How The AI Bet Pays Off + AI Lab Strategy Game — With David Cahn

How much money does the AI industry need to generate to pay back its investments, in which company is employing the winning strategy? That's coming up with Sequoia Partner, David Khan, right after this. Welcome to Big Technology, podcast a show for Cool Heated, and nuanced conversation of the tech world and beyond. We have a great show for you today, David Khan, who's writing We've Read on the Show, Week After Week, is here with us today to talk about what it's going to take to pay back all the investment going into AI, and then we'll go company by company and decide which ones have the winning strategy, and which ones might not. So David, it's great to see you finally. Welcome to the show. Thanks for having me, Alex. So your writing came on to our radar. First, when you were basically talking in 2024 about the ROI needed to make the AI Bet's payoff. This was long before the term AI ROI was invoked, and there was a sort of determination that there was some promise in this technology. And so of course you'd want to spend a lot of money in it, because you don't want to be left out, right? But you actually, very early on, started to say, "Hey, let's at least put some numbers around this to see what it will take to pay off, what was coming in." And it started in 2024 where you wrote this, "AI's $200 billion question." And for us to call our attention, because, wow, like it basically, you demonstrated that AI would have to make lifetime $200 billion to pay off its investments, and that was starting to look like real money. So let me read a quick selection from that, and we'll kind of talk about how it might apply today. So you said, "For every $1 spent on a GPU, roughly $1 needs to be spent on energy costs to run the GPU in a data center." So if NVIDIA sells $50 billion in run rate GPU revenue, by the end of the year, that implies an approximately $100 billion in cumulative data center expenditures. Let's assume those building them need to earn a 50% margin that applies for each year of current GPU capex, $200 billion of lifetime revenue would need to be generated by these GPUs to pay back the upfront capital investment. So the total number in 2024, just a mere two years ago, was $200 billion lifetime. Let me talk about where we are today. So conservatively right now, we're looking at $2 trillion in big tech capex this year next, between cumulative together, 2026, 2027 projected. It's more than $2 trillion, but I'll just say $2 trillion to be conservative about it. So by your math, that would be $4 trillion in lifetime revenue on this AI infrastructure needed to make that money back. So in terms of tech revenue, give us a scale, like a sense of scale, how much is $4 trillion really? Is this something that tech companies make regularly? They don't make often, and how feasible is it for this current level of investment to earn that money to pay back lifetime and make those investors whole? Well, maybe first a couple of comments on that. First, the $200 billion was $2,023, $600 billion. Okay. Ready for. Anyways, regardless, these numbers have gotten really big. It's funny to almost hear the numbers from then because they feel quaint, things have gotten obviously mega sized since then, and had a big reflection in 2026, which is why you had, yeah, it's $200 billion question, and $600 to a 3x growth, 2025, it was $850, so it had slowed down, and then it more than, it about doubled in 2026 to $1.5 trillion. And then as you say, if you look at 2027 and the forecast there, it's obviously going to scale past $1.5 trillion at this point. And then the last thing to say there is these numbers are cumulative. So if you really want to ask yourself, hey, what's the total catbex burden? You say for every dollar catbex, we eventually need to get an ROI. How much, how do you have to, they actually have to add up all of those numbers. So it's 200 plus 600 plus 850 plus 1.5. You basically have about $3 trillion that needs to get paid back just since chat GBT. And then as you say, if you add 2027, it's going to get larger. And yes, I think to your point, we all, I think we're all starting to recognize how big these numbers have gotten. The first post that kind of went viral was the $600 billion dollar question. That was summer of 2024. And I think the reason for that, at that point, if you remember, that was because that was when Nvidia became the most valuable company in the world. And so when I first started publishing these posts, no one really cared. It wasn't really a topic of conversation that people are focused on. And what's crazy to me is that two and a half, you know, two and a half years later, from the original post, this is still the number one topic of conversation. And so anyways, hopefully we'll take through it quickly. I think probably your listeners have heard a lot about this topic in the last year. And the AI ROI debate, which is now what everyone calls this, is raging full speed. And every time a big tech company reports earnings, you hear the debate on both sides. And so my original intent was to ask the question. I think it's important to look at both sides as you set in your intro. We try to be cool headed and just look at both sides of these things. On the one hand, AI is going to be probably the greatest revolution in human history. Today, humans do 99% of cognitive work. In 50 years, humans are going to do maybe 1% of cognitive work. So there's a huge revolution that's coming. And we all see that. And yet, on the other hand, we're having our financial analyst hats, looking at markets, things go through boom bus cycles. We have to be cool headed about the timeline that it takes to get there. Where the investment goes, who gets the returns. And one thing that we've learned from studying history is there are winners and losers. And so in these moments of immense hype and immense excitement, there's sort of this assumption that everybody's going to be a winner. And at some point, there's an assumption that everyone's going to be a loser. And in reality, and this will get into some of the game theory and some of the company's specific stuff, some people are going to be winners. And some people are going to be losers. And I think maybe the interesting conversation for those of us who are not in the arena and are looking in is, well, who's going to win, right? That's, I think, as humans, we're just fascinated by that question. And I'm fascinated by that question. I think the closer you get and the deeper you get, the more it becomes apparent that it's these human personalities, it's not some abstract math. It's actually human personalities driving it. And so it all started with a $600 billion question. But I think downstream of that is humans and companies, cultures, and ecosystem of facts as these companies interact with each other. Okay, yes, that's true. And we're going to get into that side of things. But I'm going to go back to the initial question, which is the sense of scale of that type of revenue. Why don't you compare it to like what we see in sat like the total, I think the total SaaS industry makes less than a less than a trillion a year. All big tech makes less than a trillion a year. All right, so just get us again, like a sense of scale of what's going to be needed to return this four trillion and whether you think it's feasible. Yeah, in the, in one of the first follow-up posts in summer 24, I did a post in Game Theory of AI CapEx and I sort of tried to quantify what are we talking about here. And to your point, let's just use round numbers here and say the cloud software industry has two big buckets. There's cloud infrastructure, AWS, Azure, GCP, about a $500 billion market. And then you have SaaS applications, roughly a $500 billion market. Let's call it a trillion total. It's actually less than that, but let's call it a trillion total. And so you're talking about very big numbers here. And then I think the second order thing that people talk about in AI investing is, okay fine, but we're not actually, the, the TAM that AI addresses is not software revenue. And I think that's correct. The TAM is actually human labor. And so human labor is a much bigger addressable market. Well, I think that's true. Somebody sent me a stat recently that was like, you know, there's X trillion dollars of services revenue. And if AI automates 10% of that and the labs capture all of that value, you know, then we got to pay back. And it's like, okay, that's great, but that's a huge assumption to be making, which is you need to have 10% of all of the services industries get automated. And then there's some question of value capture downstream of that. And so I do think in the long run, like I said, I think 99% of cognitive labor is going to be done by AI. So in the long run, there's no ROI question. However, there's this tension. There's always a timing tension in financial markets because the long run is not tomorrow. Companies are spending today. And I think there's this question of, and there's this constant back and forth in financial markets with people trying to figure out like when is the long run coming and are we going to get the payback in the short term? Yeah. So I guess your answer here is it is feasible. But the question is, does it come on the timetable that is going to be acceptable? And sort of the interesting thing that we're going to find out is whether there is that, you know, sort of duration mismatch. And not only is it feasible, but we're making tremendous progress, right? I think it is worth saying. And I said this in the 1.5 trillion post. I mean, what has happened within Thropic in the last year is nothing short of mind blowing to anyone who studied companies, right? It's a faster-scrolling company in history. And so I think it's more than feasible. There's real tangible progress. When I trace back, when I first published the $600 billion question, I said