Byrne Hobart: AI bubble is pro-social — it coordinates the entire supply chain to overbuild, and that's the point

justice is being served. Uh we have Bern Hobart in the reream waiting room. Let's bring him in to talk about other bubbles. More positive bubbles. More beneficial bubbles. Welcome to have you here.

Great to have you on.

Brought the bubbles.

Awesome. We brought bubbles. Great.

Bubbles. The bubble king.

It really goes everywhere. Uh for those who don't know you, please uh can you kick us off with a little bit of an introduction on yourself and and thank you so much for taking the time to be here.

Yeah, absolutely. So, hey everyone. Um I'm Burn. I am probably best known for writing the newsletter, The Diff, which you can check out at the diff.co, covering topics in tech, finance, everything adjacent to them, everything in between. Um also a partner at Anomaly, an early stage frontier tech venture capital firm. Um, also co-authored with uh Anomaly Partner, co-authored the book Boom: Bubbles in the End of Stagnation, published late last year by Stripe Press. So, uh, yeah, I'm the bubble boy.

The bubble boy on a roll.

How can we talk? How should we set the table? Do you want to talk about uh just it feels like you've been uh sort of defined as like probubble. So, I feel like asking you the question, are we in a bubble is a little bit irrelevant. Uh, but would you agree that we're in the bubble? in a bubble. Maybe at the start of a bubble, maybe at the end of the bubble, but we are. It feels like we're in a bubble and it's safe to say it now.

Yeah, totally. It's great. Um, and yeah, that that is like [laughter] that that is that is the curse of uh co-authoring a book that is trying to rehabilitate the image of bubbles is that every time the NASDAQ hits a new high, people start calling you and asking you if that's good. And um yeah, my my obligation here and and the the model advanced in the book is to say that yeah, it is pretty good. Not to say that stocks will always go up forever. Not to say that everybody's um options or their weird quasi equity participation units um will all be valued at uh at their present prices. But yeah, so the general argument we advance in the book, I guess you can rewind a little bit and say you can you can go through these different ways of talking about bubbles. One is just say they're stupid. It's when people are they just get over excited about some new technology or in the case of like housing, they get excited about some very old technology that is uh newly easy to finance and they just lose their heads and by the end everybody knows they're overpaying. Everybody assumes someone else will overpay more in the future and just when you run out of stupid people, prices collapse. So that's like the bubbles are really stupid vein of thought. And then there's one which is a little bit more nuanced which is hey yeah they're stupid but they're actually a wealth transfer from hedge fund people venture capitalists etc to everyday consumers. So I lived in San Francisco in 2015. I remember I I I long for the incredibly cheap you know universal basic Uber where every you know you could get anywhere for like $8. It was amazing. And um that was yeah it was great. It was like you know wonderful wealth transfer from the u Saudi Arabian sovereign wealth fund to me and I really appreciate it. Thanks guys. Um but but that

well it's also not notable that they they they've you know done fairly well even though there was like it it was it was a it was a bubble. It wasn't necessarily sustainable but it created a enduring business or at least one out of the out of the out of the two.

Yeah. And that's that's often what we get. Um now Uber is a little bit of an abstract case. What we often get from bubbles is we build too much infrastructure, but that means we have the infrastructure and we have enough infrastructure to build the next thing. And that was the case in the 19th century with railroads. That was the case in the late '9s with telco infrastructure. And um you know that that could end up being the case today with GPUs. But there is the probubble argument which is that what bubbles really do is they coordinate different market participants um founders, employees, investors, regulators, customers, suppliers. They convince everyone that this is happening. It's happening right now. And that if you build something, if you overbuild for today's demand, you will still have underbuilt for future demand. And if everybody's doing that at every layer in the supply chain, then you actually do build enough to satisfy future demand. And so like making it more concrete, if if TSMC does not buy into the idea that AI is a really big deal, they're not going to build enough fabs. Nvidia will not be able to ship enough chips. The next models will not be quite as good or we'll have to do more of a trade-off between training and inference. and the whole thing slows down. But if everyone is wildly optimistic, then they do build all of that infrastructure, you know, all the way from the power generation to the end use cases. They're building all of that and it all it's kind of like just in time manufacturing of the future. And the prices like the crazy prices, they are this signal that this is the time like it's happening now. If you build something on the assumption that OpenAI is going to keep shipping better models and that they will need a lot more compute and that will need a lot more power to make those models work. If you operate on that assumption, you're making the right call.

