The a16z Show - Why This Isn't the Dot-Com Bubble | Martin Casado on WSJ's BOLD NAMES

Episode Date: February 5, 2026

Christopher Mims and Tim Higgins of the Wall Street Journal sit down with a16z General Partner Martin Casado on WSJ’s Bold Names to ask whether the AI spending boom is a bubble waiting to burst. Martin explains why the fundamentals differ dramatically from the dot-com era—when WorldCom had $40 billion in debt versus today's tech giants with hundreds of billions on their balance sheets—and why a speculative valuation correction shouldn't be confused with systemic collapse. They also discuss where a16z sees opportunity in the "long tail" of AI companies beyond the state-of-the-art large language models.Follow Martin Casado on X: https://twitter.com/martin_casadoFollow Christopher Mims on X: https://twitter.com/mimsFollow Tim Higgins on X:  https://twitter.com/timkhigginsCheck out WSJ’s Bold Names: https://www.wsj.com/podcasts/wsj-the-future-of-everything Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What if everyone who's calling this a bubble has forgotten what a real bubble actually looks like? The first live video stream on the internet was a coffee pot. In 1991, a Cambridge researcher pointed a camera at the breakroom pot so he'd know whether there was coffee before walking downstairs. People called it a toy, a gimmick with no serious application. The coffee pot webcam, in no small way, became Netflix. The pattern repeats. Every major technology wave starts with use cases that look trivial

and every time skeptics confuse silliness with insignificance. 30 years later, hundreds of billions of dollars are pouring into AI infrastructure. Consultants estimate the current spending would require AI revenue to grow 40x by 2030 to justify it. The dot-com comparisons write themselves. But the dot-com crash wasn't just overvalued stocks. It was a fiberglut financed by WorldCom, a company with $40 billion in debt that was cooking its books, compounded by 9-11. The company's funding today's AI buildout have hundreds of billions of cash in their balance sheet. comparing valuations isn't the same as predicting systemic collapse.

That distinction matters. This conversation examines what separates a speculative correction from an economic crisis and why this moment may look more like the mobile or cloud booms than dot com. Martine Casato is a general partner at A16Z and a few months ago, he joined the Wall Street Journal's Bold Names podcast. We're sharing that discussion here. You know, it's interesting. You were in San Francisco and Silicon Valley during the last bubble bursting.

What are the signs you're going to be looking for that San Francisco's in a bubble again? I mean, the late 90s are just so wild. I mean, I think people forget. I think it takes maybe 20 years to forget what these things look like. It was the limos, the parties, it was the taxi drivers offering stock tips. I mean, like the janitor at one of the startups my friend worked at, like didn't want to get paid in cash, wanted to get paid in equity. It was just total, total chaos. You know, that's not where we are right now.

I mean, I think we just forgot what a true bubble looks like. Today, on bold names, we have Martin Casado. He is a general partner at venture capital firm Andresen Horowitz, where he is responsible for their billion-dollar infrastructure practice. And, MIMS, going into this episode, I think we had one big question for Martin. Are we in a new tech bubble, this time fueled by all of that excitement around AI? And also fueled by debt. specifically the debt these companies are taking on in order to pay for that AI infrastructure. It's a huge multi-billion dollar bet.

It's unclear if it's going to pay off. But for answers, let's hear from Martine. From the Wall Street Journal, I'm Christopher Mims. And I'm Tim Higgins. This is Bold Names, where you'll hear from the leaders of the bold name companies featured in the Wall Street Journal. Today we ask, will the big bet on artificial intelligence pay off? Martin, thank you so much for joining us. Hundreds of billions of dollars are pouring into artificial intelligence right now.

Help us understand where that's going. What's what exactly are, is that those dollars flowing? Is it into programs? Is it product development? Is it the picks and shovels? Where are you seeing that money go? Well, well, certainly there's a mix. The vast majority is going into actual data center capacity.

So this is GPUs. This is real estate. This is power. this is HVAC systems to cool them. Then, of course, there is the standard software costs of the teams themselves and everything else. But it's really dominated by the infrastructure. And your focus is infrastructure.

So at Andreessen Horowitz, which obviously is a tech investment firm since we go to a broad audience, want to make sure we specify that. I run the early stage infrastructure fund, yeah. But you got more than a billion dollars to invest. and when you say infrastructure, what do you mean? Yeah, so it's specifically computer science infrastructure and primarily in the software ecosystem. So we broadly define it as we invest in the stuff used to build apps.