basically, open AI is the lion's share of revenue. I think at that time, it was $12 billion. Today, it's $100 billion plus across the open AI in Thropic. Now, there's still two companies driving the vast, vast, vast majority of the revenue in AI. And so there's still a long way to go here. But there has been tremendous progress. And so I think that's important. And I think that's great. Yeah. I definitely thought about that when I was going back and reading some of those old posts that you wrote, you're sort of tallying up. Like, let's get the feasible number that, you know, we might see in terms of AI revenue. And you had open AI there and in Thropic there, and then all the cloud services. And one of the interesting things about this technology is, you know, it has sort of given birth to brand new use cases and new very lucrative lines of business. Like, I'll go back to one of your 2025 posts talking about the Tatchy PT and an Anthropic revenue. Tatchy PT has continued its epic rise north of a 12 billion run rate revenue and Anthropic has reached 5 billion in run rate revenue. And there's new club of companies quickly scaling from 0 to 100 million in revenue. You know, I think the only person that was really convinced that that Anthropic revenue was going to 10X this year, which is clearly what's going to happen was Dariel, who like said it very clearly that they went from 1 billion or they went from, yeah, they went 1 billion in 2024, they were going to do 10 billion in 2025 and who knows what happens. And it seemed crazy at the time. But because of what this technology enables Cloud Code and Cloud Co-Work showed up. And then Anthropic went from having a decent-sized AI API business to a powerhouse product business. Nothing would make me happier than to see all these questions get answered. And so I think for me it's always been, let's just look at both sides of the equation. The reality is the cost side of the equation has scaled maybe as fast as the revenue side of the equation, right? And so I think you should have this numerator and denominator and you sort of make progress on one side, but then you sort of have more of a hole to fill. And I think one thing we've seen in 2026 in particular is that on the back of all of the AI coding scaling, hyper-scalers double down on CapEx. And so I think when you look at this year in particular, AI is moving so fast that I feel like we sometimes, it's like people can't remember what happened less than two weeks ago. But you rewind back to January 2025, which feels like forever ago. You know, Microsoft and Amazon were pulling back on CapEx. And you know, there was this moment of like, well, we'll have Oracle do it. And it's super risky. Why not have Oracle do it? And then Microsoft and Amazon both got penalized by the stock market where people said, oh, they're not bullish enough on AI. And so then they had to come back to the to the to the to the to the game, if you will. And now we've reached a point of 2026 where not only is everybody in the game, but everyone is massively accelerating. And there's no, you know, I think we were at some point, there was some question of like, are people going to try to rationalize things or people are going to look at the math? I think we should have point where people just don't care about the math anymore at all. It's all in all the way. Let's see what happens. And that's how I view 2026. And I think there was a real catalyst for that earlier this year. Okay. So speaking of not caring about the math and going all in, one thing that you wrote recently, I think was in 2025 also, you wrote one thing has become clear. Nothing short of AGI will be enough to justify the investments now being proposed for the coming decade. Do you still believe that? Yeah, I do. I think this one you and I first started exchanging emails on this topic. It's funny actually I remember writing that post. I just read Hyperion, which is the name of Netizen Data Center. So there's something to that. But you at that time, again, to remind the audience at that time, everyone was talking about 10 gigawatts and 30 gigawatts and 100 gigawatts of catbacks. And so and those are just astronomically large numbers. For beyond where we are today to be clear. And so if you kind of think about those dollars of catbacks, the only possible way to pay those dollars back is going to be AGI. And so when I think the market kind of gets wrong and it's just inherent to Wall Street, but Wall Street sort of always has this view of like, is the stock market going to go up 2% or is going to go down 2% right? It's like this just custom volatility and everyone can, you know, if the market's down 10% in a month, it's like, oh my god, if the market's up 10% in a month, oh my god. In reality, I think we are reaching this like sort of bifurcated path, where path one is like, we get AGI, we pay back all these numbers and more. It's the greatest technology in human history, all of the things that we hear in the media all the time. And then path two is we got the timing wrong. There's no next application after coding and you have a big reckoning that has to come. And I think, you know, in some ways, I think the market underestimates the probability of AGI and underestimates the probability of some correction. And massively overestimates the status quo bias, which is the current status quo of we're spending $200 billion a year on CapEx. And, you know, yes, we're renting our GPUs, so revenues accelerating, but fundamentally there's no, there's not a new business line coming out of it. That is not sustainable. And so I do think, and if we're to worth, and this is why I talk about kind of the game theory and logic of the people leading these companies, you look at Sundar, you look at Sautya, you look at Sam, you look at Elon, you look at Dario, they've told us what they believe. They believe that they're chasing AGI. It's very clear. I think there is no debate about that. These guys have been extremely clear about what they're doing. The financial market kind of likes to ignore sometimes what people are saying because kind of hard to own Google on the assumption of AGI. It's just like our hard for our brains to grok that. And so we sort of pair that back into some, oh, there's going to be some ROI, you know, Microsoft's going to announce earnings. They're going to announce Azure incrementality of X or X plus 2% and we're going to, the stock's going to move on this like random number that doesn't really matter. In reality, what matters is like, are we going to get to AGI or not? And is the, are we going to have enough evidence that they can continue to spend at this scale? When now we're getting into negative free cash, let's territory, we're going to a scale where it is going to be harder and harder to keep spending at this scale. But all the evidence suggests that the, the hyper-scaler is going to continue aggressively moving in this direction. You know what's interesting is that it seems like right now a lot of the market and the investment is going into these companies sort of imagining that AGI is going to happen pretty soon. But as you pointed out, a lot of the pronouncements we're getting from the lab leaders have been sort of downplaying or sloping, you know, that timeline. Dario, of course, thinks it might happen, you know, this year, next year. But Sam Altman talked about a gentle singularity, Ilia's talked about how like pre-training is over, Jan Koon has been like on the war path talking about how LMs are not the route to AGI. So what do you make of that, that mismatch between like what we're hearing from the people who are building this technology and what the market and the investments he was pointing to, which is like, if I was thinking of places where this is out of sync, that might be like the number one place. Yeah. This is the number one thing that I was writing about in 2025. I was just obsessed with this topic in 2025. And part of the reason why I was so obsessed with it, you know, I've been investing in AI for about 10 years now. So I've been in massive AI bull for a very, very long time. And I started investing in AI a year after the transformer paper. So early, but not, you know, there were people who were earlier, right? And I always try to follow my philosophy as kind of as an investor, your job is to follow the really smart entrepreneurs. And eventually, things become consensus because the evidence is so obvious. And your job as an investor is to be ahead of that consensus. And so maybe you start investing in AI five years before chat to BT, but you're not going to start investing 10 years before chat to BT. You have to follow the the leaders. And what I started to see is that Ilya and Sam and Greg and all these guys are coming out and they're saying it's AGI's 10 years away. And by the way, as somebody who's been following AI for 10 years, that makes a lot of sense. These things take time. And in 2024 and 2025, the thing that I had gone deep on, I published this piece, server, steal and power. And I got extremely deep on just like, how do you actually build a data center? I'd flown at visited a bunch of data centers. And I sort of had this realization that, well, it takes two years from the time that you announced you're doing a data center, the data center getting built. Maybe it's going to be longer because of all of the community push back and stuff that we're seeing right now. And so just this idea, I think it in Excel spreadsheet or in a chart, it's easy to show exponential scaling. But then in the physical world, exponential scaling is very challenging. And so I had this view. And again, I was sort of obsessed with this in 2025 because I thought there was such a divergence between the way the markets were talking about this and the way the really smart, technical people were talking about this, where AGI could be 10 or 20 years away. And then the thing that I've been saying, and I think I've been