How how was how have you been processing uh some of Ben Thompson's maybe jitters around the idea that the infrastructure that gets left behind in this particular bubble might not be something that's with us for a hundred years. He's been advocating for, hey, let's do energy. Let's do nuclear, solar, let's build out a lot of energy. But if we're just like, "Yes, we way overbuilt like a bunch of H100s and then, you know, years later we're like those actually aren't that valuable. They don't they maybe depreciate over a few years." Um, that's sort of how he's articulated some of the fear of the overbuild not being as durable. Do does that resonate with you or how have you been processing that?

Yeah, I think I think you can look at these different lags and you also look at how generalizable the use cases are for these products. And so it is true that GPUs depreciate, but depreciation it is this economic concept that's tied or it's an accounting concept that's tied to an economic concept. And it actually ties in a couple different things. One of which is just if you use a machine there is friction, it can break, it can overheat, whatever. And so eventually it's trash. And then the other piece more on the economic side and much more relevant to GPUs is if the GPU is producing fewer tokens per watt and that just relative to newer ones, it can be economically worthless even though it can still actually do something useful. And so if the GPU buildout slows down, that actually decelerates the depreciation for all of the world's existing GPU fleet. And power is harder to slow down. The just the lags are a lot longer. takes a long long time to build a new power plant and to build all the equipment that goes into it. So you could have this case where bubble pops, open AAI has to do some weird recap at a much lower valuation. NASDAQ's down by half or probably not down by half, but down by a lot. [laughter] And um you know, a lot of a lot of people who felt very smart as of like a month ago um look pretty stupid, myself included. And um you know we could have that but there will still be this increase in power generation capacity because that stuff is locked in and the like the gas turbine companies they they can really lock their customers in because there are just not that many places you can go to buy one and so if you're going to buy one and they say okay you have to actually guarantee that you'll pay for it even if you really regret this that's pretty much what you'll have to do. So you could have this case where what actually happens in the aftermath is power costs decline and so these GPUs actually become higher margin when they're doing inference and so you get really really cheap abundant intelligence at today's model capabilities and that that is still

yeah the GPU is fully depreciated and power costs have come down which would make them you know the the the concern around depreciation is you just get a chip that is so much better that it's just non-economical to to run one of these old GPUs, but their the scenario that you're pointing out uh you could they could potentially be valuable for for much much longer.

Yeah. So, if you look at a company like Coreweave where their business model is we stack a bunch of GPUs in a data center, we lease them out to various people in varying terms. That is actually kind of a bet on this narrow slice where AI is not a complete flop. we don't find out that it was actually just Sam Alman typing those answers really fast all along but it's also you know doesn't completely revolutionize things because that like if there's another generation or two of GPUs or if TPUs take more share of inference then maybe those GPUs end up being economically stranded but if there's a world where we're not building many more GPUs but we are using we do find all the use cases for the ones we have that might be a world where actually core is you know reporting pretty nice gap profits and um and their investors are happy And when you look at Corwe, one of the interesting things about them is on their cap table. So they one of their big backers is this hedge fund magnetar which does incredible has done incredible things with structuring various bets. Like if you control F their um their perspectus, Magnetar is actually mentioned more often than Nvidia.

And Magnetar likes to make interesting bets on like the relative volatility of different things or the time relative timing of things. And so you could see this as them making this kind of esoteric bet that AI is actually both a really big deal and somewhat overhyped. Now it's always always hard to you know read into like what you're reading into is Magnatar is involved in this. They have done a bunch of really interesting deals throughout the core cap structure but it is it is still striking that they are very sophisticated about this exact kind of trade.

That's interesting.

Uh what is like what is the right way to view a bubble? Is it this monolithic structure in which there or or or are you viewing it as like because in in my view it's like we we just read your article or essay in the economist right before you congratulations by the way.

Uh it's great great kind of summary of everything but uh

high high honor to be in

but you're you're saying like there's pieces of a bubble that are feeding into each other and making the possibility for durable value creation to be higher and higher because you have all these parts combining. I feel like another view maybe is is similar but slightly different is like you have these rolling bubbles that are all kind of like building up and right right now there's like we have a private credit bubble maybe there's a a neocloud bubble maybe there's an LLM bubble right like do we it's unclear if we need uh you know the the 50th uh uh closed source LLM right I I don't know maybe we do right it's it's hard to predict but what what is like the right kind of like way to even just visualize the bubble uh the AI bubble broadly.

