The SpaceX IPO, Fable 5, AI Capex Update & Market Check w/ Gavin Baker, Andrew Fox & Clark Tang

And I think we're all pretty AI pilled. And I think we're all pretty AI pilled. And if you're AI pilled, that means we And if you're AI pilled, that means we And if you're AI pilled, that means we got to build a lot more compute than the got to build a lot more compute than the got to build a lot more compute than the world thinks. And that these models are world thinks. And that these models are world thinks. And that these models are going to be a lot more valuable than going to be a lot more valuable than going to be a lot more valuable than people think. You combine that with people think. You combine that with people think. You combine that with their core business, I don't know their core business, I don't know their core business, I don't know another entrepreneur or another business another entrepreneur or another business another entrepreneur or another business that's a better bet on the future, that's a better bet on the future, that's a better bet on the future, right, than SpaceX. And so I think for right, than SpaceX. And so I think for right, than SpaceX. And so I think for most institutional investors, it's a most institutional investors, it's a most institutional investors, it's a must buy, a must own, a set it and must buy, a must own, a set it and must buy, a must own, a set it and forget it, right, in order to have a a forget it, right, in order to have a a forget it, right, in order to have a a real bet on both the space and the AI real bet on both the space and the AI real bet on both the space and the AI future. All right, here we go. All right, here we go. Early morning Silicon Valley, BG2 is Early morning Silicon Valley, BG2 is Early morning Silicon Valley, BG2 is back. We're chopping it up on all things back. We're chopping it up on all things back. We're chopping it up on all things tech and markets. To do that, I have tech and markets. To do that, I have tech and markets. To do that, I have none other than GB in the house, Gavin none other than GB in the house, Gavin none other than GB in the house, Gavin Baker from a Treaties. He's brought his Baker from a Treaties. He's brought his Baker from a Treaties. He's brought his main guy, Andrew Fox. And of course, I main guy, Andrew Fox. And of course, I main guy, Andrew Fox. And of course, I had to draft Clark Tang into the mix, my had to draft Clark Tang into the mix, my had to draft Clark Tang into the mix, my partner, partner, partner, um to talk to to talk about some of the um to talk to to talk about some of the um to talk to to talk about some of the big questions of the day. You know, how big questions of the day. You know, how big questions of the day. You know, how should we be thinking about the SpaceX should we be thinking about the SpaceX should we be thinking about the SpaceX IPO? You know, what are the big levers? IPO? You know, what are the big levers? IPO? You know, what are the big levers? There are big numbers out there for There are big numbers out there for There are big numbers out there for what's going to happen over the course what's going to happen over the course what's going to happen over the course of the next few years. So, let's break of the next few years. So, let's break of the next few years. So, let's break that down a bit, help simplify it for that down a bit, help simplify it for that down a bit, help simplify it for folks. folks. folks. I Mythos launched yesterday. I want to I Mythos launched yesterday. I want to I Mythos launched yesterday. I want to talk a little bit about like who's up, talk a little bit about like who's up, talk a little bit about like who's up, who's down in the race for who's down in the race for who's down in the race for superintelligence. Where are we? What superintelligence. Where are we? What superintelligence. Where are we? What did we learn with the Mythos launch? And did we learn with the Mythos launch? And did we learn with the Mythos launch? And Clark was in uh in in in Taiwan last Clark was in uh in in in Taiwan last Clark was in uh in in in Taiwan last week um with Jensen at Computex and GTC. week um with Jensen at Computex and GTC. week um with Jensen at Computex and GTC. So, what was our takeaway there? What's So, what was our takeaway there? What's So, what was our takeaway there? What's going on with GPUs, memory? Where are going on with GPUs, memory? Where are going on with GPUs, memory? Where are the bottlenecks? And where do we go from the bottlenecks? And where do we go from the bottlenecks? And where do we go from here? To start everything off, um you here? To start everything off, um you here? To start everything off, um you know, maybe just kick it over to you, know, maybe just kick it over to you, know, maybe just kick it over to you, Gavin, talking about the SpaceX IPO. The Gavin, talking about the SpaceX IPO. The Gavin, talking about the SpaceX IPO. The IPO is in 2 days. Uh you're a big IPO is in 2 days. Uh you're a big IPO is in 2 days. Uh you're a big shareholder. Congratulations. We're also shareholder. Congratulations. We're also shareholder. Congratulations. We're also a shareholder. Uh you know, we also we a shareholder. Uh you know, we also we a shareholder. Uh you know, we also we expect to be buying in the IPO. The Wall expect to be buying in the IPO. The Wall expect to be buying in the IPO. The Wall Street Journal's reporting Street Journal's reporting Street Journal's reporting um you know, the Goldman Sachs are both um you know, the Goldman Sachs are both um you know, the Goldman Sachs are both saying 160 billion in revenue in 2028. saying 160 billion in revenue in 2028. saying 160 billion in revenue in 2028. Um we know that the IPO is $135 a share, Um we know that the IPO is $135 a share, Um we know that the IPO is $135 a share, 1.77 trillion. Um so 1.77 trillion. Um so 1.77 trillion. Um so when we think about kind of what the big when we think about kind of what the big when we think about kind of what the big levers are, there's so many moving parts levers are, there's so many moving parts levers are, there's so many moving parts in this IPO. Um nobody's better than you in this IPO. Um nobody's better than you in this IPO. Um nobody's better than you at just breaking it down, simplifying at just breaking it down, simplifying at just breaking it down, simplifying it. What are the key levers that we it. What are the key levers that we it. What are the key levers that we ought to be thinking about that you're ought to be thinking about that you're ought to be thinking about that you're thinking about over the course of the thinking about over the course of the thinking about over the course of the next few years? next few years? next few years? >> Sure. Um so great to be here. Thank you >> Sure. Um so great to be here. Thank you >> Sure. Um so great to be here. Thank you for having me. I thought we're going to for having me. I thought we're going to for having me. I thought we're going to call it B BGG B, call it B BGG B, call it B BGG B, but [laughter] we can stick with BG2. but [laughter] we can stick with BG2. but [laughter] we can stick with BG2. I've been your house. I've been your house. I've been your house. >> hey. >> hey. >> hey. All subject to revision. All subject to revision. All subject to revision. >> That's okay. That's okay. So I think >> That's okay. That's okay. So I think >> That's okay. That's okay. So I think there's two there's two there's two big levers or variables that I think big levers or variables that I think big levers or variables that I think people should focus on. And you know, people should focus on. And you know, people should focus on. And you know, I'm not going to comment on where I I'm not going to comment on where I I'm not going to comment on where I think um those variables go. think um those variables go. think um those variables go. But one is um you guys have this chart. But one is um you guys have this chart. But one is um you guys have this chart. Um did you did you post this on X? Um did you did you post this on X? Um did you did you post this on X? >> I did I did before and then we also >> I did I did before and then we also >> I did I did before and then we also included a new addition with uh XAI's included a new addition with uh XAI's included a new addition with uh XAI's new deals as well. new deals as well. new deals as well. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> Yeah. >> So Clark, who I've known for many years, >> So Clark, who I've known for many years, >> So Clark, who I've known for many years, um um um uh made a did a great analysis here. uh made a did a great analysis here. uh made a did a great analysis here. And he shows that XAI's deal um with And he shows that XAI's deal um with And he shows that XAI's deal um with Google for cloud computing Google for cloud computing Google for cloud computing uh generates more operating profit per uh generates more operating profit per uh generates more operating profit per gigawatt um than Anthropic, than Meta, gigawatt um than Anthropic, than Meta, gigawatt um than Anthropic, than Meta, than Google, than OpenAI. Uh their deal than Google, than OpenAI. Uh their deal than Google, than OpenAI. Uh their deal um um >> [clears throat] >> actually with uh Anthropic >> actually with uh Anthropic >> actually with uh Anthropic also generates probably more operating also generates probably more operating also generates probably more operating profit than anyone but um Anthropic. profit than anyone but um Anthropic. profit than anyone but um Anthropic. And so you know, um the your your And so you know, um the your your And so you know, um the your your colleague at Altimeter, Freida, also she colleague at Altimeter, Freida, also she colleague at Altimeter, Freida, also she calculated a 55% IRR calculated a 55% IRR calculated a 55% IRR >> Mhm. >> Mhm. >> Mhm. >> on Claude's one. >> on Claude's one. >> on Claude's one. >> Mhm. >> Mhm. >> Mhm. >> You know, if you can borrow money at 6, >> You know, if you can borrow money at 6, >> You know, if you can borrow money at 6, 7, 8% and invest in something with a 55% 7, 8% and invest in something with a 55% 7, 8% and invest in something with a 55% ARR, ARR, ARR, I'm not the most sophisticated thinker, I'm not the most sophisticated thinker, I'm not the most sophisticated thinker, but that math maths. but that math maths. but that math maths. >> Right. >> Right. >> Right. >> And so I think the most important >> And so I think the most important >> And so I think the most important variable, one of the two most important, variable, one of the two most important, variable, one of the two most important, is how quickly they bring on terrestrial is how quickly they bring on terrestrial is how quickly they bring on terrestrial data centers. data centers. data centers. >> Mhm. >> Mhm. >> Mhm. >> We do know from Jensen that uh Elon >> We do know from Jensen that uh Elon >> We do know from Jensen that uh Elon brings data centers up faster than brings data centers up faster than brings data centers up faster than anyone 122 days. Speed is literally cost anyone 122 days. Speed is literally cost anyone 122 days. Speed is literally cost because every day you're paying because every day you're paying because every day you're paying electricians and plumbers. That's cost. electricians and plumbers. That's cost. electricians and plumbers. That's cost. And they're now monetizing them at And they're now monetizing them at And they're now monetizing them at arguably the highest rate. And so I arguably the highest rate. And so I arguably the highest rate. And so I think, you know, everybody should run think, you know, everybody should run think, you know, everybody should run their own math on that, but that is a their own math on that, but that is a their own math on that, but that is a massive variable. massive variable. massive variable. >> Yes. >> Yes. >> Yes. >> Truly massive variable. The second thing >> Truly massive variable. The second thing >> Truly massive variable. The second thing is, you know, we have a chart and it's is, you know, we have a chart and it's is, you know, we have a chart and it's wildly out of date now. It's kind of wildly out of date now. It's kind of wildly out of date now. It's kind of freaking amazing. This chart is I think freaking amazing. This chart is I think freaking amazing. This chart is I think is this chart from 10 days ago? is this chart from 10 days ago? is this chart from 10 days ago? But it like the 10 or 12 days since this But it like the 10 or 12 days since this But it like the 10 or 12 days since this chart since we made this chart chart since we made this chart chart since we made this chart which shows the Pareto curves for Opus which shows the Pareto curves for Opus which shows the Pareto curves for Opus 4.7 for coding 4.7 for coding 4.7 for coding for Codex from OpenAI. And now we've had for Codex from OpenAI. And now we've had for Codex from OpenAI. And now we've had Opus 4.8. It was already out of date. Opus 4.8. It was already out of date. Opus 4.8. It was already out of date. And now we have Fable And now we have Fable And now we have Fable >> Totally. >> Totally. >> Totally. >> and Mythos, which is freaking wild. In >> and Mythos, which is freaking wild. In >> and Mythos, which is freaking wild. In 10 days 10 days 10 days >> Yes. >> Yes. >> Yes. >> like we would have had to update the >> like we would have had to update the >> like we would have had to update the chart twice. chart twice. chart twice. >> Right. >> Right. >> Right. >> But what the Pareto curve sure shows is >> But what the Pareto curve sure shows is >> But what the Pareto curve sure shows is how much intelligence you can get for a how much intelligence you can get for a how much intelligence you can get for a given amount of cost. And I do think given amount of cost. And I do think given amount of cost. And I do think being all revenue will accrue to the being all revenue will accrue to the being all revenue will accrue to the Pareto curve. All at least kind of Pareto curve. All at least kind of Pareto curve. All at least kind of frontier model revenue will accrue to frontier model revenue will accrue to frontier model revenue will accrue to the Pareto curve. And this is Pareto the Pareto curve. And this is Pareto the Pareto curve. And this is Pareto curve for code coding. And what I think curve for code coding. And what I think curve for code coding. And what I think is so impressive is so impressive is so impressive is that is that is that you can see in the chart that Composer 2 you can see in the chart that Composer 2 you can see in the chart that Composer 2 was Pareto dominant was Pareto dominant was Pareto dominant or, you know, at the lowest level of or, you know, at the lowest level of or, you know, at the lowest level of intelligence with very little training. intelligence with very little training. intelligence with very little training. This just reflects, and I know you know This just reflects, and I know you know This just reflects, and I know you know Cursor well. I think you know Cursor Cursor well. I think you know Cursor Cursor well. I think you know Cursor [ __ ] a lot better than I do. [ __ ] a lot better than I do. [ __ ] a lot better than I do. A vast amount better than I do. A vast amount better than I do. A vast amount better than I do. [laughter] [laughter] [laughter] But my understanding is that Cursor and But my understanding is that Cursor and But my understanding is that Cursor and Anthropic have more tokens of Anthropic have more tokens of Anthropic have more tokens of proprietary coding data than anyone proprietary coding data than anyone proprietary coding data than anyone else. And they have more tokens of else. And they have more tokens of else. And they have more tokens of proprietary coding data than exist on proprietary coding data than exist on proprietary coding data than exist on the public internet. the public internet. the public internet. And so they fed Cursor fed And so they fed Cursor fed And so they fed Cursor fed um used ChemK 0.25, used their own um used ChemK 0.25, used their own um used ChemK 0.25, used their own private data, did some RL, some private data, did some RL, some private data, did some RL, some supervised fine-tuning supervised fine-tuning supervised fine-tuning and they got a really good model. And and they got a really good model. And and they got a really good model. And then they spent 3 weeks in the Colossus then they spent 3 weeks in the Colossus then they spent 3 weeks in the Colossus 2 cluster and they got a model that 2 cluster and they got a model that 2 cluster and they got a model that 12 days ago was Pareto dominant with 12 days ago was Pareto dominant with 12 days ago was Pareto dominant with Composer 2.5. That's on their own Composer 2.5. That's on their own Composer 2.5. That's on their own benchmark. benchmark. benchmark. Um Cursor bench, so maybe take it with a Um Cursor bench, so maybe take it with a Um Cursor bench, so maybe take it with a grain of salt. But I think this just grain of salt. But I think this just grain of salt. But I think this just suggests that the Cursor data is very suggests that the Cursor data is very suggests that the Cursor data is very valuable for coding and when it is valuable for coding and when it is valuable for coding and when it is trained, you know, to Chinchilla optimal trained, you know, to Chinchilla optimal trained, you know, to Chinchilla optimal or beyond Chinchilla optimal with or beyond Chinchilla optimal with or beyond Chinchilla optimal with reinforcement learning, reinforcement learning, reinforcement learning, you know, I think it suggests that XAI you know, I think it suggests that XAI you know, I think it suggests that XAI and SpaceX AI has a shot of being a real and SpaceX AI has a shot of being a real and SpaceX AI has a shot of being a real player in coding. player in coding. player in coding. >> I mean, I think one of the interesting >> I mean, I think one of the interesting >> I mean, I think one of the interesting things is, you know, we things is, you know, we things is, you know, we you know, the way he answered the you know, the way he answered the you know, the way he answered the question, right? We didn't talk about question, right? We didn't talk about question, right? We didn't talk about launch. launch. launch. Right? We didn't talk about Starlink or Right? We didn't talk about Starlink or Right? We didn't talk about Starlink or communications. Those up until really 6 communications. Those up until really 6 communications. Those up until really 6 months ago were the business. months ago were the business. months ago were the business. >> Yeah. >> Yeah. >> Yeah. >> Right? And, you know, and then we merged >> Right? And, you know, and then we merged >> Right? And, you know, and then we merged in x.ai and we merged in Cursor and then in x.ai and we merged in Cursor and then in x.ai and we merged in Cursor and then we announced these deals where it was we announced these deals where it was we announced these deals where it was very clear he was kind of building AWS very clear he was kind of building AWS very clear he was kind of building AWS right under our nose, you know, in in in right under our nose, you know, in in in right under our nose, you know, in in in in terms of this. But what I want to do in terms of this. But what I want to do in terms of this. But what I want to do is go to go to Fox. Give us the is go to go to Fox. Give us the is go to go to Fox. Give us the breakdown. Three big lines of business, breakdown. Three big lines of business, breakdown. Three big lines of business, right? We've got the the the right? We've got the the the right? We've got the the the communication Starlink launch business, communication Starlink launch business, communication Starlink launch business, we've got the, you know, AI compute we've got the, you know, AI compute we've got the, you know, AI compute business and then I want to come back to business and then I want to come back to business and then I want to come back to x.ai that you were just clicking on. But x.ai that you were just clicking on. But x.ai that you were just clicking on. But if we just go to the core business, what if we just go to the core business, what if we just go to the core business, what do we have to assume goes right in the do we have to assume goes right in the do we have to assume goes right in the core business both with launch and with core business both with launch and with core business both with launch and with Starlink in order to achieve the numbers Starlink in order to achieve the numbers Starlink in order to achieve the numbers that are out there? that are out there? that are out there? >> Yeah, sure. So, look, I think >> Yeah, sure. So, look, I think >> Yeah, sure. So, look, I think the thing that's foundational to the thing that's foundational to the thing that's foundational to everything is the launch business. everything is the launch business. everything is the launch business. >> Right. >> Right. >> Right. >> Right? This is the kind of >> Right? This is the kind of >> Right? This is the kind of crown jewel of SpaceX. Um it's something crown jewel of SpaceX. Um it's something crown jewel of SpaceX. Um it's something that no one else really has, notably that no one else really has, notably that no one else really has, notably reusability. reusability. reusability. >> Right. >> Right. >> Right. >> And soon rapid reusability. Right? This >> And soon rapid reusability. Right? This >> And soon rapid reusability. Right? This is, I think, what you need to believe in is, I