saying this on repeat maybe for two years now. But it's like, AGI is going to be amazing, right? Like, in 50, when I'm 80 years old, it's something I said a lot of times. When I'm 80 years old, the world is going to be completely different. Everything about the way we live is going to be different. So in some ways, all of the optimistic forecasts are right. And if anything, they're underestimating how much the world is going to change. But the timeline and the path dependency, how you get there matters. Financial markets care about path dependency. And I think a lot of, you know, that's where the winners and losers come out because on the path to get there, you have to make the right strategic decisions. And if you're a grandmaster, which I think Elon is and St. Maltman is and some of these guys are grandmasters, they're trying to plan out like, okay, well, if this happens, then this happens, then this happens. And I think that the leaders of these labs are planning many, many moves ahead. And we're oversimplifying when we just look at the financial statements. We have to look at what's the master plan? And is it going to work? And whose master plan kind of makes the most sense? And that's what I'm trying to collect data on. And one of the posts I said, like every time I see an AI headline, I think like, you know, night moves to e6 or something, right? Like, what is this sort of move? What is this imply about the chessboard? And I'm kind of building this world model, if you will. Like I have a sort of a master world model. It's not correct. Obviously, it's about incorrect in a hundred ways. But I'm constantly updating this master world model with every decision. Some decisions are consistent. So it's a very small update. And some decisions are new and they actually require some updating of like, what is actually going on? What is the underlying strategy? And if you can hold that world model in your brain, that actually enables you to forecast what's going to happen in five or six years far more so than like Azure incrementality as X versus Y. And to me, one of the biggest updates was last year when all these guys came out and said the same thing, which is AGI's 10 years away. That was a huge update. Okay, I definitely want to get in twos. sort of the strategic moves, I've teased it a couple times. I'm going to ask you one more question on this section that we're going to take a break and move on. One of the things that I wanted to bring up to you was-- and you've talked about this a lot-- is this notion of an AI bubble. And you know, I'm not going to ask you whether we're an AI bubble or not. Like you've said before, that there are elements of this that is. But I guess my question for you is, why is this so confusing? Because it does seem like the consensus vacillates from, yes, this is an AI bubble to know it's not when there are a bunch of developments. Think about the turn of this year. The shift went from we're definitely an AI bubble look at all the spending to AI-concode. And so therefore, it's not a bubble because it's going to wipe out all of SAS and more encapsular of the value. And now we're like, oh, but that sort of-- I don't want to say I was plateaued, but that's been established. And now there can be even more spending. So now it feels more like a bubble. So just talk a little bit about how, when people think about whether there's an AI bubble, how they should think about it and why it's so confusing. It's such a charged word. I think that's part of it. It's like, everybody has a book. And everyone's somewhat talking their book. And so it's like, you know, you have Michael Barry going out saying it's a bubble, and he's a short guy. And so of course, he's saying that he's like short of bunch of stuff. And then you have a lot of guys who are long. And so they're saying it's all going to be-- and so I think it's just hard, at least for me. This is what makes it so hard. It's like, you're sort of trying to parse what everyone is saying, but also relative to what are they own and why are they saying what they're saying. And so I think it's really tricky. And then the word bubble sort of like has all of this emotional baggage associated with it. And so, and everyone's kind of nervous about a bubble. What is a bubble? And then any of this broader sort of mega trend, which is like Silicon Valley has eaten the world in the last 50 years. So like betting against tech eating the world seems super, super risky. And frankly seems like the wrong bet. And then also you look at AI and you say, man, if this is anything like the previous tech revolutions, wow, it's going to change the world. And then you try the technology. I remember, I think it was the first non-employee to use Devon in 2024, the coding agent, right? If you were trying the technologies, you could see the future. You saw that these things were going to be extremely powerful. And so you try these technologies. You see that they're going to replace cognitive labor. And then you also look at some of these concrete use cases. You look at what Sierra is doing in customer service. You look at what Harvey's doing in law. You look at what Juice Box is doing in recruiting. And you think, wow, like these jobs are all going to be different. So again, you're at the front lines. You kind of see that everything is going to change. And then you're trying to forecast that out. And there's a timeline to get there. And so I almost, I sort of avoid this kind of bubble conversation. Because the bubble conversation becomes very myopic. Suddenly, everything becomes about one, two, three things. And people almost stop thinking. Like you say the word bubble and people just, their brain shuts down. And like maybe they're on one side or the other, basically based on where their financial incentive is for the most part. And so you just like can't have it in intellectually interesting conversation. And the thing that I'm interested is like the nuanced conversation. Because remember, 80% of my job is I'm sitting in the boardroom of this company trying to figure out how do we navigate this AI cycle? How do we make sure that we're winners on the other side of it? That's what I'm spending my day doing. It's like, how do we win? How do we craft our strategy? I'm not a grandmaster like Elon Musk. But it's like, how do we work on our strategies that we do a good job at the end of this cycle? And sort of like bubble, no bubble is irrelevant. Like at some point, there'll be corrections. We need to survive those corrections. In the long run, the technology's obviously going to work. We need to make sure that we're the winners and we need to be massively aggressive to make sure that we're the winners on the other side of that. So I think it's better to actually have the nuanced conversation than to sort of shut things down with this kind of binary language. And that's what we try to do here. All right, so let's keep going on the other side of this break. When we're back, we're going to talk about the greatest strategy game in the history of the world as David puts it and how the big tech companies are playing it. So we'll be back right after this. Hi, everyone, Alex Cantowicz here. I want to tell you about a documentary I've made with Gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous agents, I sat down with MIT professor, Ramesh Rosker, for our White House CIO to Recipate in Michelin's Group Chief Data and AI Officer, Ambica Roger Gopal, and Sharon Guy, a former executive at Alibaba. They each offer unique insights into this evolving landscape. We conclude with Rory Blundell, CEO of Gravity, to discuss the path forward. With Gravity leading the way, join us on this journey. You can watch the full documentary at the link in the show notes. [MUSIC PLAYING] We're back here on Big Technology Podcast with David Con. He is a partner at Sequoia Capital. You could rate his writing at decondcahn.substac.com. I highly recommend you sign up. When David sends something out, I always make sure to read it. So let's talk about this great strategy game, which is what's going on between all these big tech companies against each other. What if I were to read to you sort of my one-liner on some of these players and you tell me if you think this strategy is? This is the strategy and whether it's going to be a good one. Yeah. This was my favorite post of 2026. And so I think in some ways, the thing that's interesting to me is chess is an obvious strategy game metaphor, right? So it's like obviously people are going to think about this in terms of chess. I think the more interesting metaphors are like Starcraft and a Zod. And I think this will inform how we think about the individual company strategies, which is on Starcraft for those who played Starcraft, if not how they recommend at least reading about it. But it's like Starcraft is a resource allocation game. And AI is a resource allocation game. And so I think as we get into it, what are your resources? And how do you allocate those resources? That's the most important question facing every big tech company. And then I think there's a question of like, there's three races in Starcraft. What are your intrinsic abilities? We'll get into Google, maybe one race, Microsoft's other race. But what are your intrinsic abilities and intrinsic capabilities? And then the last metaphor I used in this post was a Zod, which is this fictional game in banks, the Player of Games. Imagine the civilization where you literally play a strategy game and whoever wins becomes Emperor. And I think that's what's happening right now in AI. I think that the eight players around this 40 chess board think they are playing for the highest stakes in the universe. They are playing to be the Emperor of the universe in whatever that means, the person who controls AGI, whatever that means. And so anyways, we'll get into the company specifics. But I think everything is downstream of what do the players