So like a lot of other bubbles, it starts out as this really differentiated unique thing where most people do not know, you know, like 5 years ago, most people did not know or care that much about AI. It was kind of this thing where you would listen to the quarterly call from Google or from the company then known as Facebook and they would talk about how they're an AI company and you'd think okay like I'm glad you have your science project nerds but I really care about more ad clicks and more dollars per ad click so good luck with whatever whatever robot experiments you're doing and then when it starts growing what it starts doing is actually connecting with the rest of the economy. Now like the marginal dollar of AI capex is increasingly going into general purpose power generation infrastructure. So and and meanwhile AI is getting much more broadly distributed like initial initial use cases were one it was a really good autocomplete for coding and two if you needed to create original content in order to spam people or if you were replacing like the lowest value bloggers you could do it and it was cheaper. But then it became this thing where it's like a lot of there are just a lot of things where you wish you could apply a little intelligence to it. It's really not worth your time, but if you can get the right answer easily, then you should do it. And just like a lot of a lot of cases where you'd want like I use it a lot when I'm writing as a research tool where it's like I want examples of this phenomenon or I want

a research tool, not a writing tool.

Yeah. Right. Are you even using it as a first draft or is it more like you have a bunch of facts here and then you are actually typing out the sentences that you want?

We're like blood hounds for AI content and we did not and no alarm bells went off when we were reading

Oh yeah, the economist article was like

I've just been I've been surprised the most clearly human written.

It's so it's it's extremely notable that like using AI for writing has become the most low status thing that you can do on the internet.

It's like it just feels like disrespectful. It is lower status than making just like sloppy memes. Like

there are status things you could do.

Yeah. Okay. Like [laughter] adult content

or like use my coupon code to sign up for prize picks, [laughter] you know, here's my parlay. Who's writing?

No, but but still it just communicates it. It it it it you know, people feel disrespected by because it's like, hey, you put this out. you wanted me to read this and if it's completely obvious that that a computer generated it, it's like well was this even worth my time, right? Like if you couldn't have said it in your own words.

So there there is this dynamic where there just sometimes when there is increasing efficiency with something we find out that some of the effort was loadbearing and that doesn't mean the technology is bad. It means we do have to adapt. And so in the case of writing, one of the things that used to be the social norm was if you can produce a grammatically correct lengthy document about some topic, that is an indication that you probably know what you're talking about. And to get into a position where you can do that, you have to read a lot. So you get you acquire knowledge. And if you want to write something persuasive, you probably have to talk to a lot of people and find out what's persuasive to them, what's persuasive to you, etc. And if you can just just ask a model to admit that, then you can basically write at a level that is much higher quality than your ability to think. You can write well beyond your wisdom. It's kind of like when people use um some peptides and steroids, they end up getting weird injuries because they're just like mechanically their body is not actually suited to lift the weights of their muscles can move. So they do get serious injuries unless they train pretty rigorously. So, um, I have a a nine-year-old who has in the past used Chat TBT to write emails to me explaining her side of a fight that she had with one of her siblings. And the email is very clear, very articulate, lots of M dashes, lots of it's not X, it's Y. And it's, you know, that I think if someone that age sat down and write this coherent letter explaining their side of an argument, that would actually be impressive. Like, you'd say, okay, this person's actually thinking seriously about what happened. But in this case, it's like she can write two sentences in chat GPT and you know answer some follow-up questions from it and then produce this nice coherent looking document. So, um,

do you how much do you worry how much do you worry about a a new we have, uh, uh, kids younger than that, but how much how much do you worry about potentially a generation of young people never like, you know, maybe in a classroom setting, teachers can be like, put your phones in this box and you guys are all going to write a paper on this. And it's possible that writing will become like highly supervised because the only way to prevent somebody from just generating the written word,

it literally already is in many schools. Yeah. Yeah. Yeah. But but but even even then it's like when I think about uh growing up and being forced to think deeply about topics,

often times it was because I was assigned to write an essay on something and I didn't have the world's best autocomplete tool and I just had to sit there and kind of wrestle with an idea and actually learn about it and I had to read a book or read a bunch of essays and really put it together. And I think it's possible that just a lot of time spent like deeply thinking is just fully lost forever.