think, what you need to believe in is, I think, what you need to believe in to get to the economics in AI that make to get to the economics in AI that make to get to the economics in AI that make orbital compute something that's very orbital compute something that's very orbital compute something that's very economically attractive. economically attractive. economically attractive. >> Right. >> Right. >> Right. >> Outside of the idea that we are in >> Outside of the idea that we are in >> Outside of the idea that we are in shortage for power, shortage for chips. shortage for power, shortage for chips. shortage for power, shortage for chips. Right? Um so, I think rapid reusability Right? Um so, I think rapid reusability Right? Um so, I think rapid reusability is the main thing that we're watching is the main thing that we're watching is the main thing that we're watching for and I think most people should watch for and I think most people should watch for and I think most people should watch for. Um, for. Um, for. Um, you know, Elon talks about it a lot, but you know, Elon talks about it a lot, but you know, Elon talks about it a lot, but getting these rockets to fly at a getting these rockets to fly at a getting these rockets to fly at a cadence that's comparable to an airline, cadence that's comparable to an airline, cadence that's comparable to an airline, right? And and Gavin has used this right? And and Gavin has used this right? And and Gavin has used this analogy before, but analogy before, but analogy before, but um, the old rocket industry was kind of um, the old rocket industry was kind of um, the old rocket industry was kind of like, imagine boarding a plane, flying like, imagine boarding a plane, flying like, imagine boarding a plane, flying to California, getting off the plane, to California, getting off the plane, to California, getting off the plane, the plane explodes the plane explodes the plane explodes after. Um, so I think what SpaceX are after. Um, so I think what SpaceX are after. Um, so I think what SpaceX are ultimately trying to achieve is have a ultimately trying to achieve is have a ultimately trying to achieve is have a Starship fly both stages, not just the Starship fly both stages, not just the Starship fly both stages, not just the booster. Um, booster. Um, booster. Um, uh, uh, uh, 30, 40, 50 times 30, 40, 50 times 30, 40, 50 times before you have to retrofit that ship. before you have to retrofit that ship. before you have to retrofit that ship. Um, Um, Um, and when you do that, you're amortizing and when you do that, you're amortizing and when you do that, you're amortizing the cost of the vehicle over many the cost of the vehicle over many the cost of the vehicle over many flights, right? And that's what brings flights, right? And that's what brings flights, right? And that's what brings the cost down significantly. the cost down significantly. the cost down significantly. Um, Um, Um, >> But that's a really hard problem to >> But that's a really hard problem to >> But that's a really hard problem to solve. solve. solve. >> Extremely difficult and look, I think >> Extremely difficult and look, I think >> Extremely difficult and look, I think the company, you know, have the company, you know, have the company, you know, have been loud and clear, they're going to been loud and clear, they're going to been loud and clear, they're going to attempt to bring back the second stage attempt to bring back the second stage attempt to bring back the second stage >> Right. >> Right. >> Right. >> of Starship later this year. >> of Starship later this year. >> of Starship later this year. >> Right. >> Right. >> Right. >> Um, and then make it reusable, you know, >> Um, and then make it reusable, you know, >> Um, and then make it reusable, you know, re-fly the second stage next year. Um, re-fly the second stage next year. Um, re-fly the second stage next year. Um, and from there, ramp up the cadence. But and from there, ramp up the cadence. But and from there, ramp up the cadence. But at the end of the day, driving down the at the end of the day, driving down the at the end of the day, driving down the cost of launch cost of launch cost of launch is what enables all of these other is what enables all of these other is what enables all of these other businesses and is what makes them so businesses and is what makes them so businesses and is what makes them so attractive relative to incumbents. attractive relative to incumbents. attractive relative to incumbents. >> So how many how many times are we >> So how many how many times are we >> So how many how many times are we Starship just launched Starship 3, you Starship just launched Starship 3, you Starship just launched Starship 3, you know, just launched. How many launches know, just launched. How many launches know, just launched. How many launches are you know, do you think kind of the are you know, do you think kind of the are you know, do you think kind of the consensus out there is assuming, you consensus out there is assuming, you consensus out there is assuming, you know, two or three years from now? Like know, two or three years from now? Like know, two or three years from now? Like what is the launch cadence? Are we what is the launch cadence? Are we what is the launch cadence? Are we launching one of these every day or we launching one of these every day or we launching one of these every day or we launching one of these every week or launching one of these every week or launching one of these every week or every month? Like where are we in terms every month? Like where are we in terms every month? Like where are we in terms of expectations? of expectations? of expectations? >> Yeah, so look, I think expectations for >> Yeah, so look, I think expectations for >> Yeah, so look, I think expectations for now, you know, we're going from, you now, you know, we're going from, you now, you know, we're going from, you know, call it 160 165 launches last year know, call it 160 165 launches last year know, call it 160 165 launches last year up into the high hundreds of launches in up into the high hundreds of launches in up into the high hundreds of launches in several years and, you know, getting several years and, you know, getting several years and, you know, getting into the thousands of launches probably into the thousands of launches probably into the thousands of launches probably in the next 3 years thereafter. in the next 3 years thereafter. in the next 3 years thereafter. >> Okay. >> Okay. >> Okay. >> Um I think the company have aspirations. >> Um I think the company have aspirations. >> Um I think the company have aspirations. >> Thousands of launches, you're launching, >> Thousands of launches, you're launching, >> Thousands of launches, you're launching, you're doing two or three launches a you're doing two or three launches a you're doing two or three launches a day. day. day. >> Right. >> Right. >> Right. >> Right. And then talk to us a little bit, >> Right. And then talk to us a little bit, >> Right. And then talk to us a little bit, what is this enable? Obviously, you what is this enable? Obviously, you what is this enable? Obviously, you know, I'm here in Silicon Valley. I know, I'm here in Silicon Valley. I know, I'm here in Silicon Valley. I can't even I can't even keep a call on can't even I can't even keep a call on can't even I can't even keep a call on Sand Hill Road, two decades into the Sand Hill Road, two decades into the Sand Hill Road, two decades into the mobile revolution. I mean, it's the mobile revolution. I mean, it's the mobile revolution. I mean, it's the craziest thing. It's like a third world. craziest thing. It's like a third world. craziest thing. It's like a third world. >> It's It's a major business problem when >> It's It's a major business problem when >> It's It's a major business problem when you're freaking out here. [laughter] you're freaking out here. [laughter] you're freaking out here. [laughter] >> It's crazy. It's crazy. Right by the >> It's crazy. It's crazy. Right by the >> It's crazy. It's crazy. Right by the Starwood dead zone. And I'm like, how Starwood dead zone. And I'm like, how Starwood dead zone. And I'm like, how can this possibly be? So almost like can this possibly be? So almost like can this possibly be? So almost like it's a joke. It's the epicenter of it's a joke. It's the epicenter of it's a joke. It's the epicenter of technology in America and you can't technology in America and you can't technology in America and you can't maintain a call. Okay, so we're all maintain a call. Okay, so we're all maintain a call. Okay, so we're all going to switch to Starlink mobile when going to switch to Starlink mobile when going to switch to Starlink mobile when it comes along because I don't want to it comes along because I don't want to it comes along because I don't want to lose that call on Sand Hill Road. So lose that call on Sand Hill Road. So lose that call on Sand Hill Road. So walk me through a little bit just again walk me through a little bit just again walk me through a little bit just again high-level. Um it's a big portion of the high-level. Um it's a big portion of the high-level. Um it's a big portion of the revenue growth expected in the business revenue growth expected in the business revenue growth expected in the business over the course of the next two to three over the course of the next two to three over the course of the next two to three years. My hunch is a lot of this is years. My hunch is a lot of this is years. My hunch is a lot of this is driven uh by uh by direct to cell driven uh by uh by direct to cell driven uh by uh by direct to cell connectivity. Walk me through a little connectivity. Walk me through a little connectivity. Walk me through a little bit those economics. bit those economics. bit those economics. >> Yeah, so look, it's actually >> Yeah, so look, it's actually >> Yeah, so look, it's actually interesting. Um interesting. Um interesting. Um the broadband business is still very the broadband business is still very the broadband business is still very early stage when you think about um the early stage when you think about um the early stage when you think about um the percent of households that have actually percent of households that have actually percent of households that have actually been penetrated to date. You look at the been penetrated to date. You look at the been penetrated to date. You look at the percent of global households with percent of global households with percent of global households with Starlink, it's less than 1%. Uh and Starlink, it's less than 1%. Uh and Starlink, it's less than 1%. Uh and that's the broadband, you know, you kind that's the broadband, you know, you kind that's the broadband, you know, you kind of have a base terminal at your house, of have a base terminal at your house, of have a base terminal at your house, on your car, on your boat, um and then on your car, on your boat, um and then on your car, on your boat, um and then airlines now as well. Um airlines now as well. Um airlines now as well. Um so I actually think broadband can scale so I actually think broadband can scale so I actually think broadband can scale to hundreds of millions of terminals, to hundreds of millions of terminals, to hundreds of millions of terminals, hundreds of millions of users. And today hundreds of millions of users. And today hundreds of millions of users. And today the subscriber base the subscriber base the subscriber base >> Hundreds of millions if if they get >> Hundreds of millions if if they get >> Hundreds of millions if if they get rapid reusability of Starship, which is rapid reusability of Starship, which is rapid reusability of Starship, which is really hard. really hard. really hard. Um Um Um you know, if if there's not competition. you know, if if there's not competition. you know, if if there's not competition. Um hundreds of millions, um it's Um hundreds of millions, um it's Um hundreds of millions, um it's possible. possible. possible. But But But maybe maybe maybe >> I I always say around here, it's funny. >> I I always say around here, it's funny. >> I I always say around here, it's funny. I love seeing PM and kind of analysts in I love seeing PM and kind of analysts in I love seeing PM and kind of analysts in in this situation. It's exactly what I in this situation. It's exactly what I in this situation. It's exactly what I do with Clark. Clark will say something, do with Clark. Clark will say something, do with Clark. Clark will say something, I'll say the future is a distribution of I'll say the future is a distribution of I'll say the future is a distribution of unknown probabilities. It's either more unknown probabilities. It's either more unknown probabilities. It's either more likely or less likely, so give me the likely or less likely, so give me the likely or less likely, so give me the distribution. Are we talking 20% 30%? distribution. Are we talking 20% 30%? distribution. Are we talking 20% 30%? It's hilarious. It's the same It's hilarious. It's the same It's hilarious. It's the same >> Well, no, 100% same thing. And like I've >> Well, no, 100% same thing. And like I've >> Well, no, 100% same thing. And like I've watched Elon do many hard things and watched Elon do many hard things and watched Elon do many hard things and this is a really hard thing. So, I think this is a really hard thing. So, I think this is a really hard thing. So, I think it's reasonable to think that they're it's reasonable to think that they're it's reasonable to think that they're going to succeed with rapid reusability, going to succeed with rapid reusability, going to succeed with rapid reusability, but just I just think it's important to but just I just think it's important to but just I just think it's important to acknowledge that like acknowledge that like acknowledge that like orbital compute orbital compute orbital compute you know, Starlinks, you know, Starlink you know, Starlinks, you know, Starlink you know, Starlinks, you know, Starlink V3, Starlink direct to cell, we need we V3, Starlink direct to cell, we need we V3, Starlink direct to cell, we need we need first reusability for Starship V3 need first reusability for Starship V3 need first reusability for Starship V3 and then rapid reusability unlocks a lot and then rapid reusability unlocks a lot and then rapid reusability unlocks a lot of this. of this. of this. >> Right. When I see when I see the models >> Right. When I see when I see the models >> Right. When I see when I see the models that the banks are putting out there, that the banks are putting out there, that the banks are putting out there, right? And Wall Street Journal, right? And Wall Street Journal, right? And Wall Street Journal, everybody's reported on these. These everybody's reported on these. These everybody's reported on these. These same things have been widely leaked. same things have been widely leaked. same things have been widely leaked. They they they largely have the revenue They they they largely have the revenue They they they largely have the revenue on connectivity, so let's call it on connectivity, so let's call it on connectivity, so let's call it Starlink direct to cell, etc. Going Starlink direct to cell, etc. Going Starlink direct to cell, etc. Going from, you know, let's call it 10 billion from, you know, let's call it 10 billion from, you know, let's call it 10 billion to 50 billion uh by 2028. And so, I'm to 50 billion uh by 2028. And so, I'm to 50 billion uh by 2028. And so, I'm not asking you guys to react to, you not asking you guys to react to, you not asking you guys to react to, you know, to tell me your specific numbers, know, to tell me your specific numbers, know, to tell me your specific numbers, but when I'm talking to Clark all I'm but when I'm talking to Clark all I'm but when I'm talking to Clark all I'm trying to size up is order of magnitude. trying to size up is order of magnitude. trying to size up is order of magnitude. Do we think we can 5x the business over Do we think we can 5x the business over Do we think we can 5x the business over the course of the next 3 years? Is there the course of the next 3 years? Is there the course of the next 3 years? Is there enough TAM both in terms of broadband enough TAM both in terms of broadband enough TAM both in terms of broadband and direct to consumer? And I think the and direct to consumer? And I think the and direct to consumer? And I think the answer to that is yes. answer to that is yes. answer to that is yes. >> Yeah, here's what I just say very simply >> Yeah, here's what I just say very simply >> Yeah, here's what I just say very simply is I have um is I have um is I have um I I travel with Starlink. Um I I travel with Starlink. Um I I travel with Starlink. Um I'm I'm a big video gamer and very I'm I'm a big video gamer and very I'm I'm a big video gamer and very consistently wherever I am in the world, consistently wherever I am in the world, consistently wherever I am in the world, Starlink is the best connection. Starlink is the best connection. Starlink is the best connection. >> Yes. >> Yes. >> Yes. >> It's the fastest, it's lowest latency >> It's the fastest, it's lowest latency >> It's the fastest, it's lowest latency and I do think once they get to rapid and I do think once they get to rapid and I do think once they get to rapid reusability, it's also going to be reusability, it's also going to be reusability, it's also going to be they're going to have the cheapest cost they're going to have the cheapest cost they're going to have the cheapest cost per gigabyte or megabyte delivered um per gigabyte or megabyte delivered um per gigabyte or megabyte delivered um and better faster, cheaper has been a and better faster, cheaper has been a and better faster, cheaper has been a winning formula. winning formula. winning formula. And so, 50 billion, that's, you know, And so, 50 billion, that's, you know, And so, 50 billion, that's, you know, 0.3% 0.3% 0.3% penetration of the global telecom penetration of the global telecom penetration of the global telecom market. Now, maybe there's some market. Now, maybe there's some market. Now, maybe there's some deflation with Starlink pricing. deflation with Starlink pricing. deflation with Starlink pricing. Um but that's the way I'd frame it up. Um but that's the way I'd frame it up. Um but that's the way I'd frame it up. >> Yeah. I like betting on better, faster, >> Yeah. I like betting on better, faster, >> Yeah. I like betting on better, faster, cheaper. cheaper. cheaper. >> Um, Clark, I would say probably the >> Um, Clark, I would say probably the >> Um, Clark, I would say probably the biggest surprise of the last six six biggest surprise of the last six six biggest surprise of the last six six weeks weeks weeks is that Elon, you know, we talked about is that Elon, you know, we talked about is that Elon, you know, we talked about it on all-in podcast, we called it EWS, it on all-in podcast, we called it EWS, it on all-in podcast, we called it EWS, Elon web services, right? That that he Elon web services, right? That that he Elon web services, right? That that he struck these huge deals with Anthropic struck these huge deals with Anthropic struck these huge deals with Anthropic and Google. and Google. and Google. I don't even think people were thinking I don't even think people were thinking I don't even think people were thinking about SpaceX in the AI compute game, about SpaceX in the AI compute game, about SpaceX in the AI compute game, right? We If you looked at the models as right? We If you looked at the models as right? We If you looked at the models as of a few months ago, it was of a few months ago, it was of a few months ago, it was connectivity, so Starlink, and then it connectivity, so Starlink, and then it connectivity, so Starlink, and then it was x.ai, the model. was x.ai, the model. was x.ai, the model. But this whole category of taking all of But this whole category of taking all of But this whole category of taking all of this compute, which he's uniquely good this compute, which he's uniquely good this compute, which he's uniquely good at standing up, right? And then at standing up, right? And then at standing up, right? And then reselling it in a way that's highly reselling it in a way that's highly reselling it in a way that's highly profitable was not in a lot of people's profitable was not in a lot of people's profitable was not in a lot of people's forecast. Now it's a major component of forecast. Now it's a major component of forecast. Now it's a major component of the forecast. You know, you and I did the forecast. You know, you and I did the forecast. You know, you and I did this podcast with Jensen, where Jensen this podcast with Jensen, where Jensen this podcast with Jensen, where Jensen said Elon is an N of 1. said Elon is an N of 1. said Elon is an N of 1. >> What they achieved is is singular. Never >> What they achieved is is singular. Never >> What they achieved is is singular. Never been done before. Just to put in been done before. Just to put in been done before. Just to put in perspective, 100,000 GPUs, that's perspective, 100,000 GPUs, that's perspective, 100,000 GPUs, that's you know, easily the fastest you know, easily the fastest you know, easily the fastest supercomputer on the planet as one supercomputer on the planet as one supercomputer on the planet as one cluster. cluster. cluster. Um, a supercomputer uh, Um, a supercomputer uh, Um, a supercomputer uh, that you would build would take normally that you would build would take normally that you would build would take normally 3 years to plan. 3 years to plan. 