believe? What are the resources available to them? And what are the strengths that their race gives them to act in this chess board that is the world? OK, all right, let's go by one by one then and see how far we get. For Anthropic, I wrote the strategy is Dominate Enterprise, Dominate Code, VIA Products, and API. So how would you rate that assessment and what do you think their resource allocation play is? Disagree. I don't think that's a strategy. I think the strategy is corner of the world's talent in AI and win period. I'll elaborate on that one. I think it's been very clear. Anthropic is at a very consistent strategy, which is they stand for something, know what they stand for, and that enables them to recruit exceptional talent and that talent compounds. Because in their view, and I think in a lot of people's views, that clearly believes the same thing, talent is the scarce resource right now. And so corner of this scarce resource called talent, you get these people fighting for you, and that's how you win. Yeah, and it is interesting. If that is Anthropics play, they actually lost far fewer people than competitors did to meta during the meta superintelligence lab poaching spree back in the day. One interesting thing about that is, let's say you go into a world where let's say LLM's commoditize. Then you need to be able to win based on product. And if you have the best talent, then you could potentially have the best models and the best products. And that does seem to be downst, like, sort of one assessment of where Anthropic is playing right now. Does that track? But again, I'm going to push back on this, like-- OK. --where exactly where I think everyone gets confused. Because you sort of mix models here, and you have to focus on what's the strategy. The strategy-- and you can back test this back to before Anthropic was famously successful-- is like the strategy has always been extremely consistent. Deeply philosophically believe in AGI. It is all about getting to AGI. They believe we are going to get there. And they need talent to get there. And I think what evidence at least so far shows that they have been able to push for their frontier. And in their view, they're going to keep pushing for their frontier. And sure, they're going to have these products. And it's great that they have products. And they need revenue in order to attract investment dollars. But at the end of the day, it is all about AGI. That is my read again. This is just an outsider looking in. I'm not in the arena. I don't know. But as an outsider looking in, it's like a very consistent strategy. And they're going to keep executing that strategy. And sure, they need to deal with this noise around commoditization. But to them, it's just noise. It's just like, let's just execute the strategy. Andre Karpathy just joined all this good stuff that's happening. Let's go. I think that if you walk in the building-- and I don't spend that much time there-- I think if you walk in the building, next to people at lunch, I don't think they're talking about some China model. I think they're just like, how do we get the best people? How do we push the frontier forward? How do we make sure we have enough compute to do that? OK. So but I would then say that the flaw in their model is that there's a chance that that is commoditized. Like the chance that AGI is commoditized is the flaw in that strategy. What do you think? That's your progative. I'm not a grandmaster. I don't like to-- like to me, I think it's just like, I don't like to go say, oh, that grandmaster is bad. I'm just like-- I don't know if I can do that game. It doesn't mean that they're bad. It just means like, look, let's go with the game of chess. When you move a piece one direction, it means that the other directions are shut off for you. So I think that if in this conversation, talking about, well, it's a choice. You moved in that direction. You didn't move in that direction. That leaves your flank open. I think that if you want to talk about the game, it doesn't mean that you're a bad player. It just shows, okay, you've made that choice, and that is your weakness. - But I think that this comes back to this question of like, what are the fundamental resources available to you? - You think that is where the trust metaphor breaks down and where storecraft is more interesting? - Okay. - So what are the fundamental resources that you actually can allocate? And at the end of the day, like, if models commoditize, that's obviously not gonna be good for a startup that is driving all the frontier work. And so I think you have to bet on a certain world. Maybe that bet is wrong. That's why startups are risky. Maybe that bet is wrong, but like, you do need to be opinionated. And I think what's beautiful about, you know, as a student of the game, what's beautiful about the game that Anthropics playing is that they're so consistent on that strategy. And if the assumptions they're making proved to be correct, they will be very, very valuable. Now, everyone has to make assumptions. And generally those assumptions, you need to be based on the resources that you have available. And so, of course, Microsoft is gonna bet on commoditization because based on Microsoft's strategic position, that is a good bet worth making. And the universe is probabilistic. We don't know which scenarios are gonna play out. And so the question I would put back on you is like, if you were Dario, what would you do differently? And the answer for me is, I would do exactly what Dario is doing. I wouldn't do anything differently because I don't think you can play. You can't try to win in all probability scenarios. You need to try to drive the probability scenario that you believe is most likely. And this is where I think probability breaks down and I'm more of a chess player than a poker player where I'm like, hey, let's drive to the end game that we want. And I think when you're on the inside of companies, that's how you think and behave. Wall Street thinks of everything as a poker game. It's probabilistic. CEOs think in terms of chess. I need to checkmate the opponent. I need to get to AGI. What's the win condition? How do I get there? And so I just think it's not productive as a chess player to be wondering, hey, is this like other thing gonna happen? It's like, no, we just need to drive to the outcome. We believe in. And we might be wrong. And that's OK. Business is complicated. The world is complicated. You're not going to be right 100% of the time. But if you get distracted, and I think this is what happens at big companies, so we can get into some of the hyperscalers, where you have a committee around the table and everyone has a different opinion, that's actually much more challenging. Because you actually can't consistently execute a strategy, at least for Dario. And I think for Sam, and I think generally for startups, and for Elon for what it's worth. Dario, Sam, and Elon are all executing strategies very consistently. And it seems like they are doing a good job. OK, this is funny, because what I wrote down for OpenAI actually sounds a lot like what you said for Anthropic. My line for OpenAI is build the smartest possible AI and figure it out. Disagree again? OK. All right. I think OpenAI's strategy is everybody underestimate AGI, all in AGI. And we're going to need a most aggressive. And everybody else is going to take fewer risks than we do. And we're ultimately going to be proven right and win. And I think if you look at their compute strategy, that seems like what the strategy is. And again, that's a very coherent strategy. Sam talks about this all the time. These things are exponential. People underestimate exponentials. And we're going to get there. And by the way, I think OpenAI has the added advantage, back to my resource thing of what are your resources, where OpenAI invented the field. So I think there's always an invented the field advantage. I think they have incredible talent in there. If anything, Zuck's attempt to compete with OpenAI and just the failure of that so far just demonstrates how good the talent sled OpenAI is. And I think Sam's strategy is very coherent, which is like, we're just going to be the most aggressive. OK, so I think what you're saying about OpenAI, kind of like they're yes-ending and thropic, like they're like, yes, we're going to drive towards AGI. And we're going to try to build more aggressively than you. Yeah, and I think that's the plan. That's you, Sam, is as a leader. I think that's been very-- I mean, again, I always try to rewind the clock because everyone forgets. But I remember that he raised a billion dollars of this lab when it was like, that was a crazy thing to do. And then he raised $10 billion for Microsoft. He's always been-- I think you have to back test these strategies and ask yourself, when I look at the prior decisions, does this confirm or does this violate my assumption about the strategic thinking that is going on in these people's heads? And these are humans. They're complicated humans. Their humans whose impact is going to have global scale and so I think it's fair that we analyze their decisions. But they're humans. And I think if you back test Sam's previous decisions, he's been very consistently a big believer. And he's very consistently been the most aggressive player around the board. Yeah, all right. So if you don't want to talk about the weaknesses, I'll talk about them. And I would say if you're making that move, the flank that you leave open is what we were talking about earlier, that duration mismatch. Where if you're going all in harder and faster than anybody else, you're the most susceptible to potentially having that time frame not line up with sort of the technology's time frame in your business time frame, have a mismatch. OK. I will say that. And let's move on to the next company, which is Google. So my one liner for Google is-- this is really fun, by the way. So