Chatter about an AI bubble has been everywhere lately, and top tech companies like Google, Meta, and Microsoft have doubled down on their AI investments for 2026. But how have analysts in the past accurately identified forming tech bubbles? Hosts Michael Calore and Lauren Goode sit down with Brian Merchant, WIRED contributor and author of the newsletter Blood in the Machine, to break down the four criteria some researchers have used in the past to understand and brace for the worst.
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Transcript
Note: This is an automated transcript, which may contain errors.
Michael Calore: Hey Lauren, how are you doing?
Lauren Goode: I'm OK, Mike. It's earnings season, so a lot of us on 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.
Michael Calore: CapEx?
Lauren Goode: Capital expenditures.
Michael Calore: You say CapEx?
Lauren Goode: Yeah. Now that I'm a business desk reporter, I say CapEx.
Michael Calore: You're one of those.
Lauren Goode: 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're sharing big plans to spend on it, and especially to spend on AI infrastructure.
Michael Calore: Right. Data centers.
Lauren Goode: Yeah, more data centers. Not just data centers, but yes, that's a big part of it.
Michael Calore: 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. But today we are going to be talking about AI bubble mania.
Lauren Goode: 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 talking about. How are you doing today?
Brian Merchant: 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.
Michael Calore: 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. Since 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 dotcom era, “something profound,” 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.
Google Meta, Microsoft, and Amazon are pledging even more money in 2026. And yet, according to some reports, 95 percent 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 Calore, director of consumer tech and culture.
Lauren Goode: I'm Lauren Goode. I'm a senior correspondent.
Brian Merchant: And I am Brian Merchant, author of the newsletter and book, Blood in the Machine, and a contributor to WIRED.
Michael Calore: So 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.
Brian Merchant: I think that's why it was so important for me to try to actually sink my teeth into something a little more analytical or something a little more methodical, because so far it's been a lot of vibes-based commentary when we're looking at what's going on with AI and we see Nvidia cross these thresholds ... I mean 5 trillion is just the most recent. It 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 five to 10 years.
And so we look at what's going on and we look at the data center and it's like, it feels like a bubble. It seems like a bubble. It's got to be a bubble, right? But 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. And if more is being invested in that technology than it will ever return in its revenues and its profits down the line. You think of Pets.com or some of those early dot 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 dotcom 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.
Lauren Goode: So 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.
Brian Merchant: Yeah, so honestly, I'm 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 and what sort of things they had in common, what kind of factors played into them, what prevented one boom from becoming a bubble and what pushed other investment booms into bubble territory.
And 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 cloud compute. So 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, 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 dotcom boom, and that's a really famous example of retail investors reading an article or two maybe on WIRED and saying, “Oh, the internet is the future.” And then there's all these companies that were going public that they could just plow their money into without recognizing 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 Chat GPT 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. And at the time, that was the biggest tech demo 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, pure plays, novice investors, and alignment or coordination of beliefs.
Michael Calore: 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.
Brian Merchant: 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 lights. 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 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? Is it going to be municipalities buying these giant tower lighting fixtures? What is going to be cost-effective? And it really takes decades and decades for all that to get sorted out. We kind of think of Edison inventing the light bulb and then the world changed. It's decades. It's decades between this technology getting figured out in the lab and transitioning into something that can actually make companies money.
Michael Calore: So there's no obvious application right away. There's the raw technology and it's awe-inspiring, but translating that into a real business takes a while, and that is certainly what it seems like we're looking at with artificial intelligence right now.
Brian Merchant: Yeah, exactly. And another great example is radio. Radio was broadcast radio when that first started being demonstrated publicly, it was a really big deal and everybody kind of knew, “Well, this has to be something,” but it wasn't clear whether it was going to be used 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. And again, with AI right now, we still don't know. We know that the chatbot is popular, we know that there are a lot of people using AI. But we still don't know.
