure play investors. But the AI boom is interesting in that sort of it's all being concentrated into a handful of companies and then a few other sort of kinds of portfolio investments like real estate companies investing in data centers and that kind of thing.

16:11So if so much of this money is just changing privately between investors and companies and the public is not really involved, then what is the risk for the public that these companies are getting so huge? Yeah, I mean, it is growing. And it's a weird case, again, because we are seeing such market concentration, which is unusual. But, you know, we have to remember that NVIDIA is something like 8 % of the entire stock market. Excuse me. Yeah. I did not know that. Yeah. So if NVIDIA goes bust, then it is a big deal to a lot of retail investors. But increasingly, what's happening is that we're seeing a lot of the sort of circular investing that is sort of common in bubble eras, where, as I mentioned, investors are starting to find ways to put money into data center expansion, for example.

17:05And that might be part of a portfolio that people aren't watching really closely and they might not recognize that their fund manager has a company that's investing in real estate that's going to the data center boom that's now sort of part of their portfolio. We have these investment deals like OpenAI cut with the chipmaker AMD, where now that is a public company. And so people have investment in AMD and its fate is increasingly tied to OpenAI due to the structure of some of these deals where OpenAI now owns a chunk of AMD. And so if that goes bust, basically we have a growing number of things that expose regular sort of retail investors, regular pension holders, people with portfolios that are otherwise diversified.

17:55There's a growing exposure to an AI bust. And I think there's a lot of argument over how severe that exposure is. But to me, I look at all of these kind of deals and these kind of vulnerabilities, and it does raise some red flags for sure. Mm-hmm. So if within this framework, the third of the four pillars is novice investors, it sounds like the real concern here is, yes, the novice investors having exposure to risky investments because everyone can just go to the Robinhood app now and do that. But actually, it's the much larger institutional investments, the fact that the soft banks of the world and NVIDIAs and then OpenAI taking a 10 % stake in AMD and all of these circular large-scale investments that are actually the biggest cause for concern in this potential bubble, right?

18:36Yes, because they're the ones wielding these phenomenal sums of money. And as Goldfarb told me in an interview, AI is so loaded with uncertainty, so unknowable on some level, that it basically leaves everybody with the status of a novice investor, right? Because nobody knows what this future is going to be. So somebody tells Sam Altman he's a novice investor. I mean, Sam Altman, you know, may be one of the first to admit that he's kind of a novice investor. This is a guy who, when they were at the early stages of open AI and somebody asked him, well, how is this company going to make money? You know, I think this is five or six years ago.

19:20And he said, completely seriously, we're going to build AGI and we're going to ask it. But that's our business plan is to establish artificial general intelligence and then ask the system itself how it's going to get. So talk about uncertainty. We're cooked, Brian. I read this book. It's called Hitchhiker's Guide to the Galaxy. Well, that is to say it's just we are just in totally uncertain waters here. I mean, I think the uncertainty is off the charts. And that's part of why I make the case that this is a bubble beyond other bubbles in the past. And that does bring us to the fourth indicator, which is the alignment of beliefs or the narrative.

20:02And as we all know, the current narrative in the world of AI is that AI is going to do everything. It's going to automate jobs. It's going to cure cancer. It's going to babysit your kids. It's going to fight climate change. And all of this will usher in an era of artificial general intelligence that's going to be able to do anything that a human can do. And the promise here is of limitless potential, which is really convenient because you just don't have to define what your goal is. You know, talk about a story, right? Like, it's the story to end all stories. And when I spoke to Goldfarb, he was like, this is the one.

20:39This one is the one that's furthest out of the park because you can't just have a story, right? There has to be some level of feasibility that is present for investors to say, you know what, this is feasible enough to happen. And so the confidence with which these AI companies have come out with their products, the claims that they've made, the demo that sort of reinforces the belief in the system's capacities, it creates this stew where they can tell investors who really want to hear a lot of this stuff, right? Oh, you can automate every job ever. Like, we've been waiting 200 years for something like that.