thanks for playing the game. This is a game within a game. I'm here watching the guys on the field. It really does feel like that. OK, so for Google, mine is change search just enough to keep our customers and cross our fingers. Also try to build good models and cloud services without killing each other. Google is hard. So it will spend some time in it. Google is hard because it doesn't seem to be run by a single individual with a single vision. And I think there's varying degrees to which this is true for the hyperscalers. But let me tell you what I would have the Google strategy be. And then let's try to understand what it actually is. Google's number one-- it goes to big advantages. It has a cash machine from search that can fund a lot of things. And then number two, it has TPU. And I cannot overstate what an advantage it is to this type of TPU. They've been building this chip for a long time. It's a really good chip. Some people think they're part of Y and Throbics doing so well as they're because they're using TPUs. And so if I was Google, I would be all in TPU. And I think they are to an extent. But they're right now hoarding the TPUs for themselves for the most part. They're starting to open up the gates to let other people use TPUs. But I think something I've been surprised by is you look at Jensen. And I think one thing you've got to appreciate by Jensen is the man-- he's been doing this for 30 years. And for 30 years, he's been really consistent on one thing-- ecosystem. You go to Taiwan, Jensen's a national hero. I can probably-- there's probably a hundred people who Jensen's just made rich because he doesn't care about the incremental point. He's like, we're all going to win. It's all going to go well. Jensen makes other people rich. I think that's one of the best things about Jensen. I'm going to be the most valuable company in the world. You can have some juice too. And so Jensen's built this ecosystem. You look at Kuda. You look at sort of the ecosystem. He's built around the chip. It's like, if I was going to go into business tomorrow and start a company, I'd love to work with Jensen. Jensen's going to treat me well. He's going to be a good business partner, et cetera, et cetera. Google has a sort of more instiller thing. Google, for the last 30 years, has built everything themselves. Everything is built in house. Everything is invented here. Even though they invented all the core technology behind AI, they couldn't really figure out how to do anything with it. Now they've been good at acquisitions historically. So they've acquired a lot of companies, and they're good at acquiring talent. And their tech is-- technology team is really good. But if I was running Google, to me, it's like the all-in TPU strategy just seems so smart. It's like, hey, in video is a $5 trillion company. We can build a multi-trillion dollar company just on TPU. But Google, and now let's come back to what is Google actually doing. Because they're hoarding the TPUs, I think they're actually doing the like GoChase AGI strategy. And I just don't know if they're well set up to do that. Because I think they are actually going down the OpenAI strategy more and more. It's like, we're going to just going to keep the TPUs for ourselves. We're going to try to get the best researchers. We're going to try to make the best breakthroughs. Gemini is going to be amazing. Gemini is going to compete. And maybe that's the right strategy, because maybe AGI is such a big prize that is just not even worth some kind of hedge that or some ecosystem that. I just struggle sometimes where Google is one where-- I said, like, Daria is playing a beautiful game. Sam's playing a beautiful game. Like, as a student of the game, I can appreciate beauty in the game. And to me, it's beauty, right? It's just like, wow. What an amazing player that is. Google, like, it's an amazing company. It's got amazing resources. It's got amazing advantages. So in some ways, Google is the easiest to appreciate as a company. But then when you think about the gameplay, it does sometimes feel disjointed. So I don't know-- anyways, I don't have as clear of a view. I was very opinionated on the other two. I don't have a clear as a view at Google on what they're doing. They're spending $200 billion in CAPEX. They're accelerating. They're cloud business. I think one thing to point out-- and I think sometimes, there's some finance 101 that kind of gets lost in these conversations where it's like, if I build a house and then rent the house to you, that's CAPEX with revenue. So of course, I can get rent revenue by building something. But you have to ask the question of how much revenue you're getting. And then for these hyper-skillers, it's not just that they're building the house and then collecting rent. So the revenue goes up. But they're actually building the house and then investing in you so you can pay them rent. And so I think there is this broader question on the hyper-skillers, which is like the resource they have is cash. They're all using it to the maximum degree. And is it almost this curse of like-- I almost wonder if there's a resource curse for these big hyper-skillers where if so much cash, so they're incentivized to spend it, when really the thing that's going to drive success is not cash. And cash just can't. It can only get you so far. And sometimes I look at this CAPEX boom, and it's like, I think it's almost downstream of this resource curse, which is like, oh, I have so many resources. I have to spend those resources. I'm in this game theory with Amazon, Microsoft, and Google. I'll have these cloud businesses. And it's an oligopoly. And it's like cash cow, right? It's like the best business in history, probably. It's the greatest oligopoly in history. It's producing all these cash. So I kind of have to get to you with the other guys. Anyways, you can kind of like pull the thread, and you sort of get to a place where it's like, this strategy seems downstream of my resources available to me as opposed to being downstream of some outcome that I'm trying to get to. - What is the alternative though for them? Like is it to sit it out? - I think that's where I said like what I would do, you know, and maybe this is wrong, and I'm not in the building, I don't understand all the constraints that you have. But to me it's like, I would try to build an Nvidia competitor. - Hmm. - Like I just think the TPU is a wonderful product. If I could have 50% market share in 20 years of AI chips, that's a pretty good business. I would sort of have a management team that does that. Now sure, my internal team can be a customer of that business and there's advantages to vertical integration. But one thing, and I've been writing about vertical integration for a couple of years, the reality is when you look at the ecosystem today versus two years ago, the vertically integrated companies have not been able to leverage that vertical integration into some model advantage. And so at some point, and this is where I think a great chess player has to change strategies or a great StarGraft player, like at some point you get data, like I think there is data that vertical integration isn't resulting in the advantages of these companies thought it was going to drive. And so, okay, maybe there's a different strategy, and I think there is a coherent strategy that's like we're gonna have one team chase AGI, and we're gonna also monetize this TPU asset. And then there's the search question, right? Like in some ways, if you do the old school, you know, there's the Alistair Narn, who I love and think very highly of, and who wrote the book, The Endions That Move Markets, which is like the great book on technology investing, who says it's easier to short the canal than it is to be like figure out which row it's gonna win. Is like, is Google search like the canals, right? Is Google search just like, you know, these ad driven businesses, part of why I think meta and Google are so all in AI, is there businesses with the most at risk for AI? Like if we all move to Chad GBT, if we all move, if our time eyeballs move to these other interfaces, then these ad driven businesses have a lot of questions. And so, when you talk about what flanks you have, I think ad driven businesses are a very risky place to be. So anyways, I think it's so much coherent what they're doing. I just think all of these businesses, these big hyper-skillers, there's a lot more questions about, and part of it is like, and I think this is the beauty of founder-run businesses. It's easier for a founder, like look at Zach, his strategy is coherent, we can get into it. He's like all in, right? There's like one strategy, which is like spend the most money acquire the best talent, who cares. Fatter-led businesses can just act and behave differently than non-tender-led businesses. - Yes, okay, one question on the vertically integrated business is not being able to produce the foundational, like the leading models. Why do you think that is? So basically what you're saying is the companies who like their main business isn't necessarily selling the AI, it might be you can put it into play somewhere else. Like if you're meta, you can put it into play in a consumer product. If you're Google, maybe in Google Maps or Gmail, right? But they're all trying to build their own models, they've struggled to sort of compete with those that are not vertically integrated. So basically you have to make the money on the model itself. What do you think has been the source of that struggle? - Yeah, it's funny. I think like every 10 years, the Harvard MBA professors change their mind and like his vertical integration good or bad. (laughing) And so maybe just first principles in it, right? Like there's elements in which vertical integration is good. You look at Tesla, you look at hardware companies. Vertical integration typically is pretty good in the hardware supply chain because you have all these