Lauren Goode: OK, so we've touched on uncertainty. The next principle of a bubble you talked about is pure-play, which, as I understand it from your story, is when 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. There's the Nvidias of the world, which is OK, that's a pretty impressive single innovation, although I'm guessing Jensen Huang and others at Nvidia would say, “It's not just a single innovation,” but let's just say for this sake it is, OK. But then 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?
Brian Merchant: 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. That was one of its big business segments.
Lauren Goode: Brian, we remember the days well, when at CES, Nvidia would have a press conference every year talking about their gaming inventions. And we would be like, “Who's going to go cover the Nvidia press conference?”
Michael Calore: Cards? We're still talking about cards?
Brian Merchant: And now it's right there on top of the mountain.
Lauren Goode: Right.
Brian Merchant: Because it has essentially foregrounded all of its business into supplying the chips that make the AI boom possible. It's the classic case of selling shovels during the gold rush. It's the most certain bet you can probably make, is that even if the AI boom 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, and it's also 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, famously CoreWeave, and usually that's one of the ways that people can invest in pure-play companies. But the AI boom is interesting in that it's all being concentrated into a handful of companies, and then a few other kinds of portfolio investments like real estate companies investing in data centers and that kind of thing.
Michael Calore: So if much of this money is just changing privately between investors and companies, and if the public is not really involved, then what is the risk for the public that these companies are getting so huge?
Brian Merchant: I mean, it is growing, and it's a weird case again because we are seeing such market concentration, which is unusual, but we have to remember that Nvidia is something like 8 percent of the entire stock market.
Michael Calore: Excuse me?
Brian Merchant: Yeah.
Michael Calore: I did not know that.
Brian Merchant: 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 this 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. And 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 retail investors, regular pension holders, people with portfolios that are otherwise diversified. There'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 kinds of deals and these kinds of vulnerabilities and it does raise some red flags for sure.
Lauren Goode: So 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 SoftBanks of the world and Nvidia and OpenAI taking a 10 percent stake in AMD and all of these circular, large-scale investments that are actually the biggest cause for concern in this potential bubble, right?
Brian Merchant: Yes. 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.
Lauren Goode: So somebody tell Sam Altman he's a novice investor.
Brian Merchant: Sam Altman 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 OpenAI and somebody asked him, “Well, how is this company going to make money?” I think this is five or six years ago. And he said, completely seriously, “We're going to build AGI and we're going to ask it.” That's our business plan, to establish artificial general intelligence and then ask the system itself how it's going to give … So talk about uncertainty
Lauren Goode: We're cooked. Brian.
Michael Calore: I read this book. It's called Hitchhiker's Guide to the Galaxy.
Brian Merchant: Well, that is to say 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.
Michael Calore: And that does bring us to the fourth indicator, which is the alignment of beliefs or the narrative. And 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.
Brian Merchant: Talk about a story, right? It's the story to end all stories. And when I spoke to Goldfarb, he's like, “This is the one that's furthest out of the park,” because you can't just have a story. 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.” “Oh, it's going to cure cancer and we can just sort of put input into these systems for our pharma companies and technologies?” And, “Oh, it's going to solve climate change?”
There's something for everyone. 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, OK, 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. We don't want to have missed out on the investment opportunity for the do-literally-everything machine.
Lauren Goode: I 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 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. And she said, “You seem pretty skeptical about AI.” 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 I quoted William Gibson, “The future is here, but it's not often evenly distributed.” And that's what I think we're headed towards.
Brian Merchant: 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. But 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, the dotcom 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 makes the dotcom 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.
Michael Calore: All 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.
[Break]
Lauren Goode: 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. Just 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 dotcom crash of 25 years ago. But if it holds, AI could fundamentally reshape how we work and live, and both could end up being true in the long run. So 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 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.
Brian Merchant: Yeah. They found that all of the ingredients, so to speak, were present and 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. It's worth saying that their framework doesn't necessarily say a lot about the potential scale of calamity, that if the bubble bursts, it's just like how much of each of these four different factors do we have? And then based on that, can we say that this is a bubble? So that I think is where they would probably want to say that their analytical framework ends, and they wouldn't necessarily say what I have said, what I use their framework to conclude, which is that this could be a bubble to end all bubbles.