You know, we invest in the stuff used to build the stuff. And so this is the traditional verticals within infrastructure are compute network storage databases, but now you have a number of others like dev tool security, you know, AI models, et cetera. So this is computer science, infrastructure, or everything from, let's say, the chips all the way up to the actual apps that a technical person would use. And for those who are not familiar with, I feel like even people who think they know what venture capital is,

are probably not familiar with the modern structure of venture capital. What does it mean to be a series A? Like, what series are we talking about? So we call ourselves venture investors because these things you're right are actually pretty fluid. So we invest from first money in, which would be called a seed. all the way to, you know, pretty late series Bs. Normally we invest often, sometimes even pre-idea, if it's a second-time founder that we know,

often pre-product, sometimes pre-market traction, sometimes early market traction. And then all the way up to like, say, you know, a product is in market and they're doing pretty well, but they have not seen, you know, repeatable growth over six quarters. Like at some point in time, it moves to growth investing, which is a separate fund and a separate team.

So we tend to evaluate. technologies, teams, team market fit, market trends, things like that. I kind of think of it as you're one of these guys who are getting excited about the people involved or the wild idea that they might have to change the world and seeing the potential for the future of that. Right, without actually, you know, having the monetary data to prove it. Right, yeah. So like late stated investors, they abstract the entire world as a financial spreadsheet.

And that tells all the truth about the company. where we get very excited about founders, we get excited about certain markets before they show up. I want to talk about some of that excitement. I noticed on X earlier this year, you posted something that I thought was really insightful. I live in San Francisco, so I'm seeing some of this too. You're talking about what it's like to be in the Bay Area right now,

and you're kind of talking about how it feels like the late 1990s during the dot-com boom. And a quote, just Silicon Valley in peak disruptive glory. What do you mean by that peak disruptive? glory. So these movements tend to be much more than just technical movements, right? So they often are culture movements. And it was the same thing with the early web. You had kind of a lot of kind of these wild websites that weren't really practical, but they were super cool and people love them. Like do you,

I don't know, do you remember hamster dance? Oh yeah. Yeah. Absolutely. A website with hamster dancing. And like, you know, often companies kind of get reconstructed because you have a whole new set of Like the rules are being written because it's a new technology, right? So like when the PC came, it was an entirely new technology wave. So you didn't have like mainframe people from IBM starting these companies. You had, you know, normally a younger cohort or a younger generation. They didn't really know how to build businesses. The buyers were entirely new.

And so like, you know, there wasn't a playbook for them. So you kind of remake companies too. You know, a lot of stuff that was, you know, almost anathema, you know, five years ago is being recreated again, both from the. the company side and the consumer side. And then, of course, you know, you've got, you know, capital flooding in and all of the externalities of that. You've got entirely new user behaviors that people don't know what to deal with, right?

I mean, like, I mean, what is AI good at? It's good at, like, creating new things. Like, computers haven't been creative. It's great at creating emotional connections with humans. Like, computers haven't been good at that. And so it's just kind of this maylu of, you know, tech and culture and company building and insight. And again, yet again, Silicon Valley is at the epicenter of it, you know, just like it was for the internet.

Absolutely. And I think, but when you use the dot com boom, I think a lot of people think of the dot com bubble. And so I think you're seeing the good parts of that, the good comparisons of that era. And it sounds like, which are just telling me all the potential, all of that kind of wave of new ideas and how it can be implemented. And are you have any confidence that history isn't repeating itself with the bubble part of that boom? So all markets go in waves, right? I mean, we just saw this with COVID, right? I mean, there was a, you know, there's a, I'd call it a speculative wave where equity values went super high. And then, you know, it drops dramatically after that.

And so that will always happen. And I predict this will happen. again at some point in time. That said, I don't think that the fundamentals are anything like dot com. There's a number of things that were very different, right? So the first one was most of the infrastructure was being provided by WorldCom, right? Yeah. A lot of debt, right?

Which had $40 billion in debt. Yeah. We also had 9-11, which happened in 2001. And so, and, and, back then there's a lot of users on the internet. There weren't a lot of people that were paying for. We hadn't even really figured out a business model. So if you compare that today,

kind of every one of those is different, right? So the companies that are investing in these data centers have hundreds of billions of dollars on the balance sheet. Like, I don't know the answer to what I'm about to say, but what do you think? Do you think meta is spending more money on VR or AI? Probably has been VR, but now with the AI. and the checks they've been writing.

I mean, Zuck has talked about spending maybe $600 billion by $28. Yeah, yeah, yeah, it is. It's a ton of money, right? But like, these companies spend lots of money on infrastructure historically. And so, like, maybe they're inflating it, but these companies have, you know,

great balance sheets, great cash flow. And so, like, the fundamentals of, like, who's funding this is quite different. I want to get into the fundamentals and segment. You know, the thing, though, put your investor hat on, because I think there's some confusing signals out there, Right, because on one hand, Mark Zuckerberg, CEO of Meta, OpenAI, CEO, Sam Altman.