supply chain partners and your supply chain partners maybe don't have, you know, you look at SpaceX, right? Like you had to vertically integrate. Like your supply chain partners were bad. Everything was too expensive, the math never worked. And Elon just squeezes vertical integration, vertical integration, vertical integration. And so now vertical integration is super hot because Elon has done such a great job of vertical integration. Let's propose the hypothesis that vertical integration in between software and hardware, which is what they're trying to do with the chip and the data center and the model, isn't necessarily a good thing. Like it's just a real estate thing. I can rent the real estate thing. I don't have to buy the real estate thing. And now again, I think you could debate this. I don't think this is clear. So this is a hypothesis, right? There was an argument and I thought this was a good argument when it was first being made. Just haven't seen the evidence. There was an argument that if I control the data center then I can make a better model. That was the argument. When you look at the evidence over the last years, it doesn't seem to be the case. And maybe it's because these companies are so big that the guy building the model is like over here and the guy building the data center is like over there and they never talked to each other. So you might as well, they might as well be different companies. That would be my hypothesis. Is that like in reality, they might as well be different companies. And so if they might as well be different companies, then aren't you advantaged just buying whatever the best chip is? Aren't you advantaged just like using whatever the free market exists for a reason? The anti-vertical integration argument is capitalism, right? Like capitalism or merge where you have all these specialization and like most of the trend lines of capitalism is you specialize in the thing that you really good at you have a relative advantage at. You don't have a relative advantage. You let some other guy who has a relative advantage within that business. And maybe Apple is sort of the anti-vertical integration, right? Apple assembles the iPhone, somebody makes this chip, somebody makes the camera, somebody makes this, somebody makes that and I just make sure the product is amazing. And Apple is better off buying the camera from some guy who makes the camera because this 30 guys trying to make the camera for Apple and I get to pick the best one. So that seems to be the evidence so far. But we'll see. I think the evidence is not in yet in a way to be definitive on this. - Okay, all right, let's do a few more. So for Meti have commoditized our compliments. Spend the wheels until someone builds a good consumer AI application and then copy it and distribute it. - I'm a simplifier, so maybe to a fault sign just gonna give you all minor very, like, good, good. - Right, which is just like buy talent, it's a mercenary army, buy talent, you can pay enough and get the people that you need. And again, the question for Meta is like, can a mercenary army do as well as a missionary army? Obviously a mercenary army, we're buying these companies in St. Golden handcuffs, billions of dollars for AI researchers. I happen to think that the people they've acquired are very good and that the team is very good inside of Meta. That's my personal opinion. And so I happen to think that there's a good chance that they do figure it out and that Zux approach is coherent again. It's like, what do you, what resource do you have? Well, you have Instagram, it spits off cash. What can you do with that cash? You can buy Alex Wang. And then the question is like, okay, well, does that get you far enough? And then emergent question in 2026 is, do you have this cultural dysfunction where you have this organization with tens and tens of thousands of people and then you have these 50 people off in the ivory tower working on the thing that matters. Clearly, and you see all these leaked press releases and whatnot, clearly there's this organizational dysfunction that has emerged as a result of this. And so I think the question for Meta is what happens. And then the other question, when I graduated college, Meta was the hottest place in the world to work. And today it's not. And so the other question is like, are you fighting a losing battle against organic talent flows? Is like, do you need to win the organic talent flow game? And this is a question for all the big tech companies, which is like, organically the talent flows are not there. I spent a lot of time, we can talk about talent. I meet 300 young people a year. I spent a lot of time on talent. I'm a very talent-centric investor. The organic talent flows simply are not there for the big tech companies. In a way that 10 years ago, really good people did go to work at these companies. And today, they are not perceived as leading edge companies. - For Meta, if they figure it out, like let's say everything goes their way. Doesn't it not really matter if there's no like, consumer widespread, like consumer application of AI, like personal superintelligence? Or do you feel that if they're able to succeed in their strategy of this, you know, by talent or talent, then naturally that personal superintelligence will emerge? - And this comes back to like, I think we're all underweight AGI. And I think these brand masters are playing for AGI. It's obviously playing for AGI. And so whatever that means, I think it's, you know, I don't know, we have time to go in and like try to define AGI, it's, there's no one good definition of it. But whatever it is, you know, this definition that my partner, Constane proposed, which was basically you go from 1% of cognitive labor done by machine to 99% of cognitive labor done by machine. I think it's probably the easiest definition 'cause that's what happened in Industrial Revolution. 99% of labor was done by humans, then 99% is done by machine. Whatever version of that future is, you know, Zuck is gonna be a very wealthy man if Meta wins that race. And shareholders will be rewarded if they win that race. And so I think that is the path they are on. And again, I think it's coherent. - Whether or work is another question. - Yep, okay. So here's mine for Microsoft. I basically have it that Microsoft is gonna try to knock open AI and then throttpick down a peg, which I think we've kind of talked about is turn those models into a commodity and leverage existing enterprise relationships for profit. - I've always had a lot of respect for Sainadella. And I've always sort of, I think had this pretty optimistic view on Microsoft. I think they don't get enough credit for the fact that they own so much of open AI, right? Like the deal making that Sanya has demonstrated in his career as Microsoft TTO is just unmatched. - Yeah, it's like 27% of open AI. - In beginning, I think it was more, maybe that's what it is now, but in the beginning, it was a lot. It was, you know, there's these stories of like, Sam went to all the big tech companies. I'm pretty sure and offered them this deal and thought it was gonna do it. And so I think there's this question of, Sanya doesn't get enough credit for the fact that he's played a very, very good chess game and again to my beautiful players comment. Like I think Sanya is one of these players that you just have to stand there and go, "Wow, this guy is thinking at a level that we're not thinking." And I think to your point, to some degree, I think Microsoft's strategy right now is like, whoever wins will win, you know? And I love that. One of my favorite things about business, I think the best CEOs that I want to invest in are people who, no matter what happens in the world, I'm gonna win. Alex Wang is one of the, no matter what happens, he's gonna win. And it's just 'cause you're like nimble and you move fast and you're adaptive and you're aggressive. So I think Sanya's had this strategy. And when I looked back at 2025, when he attempted to kind of pull back on Cat-Bex and had Oracle do a lot of the Cat-Bex, I thought that was a pretty smart strategic calculation. He also updated pretty quickly that this thing is taking longer than he did. he expected and so he still has to be in the game. And so when I think about a company that's playing, its cards just really well, I think Microsoft's distribution machine is amazing. So Microsoft benefits, if OpenAI and the labs win and it's all frontier models and it's not commoditized, he owns a lot of that, he's going to do great. If they get to AGI great, like only 20% of AGI's pretty good. And then if it doesn't, and he commoditizes, he's going to win the distribution game. And so I don't know, I think it's one of these players who he's sort of slower. He's not, you know, he's not, you don't see Sia publishing every tweet every day about some new thing they're doing. He's like a little bit more slow. You know, again, on my comment at Google, I said what I would do, I think I would probably be more aggressive if I was him on M&A. Like he's been a great buyer in the past. He's someone that I think knows how to get the most out of companies. So I mean, surprise they've been less aggressive on M&A. But again, like Sia is a better chess player than I am. He, I think he does have a clear master plan here. And I think he's been extremely prudent and aggressive in how he's operated in a way that I really admire. - So here's mine for Amazon. It's do very little cell compute profit. - That's probably the closest one that I agree on, you know? - All right, good. Look at us. - Almost there. - What is Amazon strategy? It's almost, again, it comes back to this like strategy by committee thing where it's like hard to know exactly what the strategy is. Certainly the output of that is like do very little. They have their internal kind of AGI lab. It doesn't seem like it's a big priority for them. They have a huge cloud business. The