Yeah, I think it comes back to that loadbearing effort question. So I do tell my kids that there is just a qualitative difference and also that when they get an assignment at school, it's not because the teacher has this burning desire to read an essay about, you know, whatever about Charlotte's Web or something like it's not like the teacher is absolutely, you know, they have been pining for this. It's like the the point is the effort and the point is the way that you think about things and that writing is actually just a very useful way to think something through. I I don't really understand why that is. Like I don't know why it is that if you just try to talk to yourself for 20 minutes straight about a topic, you won't get to the same level of clarity that you do if you type it out. Even though the typing it out process is just really similar, it is one word after another and then a little bit of editing sometimes. Uh so I think some some of what the education system has to do which different schools do to different degrees is um is actually explain to kids what the purpose of what they're doing is so they understand what that purpose is. And then we also do have to make this adjustment of sometimes there are things that it is it used to be necessary for basically every adult to be able to do no longer as necessary and fewer people will be able to do it. and maybe the ones who do it will still take a lot of pride in their craft, but they they won't strictly have to. So, think of it as like I don't know, things like manual labor and I don't know, wilderness survival skills, things like that. There there was a time when being physically strong and knowing how to like being able to navigate in space and figure out which way is north if you're lost was actually a pretty important skill that a lot of people had to have. And there's actually you'd be mocked if you could.

Right. Right. And so then you go to this generation where there is a lot of mocking, there is a lot of bullying, but the nerds are actually probably right that this thing is not so important. And then the next generation, it's only the hobbyists who who do this. Um Thomas had this this argument about um I think it's in um his book on knowledge and decisions where he's he's talking about how if you live in a really if you live in a subsistence level tribe somewhere, you actually have to have this incredible breath of knowledge. Like you've got to know all the landmarks, how to get from one place to another, all the signs of danger, everything you can eat, everything you shouldn't eat, and you know which which local tribes are friendly, which ones aren't. And you just don't need nearly that level of knowledge to survive in a modern city today. There are all kinds of things about where your food comes from and what is safe to do and not to do that you simply don't have to know because you're not exposed to any of the risk. And so we um we actually have just a much lower knowledge requirement in in more advanced societies. On the other hand, we have much higher returns from having that having unique kinds of knowledge because now that whatever value you can create can be advertised over a much larger number of people and there's just more stuff to go around. So the the rewards from being really really smart are a lot higher. And you know I hope that when I talk to my kids about this stuff and I basically say like there's going to be a cognitive overclass and a cognitive underclass. You can opt into one of them and it's super easy if you tell your kids that it's amazing.

You must escape the cognitive underut that way. [laughter]

It is true like it is so easy to go through life without thinking and it will only get easier and so you you have to decide knowing that the thinking part is increasingly optional in a larger and larger number of domains. Do you want to be the kind of person who thinks because you like thinking and you like creating and discovering new things or do you want to be the kind of person who has just a much easier more relaxing time because they don't? uh very there's that we could continue uh on this conversation for a long time but I wanted to ask you about what scares you about this current bubble like things that are not necessarily like uh bad today but could get bad to me like te tech you know indulging in in uh in leverage for the first time uh may maybe as an industry or as like a lot of the leadership has not act they weren't in the they weren't participating in the telecom bubble. They didn't get blown up. Maybe they've never gotten blown up by leverage and and maybe that's uh a concern, but I'm curious how you think about it.