They have talked about the excitement, but they've also had kind of their own version of suggesting some of the excitement might be premature. Here's what Mark Zuckerberg had to say on the Access podcast. I do think that there's definitely a possibility, at least empirically, based on past large infrastructure buildouts and how they led to bubbles, that that's something like that. would happen here. And so on one hand, you had some warnings. On the other side, you have a lot of money going out there. What should people make of these conflicting signals and noise sorts of situations? You know, listen, I think it's very important to be sober about, you know, how long it takes for technology to be adopted.

And I think everybody, especially leaders of companies, try and temper it because otherwise, you know, expectations will outpace actual adoption. But I would say that articulating expectations for humans is very different than actual operational planning where you've got to be, you know, three to five years ahead because it takes so long to build these out. And so I wouldn't conflate a CEO trying to set expectations for a market versus an operational plan, which is what they've been in the business for for the last two decades. After the break, why Martin is skeptical that a bursting AI bubble would have disastrous consequences for the economy? It's very hard for me to see how just because you could have a speculative bubble. Absolutely. This somehow denotes that we're going to have a systemic issue. Remember, we overvalued things in mobile. We overvalued things during the early cloud boom.

All of these things we overvalued. That did not result in systemic collapse. Stay with us. There are two major concerns driving the conversation around a possible bubble, right? One, that AI is not transformative as we hoped. The other is that all this money is pouring into it. its future, these data centers and energy and chips. And it can't possibly lead to enough productive gains to justify that level of spending. And I want to set aside the kind of the question of whether the technology is developing at the progress at the levels that we've seen and just go straight to that money point.

Because I think for a few years now, one of the proof points behind why the AI spending spree wasn't the dot-com bubble was because companies. companies weren't relying on huge amounts of debt to build out that infrastructure. But as of late, we've seen that debt, right? We've seen some of that debt starting to show some flashing red lights, right? And I think of some of the numbers that my colleagues at the journal have talked about, where they see Open AI talking about a trillion dollars for data centers. Consultants at Bain have estimated that the current wave of AI infrastructure spending would require two trillion in annual AI revenue, by 2030 to justify that.

So that seems like a lot of red flags there or flashing red lights. Why are we not in the bubble? I just think we need to define bubble here. So like what you're saying is there may be a systemic collapse, which is a very different statement than saying we're in a speculative valuation bubble. So like often when we talk about bubbles, it's speculative statement. Like, oh, we're overvaluing a set of. stocks for whatever reason, right? And we see these all the time. So I would say, listen,

if you look at valuations of companies, as I mentioned before, they wax, they wane. They may be overvalued over a period of time right now. It tends to be in the long terms. These things get pretty well justified. So for example, if you look at the valuations for dot com, even though they were totally crazy, if you look at, you know, it was like the primary growth driver of the economy for the next 20 years, they're actually pretty well justified. Yeah, I mean, it seems like, you know, you talk about how AI is the biggest innovation since the Internet, but it took a little bit of time for that to correct, the ROI to be there, right?

Yeah, clearly Amazon. Yeah, yeah, but I want to actually talk to this notion of systemic collapse. Yeah, right. So the dot com was, the dot com collapse was really kind of a fiber glutton, a fiber bust. As we mentioned before, you have one company that managed demand. And it was that one company was massively levered, and it was cooking the books. And you had 9-11. So all of these things happened.

And yet, even in that, when fiber went down, it created a systemic issue in the financial system. Even then, the glut only lasted about four years. Listen, the question is not, are we over-investing relative to near-term demand? Probably. The question is, are we over-investing relative to long-term demand? And if so, do we have the economic reserves to stop some sort of, you know, systemic unraveling? The fundamentals are so different and so much better this time than they were during the late 90s. It's very hard for me to see how just because you could have a speculative bubble.

Absolutely, this somehow denotes that we're going to have a systemic issue. Remember, we overvalued things in, you know, mobile. We overvalued things during the early cloud boom. We overvalued things during the early SaaS boom. All of these things, we overvalued. That did not result in systemic collapse. And so I think we need to really tease these two things apart. It's like, yes, like valuations often get the timing wrong.

They wax and they went, you know. But you're asking specifically, does this mean there's going to be systemic collapse? I see absolutely zero indication of that. And I think I can back into that conclusion via, you know, both historical precedence on the use of bandwidth and data, plus the fundamentals of the industry that's funding these things. Let me just interject on the systemic collapse a bit. So, you know, one of my favorite columnists happens to be at Bloomberg.