Michael Calore: Well, just playing devil's advocate, even if this is the bubble to end all bubbles, history shows that these technological innovations don't just go away once the economic incentives around them lessen. I mean after the dotcom crash, the internet just kept growing and growing and I work for a dotcom 25 years later, could the same path be true for the AI industry?
Brian Merchant: Oh yeah, no doubt. I don't think even any of the fiercest bubble critics ... Well maybe the very fiercest bubble prognosticators would say that a bubble would just sort of banish AI from the world. I think that's completely unlikely, personally. I just think that there could be a lot of economic pain that happens in the interim. I think that AI has proven too popular among a certain user base for it to go away altogether. And tech companies have seen how popular these products are, and they still may need to find a way so that every query doesn't end up costing them money due to their compute power and resources that it drains. And it's very well likely that they could. But that said, I think it's obvious to me anyways, after going through this scale that the enormity of the investment sort of outpaces the level of utility that we're probably likely to see.
And I'm not alone there. There's a lot of more hardline investment banker types who are making similar conclusions, and some of the mania may be obscuring the actual balance sheets here, but it's entirely possible that AI continues to be a factor. It's interesting because one of the things that stuck around after the dotcom burst was the telecom infrastructure, right? Was like the build out of fiber-optic cables and that continue to let the internet run. The chips are in kind of a different situation because the chips are constantly being upgraded, and the chips today may not be super useful 10 years from now. So it's a little bit of a different story. I certainly think that pieces of this are going to stick around, and certainly companies will emerge victorious, but my crystal ball is just as good as yours.
Lauren Goode: Yeah. Brian, I'm glad you brought up broadband infrastructure versus chips because one of the questions that I started to have as I was reading your article, and maybe I'm so far off, but I was hoping maybe to concretize it, is I was wondering whether what ends up being enduring about generative AI is maybe its influence rather than its real potential for commercialization. If we compare it to previous bubbles, what if AI's long-term durability is more comparable to something like content, radio, social media, basically generating content, replacing knowledge work, maybe it's not as comparable to underlying infrastructure. What are your thoughts on that?
Brian Merchant: I would agree with that. I think that's its primary utility. I mean, the interesting thing about generative AI is that I think even its most full-throated proponents would agree that the quality of the content that it's putting out isn't miles better than what humans are doing. And in some cases it's even worse. Again, it's a cost proposition for a lot of companies. That's why I think that a lot of it does come down to this automation question, this question of labor. I think you've got really two buckets to look at. You've got the chatbot that sort of maps onto the social media space where it's a similar product where people are going to spend their time with this, and you can even think of that as kind of the automation of a human relationship. And then you have the labor automation bucket where a lot of people are saying, “Well, I can automate this part of my team's workflows or maybe I can cut down on labor costs over here.”
And again, the promise is that it will soon be able to do all of it. And I think that's one of those instances that I think is also indicative of a bubble, where what the sales pitch is and what's actually happening on the ground. And I think listeners may remember that a lot of this bubble talk, this most recent round of it got started when MIT published this study that said 95 percent of the firms that have invested in generative AI as an automation tool have not yet seen returns or profits as a result of their AI investment yet. So that's a pretty big number, and that started raising questions about how it is actually fairing on the ground.
So I tend to agree with you, I am still very skeptical that AI is reliable enough to be replacing the broad swath of functions in our daily digital lives where it will become an infrastructure-level thing. I more see it as potentially becoming one of the things that we do online and that is integrated into a lot of services maybe, or some services, but that isn't necessarily completely transformative in the way that the dotcom boom sort of inaugurated this era where everybody's online all the time or radio become a distinct feature of most Americans' lives or electricity being piped into every household. I think AI is probably going to be there, but I think what you're saying is right, it's a content production tool and it's a sort of a wage reduction tool.
Michael Calore: So you're saying that we're all going to be watching slop feeds and all of the YouTube videos that we're watching are going to be AI-generated, and our bosses are going to be using AI avatars to talk to us on.