3 years to plan. >> Right. >> Right. >> Right. >> And then they deliver the equipment, and >> And then they deliver the equipment, and >> And then they deliver the equipment, and it takes 1 year it takes 1 year it takes 1 year to get it all working. to get it all working. to get it all working. Yes. We're talking about 19 days. Yes. We're talking about 19 days. Yes. We're talking about 19 days. >> Wow. >> Wow. >> Wow. >> N of 1 is right. Elon is an N of 1. >> N of 1 is right. Elon is an N of 1. >> N of 1 is right. Elon is an N of 1. >> And his ability to secure supply, stand >> And his ability to secure supply, stand >> And his ability to secure supply, stand up the supply, you know, deploy it in a up the supply, you know, deploy it in a up the supply, you know, deploy it in a way that's uh, you know, coherent and way that's uh, you know, coherent and way that's uh, you know, coherent and effective for both himself and I guess effective for both himself and I guess effective for both himself and I guess now for others. So, walk us through kind now for others. So, walk us through kind now for others. So, walk us through kind of that. It looks to me again like this of that. It looks to me again like this of that. It looks to me again like this is a major component of the revenue is a major component of the revenue is a major component of the revenue story. story. story. >> Totally. I mean, so we we were all at >> Totally. I mean, so we we were all at >> Totally. I mean, so we we were all at the macro hard data center, and it was the macro hard data center, and it was the macro hard data center, and it was just very evident the amount of just very evident the amount of just very evident the amount of engineering that was that had gone into engineering that was that had gone into engineering that was that had gone into building these sites. building these sites. building these sites. Um, Um, Um, you people always talk about Google and you people always talk about Google and you people always talk about Google and their ability to to build a TPU and sell their ability to to build a TPU and sell their ability to to build a TPU and sell the TPU to Anthropic to generate the TPU to Anthropic to generate the TPU to Anthropic to generate revenues for AI. revenues for AI. revenues for AI. I think it's a pretty similar dynamic I think it's a pretty similar dynamic I think it's a pretty similar dynamic here with Elon able to secure power, uh here with Elon able to secure power, uh here with Elon able to secure power, uh build these sites faster than anyone build these sites faster than anyone build these sites faster than anyone else and also be able now to monetize it else and also be able now to monetize it else and also be able now to monetize it to um to the this massive AI market to um to the this massive AI market to um to the this massive AI market that's ahead of us. that's ahead of us. that's ahead of us. Um Um Um if you look at the the relationships if you look at the the relationships if you look at the the relationships that he's forged with a lot of his that he's forged with a lot of his that he's forged with a lot of his suppliers, you know, be it Jensen, be suppliers, you know, be it Jensen, be suppliers, you know, be it Jensen, be it, you know, all of these different um it, you know, all of these different um it, you know, all of these different um different sites that actually want xAI different sites that actually want xAI different sites that actually want xAI as a tenant. Um his his ability to as a tenant. Um his his ability to as a tenant. Um his his ability to finance these deals at at very finance these deals at at very finance these deals at at very attractive uh financing rates relative attractive uh financing rates relative attractive uh financing rates relative to a lot of the other players in in the to a lot of the other players in in the to a lot of the other players in in the space, you know, these are advantages space, you know, these are advantages space, you know, these are advantages that compound over time. And when you've that compound over time. And when you've that compound over time. And when you've built the credibility to stand up these built the credibility to stand up these built the credibility to stand up these sites and monetize at these levels, um sites and monetize at these levels, um sites and monetize at these levels, um you know, it's very it's actually a very you know, it's very it's actually a very you know, it's very it's actually a very attractive uh attractive uh attractive uh very attractive proposition for for a very attractive proposition for for a very attractive proposition for for a lot of folks involved. Um and actually, lot of folks involved. Um and actually, lot of folks involved. Um and actually, you know, you know, you know, if you look at the these deals in if you look at the these deals in if you look at the these deals in particular, particular, particular, Gavin, you you pointed out, but, you Gavin, you you pointed out, but, you Gavin, you you pointed out, but, you know, they're they're actually know, they're they're actually know, they're they're actually monetizing, you know, perhaps better monetizing, you know, perhaps better monetizing, you know, perhaps better than than than other players in the space by selling other players in the space by selling other players in the space by selling this infrastruc- this infrastruc- this infrastruc- >> a lot higher. >> a lot higher. >> a lot higher. >> Um Google is obviously paying SpaceX a >> Um Google is obviously paying SpaceX a >> Um Google is obviously paying SpaceX a huge premium for this compute. Fox, you huge premium for this compute. Fox, you huge premium for this compute. Fox, you said something that I thought was really said something that I thought was really said something that I thought was really important, which is, you know, it may important, which is, you know, it may important, which is, you know, it may very well be that in order to get, you very well be that in order to get, you very well be that in order to get, you know, first in line on space compute, know, first in line on space compute, know, first in line on space compute, which Google certainly wants to do, that which Google certainly wants to do, that which Google certainly wants to do, that they're willing to pay a premium for they're willing to pay a premium for they're willing to pay a premium for their terrestrial compute. And so, to their terrestrial compute. And so, to their terrestrial compute. And so, to me, that's how you kind of square the me, that's how you kind of square the me, that's how you kind of square the circle as to why the premium. Any circle as to why the premium. Any circle as to why the premium. Any thoughts? thoughts? thoughts? >> Yeah, look, I think there's some of that >> Yeah, look, I think there's some of that >> Yeah, look, I think there's some of that embedded there, but um look, at the end embedded there, but um look, at the end embedded there, but um look, at the end of the day, SpaceX can stand up compute of the day, SpaceX can stand up compute of the day, SpaceX can stand up compute quickly. quickly. quickly. They can stand it up coherently. And They can stand it up coherently. And They can stand it up coherently. And they can stand up a lot of it in one they can stand up a lot of it in one they can stand up a lot of it in one place and have it readily available. So, place and have it readily available. So, place and have it readily available. So, look, I think that's most of the look, I think that's most of the look, I think that's most of the premium, but outside of that certainly premium, but outside of that certainly premium, but outside of that certainly people are going to space over time. people are going to space over time. people are going to space over time. >> I have to pay a little call option to >> I have to pay a little call option to >> I have to pay a little call option to get first in line for space. get first in line for space. get first in line for space. >> There you go. Good one. >> There you go. Good one. >> There you go. Good one. >> We've all been investing in the neo >> We've all been investing in the neo >> We've all been investing in the neo cloud space. So, like there's a cloud space. So, like there's a cloud space. So, like there's a fundamental belief around this table I fundamental belief around this table I fundamental belief around this table I that that we lack the compute needed to that that we lack the compute needed to that that we lack the compute needed to continue to push the frontier on continue to push the frontier on continue to push the frontier on intelligence. So, we have to build a lot intelligence. So, we have to build a lot intelligence. So, we have to build a lot of compute, okay? Now there's a there's of compute, okay? Now there's a there's of compute, okay? Now there's a there's competition going on. On one end you competition going on. On one end you competition going on. On one end you have the hyper scalers who are building have the hyper scalers who are building have the hyper scalers who are building out that capability. Then we have AI out that capability. Then we have AI out that capability. Then we have AI dedicated clouds that are building out dedicated clouds that are building out dedicated clouds that are building out that capability. And now literally in a that capability. And now literally in a that capability. And now literally in a matter of weeks, right, we have a a you matter of weeks, right, we have a a you matter of weeks, right, we have a a you know a giant that's emerged in this know a giant that's emerged in this know a giant that's emerged in this category which is SpaceX. category which is SpaceX. category which is SpaceX. The question to you Gavin is can they The question to you Gavin is can they The question to you Gavin is can they consolidate this market, right? Because consolidate this market, right? Because consolidate this market, right? Because if I think about a marketplace, Elon has if I think about a marketplace, Elon has if I think about a marketplace, Elon has a unique ability to get the supply. He a unique ability to get the supply. He a unique ability to get the supply. He has a unique ability to cut deals on the has a unique ability to cut deals on the has a unique ability to cut deals on the other side and nobody can stand it up other side and nobody can stand it up other side and nobody can stand it up like he can stand it up. So, I think like he can stand it up. So, I think like he can stand it up. So, I think there might be a real consolidation in there might be a real consolidation in there might be a real consolidation in the AI compute market where you have the the AI compute market where you have the the AI compute market where you have the hyper scalers on the one hand and on the hyper scalers on the one hand and on the hyper scalers on the one hand and on the other hand, you know, he may emerge as other hand, you know, he may emerge as other hand, you know, he may emerge as the largest, strongest player in the AI the largest, strongest player in the AI the largest, strongest player in the AI compute market. compute market. compute market. >> Yeah, so I think they're are they the >> Yeah, so I think they're are they the >> Yeah, so I think they're are they the number four number five hyper scaler number four number five hyper scaler number four number five hyper scaler today after the Google deal? today after the Google deal? today after the Google deal? >> Um it will be number four. >> Um it will be number four. >> Um it will be number four. >> Kind of wild. >> Kind of wild. >> Kind of wild. >> Yeah. >> Yeah. >> Yeah. >> In 30 days >> In 30 days >> In 30 days we went from not being an AI hyper we went from not being an AI hyper we went from not being an AI hyper scaler to being number four. And we scaler to being number four. And we scaler to being number four. And we passed a lot of companies including passed a lot of companies including passed a lot of companies including Oracle. Oracle. Oracle. >> Coreweave is a huge business, right? >> Coreweave is a huge business, right? >> Coreweave is a huge business, right? That we're we're investors in, you know, That we're we're investors in, you know, That we're we're investors in, you know, and have been investors in, right? But and have been investors in, right? But and have been investors in, right? But there are a lot of other players, the there are a lot of other players, the there are a lot of other players, the Nebius's of the world, the Iron's of the Nebius's of the world, the Iron's of the Nebius's of the world, the Iron's of the world. And I would say that they're world. And I would say that they're world. And I would say that they're probably 50 neo labs being funded in probably 50 neo labs being funded in probably 50 neo labs being funded in Silicon Valley right now as we speak Silicon Valley right now as we speak Silicon Valley right now as we speak because of the shortage in compute. because of the shortage in compute. because of the shortage in compute. >> Absolutely. >> Absolutely. >> Absolutely. So, that's So, that's So, that's kind of crazy in 30 days. That's just kind of crazy in 30 days. That's just kind of crazy in 30 days. That's just extraordinary. extraordinary. extraordinary. What I would say is there I think there What I would say is there I think there What I would say is there I think there is a belief that these data centers are is a belief that these data centers are is a belief that these data centers are commodities. commodities. commodities. >> Mhm. >> Mhm. >> Mhm. >> And I do not share that belief. >> And I do not share that belief. >> And I do not share that belief. Um I don't think anybody around this Um I don't think anybody around this Um I don't think anybody around this table shares that belief. table shares that belief. table shares that belief. And in the same way that Elon was able And in the same way that Elon was able And in the same way that Elon was able to re-engineer a rocket from first to re-engineer a rocket from first to re-engineer a rocket from first principles and make it reusable, principles and make it reusable, principles and make it reusable, he engineered an electric car from first he engineered an electric car from first he engineered an electric car from first principles. You know, everyone else was principles. You know, everyone else was principles. You know, everyone else was trying to, you know, make an electric trying to, you know, make an electric trying to, you know, make an electric car like an internal combustion engine car like an internal combustion engine car like an internal combustion engine car and he thought about it differently. car and he thought about it differently. car and he thought about it differently. And um And um And um I think he looked at data center design I think he looked at data center design I think he looked at data center design from first principles and he designed from first principles and he designed from first principles and he designed something fundamentally different. And I something fundamentally different. And I something fundamentally different. And I did actually ask the team. I said, "Hey did actually ask the team. I said, "Hey did actually ask the team. I said, "Hey guys, maybe I'd be a little less public guys, maybe I'd be a little less public guys, maybe I'd be a little less public about things that are very obvious to about things that are very obvious to about things that are very obvious to you [laughter] you [laughter] you [laughter] >> Right. >> Right. >> Right. >> about how to design a data center, but >> about how to design a data center, but >> about how to design a data center, but are revelations to other people are revelations to other people are revelations to other people because I think what you're doing is because I think what you're doing is because I think what you're doing is maybe um maybe um maybe um more differentiated than you perhaps more differentiated than you perhaps more differentiated than you perhaps realize cuz what you're doing is so realize cuz what you're doing is so realize cuz what you're doing is so logical to you, logical to you, logical to you, but maybe lot not logical to everyone but maybe lot not logical to everyone but maybe lot not logical to everyone else. And that's how he was able to do else. And that's how he was able to do else. And that's how he was able to do it in 122 days. it in 122 days. it in 122 days. >> Yeah, to I mean to that point, Brad, >> Yeah, to I mean to that point, Brad, >> Yeah, to I mean to that point, Brad, yesterday we were meeting one of our yesterday we were meeting one of our yesterday we were meeting one of our portfolio companies and we were talking portfolio companies and we were talking portfolio companies and we were talking about behind the meter and we're, you about behind the meter and we're, you about behind the meter and we're, you know, really thinking about it. There's know, really thinking about it. There's know, really thinking about it. There's only maybe two or three two or three only maybe two or three two or three only maybe two or three two or three players now that can actually reliably players now that can actually reliably players now that can actually reliably engineer behind the meter data center. engineer behind the meter data center. engineer behind the meter data center. And you know, there's real engineering And you know, there's real engineering And you know, there's real engineering work that goes into all of this. So, if work that goes into all of this. So, if work that goes into all of this. So, if you think about this, you think about this, you think about this, if you're a gas combustion if you're if you're a gas combustion if you're if you're a gas combustion if you're Vernova and you say we only have a Vernova and you say we only have a Vernova and you say we only have a certain number of gas combustion certain number of gas combustion certain number of gas combustion engines. Now, we can sell them to x.ai engines. Now, we can sell them to x.ai engines. Now, we can sell them to x.ai or we can sell them to one of these or we can sell them to one of these or we can sell them to one of these startup neo clouds. Who are you going to startup neo clouds. Who are you going to startup neo clouds. Who are you going to sell them to? sell them to? sell them to? >> Well, and there's another dynamic. >> Well, and there's another dynamic. >> Well, and there's another dynamic. Everyone starts making more money when Everyone starts making more money when Everyone starts making more money when the GPUs get energized and sold faster. the GPUs get energized and sold faster. the GPUs get energized and sold faster. So, literally speed is money for all of So, literally speed is money for all of So, literally speed is money for all of the suppliers. Power, land, turbines. the suppliers. Power, land, turbines. the suppliers. Power, land, turbines. So, So, So, I think it's we'll we'll see. I think it's we'll we'll see. I think it's we'll we'll see. >> Right. >> Right. >> Right. >> Hey Brad, man. >> Hey Brad, man. >> Hey Brad, man. >> And but but this is just we're just >> And but but this is just we're just >> And but but this is just we're just talking terrestrial. I do I do want to talking terrestrial. I do I do want to talking terrestrial. I do I do want to hit on and then you can flip it back on hit on and then you can flip it back on hit on and then you can flip it back on me. Talk to me, okay, So, let's let's me. Talk to me, okay, So, let's let's me. Talk to me, okay, So, let's let's assume, right, that they continue to assume, right, that they continue to assume, right, that they continue to build out the terrestrial landscape. build out the terrestrial landscape. build out the terrestrial landscape. They continue to find buyers for that. They continue to find buyers for that. They continue to find buyers for that. Um, walk us through, you know, what this Um, walk us through, you know, what this Um, walk us through, you know, what this unlocks, you know, and how this is unlocks, you know, and how this is unlocks, you know, and how this is related to space data centers because I related to space data centers because I related to space data centers because I think, you know, once you start talking think, you know, once you start talking think, you know, once you start talking terrafab capacity and beyond. So, we're terrafab capacity and beyond. So, we're terrafab capacity and beyond. So, we're talking 1,000 gigs, right? And this year talking 1,000 gigs, right? And this year talking 1,000 gigs, right? And this year what what we're doing, 25 or 30 gigs what what we're doing, 25 or 30 gigs what what we're doing, 25 or 30 gigs just to put it all in perspective. just to put it all in perspective. just to put it all in perspective. >> 20, yeah. >> 20, yeah. >> 20, yeah. >> Right? >> Right? >> Right? >> 20, 25 gigs. >> 20, 25 gigs. >> 20, 25 gigs. >> Okay, so so once we start scaling up, >> Okay, so so once we start scaling up, >> Okay, so so once we start scaling up, walk us through, walk us through, walk us through, do we have to have space data centers in do we have to have space data centers in do we have to have space data centers in order to get excited about buying the order to get excited about buying the order to get excited about buying the IPO, right? And then there's obviously IPO, right? And then there's obviously IPO, right? And then there's obviously this this debate in the world. I I heard this this debate in the world. I I heard this this debate