cloud business does seem to be like losing share to Azure and Google over time. I mean, it was the best of the cloud businesses in the software era. In the AI era, will it be quite as differentiated? I'm not sure. So I think Azure and Google and GCP have kind of caught up and have pretty some pretty good advantages on AI cloud. And so you look at the cloud business and you think, I think your analysis is probably correct. It's like, hey, we're just going along for the ride. We're going to do kind of whatever else is doing. We're not going to do anything crazy. We're just going to do whatever else is doing. We're the best at building data centers. Maybe to give them some credit on the things they're really good at. Like they're the best in the world at building data centers. They have the best cloud business in the world. They know how to build a cloud business and it's kind of steady as she goes. On all these earnings calls, they basically talk about like, hey, we're really good at building data centers and that's an advantage. Again, I'm waiting to see those advantages kind of materialize. I think they're a good argument, but let's see it materialize. And so maybe again, maybe we're going to fast forward the clock in 10 years and it's kind of the simple strategies did end up working well. Obviously there's less to, there's less intellectually interesting about Microsoft strategies. It's just like, and the thing is like this do nothing approach is not really doing nothing. They're blowing hundreds of billions of dollars on cat-backs. So maybe that's the other thing that's kind of weird about the hyperscalers is the baseline is so high. You can't actually say you're doing nothing. Like you're spending hundreds of billions of dollars. You're spending all of your investors free cash flow. And I think this is something that does happen in these kind of crazy cycles is that our baseline expectation for what's normal adjusts. So it's like, oh yeah, spending hundreds of billions of dollars cat-backs, that's like a do nothing strategy. That's actually insane a lot of money that you're spending. And so in some ways I worry for Amazon, which is like, heads I lose, tails I lose, it's like if this all goes well on AGI happens, they're not well positioned for that. And if it doesn't happen, they're kind of in the bath with everybody else on the cat-backs that they've spent. And so it's like where's the edginess, where's the spike? That's nice you think like, man, this is a great strategy. So again, I don't know, some strategies I understand more than others, I assume these companies have better strategies, but I do think the founder led companies have some advantage in their ability to be aggressive and opinionated, whereas you have this kind of committee washing in these big companies. Apple is the one, by the way, with the do nothing strategy. You got it, look at Apple and say, wow, there's some chance that Apple just comes out, like a genius on this. Or like if it does commoditize, Apple's not spending money on cat-backs, Apple really is the do nothing strategy. Obviously investors are penalizing that for them today, but you can imagine the scenario where Apple looks great in 10, 20 years because of the decisions they made. And so Apple in some ways is also an opinionated strategy in a way that some of the hyperscalers are not. And I think the reason the hyperscalers can't be opinionated coming back to this game theory thing is they have this golden goose where they fight with each other, and it's this amazing oligopoly. And I do think there's this like winners curse or resource curse in competitions where you're actually saddled by the fact that you have this cash machine, and you just have to like, you use it because it's the thing you have, whereas everyone always said about inthropic, like they're on a knife's edge. You know, if they lose their frontier, that's bad for them. But people perform at their best to run around the knife's edge. And I think that has been the empirical evidence of the last two years. In video, this is probably too glib, but I just wrote a pray that inferences and commoditized. I would say in video, prop up the AI ecosystem. And this is very consistent with Jensen's history. And I think to his credit, like a pretty smart strategy, Jensen just needs AI to work. If AI works and doesn't crash, in videos, it's going to be in a great spot. It's the one of the most valuable companies in the world. Jensen is one thing I like to talk about in AI in 2026. It's like the AI trade has sort of degraded in the last three years. 2023 is the year of Nvidia, right? It is like the greatest company ever. It's an amazing chip. It's an amazing technology. Kudos amazing. He spent 30 years building this thing. It's like, when you think about 2023, it was like this amazing, crowning moment for Nvidia. 2024, you get Broadcom. Hock-Tan is one of the all-time great CEOs in business history. Broadcom's a premier brand. It's the number one design firm and semi-conductors. Of course, Broadcom's going to do really well. 2025, you get GE Vernova, you get Vertiv, you get Siemens. You basically have these industrial giants that step in and look, their good business is GE is an American national hero. Siemens is a German hero. They're not quite as good as Broadcom or Nvidia from just like a raw, amazingness perspective, if you will. But fine. In 2026, you have like SK Heinex, Micron, and Samsung, which are like right place, right time, price hikes, just like squeeze margin out of the hyperscalers. So he's on Nvidia, I just think it's worth saying. It's one of the greatest businesses in human history. And so he just needs the thing to keep going. And I think the circular strategy where he's investing in everybody and trying to make sure everyone also wins, to some degree, I think it's like Jensen is leaving so much money on the table. And it's because he genuinely at the bottom of his heart, I think just wants this whole thing to work. And I think he probably should get a lot of credit for that. And obviously doesn't. Nvidia stocks barely move this year, even though these high-flying beta companies are going crazy, because they're raising price. Jensen isn't being insane on price. He probably could have been. He hasn't been crazy on price. He's good to the ecosystem. And so I think anyways, ecosystem first is my summary of what Jensen is all about. And I think he's been about that for 30 years. So this idea that he'll like give startups GPUs in exchange for like percentage of their company, that's basically his-- if you have a chance of working in AI, I'm just going to make sure that you at least get a chance to take that swing. And I'm going to give you the most precious commodity. And that's kind of part of that strategy. I think sometimes people's egos get in the way of doing what's right for them. And Jensen is someone who I think is the opposite. He's pretty low ego about it all. He's like, hey, I'm just going to make everybody win. Maybe I'll leave some dollars on the table. It's all great. We're all going to do great. AI's going to be amazing. It's going to change the world. Every consumer in the world is going to have a better life because of AI. I do actually view Jensen as kind of the-- make the pie big. Yeah. The way that everybody else is fighting over share in this and that, I think Jensen just wants the pie to be really big. For the tour of ending, that's why he's so obsessed with open source. He just wants the pie to be big. He just wants AI to make people's lives better. I think he's very well-intentioned. And I think it's because he's-- think about how many years they spent building Kuda for this moment. In some ways, Jensen made a bet on AI a long, long time ago. Jensen was one of the first people to make a bet on AI. And I think that he can't change who you are. That's maybe why, also, whenever we talk with these companies, I come back to the human personalities. In the game theory, what's their world model? I have my world model, which I've shared on this podcast. What's their world model? I think you can't change. Some age, you're not changing your fundamental world model. And Jensen's fundamental world model is you win, I win. Right. OK, so for SpaceX, is SpaceX kind of pursuing the strategy that you wanted for Google? Not-- they don't have their chip. But they started to try to build their own model. They're still building it with GROC. But I think they realized that they were-- they're all in pursuit of AI through their own model. It might not have been the winning horse and then pivoted to being an AI cloud or a Neo cloud. And that's sort of how they're going to live or die here. To me, SpaceX is like a meta commentary on financial markets, which I love. Which is like-- I mentioned earlier, Silicon Valley's eat in the world over the last 60 years. It's been amazing. We have chips. We have Moore's Law. So many of the things have happened. And then we have resulted in Instagram and TikTok and whatever. It's just like not-- I don't think that the hope for humanity element has been a little missing. And Elon is almost-- it's like meta commentary, which is like if you move civilization forward, then you make a lot of money. And-- but he really cares about moving civilization forward. Again, to the underlying incentives, I don't think Elon is fundamentally-- dollar motivated. I think Elon is fundamentally motivated. And so let's go to space. Let's colonize other planets. Let's advance human civilization. AI's the way to get there. We're going to have robots. Elon, in some ways, is working on every important idea that matters for civilization. It's like we need tunnels. So we're going to have the boring company. And we need neural links. So we're going to have neural link. And he's able to aggregate talent. And I just think his leadership style is so masterful. He attracts the best people in the world. He makes them want to do their