Lauren Goode: They already do. Have you ever gone through one of those, like, choose your health care plan bots? It's there.
Michael Calore: So this does sound like a sort of healthier path towards the future, the fact that these things are encroaching on our daily lives and not being as transformative as the companies are promising possibly. I'm just trying to imagine a future here in which this bubble actually sustains and does not result in a crash, does not pop. It's not an 8 on the scale, maybe it's more of a 6 and there's a way to turn it around. But then again, I'm the optimist on the show, so I would say that.
Brian Merchant: Look, I think we are in a very unique moment right now, both socially, technologically, and politically, right? There are also a number of sort of avenues that have not often been explored in sort of this socioeconomic and political formation of a technology that is experiencing a bubble.
So for example, we have not really seen an administration that's been willing to take a 10 percent stake in an American technology company before. So Intel is now 10 percent owned by the federal government, and we know that the administration is a big fan of AI. Just look at its Twitter feed at any given time, you'll see AI-generated content, and that it finds this technology useful on a number of levels. So I think one possible outcome is if the bubble does start to burst, then we might see an administration that's willing to economically intervene in that bursting and maybe prop up some of the firms or buy stakes in them, which would, again, we're in an unprecedented moment on a number of levels, and I certainly wouldn't discount that from happening, from the state becoming a partner in these AI companies should they face a financial crisis.
Michael Calore: Well, that is a very strange place to end the conversation, but we do have to take a break. So let's take a break and we'll come right back.
[Break]
Michael Calore: Lauren and Brian, thank you so much for a great conversation about this bubble that we're all sitting on top of. I think everybody will be watching closely to see how it plays out over the next few months and years. In the meantime, you can read Brian's story on WIRED.com about the bubble, but for now we're going to dive into our new segments called WIRED and TIRED. Whatever is new and cool is WIRED. Whatever passé thing is on the way out is TIRED. So Lauren, do you want to go first?
Lauren Goode: OK, I'm going to start with my TIRED. My TIRED is meetings.
Michael Calore: OK.
Lauren Goode: I think there are probably two specific reasons you should have a meeting and then otherwise you should not have a meeting. And the first form of meeting that I think works really well is when you have an agenda and you bullet point it out and you get through it as quickly as possible because there are just things that you need to say to people in person or on a Zoom or something like that. But you should not go in with an agenda and meander. You should be very clear and explicit about the agenda.
And then the other kind of meeting that can be useful are the free-flowing, open brainstorms where you get together specifically without an agenda. But the idea is that you're going to benefit some of your team or your group of people. Your people are going to benefit from just having that free-flowing conversation.
Michael Calore: The no-bad-ideas meeting.
Lauren Goode: The no-bad-ideas, the yes and meeting, it's the in between that just kind of drives me crazy and we don't have time for it, folks. We don't have time. We got to get stuff done. It's like, sorry to be the Q4 person, but it's like getting to the end of the year.
Michael Calore: Sorry to be the—
Lauren Goode: Sorry to the KPI person.
Brian Merchant: It's your business desk instincts taking over again.
Lauren Goode: Yeah, it's all right. It's just in your personal life too, right? It's like, let's not have the in-between where you say we have something to accomplish, but then we're going to just completely monopolize the time.
Michael Calore: Your TIRED is all meetings.
Lauren Goode: A certain category of meeting.
Michael Calore: The meeting without the agenda.
Lauren Goode: But ends up being the meeting that everyone has.
Michael Calore: OK.
Lauren Goode: Yeah. So either be agenda or brainstorming and nothing in between.
Michael Calore: What's your WIRED? No meetings.
Lauren Goode: My WIRED is read people's faces better. I think when we communicate, this is like Lauren's life hacks. I think now these days when we talk to people often in person, we're still consumed by screens. Our phones are on the table in front of us. Maybe we're taking meetings with laptops in person. Just look at people's faces when they talk to you. It's remarkable how much more you can understand about what they're feeling and what they're trying to convey, and you actually remember what they say better when you're just looking them in the eye. And it's hard for some people. I do understand that, but just try to do the best you can to actually look at someone and be present with them when you're talking to them.