21:18Oh, it's going to cure cancer and we can just sort of like put input into these systems for our pharma companies and technologies. And, oh, it's going to solve climate change. All of these, there's something for everyone, right? There's something that every single investor or corporate partner wants to hear in this and could feasibly buy into. Because for the last three years now, there has been this sort of coordination of beliefs, certainly among the majority of the investor class, that, okay, this is the real deal or it's a real enough deal that we're willing to put billions of dollars on the line because we don't want to get caught if it doesn't pan out.

21:56We don't want to have missed out on the investment opportunity for the do literally everything machine.

22:04I had a conversation earlier this year for a Wired story with the AMD CEO, Lisa Su. And we went back and forth, of course, for a while about AI. And we did both agree, based on experiences we'd had with a parent being in the ICU, that we thought that there was potential for AI to really help in health and medicine. But other than that, we sort of diverged. And I was asking her questions about, like, you know, the future of scaling and inference versus training and also content moderation and what happens when we get to this point in society where AI is like people actually can't interpret what's real from what's fake.

22:43And she said, you seem pretty skeptical about AI. She turned to me eventually and I said, well, I just think the people who are most positive about it right now are those who stand to benefit the most. And, you know, like quoted William Gibson, the future is here, but it's not often evenly distributed. And that's what I think we're headed towards. Yeah, I think that's a really good point. And I think it's clear that there are going to be some victors from the AI boom, even if the bubble does burst. And I think I may be even more skeptical about some of these elements of the AI blueprint or the AI business model than even you are.

23:24But it's clear that even if the bubble bursts, there's going to be utility that's found. And that's one thing you often hear to counteract those worried about a bubble. They say, well, you know, the dot-com bubble burst and then we got all of this good stuff out of it. And that could be true on a number of levels. It could also be true that, again, the scale of this bubble could prove truly disastrous on an economic level that, you know, makes the dot-com burst pale in comparison. But it could also be that some of these use cases for AI that persist, like automating work or automating especially sort of the production of creative goods, are things that more permanently shape our economy in ways that aren't good for workers in the long term.

24:15All right. So the fundamental question of are we headed for a pop? We're going to put a pin in that question and we're going to come back to it after the break.

24:31There's a reason millions of people start their day with Morning Brew Daily. Each morning, we break down the biggest news in business from the latest tech headlines to why nobody can afford a house right now. All in a format that fits seamlessly into your day. You'll leave each episode of Morning Brew Daily smarter and ready to take on the world around you. I'm Toby Howell. And I'm Neil Freiman. And we're ready to show you that business news doesn't have to be boring. So join us on Morning Brew Daily, available wherever you get your podcasts. Welcome back to Uncanny Valley. Today, we're talking with Brian Merchant about whether AI is a bubble and what happens if that bubble bursts.

25:09Just last week, NVIDIA CEO Jensen Huang said he doesn't believe we are in an AI bubble at all. It's just a quote unquote natural transition from general-purpose computing to accelerated computing, which is something he's been saying for a long time. It makes sense that he wouldn't think of AI as a bubble, but it also consistently seems like, depending on who you ask, the answer changes. If the bubble bursts, the economy could take a hit harder than the dot-com crash of 25 years ago. But if it holds, AI could fundamentally reshape how we work and live. both could end up being true in the long run.

25:50So Brian, in the first half of this show, we talked a lot about the four-point framework that was created by Goldfarb and Kirsch. They also use a scale, a scale of 0 to 8, whether or not we're in a bubble. And when you ask them about AI, they came back with a big fat 8, a buyer beware. Yeah, they found that all of the ingredients, so to speak, were present in some of them in quite large doses. And that according to this historically informed framework, we've got the maximum level of bubble alert here. You know, it's worth saying that their framework doesn't necessarily say a lot about the potential scale of calamity, right?