best work. And he cuts all the blockers. I mentioned management by committee. It's like great performers don't want to be managed by a committee. Great performers want all the blockers to go away. Let's just focus on achieving the mission. And you meet the veterans who are at Tesla for 15 years. They're so proud of what they've done. achieved. My partner, Ravi Gupta, has this thing. He says, "The meaning of life is earned achievement." Isn't Elon all about helping other people achieve that meaning of life? What an achievement you feel when you were part of one of these companies. Elon is just so consistent to me, Elon is about chasing what's good for humanity while bringing out the best in the human beings who work in his organizations. Obviously, they're intense, obviously people come and go, and it's imperfect, but SpaceX is pursuing the biggest missions for humanity right now, and it has the most optimistic vision of what--do you think Elon is correct in a lot of his commentary on, like, he is pro-human. He's like fundamentally the most optimistic pro-human pro-progress vision out there, and that's why I think retail has lined up behind him. Okay, I want to end here, and this is sort of a turn from where we've been the entire conversation, but I'm so glad that you wrote about this because I don't think it's talked about enough, and this is something that you wrote about how AI might change spirituality. And you even asked this question in the extreme human's worship AI, and what would such worship even look like? And, you know, I mean, obviously the history of spirituality is human's worshiping, what they think is this higher power, so it would go to stand to reason that, like, if we invent AI or superintelligence, there will certainly be some people who will be like, "This is the manifestation of the--like, effectively, like, we've created the creator." So, I--which is crazy and weird to think about. So I'd love to hear your thoughts on where this goes, and also just as a corollary to that, how the pursuit of building an artificial brain may change the way that people think about religion itself, at least the traditional forms of religion that we have on this planet today. Great question. It's a deep thread to pull. So I'll try to give an answer to it in the longer conversation, obviously. You know, sometimes I sort of have these, like, side quests that I go down, and it can be multiple-year side quests, so I'm just going to say that out loud, right? Like, these side quests don't always converge on the main quest. The main quest is, like, technology investing, how do we build the future of humanity? I'm motivated by intellectual interest. The reason I got into AI, you know, almost 10 years ago, is intellectual interest in what AI was and what it was doing and the potential. I sort of have this feeling--it's funny to think back to those times--at this feeling of, like, "Man, it's like so crazy that we have all this data and we just make dashboards with it." You know, like, that's what I was thinking 10 years ago. And then, and I had this view of, like, maybe we could do more with the data, right? It was really that basic of an insight, and I'd pull that thread. And the nice thing is, like, you pull these threads, and then some of the threads are longer than others, and look, this AI thread has taken us 10 years, and we're still pulling the thread, and we're going to be pulling this thread for a long time. This other thread, and this is a personal interest, not a career interest, is like spirituality and God and religion, and I grew up religious and, um, believing God and, um, so what is God, right? And I think when I invest, you sort of try to invest in, like, emotionally mature humans, and so part of our quest, I think, as individuals, is, like, to become emotionally mature ourselves. And part of becoming emotionally mature, I think, sort of trying to understand the universe. I think this is why a lot of physicists are trying to understand the universe. And religion, in my mind, is this, like, you know, thousands of years quest to understand the universe. And I have a belief that, you know, 5,000 years of wisdom probably got us somewhere, um, and that we were making progress. And humans, I don't think our IQs have, like, fundamentally changed that much. Humans were pretty smart. And what humans did is they constructed whatever we call religion, and they constructed these systems to try to get it ground truth in the universe. And in my view, there's like, it's all pointing at one truth. I think a lot of people would believe this. Like, you can read the Quran. You can read the Torah. You can read the gospels. You can read the Marbarta. Like, there's kind of one truth in the universe. A lot of people have been trying to get out with that truth is we call that truth. God. Anyways, this is a side quest, right? There's nothing to do with AI. And yet, it really informs our day to day lives, especially in the valley. Like, the valley is this place that is so devoid of God. And you can pull the thread of, like, why is America not just the valley, the valley is an extreme version of this. But why is America so empty of godliness, right? And then you kind of pull the thread and you go back to Freud. And Freud basically said, like, hey, actually God died. Freud didn't kill God. He just noticed that God was dying, that religion was dying in the West. And Freud invents therapy and therapy culture, if you look at the last 20, 30 years, therapy culture was, like, sort of taken over. And yet, you look at the stats. People are lonely. People are depressed. People are unhappy. And so, I actually don't think this, these are parallel threads, like, AI is happening. And then there's just a broader cultural thing that's happening. And frankly, people don't really want to hear from me on the cultural thing, which I get is like, I'm just a VCs investing in startups fine. But I think that the cultural thing has echoes inside of this AI universe, where I think one view of it is it's the Tower of Babel quest, right? We're trying to build God that didn't end well in the Tower of Babel. What happens? How does that play out? So, neat, that's one view. And then the other view is that, hey, God died in the early 1900s, just as societal level. And then these people are trying to create a new God. And maybe they will successfully create a new God. And maybe that will be infused the world with some spirituality. And I think there's going to be a lot of debate along the way of, is this good? Is this bad? It's hard to have an opinion. I'm still in the, you know, I'm in the 10 years ago on AI. Like, I'm in the early phases of pulling this thread. But I will say the more I've pulled this thread, the more I've learned, I think it's intellectually extremely deep area. There's a lot of people who've written about it. And I'm probably in ining one or two of learning more about what I would call transcendence. It's the transcendence quest, like spirituality and religion is one version of that. But I think we all sort of crave transcendence. And in some ways, this AI quest is sort of fulfilling this need that people have for transcendence. And I wonder if that is part of what's powering it, why it's become so big. And I wonder how that plays out because the technology evolves. Yeah, no, it's, it's crazy to think about. And I think you're right. Like, it isn't talked about in terms of AI development. But there's no avoiding it. I think it has to play some role when you're trying to create intelligent life on your own, whether you believe in God or not. There is a, some spiritual parallel there. So, there's a question that we all share in common, which is like, how do we heal society? Like society is hurting right now. How do we heal society? Okay. Do you think AI could do that? I don't know. I think there's elements in which it's been good and there's elements that are some bad, right? And I think we, you know, to some degree, I think the exacerbating loneliness is concerning. Certainly technology has made people very lonely. We don't have, communities are breaking down. We don't have people don't have the sense of community right now in this country. And so, how do you do that? I think there's one argument which is like, return to the religions of old. Maybe that will work. I don't know, right? It's, you see, as a technology guy, it just always seems hard to go backwards. So, how do you go forwards? But then you have all, you know, you have Peter Tiel saying that it's like the anti-Christ, right? So, it's like, what? I don't know is the answer. And this is why I'm pulling this red. And I think it's interesting. And I hope that through developing this technology, we come out with some optimistic answer that enables people to have enriched lives. And I do think a lot of the people who are building this technology believe that once we're freed from daily labor, you know, we're going to be able to have these rich lives. I do think it's a Jewish phrase, which is there's no work with that Torah and there's no Torah without work. Meaning like, we need the spirituality crest and we need to be grounded by some real things in life that we're working on. And so, I think this dual quest is in some ways like the dual quest of humanity. It's like, you need to earn your daily bread. And then you also want some version of transcendence. Well, I hope you keep writing about this. And folks, I do urge you to go sign up for David's newsletter, decon, dcahn.substack.com. I'm looking forward to reading more of your writing. And I know you don't do this often. So I really appreciate you coming on the show. And I hope we'll see you again, David. Thanks for coming on. Thanks Alex. Thanks for having me. Fun conversation. Really fun. Really fun. Well, one of our better ones here. So thank you again. And thank you all for listening and watching and we'll see you next time on Big Technology podcast. [Music]