Shout out to our colleagues slash competitors there. Connerson, he says, you know, every time is different. People like to say every time is different, but that's true. Every time that we have a bubble or a systemic collapse, it is different. So here's some things that I think are relevant that are different now, right? Yeah. We've never had the level of concentration in the stock market in terms of the proportion of the value of the stock market represented by the top five or so companies that we have now, all of which are tech companies. So, you know, the stakes are higher, right?

Like there's lots of cash on the side, but people are also pouring cash into the market as a whole. The current level of investment implies, just with a basic back of the envelope calculation, that the total amount of revenue generated by, let's say, AI in all of its manifestations at all these companies, needs to increase something like 40x in the next four years to justify the current level of investment. Isn't it possible that that gap is so large that we could have a significant dip in markets and an investment in this infrastructure in a way that people look back on and say, oops, that really was a mistake.

It's totally possible. There's an interesting thing in the way that you phrase it that I thought was absolutely correct, and I think it's worth calling out. So the companies that are implementing a lot of this AI spend have existing businesses, right? And the AI portion has to grow 40X, which is a lot. But it is actually not a lot relative to their businesses. And if you look at their existing businesses, you'll actually see this is the largest shift in budget. we have ever seen, right? It's going from basically one side to the other side. So I would,

I would agree that you need the AI portion to grow. But what I would disagree is you need those companies to grow. And this is so often confused when people talk about these numbers. I mean, you have companies that their entire job, like meta, is like, okay, we launch new technologies and new things, and then we shift spend budget user behavior onto those things. And so, again, let's not confuse something like the internet, which was kind of like a net new behavior, net new spend, net new companies drove basically all of it, with existing companies that are shifting spend from one column to another column, which is actually what we're saying.

Coming up, it's a heady time for AI investors. So where does Martine see the opportunities? We're very excited about the fact they're going to have new companies. I think even when you ask questions, you're like, in your head, you're thinking open AI. That's your model. But I will tell you, that's not what the landscape looks like. That's one company.

And the state of the art models is a very strong. small subset of the long tail of AI companies. That's next. So you're an optimist, right? I've heard you say that, you know, especially now, is not a time for zero-some thinking. You know, and that makes sense. The market's clearly growing, yeah. Yeah, yeah, the market's clearly growing.

I mean, listen, it's not optimist. I mean, the realistic view of tech in the last 30 years is that it keeps growing. True, true, true. although not every idea pans out right not every second this one is clearly already generating a lot of revenue as you said yeah um that said on your investments when do you expect to see real ROI on the things that you've been investing in over the past couple of years you know this is this is such an interesting question probably for a different reason than you're intending which is um a lot of the best companies don't go public right which is kind of a new thing by the way right It is a super, it's a definitely new thing.

I mean, and so I actually. And that's because, and I just slow it down there, that's because there's so much capital out there that they can use that money to grow without going to the public markets. Exactly. It's a very, like many of the best companies don't go public.

And so I actually think there's a very interesting discussion to have on exactly this question, but I think it's orthogonal to AI. Meaning I think that's kind of the, like, the dominant trend there. I mean, these AI companies, many of them have like, you know, they're profitable, they're at scale and so that they could. But there's a bigger question of, well, if there's enough money in the private markets, why would we?

You know, it's just a lot of overhead that I don't need and I can actually be more aggressive as a company without doing that. And so I think, you know, from my vantage point, that kind of shift is something we're also trying to understand. Like LPs are trying to understand that we're all trying to understand what that means. means. Because how do you make your money? Because either traditionally it's been through going public or getting acquired by a bigger company, that has been usually the path. Yeah, that's right. Well, but it's also interesting because actually the answer is very simple, which is, well, we can just sell it to somebody else later stage and we can cash out. But if it's the best companies that are doing that, why would you ever sell? Because they're growing very nicely. And so it just, all I will say is like,

like it doesn't pose a problem with liquidity. It just changes how you think about this in a way that I don't think we've institutionally had to think about it. Like, you know, you end up with these kind of captive markets, you know, that you have exposure to that are growing really well. And so like, should you sell those? Should you not sell those? What are the expectations of our investors?

And I don't have good answers for you now, but I will say this is, I would say every VC is having exactly this discussion to try and understand. and what is the right posture. Where do you see the biggest opportunities for investors like yourself in AI? I mean, without giving it away to your frenemies at Sequoia or whatever? Oh, no.