Yeah, I'm I'm less concerned about that. I I think the current generation of tech leaders, there's a lot more tech history that they can know about, and they just seem more interested in tech history. You can actually go back and see that the people who were more obsessed with tech history tended to do better. Like Steve Jobs was obsessed with the story of Polaroid. It's this beautiful consumer device. Changes everybody's behavior. Really simple tool. You look at it. You know exactly how to use it. You know what it does and it does what it's what it looks like it's supposed to do. Um Jeff Bezos gave a TED talk when that was a much cooler thing to do. I think right after the com bubble had rolled over where he's talking about the early days of electrification and how the internet is like that partly in the sense that we we did not know how to use it. We didn't know all the applications. And he I think he he said I think that's where I I heard that the original appliances like if you bought an iron originally it would actually plug into a light socket. Like you'd unscrew a light bulb, screw in the iron, and then iron your clothes in the dark and then screw in the light bulb again. Or I guess you'd iron your clothes during the day. But anyway, [laughter] like we it was very janky. And so you could have looked at it at that time and said like this is just a clown show. like, okay, sure, electric lighting, I get it, but what are you doing with all these other weird gadgets? And who needs that? Like, we already had irons. They were fine. Um, so I I I think that a lot of tech people are actually pretty keenly aware of history. And a lot of them are just they're they're way more obsessed than you would think with the prospect of their company becoming irrelevant in six months and a total failure in two years. So, I think we're, you know, it is riskier to borrow than not to borrow, but we're probably safe on that front. I think one thing that could go wrong is some combination of um corporate behavioral norms and regulatory norms and investor assumptions where we decide that this stuff is really dangerous. We should not touch it. It will blow a giant hole in somebody's balance sheet because we know it happened and it'll happen again and it just becomes untouchable for a while which did kind of happen in the dot space. And I think people underestimate that when they look at things like Mark Zuckerberg starting a a social network in 2004 is that that was you could have looked at that as really like now it looks really forward thinking. At the time it kind of looked dated. It kind of like the example I use is like if you if in 1999 you moved to Seattle to start a grunge band like you missed it. You were you were way out of date and that's what it looks like. Um so it was still a kind of contrarian thing to do and it was still a company that was started in the aftermath of this dotcom bust when people were were cautious. So um but it with AI the capital requirements are so high that it is actually a really big deal if investors decide that the space is uninvestable progress actually stops. Whereas you just don't need a lot of capital if you're in your dorm room on your laptop just slaying PHP.

Yeah. Or or you can at least monetize much much earlier. And you see that with like the Google uh earnings like preipo is like a massively profitable business undeniable didn't need any permission. Uh what do you think about sovereign AI international uh how bubbles spread internationally? I was listening to Tyler Cowan uh talk about uh one of the weird side effects of tariffs is that other countries might copy America's tariffs just for sort of mimemetic reasons and America might be in such a powerful position that tariffs might not actually wind up hurting America because of its position in the global economy but if another country says oh let's copy that they might be hurt more uh I'm wondering about how bubbles propagate uh at the same time a lot the telecom uh magnates in foreign countries that just kind of copied our telecom build out. Well, they're the richest people in those company in those countries now. So, how are you thinking about like the bubble spreading internationally?

Like I think it is a it is a really cool toy for pro states and some of them have actually done some really impressive work. So, um you know I I don't really begrudge that. I'm not sure how many how many general purpose models the world needs. I suspect what the world needs is lots and lots of special purpose models. And that can be the level of okay, this model just knows Rust, but it is insanely good at Rust and it has not polluted its mind with any bad habits from C or C++ or anything else. It's just pure Rust. And then you could also have even more narrowly scoped models where it's like this model is this one person and it will give you the best approximation it can of what this one person does. And if you have a lot of different models and you and people who interact with models interact through a router where the first thing the router does is figure out which submodel to send things to and it can do many iterations of that and eventually might be sending some things to you know maybe delegating some things to an agent that ends up talking to an agent at some third party service. Um so like I'm thinking of things like if you are planning I mean everyone says if you're planning a trip let's say you're planning a really complicated tax-sensitive global M&A transaction. So maybe you need like the French tax law bot to interact with the US tax law bot and they both need to make sure that the economics of your weird tax thing also make sense in that world. You could actually have this great diversity of models with a great diversity of model use cases. But for the general purpose stuff, like I don't I don't think there is I I think that there is enough room for customization at the user level that we probably don't need 50 different models that are close to the frontier.

Yeah.

What are your labor displacement timelines? Because every CEO over the last year has has used AI as the reason behind layoffs and I think everyone has been calling BS on a lot of that. It's just like they need a good reason to do a round of layoffs for other more real reasons. Um, and everyone I think has seen the chart by now of of of job openings versus uh uh uh you know when when HGBT was released and at the same time you know if you've used these tools uh you know you're not a lot of people

it doesn't feel like a drop in replacement.