in the world. I I heard Jeff Bezos say, you know, I think it's Jeff Bezos say, you know, I think it's Jeff Bezos say, you know, I think it's more like 6 years, but Elon's going to more like 6 years, but Elon's going to more like 6 years, but Elon's going to say three because if if he says six, say three because if if he says six, say three because if if he says six, then it will take even longer. So, say then it will take even longer. So, say then it will take even longer. So, say three and we may get it in four or five. three and we may get it in four or five. three and we may get it in four or five. But are space data centers integral and But are space data centers integral and But are space data centers integral and essential to, you know, the IPO? And essential to, you know, the IPO? And essential to, you know, the IPO? And what do you think the timeline is, Erin? what do you think the timeline is, Erin? what do you think the timeline is, Erin? There are you guys. There are you guys. There are you guys. >> So, I don't think I think if you think >> So, I don't think I think if you think >> So, I don't think I think if you think about those variables around what about those variables around what about those variables around what Crusher could mean for XAI. Crusher could mean for XAI. Crusher could mean for XAI. And we do have an existence proof that And we do have an existence proof that And we do have an existence proof that once you really get on that Pareto once you really get on that Pareto once you really get on that Pareto frontier, frontier, frontier, revenue can scale rapidly and it's revenue can scale rapidly and it's revenue can scale rapidly and it's called Entropic. And there does seem to called Entropic. And there does seem to called Entropic. And there does seem to be an exhaust There seems to be a lot of be an exhaust There seems to be a lot of be an exhaust There seems to be a lot of demand for coding. And I do think I'm demand for coding. And I do think I'm demand for coding. And I do think I'm John Massad posted something very John Massad posted something very John Massad posted something very interesting. interesting. interesting. >> The The founder of Replit. >> The The founder of Replit. >> The The founder of Replit. >> The founder of Replit. It he called it >> The founder of Replit. It he called it >> The founder of Replit. It he called it bitter lesson adjacent that coding may bitter lesson adjacent that coding may bitter lesson adjacent that coding may be the fastest path to AGI and ASI be the fastest path to AGI and ASI be the fastest path to AGI and ASI because if you really go to coding, you because if you really go to coding, you because if you really go to coding, you can write code if a model's good at can write code if a model's good at can write code if a model's good at coding to do anything. So, I think coding to do anything. So, I think coding to do anything. So, I think that's a profound point and I think that's a profound point and I think that's a profound point and I think coding is going to continue to be very coding is going to continue to be very coding is going to continue to be very important. So, I think if you think important. So, I think if you think important. So, I think if you think about that variable, about that variable, about that variable, if you think about Starlink direct to if you think about Starlink direct to if you think about Starlink direct to cell enabled by Starlink V3, and you cell enabled by Starlink V3, and you cell enabled by Starlink V3, and you think about how quickly they can or think about how quickly they can or think about how quickly they can or cannot bring on terrestrial compute, I I cannot bring on terrestrial compute, I I cannot bring on terrestrial compute, I I think orbital compute is is think orbital compute is is think orbital compute is is is necessary for the IPO valuation, is necessary for the IPO valuation, is necessary for the IPO valuation, but it's certainly important and it's but it's certainly important and it's but it's certainly important and it's >> Well, maybe another way to say it is you >> Well, maybe another way to say it is you >> Well, maybe another way to say it is you think you think you think you you may think we're going to get to ASI you may think we're going to get to ASI you may think we're going to get to ASI faster than we're going to get to faster than we're going to get to faster than we're going to get to orbital compute. That may take us from orbital compute. That may take us from orbital compute. That may take us from 300 IQ to 400 IQ, 500 IQ, and beyond. 300 IQ to 400 IQ, 500 IQ, and beyond. 300 IQ to 400 IQ, 500 IQ, and beyond. Um and the ability to scale it up to Um and the ability to scale it up to Um and the ability to scale it up to consume 10% of, you know, global GDP, consume 10% of, you know, global GDP, consume 10% of, you know, global GDP, but maybe maybe that's where we should but maybe maybe that's where we should but maybe maybe that's where we should move next. move next. move next. >> No, no, I think on the orbital compute, >> No, no, I think on the orbital compute, >> No, no, I think on the orbital compute, I think Foxy would be great our Clark to I think Foxy would be great our Clark to I think Foxy would be great our Clark to lay out the math from first principles lay out the math from first principles lay out the math from first principles on, you know, Clark has this great chart on, you know, Clark has this great chart on, you know, Clark has this great chart on, you know, the gigawatts it costs, on, you know, the gigawatts it costs, on, you know, the gigawatts it costs, you know, the dollars per gigawatt. you know, the dollars per gigawatt. you know, the dollars per gigawatt. >> Right. >> Right. >> Right. >> Walk us through the economic case. >> Walk us through the economic case. >> Walk us through the economic case. >> Yeah. Yeah, so I mean, on this point of >> Yeah. Yeah, so I mean, on this point of >> Yeah. Yeah, so I mean, on this point of is orbital key to investing here? I is orbital key to investing here? I is orbital key to investing here? I don't think it is and I'll first point don't think it is and I'll first point don't think it is and I'll first point I'll make is what are the implied I'll make is what are the implied I'll make is what are the implied monetization rates monetization rates monetization rates based on expectations today for the AI based on expectations today for the AI based on expectations today for the AI business? And you know, I think you business? And you know, I think you business? And you know, I think you threw out the $160 billion number that's threw out the $160 billion number that's threw out the $160 billion number that's been leaked out there that people are been leaked out there that people are been leaked out there that people are talking about. talking about. talking about. The implied monetization rate on that The implied monetization rate on that The implied monetization rate on that number is something like $14 billion per number is something like $14 billion per number is something like $14 billion per gigawatt per year for the AI business. gigawatt per year for the AI business. gigawatt per year for the AI business. They just signed Anthropic at 22 to 23. They just signed Anthropic at 22 to 23. They just signed Anthropic at 22 to 23. They just signed Google at 50. They just signed Google at 50. They just signed Google at 50. >> Right. >> Right. >> Right. >> Right. So, I I think you can invest >> Right. So, I I think you can invest >> Right. So, I I think you can invest behind the AI business terrestrially and behind the AI business terrestrially and behind the AI business terrestrially and still be excited about it. But with still be excited about it. But with still be excited about it. But with orbital orbital orbital >> an important point. Excited about it if >> an important point. Excited about it if >> an important point. Excited about it if they can get the land and the power. they can get the land and the power. they can get the land and the power. >> Right. But but but I mean I I think for >> Right. But but but I mean I I think for >> Right. But but but I mean I I think for most investors, right? They get They most investors, right? They get They most investors, right? They get They have an easier time getting their head have an easier time getting their head have an easier time getting their head around how SpaceX wins terrestrially. around how SpaceX wins terrestrially. around how SpaceX wins terrestrially. Like can they go get land, power, and Like can they go get land, power, and Like can they go get land, power, and chips? The answer to that is high chips? The answer to that is high chips? The answer to that is high probability yes, okay? And what we're probability yes, okay? And what we're probability yes, okay? And what we're saying is at the rate they're monetizing saying is at the rate they're monetizing saying is at the rate they're monetizing that, that gets you to the numbers that that, that gets you to the numbers that that, that gets you to the numbers that are being leaked out there before you are being leaked out there before you are being leaked out there before you even have to take the leap of faith that even have to take the leap of faith that even have to take the leap of faith that they're going to extend the lead with they're going to extend the lead with they're going to extend the lead with orbital data centers. But take us there orbital data centers. But take us there orbital data centers. But take us there on that, too. on that, too. on that, too. >> Sure. Yeah, so so look, with orbital, I >> Sure. Yeah, so so look, with orbital, I >> Sure. Yeah, so so look, with orbital, I think the key thing is um two-stage think the key thing is um two-stage think the key thing is um two-stage reusability. reusability. reusability. >> Yeah. >> Yeah. >> Yeah. >> And beyond that, rapid two-stage >> And beyond that, rapid two-stage >> And beyond that, rapid two-stage reusability. reusability. reusability. >> Yeah. >> Yeah. >> Yeah. >> So, today with Starship, they've shown >> So, today with Starship, they've shown >> So, today with Starship, they've shown that they can successfully reland the that they can successfully reland the that they can successfully reland the booster. booster. booster. The second stage, we'll see what happens The second stage, we'll see what happens The second stage, we'll see what happens later this year. I think they're later this year. I think they're later this year. I think they're attempting to bring that back and then attempting to bring that back and then attempting to bring that back and then make it reusable by next year. make it reusable by next year. make it reusable by next year. Um Um Um but the thing that's important about but the thing that's important about but the thing that's important about two-stage reusability when it comes to two-stage reusability when it comes to two-stage reusability when it comes to the economics for orbital compute, the economics for orbital compute, the economics for orbital compute, right, is the cost per kg comes down right, is the cost per kg comes down right, is the cost per kg comes down significantly. You know, we're talking significantly. You know, we're talking significantly. You know, we're talking about going from $1,500 per kg on about going from $1,500 per kg on about going from $1,500 per kg on Falcon, somewhere in that range, to 250 Falcon, somewhere in that range, to 250 Falcon, somewhere in that range, to 250 per kg, something lower. Um and the more per kg, something lower. Um and the more per kg, something lower. Um and the more that you can reuse the rocket, that you can reuse the rocket, that you can reuse the rocket, the more that price comes down. the more that price comes down. the more that price comes down. >> Right. >> Right. >> Right. >> Right, cuz you're just depreciating the >> Right, cuz you're just depreciating the >> Right, cuz you're just depreciating the cost of the launch. cost of the launch. cost of the launch. And eventually, you asymptote to the And eventually, you asymptote to the And eventually, you asymptote to the cost of the fuel. cost of the fuel. cost of the fuel. >> Right. >> Right. >> Right. >> Right. >> Right. >> Right. Assuming you can use a rocket for Assuming you can use a rocket for Assuming you can use a rocket for forever. forever. forever. >> Yes. >> Yes. >> Yes. >> Right, which will take a very long time >> Right, which will take a very long time >> Right, which will take a very long time for us to to really achieve that. But, for us to to really achieve that. But, for us to to really achieve that. But, um and at that point, we're talking um and at that point, we're talking um and at that point, we're talking about something well south of 250 per about something well south of 250 per about something well south of 250 per kg. kg. kg. So, So, So, then you look at the specs of these AI then you look at the specs of these AI then you look at the specs of these AI satellites. You know, Elon did a great satellites. You know, Elon did a great satellites. You know, Elon did a great >> Yeah, that pod that that pod was >> Yeah, that pod that that pod was >> Yeah, that pod that that pod was incredible that he laid out the other incredible that he laid out the other incredible that he laid out the other day, the specs on the satellites. day, the specs on the satellites. day, the specs on the satellites. >> It was really great because I think they >> It was really great because I think they >> It was really great because I think they are finally showing people, here's how are finally showing people, here's how are finally showing people, here's how you could viably design one of these you could viably design one of these you could viably design one of these satellites. satellites. satellites. And how heavy is the satellite? How many And how heavy is the satellite? How many And how heavy is the satellite? How many could you fit into a Starship launch? could you fit into a Starship launch? could you fit into a Starship launch? And when you back into the numbers, you And when you back into the numbers, you And when you back into the numbers, you get to something like 5 MW of capacity get to something like 5 MW of capacity get to something like 5 MW of capacity per Starship launch. per Starship launch. per Starship launch. >> Right. >> Right. >> Right. >> There's 100 metric tons in one of those >> There's 100 metric tons in one of those >> There's 100 metric tons in one of those Starships. So, you can back into the Starships. So, you can back into the Starships. So, you can back into the math of how much will it cost per math of how much will it cost per math of how much will it cost per gigawatt gigawatt gigawatt to launch these satellites into space. to launch these satellites into space. to launch these satellites into space. >> Right. >> Right. >> Right. >> Launch this compute into space. Um >> Launch this compute into space. Um >> Launch this compute into space. Um and the math that you get to before you and the math that you get to before you and the math that you get to before you account for things like account for things like account for things like bad GPUs, bad satellites, right, these bad GPUs, bad satellites, right, these bad GPUs, bad satellites, right, these will all be things that happen. will all be things that happen. will all be things that happen. But the math you get to is it's about $5 But the math you get to is it's about $5 But the math you get to is it's about $5 billion per gigawatt of CapEx to put billion per gigawatt of CapEx to put billion per gigawatt of CapEx to put these in space. these in space. these in space. >> Right. >> Right. >> Right. >> For comparison, terrestrially, >> For comparison, terrestrially, >> For comparison, terrestrially, talk about the switch gears, the talk about the switch gears, the talk about the switch gears, the generators, the transformers, the shell, generators, the transformers, the shell, generators, the transformers, the shell, getting the power, that today is about getting the power, that today is about getting the power, that today is about 25 20 to 25 billion per gigawatt. 25 20 to 25 billion per gigawatt. 25 20 to 25 billion per gigawatt. So we're talking about a 5x reduction So we're talking about a 5x reduction So we're talking about a 5x reduction in cost in cost in cost on half of your bill of materials on half of your bill of materials on half of your bill of materials >> Right. >> Right. >> Right. >> for the data center. >> for the data center. >> for the data center. >> Right. >> Right. >> Right. >> Which is a huge number. >> Which is a huge number. >> Which is a huge number. >> Yeah, just just very simply, I mean just >> Yeah, just just very simply, I mean just >> Yeah, just just very simply, I mean just to say that put it cost $60 to put a to say that put it cost $60 to put a to say that put it cost $60 to put a gigawatt gigawatt gigawatt on the ground today. And we'll call it on the ground today. And we'll call it on the ground today. And we'll call it 35 of that is are the GPUs and the 35 of that is are the GPUs and the 35 of that is are the GPUs and the silicon that's doing the training and silicon that's doing the training and silicon that's doing the training and the inference. the inference. the inference. And 25 billion is the land, the shell, And 25 billion is the land, the shell, And 25 billion is the land, the shell, the power, and the cooling. the power, and the cooling. the power, and the cooling. I would hypothesize that those elements I would hypothesize that those elements I would hypothesize that those elements are probably going to be inflationary, are probably going to be inflationary, are probably going to be inflationary, so that 25 billion may not go down. so that 25 billion may not go down. so that 25 billion may not go down. And because space, power, cooling are And because space, power, cooling are And because space, power, cooling are effectively free in space, and when I effectively free in space, and when I effectively free in space, and when I say space, I mean land. You know, say space, I mean land. You know, say space, I mean land. You know, there's no land in space, but there is there's no land in space, but there is there's no land in space, but there is space. Um Um you're you're talking about putting a you're you're talking about putting a you're you're talking about putting a gigawatt into space for 30 billion and gigawatt into space for 30 billion and gigawatt into space for 30 billion and having lower operating costs. Now the having lower operating costs. Now the having lower operating costs. Now the dynamic versus 60 billion that's dynamic versus 60 billion that's dynamic versus 60 billion that's inflationary, and that third and that 30 inflationary, and that third and that 30 inflationary, and that third and that 30 billion, that five may be deflationary billion, that five may be deflationary billion, that five may be deflationary over time. over time. over time. But what we need to consider But what we need to consider But what we need to consider is you know, the reliability and the is you know, the reliability and the is you know, the reliability and the maintenance. And so as long as you know, maintenance. And so as long as you know, maintenance. And so as long as you know, everybody can do the math, everybody can do the math, everybody can do the math, but as long as these satellites in space but as long as these satellites in space but as long as these satellites in space aren't failing at an at an astronomical aren't failing at an at an astronomical aren't failing at an at an astronomical rate, the math maths. As you can see, rate, the math maths. As you can see, rate, the math maths. As you can see, and by the way, we know GPUs melt and and by the way, we know GPUs melt and and by the way, we know GPUs melt and lasers fail. We know this happens in lasers fail. We know this happens in lasers fail. We know this happens in data centers, data centers, data centers, particularly during big training runs. particularly during big training runs. particularly during big training runs. And yeah, I mean GPUs melt. And yeah, I mean GPUs melt. And yeah, I mean GPUs melt. Um so as long as the reliability and Um so as long as the reliability and Um so as long as the reliability and maintenance is not dramatically lower, maintenance is not dramatically lower, maintenance is not dramatically lower, the math is there once we have the math is there once we have the math is there once we have reusability and then rapid reusability reusability and then rapid reusability reusability and then rapid reusability for Starship V3. for Starship V3. for Starship V3. >> I when you >> I when you >> I when you when we look at this, okay, so we we when we look at this, okay, so we we when we look at this, okay, so we we went through Starlink and we said, went through Starlink and we said, went through Starlink and we said, "Okay, like it it just stands to reason "Okay, like it it just stands to reason "Okay, like it it just stands to reason we're going to have direct to cell on we're going to have direct to cell on we're going to have direct to cell on Starlink." Like the assumptions there Starlink." Like the assumptions there Starlink." Like the assumptions there are, you know, again, seem like you can are, you know, again, seem like you can are, you know, again, seem like you can get your head around. Then when it comes get your head around. Then when it comes get your head around. Then when it comes to building