Who are big listeners of this podcast, I have no doubt. Sequoia is great. Every time you have a new capability and a new behavior, you get new companies. And that's what's exciting. Like, a lot of times,

it's like you've got this tech, but like the technology isn't disruptive enough or isn't a new behavior. And so it's very, very hard for to get like new iconic companies. I mean, that's actually been the story of AI previously. Like, like before this wave, AI has been a thing since the 60s, right? I mean, I took my first AI course in 1999. So then where are all the AI companies? We've had 30 years of this. Like, where are they? Why hasn't there been any AI companies? And the answer is because the economics have been terrible. It's like they give you a 20% gain and the product kind of sucks. And

And it's been okay. And so it's been a technology that, like, large companies can use to get 20% better. I can, you know, I know your preference 20% better. I can detect fraud 20% better. Like, that's been the story of AI until this generative wave. The generative wave is like, this is a totally new behavior and it's a thousand times better than the traditional way. And when you have those disruptions, then you end up in, um,

you know, in these super cycles where you have new generational companies. So I would say generally, this is very exciting because you're going to definitely have new companies that rise up like the Open AIs or the Anthropics or the cursors or whatever it is that are clearly going to be new. And then as particular markets, we kind of break the world into two pieces. There's like there's the state of the art, large language models like the Open AIs. And, you know, in our view, those require tremendous amount of capital. But then there's all of these other companies that people don't. talk as much about, the ones that do like image diffusion or video diffusion or speech or music or whatever. These are all generative AI. The companies are great. And so we invest heavily

in those too. And so we invest broadly like you do in any cycle. We're very excited about the fact they're going to have new companies. But we do kind of segment the market based on, I think a lot of the time, I think even when you ask questions, you're like in your head, you're thinking open AI. That's your model. But I will tell you, that's not what the landscape looks like. That's one company. And the state of the art models is a very small. upset of the long tail of AI company. So we're very interested in the long tail in addition to, of course, like the, you know, the open AIs, et cetera. You feel like we're at a tipping point for AI, that the economics are, you can see the economics are coming, that this is real businesses

here on the horizon. I mean, yeah, for sure, 100%. I mean, but we've seen that, this isn't even, like, for some uses cases of AI, I just don't think this is a controversial take. Like, the, the economics are great for a number of use cases. Now, listen, I think we just tend to think pretty muddy about a lot of these things. So, for example, the statement that there's no long-term devensibility is very different than can you build a profitable company. And the answer is we can definitely build profitable companies. We have those that have great growth. Like, those exist today.

And there's a number of examples of those. Then people say, well, are they long-term defensible? Maybe. Right. But I will say, like, you know, traditional defenseability does pertain to these companies. So if you can build a two-sided marketplace or long-term integration or whatever it is, you can get defensibility. And so I just feel like a lot of the times we kind of move the goalposts on simple questions. But we can be very, very clear that, yes, you can build a company today that is AI-based, that is profitable, that grows at levels that we view is incredibly healthy.

So what story do you think we'll be telling about today's? AI boom 20 years from now. Do you remember what the first video for the web was? Like the subject of the first video for the web? The first, the first video on the web. You got me. The first video was a coffee pot. Oh, the live web cam of the coffee pot.

I do remember this. The Cambridge University coffee machine gained international stardom with more than 150,000 people around the world avidly watching it. And the reason, you know, someone put it up as a researcher, I think, in Cambridge, and he's like, I want to see if the coffee pot has coffee in it before I, like, go down to, like, waste my time to go get some coffee. All I have to do is to click on a little button that says coffee machine like this, and eventually I get a picture on my workstation. And it was like this total sensation. And so if you look back, you know, in the mid-90s, you had these things that just looked like, toys and they were so silly and you made fun of them. But the reality is, is that video of a coffee

pot in no small way became Netflix and people could see that. Right. People could see it. And so there was like, you know, like we kind of like would make fun of all of the excitement about what seemed like trivial things. But it all turned out to be true in the long run. And I think that that will be the story of this realm, which is that we see a lot of like anime and a lot of silly use cases. And then we tend to poo this is like, oh, this isn't serious stuff and where are the enterprise use cases and blah, blah, blah. But like, this is what the future always looks like. Well, Martin, thank you for coming on. It was such a pleasure. I appreciate it.

A quick note, the coffee pot video that Martine referenced was the first live webcam video on the internet, not the first video on the web. Thanks for listening to the A16Z podcast. If you enjoy the episode, let us know by leaving a review at rate thispodcast.com slash a 16Z. We've got more great conversations coming your way. See you next time. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice,

or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a lot of the company, a link to our investments. Please see a16.com forward slash disclosures.