Yeah. Meanwhile, you have engineers like, you know, LMS are incredible at coding and you have engineer if you're a talented engineer or even a high agency engineer, you probably have more opportunity than ever. And but I'm so I'm curious about uh how you're thinking about timelines. Yeah, like this stuff takes a surprisingly long time to deploy because one of the loadbearing inefficiencies is that if something required intelligence, there's a single there's at least one human being whose judgment is implicitly tied to the output of that product. And it's really hard to go from there is some specific person to blame like if a mistake was made, some someone made it to if you scale up your work by, you know, 100x and now 95% of the time you do just fine and 5% of the time you mess up. Is that your fault? Is that Claude's fault? We don't want to blame Claude. Claude's so nice to us. Um, [laughter] we don't know. So like we actually have to rethink how people get judged. There's this sense in which everyone becomes a kind of engineering manager who like everyone in software becomes this engineering manager who is describing what needs to get done and vetting what has been done but is writing less code themselves. On the other hand, LMS are actually pretty good at doing the opposite where you are the junior coder. You are doing the grunt work and what it's doing is looking at your overall architecture and telling you what things you missed and what design mistakes you have made that are just a lot easier to fix up front. But like a lot of organizations, they they don't want they they don't want the risk of their workers are massively more productive, but they're also producing some mistaken things and that's actually going to be a big hit to the company's reputation. So you'll you'll probably see what I think you'll see is that a lot of AI deployment is that there will be a legacy version of something. There will be an AI native version of that thing. The AI native version will sell to smaller customers. Those customers will grow faster than legacy companies. and then the AI native product gets sold to all the legacy companies. So this is kind of the strike model where they started out doing payments for early stage companies that had pretty simple requirements and had some tolerance for error and then they as long as they stay good enough to maintain whatever their biggest customer is they are necessarily building out the feature set for other companies the size of that biggest customer. So you get some deployment that way, but it has this it it actually takes a while because the big companies, they just they want to be somewhat cautious on this. And you sometimes have this case where there's a top- down mandate at a big company saying everybody's got to use AI. And there's also this bottom up insurgency of I can use AI and it makes my job more effective. And

also there's going to be a dynamic there's a dynamic too where we will see scenarios where employees say, well, I don't want to adopt this AI. this one's a little too good. I'd be worried about losing my job, right? And so I think we're going to see like more friction between even even with tools that actually can replace labor like truly not just being like a co-pilot and the friction to adoption because the people that would be adopting them and and that's probably you know years out. Certain investors have been underwriting early stage private market bets to uh uh they're saying like labor is the TAM. Like how do you view that framework? is is that like it it feels overly simplistic to just say like any dollar that is spent, you know, that goes out through any type of payroll system today is up for grabs. Um but there seems to be some some element of truth to it.

Yeah, there there's a little truth to that, but I think it's in the same way that Netflix says that time is the TAM and their biggest competitor is sleep. Like it is it is broadly true marketing that Yeah, like it is marketing, but it's also a way to frame the scope of the opportunity. So what I would say is that when the labor in question is mostly delivering value by producing a sequence of tokens whether that is writing a document or building an Excel model or writing some code that that is the addressable market for an AI tool. But the real world just has enough complexity that models have to develop a really good world model. And one way you can think of it is in software they have a really good world model because that is their world. like their world is this abstract world that is defined by whoever wrote the compiler. And to a lesser extent, that world has some complexities if you're actually working with real world physical systems where someone can trip over a cord and unplug one of the servers in your distributed system. And that is just not contemplated in the purely software world model. But as soon as you move out of pure software that is running on one machine for one user, you start to get some real world complexity. And then when you're trying to automate something like building up a financial model, you need pretty tight feedback between what assumption works in the real world, what actually maps to economic realities, and then what assumption is the the most probable token in this cell that needs a token. And I think that we'll like as AI gets deployed in messier parts of the world, what you'll actually see is that more of the world will get structured in an AI friendly way and that more more of GDP will be in that world that is already pre-structured for AI. But then you still have the rest of the real world where it's just really hard to get eagle onboarded. And you can actually see that with things like when when the company that noticed Facebook was growing internationally, one of the obstacles they ran into was in many places almost nobody has a computer. So that's one of the reasons that they went into mobile early, but they also realized they could market themselves through internet cafes and that the apparently for a while in the developing world if you went to any internet cafe half of the unused computers would have the Facebook logout screen and that was actually a huge source of user acquisition for them in develop in developing markets. And then once smartphones came out those people migrated onto smartphones and then Meta was able to keep them and continue to sell them. So somebody would be on Facebook, they would use a computer in an internet cafe. They would get up and leave and then somebody would sit down and they'd be like, "What is what is Facebook?" And then they would just create

Exactly.

Yeah.

Yes. There's there's a great story in Chaos Monkeys about this and about how they wanted to have an ad on the logout page because they're like, "This is otherwise just wasted real estate." And it turned out the logout page is this missionritical thing in all the in many of the non- US markets. So there's a big internal fight on that.