terrestrial data centers, to building terrestrial data centers, to building terrestrial data centers, again, not a hard one to think that again, not a hard one to think that again, not a hard one to think that based on these couple deals that Elon's based on these couple deals that Elon's based on these couple deals that Elon's going to build a much bigger Starlink's going to build a much bigger Starlink's going to build a much bigger Starlink's going to build or SpaceX is going to going to build or SpaceX is going to going to build or SpaceX is going to build a much bigger business there. And build a much bigger business there. And build a much bigger business there. And then you have this call option on space then you have this call option on space then you have this call option on space that would drop the price even further. that would drop the price even further. that would drop the price even further. The one thing we haven't talked about is The one thing we haven't talked about is The one thing we haven't talked about is their model, right? And I find this their model, right? And I find this their model, right? And I find this surprising, right? Six Six months ago, surprising, right? Six Six months ago, surprising, right? Six Six months ago, x.ai was competing, they were doing x.ai was competing, they were doing x.ai was competing, they were doing pretty well, but they've done something pretty well, but they've done something pretty well, but they've done something dramatic over the course of the past dramatic over the course of the past dramatic over the course of the past couple couple couple couple months, which is they bought couple months, which is they bought couple months, which is they bought Cursor, right? Cursor is 700 800 people Cursor, right? Cursor is 700 800 people Cursor, right? Cursor is 700 800 people was already doing incredibly well from a was already doing incredibly well from a was already doing incredibly well from a revenue perspective. Our own revenue perspective. Our own revenue perspective. Our own projections were that they could exit projections were that they could exit projections were that they could exit this year at up to $10 billion this year at up to $10 billion this year at up to $10 billion of revenue, so they were growing very of revenue, so they were growing very of revenue, so they were growing very fast fast fast one of the leading coding agents, but one of the leading coding agents, but one of the leading coding agents, but they also had this incredible team with they also had this incredible team with they also had this incredible team with the potential, right, to really build a the potential, right, to really build a the potential, right, to really build a frontier level model, but they were frontier level model, but they were frontier level model, but they were compute constrained. So all of a sudden, compute constrained. So all of a sudden, compute constrained. So all of a sudden, they get bought by X. X has massive they get bought by X. X has massive they get bought by X. X has massive compute that they can now train on. compute that they can now train on. compute that they can now train on. And when I think about the revenue in AI And when I think about the revenue in AI And when I think about the revenue in AI that like if I look at that line item in that like if I look at that line item in that like if I look at that line item in the models having it go from $10 billion the models having it go from $10 billion the models having it go from $10 billion to $150 billion, yes, a lot of that will to $150 billion, yes, a lot of that will to $150 billion, yes, a lot of that will be the core weave type business that be the core weave type business that be the core weave type business that they have, but the question is how much they have, but the question is how much they have, but the question is how much of that is going to be the core x.ai of that is going to be the core x.ai of that is going to be the core x.ai business that's really powered by the business that's really powered by the business that's really powered by the new team from Cursor. So any thoughts on new team from Cursor. So any thoughts on new team from Cursor. So any thoughts on that, Kevin? that, Kevin? that, Kevin? >> Right now, so Composer 2.5 was Pareto >> Right now, so Composer 2.5 was Pareto >> Right now, so Composer 2.5 was Pareto dominant 12 days ago. It was trained on dominant 12 days ago. It was trained on dominant 12 days ago. It was trained on the Kimmy K2.5 base model. the Kimmy K2.5 base model. the Kimmy K2.5 base model. >> Right. >> Right. >> Right. >> Now, what's happening is the Grok 4.3 >> Now, what's happening is the Grok 4.3 >> Now, what's happening is the Grok 4.3 1.5 trillion parameter model is 1.5 trillion parameter model is 1.5 trillion parameter model is training. training. training. One would hypothesize based on scaling One would hypothesize based on scaling One would hypothesize based on scaling laws that that will might be a better laws that that will might be a better laws that that will might be a better base model. And then the cursor data is base model. And then the cursor data is base model. And then the cursor data is being injected into the pre-training being injected into the pre-training being injected into the pre-training process, not just reinforcement process, not just reinforcement process, not just reinforcement learning. learning. learning. And we'll see, and I think that is going And we'll see, and I think that is going And we'll see, and I think that is going to be a very important data point when to be a very important data point when to be a very important data point when that comes out. And I just think that comes out. And I just think that comes out. And I just think everyone should keep in mind that once everyone should keep in mind that once everyone should keep in mind that once you are at multiple places on that you are at multiple places on that you are at multiple places on that Pareto curve, if you have compute, you Pareto curve, if you have compute, you Pareto curve, if you have compute, you can scale really rapidly. can scale really rapidly. can scale really rapidly. >> You know, that that to me is if I had to >> You know, that that to me is if I had to >> You know, that that to me is if I had to say what the one piece that's being lost say what the one piece that's being lost say what the one piece that's being lost in the story, in the story, in the story, right? Like it's easy for everybody to right? Like it's easy for everybody to right? Like it's easy for everybody to get excited about the deals with get excited about the deals with get excited about the deals with Anthropic because you can put your hands Anthropic because you can put your hands Anthropic because you can put your hands around that. You know how much revenue around that. You know how much revenue around that. You know how much revenue it is. I see debate about, you know, the it is. I see debate about, you know, the it is. I see debate about, you know, the 90-day termination and how long they 90-day termination and how long they 90-day termination and how long they last and what multiple do you put on last and what multiple do you put on last and what multiple do you put on those revenues. But I think the thing those revenues. But I think the thing those revenues. But I think the thing that's getting lost is I think they've that's getting lost is I think they've that's getting lost is I think they've dramatically advanced their capability dramatically advanced their capability dramatically advanced their capability when it comes to building a frontier when it comes to building a frontier when it comes to building a frontier model. People outside Silicon Valley may model. People outside Silicon Valley may model. People outside Silicon Valley may not know, you know, Michael and the team not know, you know, Michael and the team not know, you know, Michael and the team at Cursor as well. This is an at Cursor as well. This is an at Cursor as well. This is an extraordinary team that he just extraordinary team that he just extraordinary team that he just downloaded, right, into SpaceX. SpaceX downloaded, right, into SpaceX. SpaceX downloaded, right, into SpaceX. SpaceX was already building good models. And was already building good models. And was already building good models. And what they have is what they have is what they have is they have this way to monetize compute they have this way to monetize compute they have this way to monetize compute that gives you this call option that you that gives you this call option that you that gives you this call option that you can pull all that compute in-house, can pull all that compute in-house, can pull all that compute in-house, right, to train a model and then to run right, to train a model and then to run right, to train a model and then to run the model. I suspect if there's an the model. I suspect if there's an the model. I suspect if there's an upside surprise, if we went around the upside surprise, if we went around the upside surprise, if we went around the table, I'd say this is the place that's table, I'd say this is the place that's table, I'd say this is the place that's getting the least amount of attention getting the least amount of attention getting the least amount of attention and could have the biggest upside and could have the biggest upside and could have the biggest upside surprise. Any any thoughts, Clark, on surprise. Any any thoughts, Clark, on surprise. Any any thoughts, Clark, on what you think is being overlooked or what you think is being overlooked or what you think is being overlooked or areas that you think are misunderstood areas that you think are misunderstood areas that you think are misunderstood about the business today? about the business today? about the business today? >> I I would say I would say >> I I would say I would say >> I I would say I would say what the last few weeks have proven is what the last few weeks have proven is what the last few weeks have proven is that Elon, um, that Elon, um, that Elon, um, their team can stand up all this their team can stand up all this their team can stand up all this compute. Actually, if you just, you compute. Actually, if you just, you compute. Actually, if you just, you know, went back 1 and 1/2 years, you know, went back 1 and 1/2 years, you know, went back 1 and 1/2 years, you know, they were behind in the race to know, they were behind in the race to know, they were behind in the race to stand up compute. They were you know stand up compute. They were you know stand up compute. They were you know they they didn't have that many H100s. they they didn't have that many H100s. they they didn't have that many H100s. They brought in Colossus. Then they They brought in Colossus. Then they They brought in Colossus. Then they brought in Colossus 2 at a scale much brought in Colossus 2 at a scale much brought in Colossus 2 at a scale much larger than anyone else. And now you larger than anyone else. And now you larger than anyone else. And now you know as we gear for Vera Rubin know as we gear for Vera Rubin know as we gear for Vera Rubin you know you know you know from you know a lot of my conversations from you know a lot of my conversations from you know a lot of my conversations it looks like they've you know secured it looks like they've you know secured it looks like they've you know secured maybe up to 20% of Vera Rubin capacity maybe up to 20% of Vera Rubin capacity maybe up to 20% of Vera Rubin capacity especially in the early days of you know especially in the early days of you know especially in the early days of you know when you know these these chips are very when you know these these chips are very when you know these these chips are very scarce that that they're going to have a scarce that that they're going to have a scarce that that they're going to have a a a a a lead on all of this because you know a lead on all of this because you know a lead on all of this because you know people think that they can stand up this people think that they can stand up this people think that they can stand up this compute better. So I think they'll all compute better. So I think they'll all compute better. So I think they'll all you know what what the last few weeks you know what what the last few weeks you know what what the last few weeks have actually shown is that Elon you have actually shown is that Elon you have actually shown is that Elon you know Elon will know Elon will know Elon will take you know take a shot at hitting the take you know take a shot at hitting the take you know take a shot at hitting the frontier but if it you know if for frontier but if it you know if for frontier but if it you know if for whatever reason whatever reason whatever reason um they um they um they they have over procured some capacity they have over procured some capacity they have over procured some capacity this is a very scarce asset that they've this is a very scarce asset that they've this is a very scarce asset that they've shown that they can monetize at actually shown that they can monetize at actually shown that they can monetize at actually you know best in class margins and you know best in class margins and you know best in class margins and payback periods. payback periods. payback periods. >> The irony is like you know >> The irony is like you know >> The irony is like you know you and I've been doing this long enough you and I've been doing this long enough you and I've been doing this long enough to know I mean that's why Bezos built to know I mean that's why Bezos built to know I mean that's why Bezos built AWS. Right? He had to build capacity for AWS. Right? He had to build capacity for AWS. Right? He had to build capacity for Black Friday. Black Friday. Black Friday. >> Yeah. >> Yeah. >> Yeah. >> Right? But then the rest of the year he >> Right? But then the rest of the year he >> Right? But then the rest of the year he sat on all this capacity they had to sat on all this capacity they had to sat on all this capacity they had to build and he figured out a really build and he figured out a really build and he figured out a really incredible way to monetize this. And by incredible way to monetize this. And by incredible way to monetize this. And by the way investors at the time 2009 2010 the way investors at the time 2009 2010 the way investors at the time 2009 2010 when he was building out the capability when he was building out the capability when he was building out the capability around AWS hated it. around AWS hated it. around AWS hated it. >> Of course. >> Of course. >> Of course. >> Because he was consuming all that free >> Because he was consuming all that free >> Because he was consuming all that free cash flow. My meanwhile he was digging cash flow. My meanwhile he was digging cash flow. My meanwhile he was digging the biggest gold mine in the history of the biggest gold mine in the history of the biggest gold mine in the history of the world. One of the biggest. the world. One of the biggest. the world. One of the biggest. >> One of the biggest. >> One of the biggest. >> One of the biggest. >> Among them among them at the time was >> Among them among them at the time was >> Among them among them at the time was probably the biggest. probably the biggest. probably the biggest. >> Yeah Google search might want to have a >> Yeah Google search might want to have a >> Yeah Google search might want to have a we'll have a we'll have a discussion. we'll have a we'll have a discussion. we'll have a we'll have a discussion. >> [clears throat] >> [clears throat] >> [clears throat] >> By the way I do think it is important. >> By the way I do think it is important. >> By the way I do think it is important. Grok 4.3 I think the cursor if they Grok 4.3 I think the cursor if they Grok 4.3 I think the cursor if they acquire it that may end up being very acquire it that may end up being very acquire it that may end up being very important. But Grok 4.3 was on the important. But Grok 4.3 was on the important. But Grok 4.3 was on the Pareto frontier and has of 10 or 12 days Pareto frontier and has of 10 or 12 days Pareto frontier and has of 10 or 12 days ago and this these things move fast. But ago and this these things move fast. But ago and this these things move fast. But most intelligent 500 billion parameter most intelligent 500 billion parameter most intelligent 500 billion parameter model in the world. And they were on the model in the world. And they were on the model in the world. And they were on the frontier and there are four companies on frontier and there are four companies on frontier and there are four companies on the frontier. the frontier. the frontier. xAI, SpaceX AI, Google one with Gemini xAI, SpaceX AI, Google one with Gemini xAI, SpaceX AI, Google one with Gemini 3.1 Pro, and then the rest of it was 3.1 Pro, and then the rest of it was 3.1 Pro, and then the rest of it was dominated by Anthropic and OpenAI. But dominated by Anthropic and OpenAI. But dominated by Anthropic and OpenAI. But they were on the Pareto frontier and now they were on the Pareto frontier and now they were on the Pareto frontier and now we'll see what they do with Cursor. we'll see what they do with Cursor. we'll see what they do with Cursor. >> Yeah. Um I want to come back to that in >> Yeah. Um I want to come back to that in >> Yeah. Um I want to come back to that in a second. a second. a second. >> way, man, I want to ask you some >> way, man, I want to ask you some >> way, man, I want to ask you some questions. questions. questions. >> go go go. What do you think? So you >> go go go. What do you think? So you >> go go go. What do you think? So you think the biggest source of potential think the biggest source of potential think the biggest source of potential upside is the model? upside is the model? upside is the model? >> Yes. >> Yes. >> Yes. >> What do you think? >> What do you think? >> What do you think? >> I think that's the I think that's the >> I think that's the I think that's the >> I think that's the I think that's the thing that's least talked about. thing that's least talked about. thing that's least talked about. >> Least talked about. >> Least talked about. >> Least talked about. >> Right? And so, listen. >> Right? And so, listen. >> Right? And so, listen. When I look at the bull bear case on the When I look at the bull bear case on the When I look at the bull bear case on the IPO, right? The bears are looking at IPO, right? The bears are looking at IPO, right? The bears are looking at last year's revenue. Say it was $18 last year's revenue. Say it was $18 last year's revenue. Say it was $18 billion billion billion and they're looking at the forecast from and they're looking at the forecast from and they're looking at the forecast from the banks of $160 billion, you know, 3 the banks of $160 billion, you know, 3 the banks of $160 billion, you know, 3 years from now and they're saying, years from now and they're saying, years from now and they're saying, "Listen, not many companies in the "Listen, not many companies in the "Listen, not many companies in the history of the world have basically 8x history of the world have basically 8x history of the world have basically 8x their revenue over 3 to 4 years." Right? their revenue over 3 to 4 years." Right? their revenue over 3 to 4 years." Right? So that's where, you know, I think and So that's where, you know, I think and So that's where, you know, I think and people get nervous about the valuation. people get nervous about the valuation. people get nervous about the valuation. When I look at this, again, when you When I look at this, again, when you When I look at this, again, when you break it down as an analyst first break it down as an analyst first break it down as an analyst first principles, part by part, which is what principles, part by part, which is what principles, part by part, which is what I tried to do here, right? When you look I tried to do here, right? When you look I tried to do here, right? When you look at Starlink, it looks totally doable. at Starlink, it looks totally doable. at Starlink, it looks totally doable. When I look at what they're building in When I look at what they're building in When I look at what they're building in AI compute terrestrially, looks totally AI compute terrestrially, looks totally AI compute terrestrially, looks totally doable over the course of next 3 years. doable over the course of next 3 years. doable over the course of next 3 years. When I look at the model itself after When I look at the model itself after When I look at the model itself after the acquisition of Cursor, you know, the acquisition of Cursor, you know, the acquisition of Cursor, you know, combining those things around the combining those things around the combining those things around the compute they have, that looks to me like compute they have, that looks to me like compute they have, that looks to me like it could be an upside surprise. So I it could be an upside surprise. So I it could be an upside surprise. So I would say that I think that uh you know, would say that I think that uh you know, would say that I think that uh you know, in the IPO, but I think when you look in the IPO, but I think when you look in the IPO, but I think when you look back 3 years from now, there's a decent back 3 years from now, there's a decent back 3 years from now, there's a decent chance that everybody's like, "Oh my chance that everybody's like, "Oh my chance that everybody's like, "Oh my god, that was super obvious." Right? god, that was super obvious." Right? god, that was