Michael Calore: Solid.
Lauren Goode: Those are my WIRED and TIRED or TIRED and WIRED or whatever we're calling it. I literally just came up with those. Brian, please do something better. Please save us. Save me.
Brian Merchant: I'm going to say TIRED are AI companions because talk about no face time at all. I've seen enough, right? This is a toxic development for society in general. No shade on anyone who is using an AI companion, but I think it's time to wind it down. We've seen what these things are doing to people in vulnerable states. We've seen what happened with social media in decades past, and this feels like just like social media on steroids and just catering to your every whim. And as Lauren said, I am a firm believer we got to get back at looking at people's faces and crawl out of these solipsistic digital relationships. So TIRED, AI companions—get rid of your Friend pendant, if you are one of the 14 people who have one.
And WIRED, I will say, again, calling back to what Lauren said are the Luddite clubs, these kids in New York City growing chapters around the country who are embracing, I think that exact tendency that you're talking about, the need to see more people's faces, more person-to-person connection. That great funny campaign in New York City where people are scrawling on the AI friend graffiti on the subways, and it's turning into this real thing. I write about this stuff. I am the Luddite Rehabilitator in Chief in some ways. I wrote a whole book about why the Luddites were actually right, and we've got them all wrong. So I hear from people and people who are trying to stand up to Big Tech to try to get more in-person time and to sort of stop AI from overrunning their lives completely. So WIRED, the Luddites.
Michael Calore: Nice.
Lauren Goode: All right, Mike, what's your WIRED/TIRED?
Michael Calore: OK, so we're in shoulder season right now, right? It's between the summer and the fall, at least here in San Francisco. And I think it's kind of the same in New York where it's like it's still nice during the day, but it's chilly at night and it's time to start wearing your long unders, your base layers. So I'm going to say for right now my WIRED is capilene base layers and my TIRED is wool base layers. Now I love wool. Wool is great. It's a little itchy. Some people have problems with it for ethical sourcing reasons, for veganism reasons, some people are allergic to it. So I have this great alternative which I've been testing, which is a capilene base layer.
Capilene is a fabric that is 100 percent recycled polyester that has been dyed and treated and everything and performs very close to wool. It's also lighter in weight and it is not as stuffy as wool is. So when you need a base layer, which is like when you're going out exercising early in the morning or you're going to be out hiking and you don't know what the weather is going to be like, you want to wear a good base layer. If you wear capilene, you can get something that's a little bit lighter, performs about the same as wool without the intense heat trapping and the intense itchiness that wool can bring you. So they are about as expensive as wool. They're a little bit stinkier than wool is after you wear them all day or if you sleep in them when you wake up in the morning. But they're really nice. So give capilene a shot if that's—
Lauren Goode: Huge fan of capilene.
Michael Calore: Are you? That's great. Great to hear.
Lauren Goode: I mean, I don't do snow sports that often, but when I do, great base layers.
Michael Calore: Yeah. All right, well thank you both. Brian, thank you for joining us.
Brian Merchant: It was my pleasure. This was a lot of fun.
Lauren Goode: We'll have to have you on again soon
Michael Calore: When the bubble bursts.
Lauren Goode: Oh, dear.
Michael Calore: Thank you for listening to Uncanny Valley. If you liked what you heard today, make sure to follow our show and rate it on your podcast app of choice. If you'd like to get in touch with us with any questions, comments, or shows, suggestions you can write to us at uncannyvalley@wired.com. Today's show is produced by Dara Lookpots and Mark Leyda, Amar Lal and Macrosound mixed this episode. Mark Leyda is our San Francisco studio engineer. Daniel Roman fact-checked this episode. Kate Osborn is our executive producer. Katie Drummond is WIRED's global editorial director. Chris Bannon is Condé Nast's head of global audio.