Interesting. And they did end up doing ads on the logout page only in developed markets where growth had slowed down enough and they were already a dominant market share. But like they they needed the outside infrastructure to catch up with the product. And once it did, the product was already there kind of waiting for that infrastructure and saturated it really quickly. But this is this is another thing that happens with bubbles in general, technology bubbles in general. It's like you you don't consider the podcast an electricity company. Like you don't think of yourselves as that business, but the business doesn't really function if you can't plug something into an outlet or use a battery and actually get power from it.

Nothing can stop us from podcasting. Let's be clear. We will

do it without microphones, without cameras.

We'll just find a crowd of people and scream at them.

Megaphone on the rooftops.

But like so in one sense, the 1920s bulls who were like, I'm all in on electricity. This is the future. They were absolutely right. But if you transport a trader, a stock trader from 1925 to 2025 and you're like, okay, go buy all the electricity stocks, it's like, well, that's everything. Like every company uses this, so [laughter] there is no real way to make a direct bet on it. And that to the extent that there is, the direct bet is now a totally different bet. Now, actually, that that particular time traveler, if if he arrives in um in 2023 instead of 2025, actually his his 1925 thesis of just buy levered power generating companies and put all your money to that. It's actually brilliant. So these things, you know, the cycle does repeat itself a little bit, but they as it as it disperses, you've got a little bit of AI and everything. And it's, you know, internet is the same way. Like you don't consider Target an internet retailer. They do a lot of ecom. All the physical ret basically all the physical retailers do a lot of um online sales. The the fast food restaurants do a lot of their sales through apps and through kiosks. So there is just this convergence where by the time the bet is such a big scary bet that you're like the whole economy is dependent on this. You're also like well it's just mixed in with the whole econom like you can't actually take the AI part out of the US economy and the US growth story without completely breaking things and at that point it kind of converges. It settles

yeah makes a lot of sense.

I have one more question that is probably worthy of like a 10-minute answer but we'll see. We only have a few minutes. Uh, how how is it the CEO's job to disconnect the stock price from reality?

Well, it's partly the market's job to tell employees where their equity where they should go if they want to max out the value of their equity comp. And this is something that I I used to not really believe. And what happened with Meta in 2022 kind of converted me this view where Mark Zuckerberg did not actually have to care that his stock was under $100 a share. He it's not like the board is going to vote him out even if even if he didn't have voting control. They're just not going to kick him out. But it did mean that it was harder to recruit people. And so if your dream is we're all going to live in the metaverse, we're going to have this legless utopia. You could only hire the people who make that possible if they think your stock is going to go up. Otherwise, you have to pay them entirely in cash. And then your stock goes down even more. And suddenly you're in this position of making really hard decisions that you don't want to make. So sometimes you Yeah. you take a foot off the gas pedal in terms of massive capex for something investors are skeptical of and as long as you're still in the lead and this is what like investors would send like hedge fun people there was a great hedge fun letter that was completely plain was like Mark even if you cut your metaverse spending in half you'd still be spending the majority of the world's metaverse money like you you know you're still a winner you're still you still get the trophy but please just give us some free cash flow buy back some stock it's cheap

yeah it feels like such like disconnecting your stock price from reality uh uh at least to the positive uh can be like a massive advantage and and you can see different like like um uh uh Palanteer is like a good example of this or or Tesla is a good example of this and if you can keep it going it's like tremendously effective because investors want to be in companies where the stock price is not necessarily always going to be tied to fundamentals and even employees can benefit. Uh and maybe like the the opposite side of that is like is like Dylan Field with Figma. like I feel like he just wants he wants to be valued like like fairly and like accurately and just wants to make the business better and better and better every day. Um but uh of course it's a double-edged sword and it's great when it's uh disconnected uh uh to to to the higher end. But anyways, this was super fun. Thank you. Thank you so much for joining. Would love to have you back on again soon.

Let's do this again soon. This is fun.

Absolutely.

Great time. Have a great rest of your day. We'll talk to you soon.

We'll do. You too.

Uh before we hop on with our next guest, let me tell you about Privy. Privy makes it easy to build on crypto rails, securely spin up white label wallets, sign transactions, and integrate onchain infrastructure all through one simple API. Our next guest is Glenn Hutchkins. He is the co-founder of Silverlake Partners and uh the chairman of North Island, North Island Ventures. I believe he's in the Restream waiting room. We will bring him into the TVPN Ultra Dome.