super obvious." Right? Even though today all of these things Even though today all of these things Even though today all of these things have risk associated and back to where have risk associated and back to where have risk associated and back to where we started. I'm not, you know, none of we started. I'm not, you know, none of we started. I'm not, you know, none of us are here to pump the IPO at 1.77 us are here to pump the IPO at 1.77 us are here to pump the IPO at 1.77 trillion. It's really to just break it trillion. It's really to just break it trillion. It's really to just break it down as we do inside our shop and to down as we do inside our shop and to down as we do inside our shop and to say, "What is that distribution of say, "What is that distribution of say, "What is that distribution of future probabilities? What's the future probabilities? What's the future probabilities? What's the probability that it's higher from here? probability that it's higher from here? probability that it's higher from here? What's the prob" And I think we're all What's the prob" And I think we're all What's the prob" And I think we're all pretty AI pilled. And if you're AI pretty AI pilled. And if you're AI pretty AI pilled. And if you're AI pilled, that means we got to build a lot pilled, that means we got to build a lot pilled, that means we got to build a lot more compute than the world thinks and more compute than the world thinks and more compute than the world thinks and that these models are going to be a lot that these models are going to be a lot that these models are going to be a lot more valuable than people think. You more valuable than people think. You more valuable than people think. You combine that with their core business. I combine that with their core business. I combine that with their core business. I don't know another entrepreneur or don't know another entrepreneur or don't know another entrepreneur or another business that's a better bet on another business that's a better bet on another business that's a better bet on the future, the future, the future, right, than SpaceX. And so I think for right, than SpaceX. And so I think for right, than SpaceX. And so I think for most institutional investors, it's a most institutional investors, it's a most institutional investors, it's a must buy, a must own, a set it and must buy, a must own, a set it and must buy, a must own, a set it and forget it, right, in order to have a forget it, right, in order to have a forget it, right, in order to have a real bet on both the space and the AI real bet on both the space and the AI real bet on both the space and the AI future. future. future. >> From your lips to God's ears. >> From your lips to God's ears. >> From your lips to God's ears. >> I mean, listen, I I again, I think that >> I mean, listen, I I again, I think that >> I mean, listen, I I again, I think that I I I think that you're going to have to I I I think that you're going to have to I I I think that you're going to have to wait, but you know, we had this chart wait, but you know, we had this chart wait, but you know, we had this chart last week, right, that came out. last week, right, that came out. last week, right, that came out. Everybody was sending around Twitter, Everybody was sending around Twitter, Everybody was sending around Twitter, conveniently timed, and you know, it's conveniently timed, and you know, it's conveniently timed, and you know, it's like shows the average max drawdown post like shows the average max drawdown post like shows the average max drawdown post IPO for like 20 companies from Facebook, IPO for like 20 companies from Facebook, IPO for like 20 companies from Facebook, Twitter, Alibaba, Shopify is, you know, Twitter, Alibaba, Shopify is, you know, Twitter, Alibaba, Shopify is, you know, over 50%. And so maybe that again will over 50%. And so maybe that again will over 50%. And so maybe that again will will will end this section here. You will will end this section here. You will will end this section here. You know, Gavin, you and I've been doing know, Gavin, you and I've been doing know, Gavin, you and I've been doing this a long time. We know it's going to this a long time. We know it's going to this a long time. We know it's going to be bouncy around the IPO. Um, be bouncy around the IPO. Um, be bouncy around the IPO. Um, you know, how do you as a manager try to you know, how do you as a manager try to you know, how do you as a manager try to try to manage that? Um, do you try to try to manage that? Um, do you try to try to manage that? Um, do you try to trade around the IPO? Do you set it kind trade around the IPO? Do you set it kind trade around the IPO? Do you set it kind of and forget it? I would say from an of and forget it? I would say from an of and forget it? I would say from an Altimeter perspective, what we tend to Altimeter perspective, what we tend to Altimeter perspective, what we tend to do is we take a base position that we do is we take a base position that we do is we take a base position that we set and forget, right? And then we may set and forget, right? And then we may set and forget, right? And then we may size up or size down depending upon how size up or size down depending upon how size up or size down depending upon how the market reacts in, you know, in a the market reacts in, you know, in a the market reacts in, you know, in a particular moment. Um, but any thoughts particular moment. Um, but any thoughts particular moment. Um, but any thoughts on on this chart or on on this chart or on on this chart or you know, how how people you guys are you know, how how people you guys are you know, how how people you guys are thinking about it in particular. You thinking about it in particular. You thinking about it in particular. You obviously own a lot going into it. obviously own a lot going into it. obviously own a lot going into it. >> First agree with absolutely everything >> First agree with absolutely everything >> First agree with absolutely everything you said and I actually think about it you said and I actually think about it you said and I actually think about it the same way, set it and forget it. the same way, set it and forget it. the same way, set it and forget it. You've talked about you have ballast, You've talked about you have ballast, You've talked about you have ballast, you move around and you move the ballast you move around and you move the ballast you move around and you move the ballast to one side of the ship when you want to to one side of the ship when you want to to one side of the ship when you want to the ship to lean into the wind to go the ship to lean into the wind to go the ship to lean into the wind to go faster and you move it to the other side faster and you move it to the other side faster and you move it to the other side when you don't want the ship to tip when you don't want the ship to tip when you don't want the ship to tip over. I think that's a great analogy. over. I think that's a great analogy. over. I think that's a great analogy. Think about all Think about all Think about all important companies in the portfolio the important companies in the portfolio the important companies in the portfolio the same way. So 100% agree. I mean, this same way. So 100% agree. I mean, this same way. So 100% agree. I mean, this this chart is a bummer. What I would say this chart is a bummer. What I would say this chart is a bummer. What I would say is, you know, this data on IPOs, but is, you know, this data on IPOs, but is, you know, this data on IPOs, but what I would just say is this is a what I would just say is this is a what I would just say is this is a really unprecedented situation. really unprecedented situation. really unprecedented situation. >> Yes. We've never had an IPO this big. >> Yes. We've never had an IPO this big. >> Yes. We've never had an IPO this big. We've never had an IPO that's going to We've never had an IPO that's going to We've never had an IPO that's going to go into an index this quickly. go into an index this quickly. go into an index this quickly. We We We simply do not know how much selling simply do not know how much selling simply do not know how much selling there will be from investors. there will be from investors. there will be from investors. I would hazard a guess. I mean, I'm I I would hazard a guess. I mean, I'm I I would hazard a guess. I mean, I'm I don't know. don't know. don't know. But Elon, I don't think he needs But Elon, I don't think he needs But Elon, I don't think he needs liquidity and I think he owns What does liquidity and I think he owns What does liquidity and I think he owns What does he own, Foxy? he own, Foxy? he own, Foxy? >> It's >> It's >> It's 50% 50% 50% >> 50% of the company. >> 50% of the company. >> 50% of the company. >> way, he's locked up for 365 days or 366 >> way, he's locked up for 365 days or 366 >> way, he's locked up for 365 days or 366 days. So, we know he's not selling, days. So, we know he's not selling, days. So, we know he's not selling, right? right? right? >> So, I just think it's an unprecedented >> So, I just think it's an unprecedented >> So, I just think it's an unprecedented situation and the right answer situation and the right answer situation and the right answer >> Yeah. >> Yeah. >> Yeah. >> is I don't know what's going to happen >> is I don't know what's going to happen >> is I don't know what's going to happen in the short term. And the right answer in the short term. And the right answer in the short term. And the right answer that I would just, you know, encourage that I would just, you know, encourage that I would just, you know, encourage every investor making their own decision every investor making their own decision every investor making their own decision is to just think exactly [clears throat] is to just think exactly [clears throat] is to just think exactly [clears throat] the way you articulated it. We have the way you articulated it. We have the way you articulated it. We have these different levers. We have these these different levers. We have these these different levers. We have these different variables. Think about each different variables. Think about each different variables. Think about each one of them from first principles. Make one of them from first principles. Make one of them from first principles. Make your own decision. your own decision. your own decision. Do your own due diligence. Be Do your own due diligence. Be Do your own due diligence. Be thoughtful. But, there are a lot of thoughtful. But, there are a lot of thoughtful. But, there are a lot of variables here and that it is a little variables here and that it is a little variables here and that it is a little funny to me that uh you know, it was 100 funny to me that uh you know, it was 100 funny to me that uh you know, it was 100 times trailing TTM revenue. Well, after times trailing TTM revenue. Well, after times trailing TTM revenue. Well, after the deals they signed, I think it's at the deals they signed, I think it's at the deals they signed, I think it's at 39 times. 39 times. 39 times. >> That can change fast. >> That can change fast. >> That can change fast. >> So, they added $29 billion in a month. >> So, they added $29 billion in a month. >> So, they added $29 billion in a month. >> Yes. Now, it's >> Yes. Now, it's >> [laughter] >> By the way, have you ever seen that >> By the way, have you ever seen that >> By the way, have you ever seen that happen? happen? happen? >> Never. Never. And you know, it just goes >> Never. Never. And you know, it just goes >> Never. Never. And you know, it just goes to show to show to show first um Elon is not only a great first um Elon is not only a great first um Elon is not only a great engineer. engineer. engineer. He and Gwen and the team are great at He and Gwen and the team are great at He and Gwen and the team are great at business. business. business. >> And Brad, >> And Brad, >> And Brad, >> They they they understand what needs to >> They they they understand what needs to >> They they they understand what needs to be done to raise the capital to get to be done to raise the capital to get to be done to raise the capital to get to the next phase. They have a long-term the next phase. They have a long-term the next phase. They have a long-term mission in the business. And so, to me, mission in the business. And so, to me, mission in the business. And so, to me, again, what we saw in the course of the again, what we saw in the course of the again, what we saw in the course of the last few weeks with cursor, what we saw last few weeks with cursor, what we saw last few weeks with cursor, what we saw with these deals that they cut, I don't with these deals that they cut, I don't with these deals that they cut, I don't know that any of the mag seven could know that any of the mag seven could know that any of the mag seven could have moved that quickly to adjust the have moved that quickly to adjust the have moved that quickly to adjust the business that they did. It's business that they did. It's business that they did. It's exceptionally entrepreneurial at scale, exceptionally entrepreneurial at scale, exceptionally entrepreneurial at scale, which we very rarely see in businesses. which we very rarely see in businesses. which we very rarely see in businesses. Two other things I would just say Two other things I would just say Two other things I would just say >> you a hug, Brad? >> you a hug, Brad? >> you a hug, Brad? >> Two Two other things I I I I would just >> Two Two other things I I I I would just >> Two Two other things I I I I would just say. Number one is people talk a lot say. Number one is people talk a lot say. Number one is people talk a lot about the total amount of capital being about the total amount of capital being about the total amount of capital being raised. If you add up the capital here, raised. If you add up the capital here, raised. If you add up the capital here, right, for Anthropic what they may right, for Anthropic what they may right, for Anthropic what they may raise, what OpenAI may raise, what, you raise, what OpenAI may raise, what, you raise, what OpenAI may raise, what, you know, SpaceX may raise, let's call it know, SpaceX may raise, let's call it know, SpaceX may raise, let's call it $250 billion. $250 billion. $250 billion. That's 1% of the Mag 7. That's 1% of the Mag 7. That's 1% of the Mag 7. Okay, it's 1% of the Mag 7. Okay, it's 1% of the Mag 7. Okay, it's 1% of the Mag 7. >> I Yeah. And >> I Yeah. And >> I Yeah. And And we will as well. You know, like that And we will as well. You know, like that And we will as well. You know, like that to me is like a bet on the future that to me is like a bet on the future that to me is like a bet on the future that we all believe in. And so, if I said, we all believe in. And so, if I said, we all believe in. And so, if I said, "Where are we out of consensus? What is "Where are we out of consensus? What is "Where are we out of consensus? What is our variant perception?" We actually our variant perception?" We actually our variant perception?" We actually think it's going to be bigger, faster, think it's going to be bigger, faster, think it's going to be bigger, faster, and we've thought that for a couple and we've thought that for a couple and we've thought that for a couple years. Um so, first, it's only 1% of the years. Um so, first, it's only 1% of the years. Um so, first, it's only 1% of the Mag 7 market cap. And then you Mag 7 market cap. And then you Mag 7 market cap. And then you referenced it, the amount of selling. Um referenced it, the amount of selling. Um referenced it, the amount of selling. Um I've got a chart we'll post here. This I've got a chart we'll post here. This I've got a chart we'll post here. This is, you know, the the dribble share is, you know, the the dribble share is, you know, the the dribble share release for SpaceX shareholders. You release for SpaceX shareholders. You release for SpaceX shareholders. You know, so there's not a lot that can be know, so there's not a lot that can be know, so there's not a lot that can be released um up until after the first released um up until after the first released um up until after the first earnings. This We saw this in the earnings. This We saw this in the earnings. This We saw this in the Cerebras IPO. Um there's a version of it Cerebras IPO. Um there's a version of it Cerebras IPO. Um there's a version of it here in this IPO. And so, again, I think here in this IPO. And so, again, I think here in this IPO. And so, again, I think the banks have been thoughtful here, the banks have been thoughtful here, the banks have been thoughtful here, knowing that this is a very large IPO. knowing that this is a very large IPO. knowing that this is a very large IPO. And I'm not saying that won't trade And I'm not saying that won't trade And I'm not saying that won't trade down. Like there's possibility, you down. Like there's possibility, you down. Like there's possibility, you know, these things trade down. But know, these things trade down. But know, these things trade down. But again, for me, telescope out, is there again, for me, telescope out, is there again, for me, telescope out, is there any company better positioned as a bet any company better positioned as a bet any company better positioned as a bet on the future? I think what they've on the future? I think what they've on the future? I think what they've shown over the course of last 5 weeks, shown over the course of last 5 weeks, shown over the course of last 5 weeks, they're they're they're they're they're probably number one. But they're they're probably number one. But they're they're probably number one. But let's move on. let's move on. let's move on. >> No, no, can I just say one thing about >> No, no, can I just say one thing about >> No, no, can I just say one thing about the employees? I think another thing the employees? I think another thing the employees? I think another thing that's unprecedented here is the that's unprecedented here is the that's unprecedented here is the employees employees employees >> Yeah. >> Yeah. >> Yeah. >> and to a large degree the investors here >> and to a large degree the investors here >> and to a large degree the investors here have had liquidity every 6 months. have had liquidity every 6 months. have had liquidity every 6 months. >> Exactly. >> Exactly. >> Exactly. >> the last 10 years. >> the last 10 years. >> the last 10 years. >> Yes. >> Yes. >> Yes. >> So, if you're a SpaceX employee or >> So, if you're a SpaceX employee or >> So, if you're a SpaceX employee or former employee, and you wanted to sell former employee, and you wanted to sell former employee, and you wanted to sell you've had whatever that is, you've had whatever that is, you've had whatever that is, close to 20 chances. And it is a matter close to 20 chances. And it is a matter close to 20 chances. And it is a matter of historical record that large of historical record that large of historical record that large investors have been able to sell. So investors have been able to sell. So investors have been able to sell. So I would think a lot of the people I would think a lot of the people I would think a lot of the people >> they've >> they've >> they've >> chosen to own it. Now, there's a new >> chosen to own it. Now, there's a new >> chosen to own it. Now, there's a new valuation and we'll see what they do, valuation and we'll see what they do, valuation and we'll see what they do, but just this is utterly unprecedented but just this is utterly unprecedented but just this is utterly unprecedented and we'll see. and we'll see. and we'll see. >> Yeah, I know. It's It's It's a great >> Yeah, I know. It's It's It's a great >> Yeah, I know. It's It's It's a great point. We have in fact called these point. We have in fact called these point. We have in fact called these companies quasi-public. Um you and I companies quasi-public. Um you and I companies quasi-public. Um you and I both know that SpaceX and I'd put both know that SpaceX and I'd put both know that SpaceX and I'd put Anthropic in in in this category as Anthropic in in in this category as Anthropic in in in this category as well, Databricks in this category. These well, Databricks in this category. These well, Databricks in this category. These things in many ways have been more things in many ways have been more things in many ways have been more liquid over the course of the past 3 liquid over the course of the past 3 liquid over the course of the past 3 years than some public biotech companies years than some public biotech companies years than some public biotech companies we know. Right? And so there's a we know. Right? And so there's a we know. Right? And so there's a continuum of liquidity here. We We treat continuum of liquidity here. We We treat continuum of liquidity here. We We treat it as a binary, private versus public, it as a binary, private versus public, it as a binary, private versus public, but it's really about this continuum. but it's really about this continuum. but it's really about this continuum. You know, let's keep going on models. You know, let's keep going on models. You know, let's keep going on models. You know, um Anthropic launched Fable 5, You know, um Anthropic launched Fable 5, You know, um Anthropic launched Fable 5, which you referenced um yesterday, which which you referenced um yesterday, which which you referenced um yesterday, which is basically Mythos um with some is basically Mythos um with some is basically Mythos um with some classifiers and safeguards um around classifiers and safeguards um around classifiers and safeguards um around cyber and biology, chemistry, cyber and biology, chemistry, cyber and biology, chemistry, um and distillation. When those things um and distillation. When those things um and distillation. When those things get triggered, it fails back [snorts] to get triggered, it fails back [snorts] to get triggered, it fails back [snorts] to Opus 4.8. Um you know, there was a Opus 4.8. Um you know, there was a Opus 4.8. Um you know, there was a Copart tweet about this yesterday. He Copart tweet about this yesterday. He Copart tweet about this yesterday. He said, you know, it sold on all the said, you know, it sold on all the said, you know, it sold on all the benchmarks, but what really makes it benchmarks, but what really makes it benchmarks, but what really makes it special is long-running tasks. Okay? You special is long-running tasks. Okay? You special is long-running tasks. Okay? You retweeted our good friend, you know, retweeted our good friend, you know, retweeted our good friend, you know, Noam Brown. Um you know, ChatGPT 5.5 Noam Brown. Um you know, ChatGPT 5.5 Noam Brown. Um you know, ChatGPT 5.5 also exhibited these capabilities. also exhibited these capabilities. also exhibited these capabilities. Um you know, it it led Noam, right, to Um you know, it it led Noam, right, to Um you know, it it led Noam, right, to suggest that it's not very relevant to suggest that it's not very relevant to suggest that it's not very relevant to do these snapshot benchmarks anymore. do these snapshot benchmarks anymore. do these snapshot benchmarks anymore. Yeah, like the x-axis has to be time or Yeah, like the x-axis has to be time or Yeah, like the x-axis has to be time or tokens or compute because we can solve tokens or compute because we can solve tokens or compute because we can solve most problems now if we just let these most problems now if we just let these most problems now if we just let these frontier models for a very long uh point frontier models for a very long uh point frontier models for a very long uh point in time. So, Gavin, what is this new in time. So, Gavin, what is this new in time. So, Gavin, what is this new class of model, right, Fable Fable 5, class of model, right, Fable Fable 5, class of model, right, Fable Fable 5, ChatGPT 5.5? What does it mean for the ChatGPT 5.5? What does it mean for the ChatGPT 5.5? What does it mean for the race in superintelligence? Who's up? race in superintelligence? Who's up? race in superintelligence? Who's up? Who's down? Who's still on the frontier? Who's down? Who's still on the frontier? Who's down? Who's still on the frontier? Um give us your thoughts. Um give us your thoughts. Um give us your thoughts. >> I mean, it's hard to say that >> I mean, it's hard to say that >> I mean, it's hard to say that Anthropic's not up. Anthropic's not up. Anthropic's not up. >> Yeah. >> Yeah. >> Yeah. >> Like after >> Like after >> Like after the revenue numbers they've put up, the revenue numbers they've put up, the revenue numbers they've put up, after the Fable 5 release, and Mythos is after the Fable 5 release, and Mythos is after the Fable 5 release, and Mythos is evidently even better. evidently even better. evidently even better. But I just think that Gnome Brown post But I just think that Gnome Brown post But I just think that Gnome Brown post from yesterday, polynomial, is so from yesterday, polynomial, is so from yesterday, polynomial, is so profound. profound. profound. And just the idea that we do not know And just the idea that we do not know And just the idea that we do not know how smart these models are. how smart these models are. how smart these models are. And we made And we made And we made >> Say more about that. Why don't we know >> Say more about that. Why don't we know >> Say more about that. Why don't we know how smart they are? how smart they are? how smart they are? >> Because nobody has run Mythos for a year >> Because nobody has run Mythos for a year >> Because nobody has run Mythos for a year continuously. And we may never know how continuously. And we may never know how continuously. And we may never know how smart each generation of models actually smart each generation of models actually smart each generation of models actually is or was, but because we don't have is or was, but because we don't have is or was, but because we don't have time to appropriately evaluate their time to appropriately evaluate their time to appropriately evaluate their intelligence before the next model comes intelligence before the next model comes intelligence before the next model comes out. I mean, this is a profound out. I mean, this is a profound out. I mean, this is a profound statement. And just just imagine, okay? statement. And just just imagine, okay? statement. And just just imagine, okay? So, I always say like when you think So, I always say like when you think So, I always say like when you think about FSD, about FSD, about FSD, just imagine a human being who never just imagine a human being who never just imagine a human being who never gets distracted, never gets tired, never gets distracted, never gets tired, never gets distracted, never gets tired, never talks on the phone in the car, never talks on the phone in the car, never talks on the phone in the car, never drinks and drives, never yells at their drinks and drives, never yells at their drinks and drives, never yells at their kids, never has to go to the backseat to kids, never has to go to the backseat to kids, never has to go to the backseat to give their baby a bottle. give their baby a bottle. give their baby a bottle. And like of course you would think that And like of course you would think that And like of course you would think that over time that is superior to humans who over time that is superior to humans who over time that is superior to humans who are distracted. are distracted. are distracted. I don't know how long How long can you I don't know how long How long can you I don't know how long How long can you think deeply about one topic, Brad? think deeply about one topic, Brad? think deeply about one topic, Brad? >> What do you Give me an hour. Give me an >> What do you Give me an hour. Give me an >> What do you Give me an hour. Give me an [laughter] hour. Give me an hour. [laughter] hour. Give me an hour. [laughter] hour. Give me an hour. >> A BIT. >> A BIT. >> A BIT. THAT MAKES me feel terrible cuz I think THAT MAKES me feel terrible cuz I think THAT MAKES me feel terrible cuz I think I can think deeply about one topic I can think deeply about one topic I can think deeply about one topic continuously before having a stray continuously before having a stray continuously before having a stray thought enter my mind thought enter my mind thought enter my mind for like maybe 5 minutes. Then I can for like maybe 5 minutes. Then I can for like maybe 5 minutes. Then I can come back to that. come back to that. come back to that. Imagine if Albert Einstein Imagine if Albert Einstein Imagine if Albert Einstein had been able instead of, you know, and had been able instead of, you know, and had been able instead of, you know, and maybe that maybe I maybe that maybe I maybe that maybe I maybe he could think for 3 hours at a maybe he could think for 3 hours at a maybe he could think for 3 hours at a time. Clearly an exceptional intellect. time. Clearly an exceptional intellect. time. Clearly an exceptional intellect. But imagine Albert Einstein had just But imagine Albert Einstein had just But imagine Albert Einstein had just thought about fundamental physics thought about fundamental physics thought about fundamental physics 24 hours a day. 24 hours a day. 24 hours a day. He doesn't have to eat, he doesn't have He doesn't have to eat, he doesn't have He doesn't have to eat, he doesn't have to sleep, he doesn't have to relax, he to sleep, he doesn't have to relax, he to sleep, he doesn't have to relax, he doesn't drink, doesn't drink, doesn't drink, >> never gets old, >> never gets old, >> never gets old, >> never gets old, >> never gets old, >> never gets old, >> never has diminished intelligence, >> never has diminished intelligence, >> never has diminished intelligence, >> and he thought for 1 year. I mean, we >> and he thought for 1 year. I mean, we >> and he thought for 1 year. I mean, we might already, you know, might already, you know, might already, you know, >> have solved a lot of these intractable >> have solved a lot of these intractable >> have solved a lot of these intractable problems. problems. problems. >> So, I just think that's an extraordinary >> So, I just think that's an extraordinary >> So, I just think that's an extraordinary thought. And just my takeaway was thought. And just my takeaway was thought. And just my takeaway was however however however bullish I was on compute before then, bullish I was on compute before then, bullish I was on compute before then, I'm just a lot more bullish. I'm just a lot more bullish. I'm just a lot more bullish. >> Right. Right. Right. So, so, so that is >> Right. Right. Right. So, so, so that is >> Right. Right. Right. So, so, so that is a, you know, we saw when a, you know, we saw when a, you know, we saw when that was probably what really unlocked that was probably what really unlocked that was probably what really unlocked Opus 4.6. It was the first really Opus 4.6. It was the first really Opus 4.6. It was the first really long-running model that could maintain long-running model that could maintain long-running model that could maintain that context, maintain that memory, um that context, maintain that memory, um that context, maintain that memory, um solve some of these longer-running solve some of these longer-running solve some of these longer-running problems, right? For us, the signal was problems, right? For us, the signal was problems, right? For us, the signal was in January. We knew we felt like that in January. We knew we felt like that in January. We knew we felt like that was a big moment, but then when you was a big moment, but then when you was a big moment, but then when you started to see the revenue go up, we started to see the revenue go up, we started to see the revenue go up, we knew that lots of people were voting knew that lots of people were voting knew that lots of people were voting independently, that that was a profound independently, that that was a profound independently, that that was a profound moment that they became much, much more moment that they became much, much more moment that they became much, much more useful. So, useful. So, useful. So, but one of the things that the consensus but one of the things that the consensus but one of the things that the consensus going into this year, right? So, the big going into this year, right? So, the big going into this year, right? So, the big question going into this year was was question going into this year was was question going into this year was was the AI revenue going to show up? Were we the AI revenue going to show up? Were we the AI revenue going to show up? Were we going to get to these thresholds of going to get to these thresholds of going to get to these thresholds of intelligence that caused enterprises and intelligence that caused enterprises and intelligence that caused enterprises and consumers to use them more? And I think consumers to use them more? And I think consumers to use them more? And I think the consensus at the time, at least on the consensus at the time, at least on the consensus at the time, at least on this podcast, um the the the debate with this podcast, um the the the debate with this podcast, um the the the debate with with my with with with Bill was the with my with with with Bill was the with my with with with Bill was the open-source models, cheap tokens, were open-source models, cheap tokens, were open-source models, cheap tokens, were catching up on the frontier, that catching up on the frontier, that catching up on the frontier, that perhaps these models were beginning to perhaps these models were beginning to perhaps these models were beginning to asymptote, um that people wouldn't asymptote, um that people wouldn't asymptote, um that people wouldn't really pay for premium tokens, really pay for premium tokens, really pay for premium tokens, and it seems to me that the evidence on and it seems to me that the evidence on and it seems to me that the evidence on the field, 6 months into the year, is the field, 6 months into the year, is the field, 6 months into the year, is just the opposite, right? That frontier just the opposite, right? That frontier just the opposite, right? That frontier tokens are capturing the vast majority tokens are capturing the vast majority tokens are capturing the vast majority of all the revenues, of all the revenues, of all the revenues, and that in fact, if you believe in the and that in fact, if you believe in the and that in fact, if you believe in the long-running capabilities and more long-running capabilities and more long-running capabilities and more compute allows you to do that, they may compute allows you to do that, they may compute allows you to do that, they may actually be extending their lead, right? actually be extending their lead, right? actually be extending their lead, right? On some of these models that were built On some of these models that were built On some of these models that were built on distillation. So, I just open it up on distillation. So, I just open it up on distillation. So, I just open it up to anyone around the table, what are to anyone around the table, what are to anyone around the table, what are your thoughts on whether or not, you your thoughts on whether or not, you your thoughts on whether or not, you know, have we challenged this thesis know, have we challenged this thesis know, have we challenged this thesis that cheap open-source tokens are going that cheap open-source tokens are going that cheap open-source tokens are going to always, you know, close the gap on to always, you know, close the gap on to always, you know, close the gap on these frontier models, or are they these frontier models, or are they these frontier models, or are they extending their leads? extending their leads? extending their leads? >> I I think this debate, like this same >> I I think this debate, like this same >> I I think this debate, like this same debate has existed since the beginning debate has existed since the beginning debate has existed since the beginning of of of since we started training these models since we started training these models since we started training these models to begin with, which was hey, we're to begin with, which was hey, we're to begin with, which was hey, we're always kind of three, six months behind always kind of three, six months behind always kind of three, six months behind the frontier. But empirically, like you the frontier. But empirically, like you the frontier. But empirically, like you can just see all of the revenue has can just see all of the revenue has can just see all of the revenue has actually just accrued at the frontier. actually just accrued at the frontier. actually just accrued at the frontier. And that I think that's because every And that I think that's because every And that I think that's because every time we release the frontier, time we release the frontier, time we release the frontier, a whole new like slew of use cases a whole new like slew of use cases a whole new like slew of use cases >> Right. >> Right. >> Right. >> that that previously we could have never >> that that previously we could have never >> that that previously we could have never tackled before, like coding. tackled before, like coding. tackled before, like coding. Um but also just, you know, you know, Um but also just, you know, you know, Um but also just, you know, you know, we've we've just been locked at our desk we've we've just been locked at our desk we've we've just been locked at our desk for the last last day just, you know, for the last last day just, you know, for the last last day just, you know, hammering Claude because, you know, it's hammering Claude because, you know, it's hammering Claude because, you know, it's just fascinating the things that now we just fascinating the things that now we just fascinating the things that now we can do with fable five that we could can do with fable five that we could can do with fable five that we could just couldn't do with opus 48 just a day just couldn't do with opus 48 just a day just couldn't do with opus 48 just a day before. before. before. >> So what are some of those things, man? >> So what are some of those things, man? >> So what are some of those things, man? I'm curious. I'm curious. I'm curious. >> So So I think it's really really good at >> So So I think it's really really good at >> So So I think it's really really good at multi-agent orchestration. So they they multi-agent orchestration. So they they multi-agent orchestration. So they they Anthropic released a um a blog post Anthropic released a um a blog post Anthropic released a um a blog post about like different uh agent um six about like different uh agent um six about like different uh agent um six different agent like orchestration different agent like orchestration different agent like orchestration patterns that, you know, they've they've patterns that, you know, they've they've patterns that, you know, they've they've talked about. But really like once you talked about. But really like once you talked about. But really like once you start being able to manage all these start being able to manage all these start being able to manage all these agents, the harness and the model itself agents, the harness and the model itself agents, the harness and the model itself is being arled with one another, they're is being arled with one another, they're is being arled with one another, they're actually being, actually being, actually being, you know, fused closer and closer you know, fused closer and closer you know, fused closer and closer together, but the model can understand together, but the model can understand together, but the model can understand the, you know, the extent of your work. the, you know, the extent of your work. the, you know, the extent of your work. So, you know, one of the things, for So, you know, one of the things, for So, you know, one of the things, for instance, is um instance, is um instance, is um I just threw in like seven of our models I just threw in like seven of our models I just threw in like seven of our models and just said, "Okay, like I want to and just said, "Okay, like I want to and just said, "Okay, like I want to create a master view of like my beliefs create a master view of like my beliefs create a master view of like my beliefs given all of these assumptions of all given all of these assumptions of all given all of these assumptions of all these companies, TSMC capacity, like and these companies, TSMC capacity, like and these companies, TSMC capacity, like and then and then produce me a report on all then and then produce me a report on all then and then produce me a report on all this stuff." And you know, the the model this stuff." And you know, the the model this stuff." And you know, the the model is able to reason through all of our is able to reason through all of our is able to reason through all of our assumptions. Like actually, if you assumptions. Like actually, if you assumptions. Like actually, if you believe this believe this believe this >> Right. What are the contradictions >> Right. What are the contradictions >> Right. What are the contradictions exactly? exactly? exactly? >> Yeah, it's it was fascinating. And and >> Yeah, it's it was fascinating. And and >> Yeah, it's it was fascinating. And and and you know, before we'd never do that, and you know, before we'd never do that, and you know, before we'd never do that, but but now, you know, I think we're but but now, you know, I think we're but but now, you know, I think we're just step one into multi-agent just step one into multi-agent just step one into multi-agent orchestration. We're going to do this orchestration. We're going to do this orchestration. We're going to do this even further and that's one example. even further and that's one example. even further and that's one example. I've also dumped all my all my notes I've also dumped all my all my notes I've also dumped all my all my notes into it and it's reason across all my into it and it's reason across all my into it and it's reason across all my notes from the last 3 years and said, notes from the last 3 years and said, notes from the last 3 years and said, you know, here are some of your ideas you know, here are some of your ideas you know, here are some of your ideas that were consistent. Here are like, you that were consistent. Here are like, you that were consistent. Here are like, you know, the sources that were actually the know, the sources that were actually the know, the sources that were actually the highest signal to what actually played highest signal to what actually played highest signal to what actually played out, you know, and then it is actually out, you know, and then it is actually out, you know, and then it is actually just super fascinating what you could do just super fascinating what you could do just super fascinating what you could do and we've just blown through our blown and we've just blown through our blown and we've just blown through our blown through our limits. through our limits. through our limits. >> mean it's it's it's unlocking all this. >> mean it's it's it's unlocking all this. >> mean it's it's it's unlocking all this. I mean like they gave examples yesterday I mean like they gave examples yesterday I mean like they gave examples yesterday and the release Anthropic did, you know, and the release Anthropic did, you know, and the release Anthropic did, you know, 50 million line Ruby code base at Stripe 50 million line Ruby code base at Stripe 50 million line Ruby code base at Stripe that was, you know, refactored in a day that was, you know, refactored in a day that was, you know, refactored in a day versus many weeks with many people. You versus many weeks with many people. You versus many weeks with many people. You think about where this is impacting think about where this is impacting think about where this is impacting biology and life sciences just across biology and life sciences just across biology and life sciences just across the spectrum. the spectrum. the spectrum. Um Um Um and to me it really gets back to this and to me it really gets back to this and to me it really gets back to this fundamental point. Number one, if you fundamental point. Number one, if you fundamental point. Number one, if you believe this to be true about believe this to be true about believe this to be true about long-running agents, then we're going to long-running agents, then we're going to long-running agents, then we're going to produce and consume more tokens in the produce and consume more tokens in the produce and consume more tokens in the future as far as the eye can see. So the future as far as the eye can see. So the future as far as the eye can see. So the world this gets me back to, you know, world this gets me back to, you know, world this gets me back to, you know, terrafab and space orbital and all this terrafab and space orbital and all this terrafab and space orbital and all this because because because we we we may in fact unlock real we we we may in fact unlock real we we we may in fact unlock real thresholds of intelligence, but we're thresholds of intelligence, but we're thresholds of intelligence, but we're going to have to let these horses run going to have to let these horses run going to have to let these horses run for a long time in order to get there. for a long time in order to get there. for a long time in order to get there. Yeah, I would just say two things I two Yeah, I would just say two things I two Yeah, I would just say two things I two things can be true. things can be true. things can be true. >> Mhm. >> Mhm. >> Mhm. >> The majority of economic value may >> The majority of economic value may >> The majority of economic value may continue to accrue to the frontier and continue to accrue to the frontier and continue to accrue to the frontier and man has it ever accrued to the frontier man has it ever accrued to the frontier man has it ever accrued to the frontier thus far and for sure the first 6 months thus far and for sure the first 6 months thus far and for sure the first 6 months of this year, but the majority of tokens of this year, but the majority of tokens of this year, but the majority of tokens consumed in the world may be open consumed in the world may be open consumed in the world may be open source. source. source. >> And they are >> And they are >> And they are >> today. >> today. >> today. >> Yes. I and I think that this current >> Yes. I and I think that this current >> Yes. I and I think that this current state is likely to persist. Harvey had a state is likely to persist. Harvey had a state is likely to persist. Harvey had a great blog post that they put out on X great blog post that they put out on X great blog post that they put out on X and they used and it's just amazing how and they used and it's just amazing how and they used and it's just amazing how everything gets out out of date like in everything gets out out of date like in everything gets out out of date like in 5 days, you know. 5 days, you know. 5 days, you know. But they used their own proprietary But they used their own proprietary But they used their own proprietary legal data to do reinforcement learning legal data to do reinforcement learning legal data to do reinforcement learning and supervised fine-tuning and supervised fine-tuning and supervised fine-tuning with Fireworks on an open source model with Fireworks on an open source model with Fireworks on an open source model and then And used a router and a router and then And used a router and a router and then And used a router and a router being something that picks which model being something that picks which model being something that picks which model you send which query to, and which model you send which query to, and which model you send which query to, and which model you use to check which model. And they you use to check which model. And they you use to check which model. And they got better outcomes than Opus 4 either got better outcomes than Opus 4 either got better outcomes than Opus 4 either 4.7 or 4.8 at a lower cost. 4.7 or 4.8 at a lower cost. 4.7 or 4.8 at a lower cost. >> Yes. >> Yes. >> Yes. >> And I think that is the future. And the >> And I think that is the future. And the >> And I think that is the future. And the reality is reality is reality is they were still consuming a lot of Opus, they were still consuming a lot of Opus, they were still consuming a lot of Opus, but a majority of the tokens they were but a majority of the tokens they were but a majority of the tokens they were processing probably were in their own processing probably were in their own processing probably were in their own open-source models. open-source models. open-source models. >> We heard the same thing. We did a We did >> We heard the same thing. We did a We did >> We heard the same thing. We did a We did um um um We did an enterprise survey that we'll We did an enterprise survey that we'll We did an enterprise survey that we'll post of 300 companies how which ones post of 300 companies how which ones post of 300 companies how which ones were optimizing, so these are folks who were optimizing, so these are folks who were optimizing, so these are folks who are kind of looking at model routing and are kind of looking at model routing and are kind of looking at model routing and saying we're going to send certain saying we're going to send certain saying we're going to send certain tokens over here, which ones are tokens over here, which ones are tokens over here, which ones are thinking about optimizing, which ones thinking about optimizing, which ones thinking about optimizing, which ones aren't optimizing yet, and then what is aren't optimizing yet, and then what is aren't optimizing yet, and then what is their expected use of frontier model their expected use of frontier model their expected use of frontier model tokens, right? And they're all expecting tokens, right? And they're all expecting tokens, right? And they're all expecting to consume a lot more even though to consume a lot more even though to consume a lot more even though they're already in the process of they're already in the process of they're already in the process of optimizing. Think of it in the in in the optimizing. Think of it in the in in the optimizing. Think of it in the in in the context of JP Morgan. If they're doing context of JP Morgan. If they're doing context of JP Morgan. If they're doing some back of the house stuff, right, on some back of the house stuff, right, on some back of the house stuff, right, on customer service or whatever, they may customer service or whatever, they may customer service or whatever, they may very well use an open-source model. Now, very well use an open-source model. Now, very well use an open-source model. Now, I think they're loath to use Chinese I think they're loath to use Chinese I think they're loath to use Chinese open-source models, so they're waiting open-source models, so they're waiting open-source models, so they're waiting on kind of US open-source models to, you on kind of US open-source models to, you on kind of US open-source models to, you know, be able to really deliver the bang know, be able to really deliver the bang know, be able to really deliver the bang that they need, but my hunch is for that they need, but my hunch is for that they need, but my hunch is for these enterprises, a lot of that back of these enterprises, a lot of that back of these enterprises, a lot of that back of the house stuff will get rooted there. the house stuff will get rooted there. the house stuff will get rooted there. That will probably be a majority of the That will probably be a majority of the That will probably be a majority of the tokens, but I think the really tokens, but I think the really tokens, but I think the really high-value stuff, you know, coding as an high-value stuff, you know, coding as an high-value stuff, you know, coding as an example, they don't want to write example, they don't want to write example, they don't want to write second-tier code. I think the vast second-tier code. I think the vast second-tier code. I think the vast majority of that will continue to be on majority of that will continue to be on majority of that will continue to be on the frontier. the frontier. the frontier. >> Um >> Um >> Um >> You don't need Albert Einstein to book >> You don't need Albert Einstein to book >> You don't need Albert Einstein to book you a trip. You don't need Albert you a trip. You don't need Albert you a trip. You don't need Albert Einstein to do KYC. Einstein to do KYC. Einstein to do KYC. >> But but but this is the debate we had at >> But but but this is the debate we had at >> But but but this is the debate we had at literally at this table two years ago. literally at this table two years ago. literally at this table two years ago. However, if you just look at the revenue However, if you just look at the revenue However, if you just look at the revenue curves, right? What bill What what folks curves, right? What bill What what folks curves, right? What bill What what folks concluded when they said that, they concluded when they said that, they concluded when they said that, they said, "Therefore, the frontier models said, "Therefore, the frontier models said, "Therefore, the frontier models will not accrue most of the revenue." will not accrue most of the revenue." will not accrue most of the revenue." And what we're seeing right now, it's And what we're seeing right now, it's And what we're seeing right now, it's 90% of the 90% of the 90% of the >> That has been decisively wrong. Probably >> That has been decisively wrong. Probably >> That has been decisively wrong. Probably more than 90%, and it may continue to be more than 90%, and it may continue to be more than 90%, and it may continue to be decisively wrong. Frontier might be 90% decisively wrong. Frontier might be 90% decisively wrong. Frontier might be 90% of the of the of the economic value. Open-source economic value. Open-source economic value. Open-source >> might be 80% of tokens. >> might be 80% of tokens. >> might be 80% of tokens. Something that I think is very important Something that I think is very important Something that I think is very important on open source on open source on open source is that you know, I think there's this is that you know, I think there's this is that you know, I think there's this belief that it's bearish for AI. belief that it's bearish for AI. belief that it's bearish for AI. It's actually it may be very bearish for It's actually it may be very bearish for It's actually it may be very bearish for the frontier models. There's that bear the frontier models. There's that bear the frontier models. There's that bear case you talked about. It's actually case you talked about. It's actually case you talked about. It's actually really bullish for compute and hardware really bullish for compute and hardware really bullish for compute and hardware because if the frontier models are because if the frontier models are because if the frontier models are capturing less of the margin, then capturing less of the margin, then capturing less of the margin, then you're going to spend more on compute. you're going to spend more on compute. you're going to spend more on compute. So, the better open source does, the So, the better open source does, the So, the better open source does, the better it is for compute providers. better it is for compute providers. better it is for compute providers. >> And I yeah, I I will say it there is a >> And I yeah, I I will say it there is a >> And I yeah, I I will say it there is a very very very I would say between um spending time in I would say between um spending time in I would say between um spending time in the heart of like the West, Silicon the heart of like the West, Silicon the heart of like the West, Silicon Valley, and also spending time in Asia, Valley, and also spending time in Asia, Valley, and also spending time in Asia, there is like a very there is like a very there is like a very big um big um big um like a deep-seated belief in one versus like a deep-seated belief in one versus like a deep-seated belief in one versus the other, which is like if you spend a the other, which is like if you spend a the other, which is like if you spend a lot of time here, it's like all closed lot of time here, it's like all closed lot of time here, it's like all closed source, cloud, every all traffic is source, cloud, every all traffic is source, cloud, every all traffic is going to go, you know, by way of this going to go, you know, by way of this going to go, you know, by way of this direction. And then you spend time in direction. And then you spend time in direction. And then you spend time in Asia, you know, Asia, you know, Asia, you know, the the overwhelming belief is that the the overwhelming belief is that the the overwhelming belief is that we're going to find the right model to we're going to find the right model to we're going to find the right model to the right workload, and we're not going the right workload, and we're not going the right workload, and we're not going to overspend. to overspend. to overspend. >> Right. >> Right. >> Right. >> And I think, you know, I would say I >> And I think, you know, I would say I >> And I think, you know, I would say I would say would say would say the next year is probably going to be the next year is probably going to be the next year is probably going to be the most indicative of which way this the most indicative of which way this the most indicative of which way this falls falls falls um because um because um because I think I think the reason why I think I think the reason why I think I think the reason why uh closed source models have captured so uh closed source models have captured so uh closed source models have captured so much of the value is because um the much of the value is because um the much of the value is because um the models actually get the intention and models actually get the intention and models actually get the intention and actually carry through the work. And actually carry through the work. And actually carry through the work. And this is the first year where we actually this is the first year where we actually this is the first year where we actually had agents that actually carried out had agents that actually carried out had agents that actually carried out user intention from just answering a user intention from just answering a user intention from just answering a chatbot request to actually producing chatbot request to actually producing chatbot request to actually producing useful work. useful work. useful work. >> Right. >> Right. >> Right. >> Um now the the the level of this >> Um now the the the level of this >> Um now the the the level of this intelligent has scaled so rapidly, and intelligent has scaled so rapidly, and intelligent has scaled so rapidly, and we continue to push against like the we continue to push against like the we continue to push against like the most economically valuable tasks, which most economically valuable tasks, which most economically valuable tasks, which are coding and finance and all these are coding and finance and all these are coding and finance and all these like knowledge work tasks. But like for like knowledge work tasks. But like for like knowledge work tasks. But like for the long tail of tasks, if open source the long tail of tasks, if open source the long tail of tasks, if open source continues to maintain a 6-month lag, we continues to maintain a 6-month lag, we continues to maintain a 6-month lag, we might actually see a lot more open might actually see a lot more open might actually see a lot more open source used for source used for source used for you know, our everyday tasks that we you know, our everyday tasks that we you know, our everyday tasks that we might actually might actually might actually >> basically Jensen's argument, right? >> basically Jensen's argument, right? >> basically Jensen's argument, right? Jensen's argument is you're going to Jensen's argument is you're going to Jensen's argument is you're going to have model routing have model routing have model routing and we're just in a moment in time where and we're just in a moment in time where and we're just in a moment in time where the frontier models gain the advantage the frontier models gain the advantage the frontier models gain the advantage can do long-running tasks that open can do long-running tasks that open can do long-running tasks that open source models couldn't do it very well source models couldn't do it very well source models couldn't do it very well and so they're accruing all of the and so they're accruing all of the and so they're accruing all of the value, but as soon as the open source value, but as soon as the open source value, but as soon as the open source models can do the long-running tasks as models can do the long-running tasks as models can do the long-running tasks as well, which is not far away that they well, which is not far away that they well, which is not far away that they too will grab a bunch a bunch of this too will grab a bunch a bunch of this too will grab a bunch a bunch of this revenue. revenue. revenue. >> Are you about to burst into reflection? >> Are you about to burst into reflection? >> Are you about to burst into reflection? >> I'm not. >> I'm not. >> I'm not. >> Okay. No, no, no, no are we, but I'm >> Okay. No, no, no, no are we, but I'm >> Okay. No, no, no, no are we, but I'm very impressed by Misha and and the team very impressed by Misha and and the team very impressed by Misha and and the team and what they're doing. I very much want and what they're doing. I very much want and what they're doing. I very much want a frontier open source US lab to win. We a frontier open source US lab to win. We a frontier open source US lab to win. We know that, you know, I heard you say know that, you know, I heard you say know that, you know, I heard you say recently and I believe it to be true recently and I believe it to be true recently and I believe it to be true Nvidia any day that they really wanted Nvidia any day that they really wanted Nvidia any day that they really wanted to, right? They already have some great to, right? They already have some great to, right? They already have some great open source models. They could open source models. They could open source models. They could absolutely build a frontier open source absolutely build a frontier open source absolutely build a frontier open source model whenever they chose to do it and model whenever they chose to do it and model whenever they chose to do it and so it's not a question in my mind as to so it's not a question in my mind as to so it's not a question in my mind as to whether or not the US is going to have a whether or not the US is going to have a whether or not the US is going to have a frontier open source model. It's just a frontier open source model. It's just a frontier open source model. It's just a question about timing and then like at question about timing and then like at question about timing and then like at that point in time is that you know, that point in time is that you know, that point in time is that you know, let's say let's assume they get these let's say let's assume they get these let's say let's assume they get these long-running capabilities. long-running capabilities. long-running capabilities. Have the frontier labs now achieved Have the frontier labs now achieved Have the frontier labs now achieved something yet again that allows them to something yet again that allows them to something yet again that allows them to keep keep the the stranglehold on the keep keep the the stranglehold on the keep keep the the stranglehold on the revenues? revenues? revenues? >> Yeah, and I just think it's if you're >> Yeah, and I just think it's if you're >> Yeah, and I just think it's if you're Wow, that's a cute ASIC you've built Wow, that's a cute ASIC you've built Wow, that's a cute ASIC you've built there. That is so cute. How would you there. That is so cute. How would you there. That is so cute. How would you like like like open source to join the frontier? open source to join the frontier? open source to join the frontier? >> Right. >> Right. >> Right. >> How would you like that? How do you like >> How would you like that? How do you like >> How would you like that? How do you like them apples? So, I mean I'm not sure them apples? So, I mean I'm not sure them apples? So, I mean I'm not sure that's the explicit calculation, but I that's the explicit calculation, but I that's the explicit calculation, but I do think Jensen do think Jensen do think Jensen >> Say more. Just double click on that for >> Say more. Just double click on that for >> Say more. Just double click on that for everybody at home. everybody at home. everybody at home. >> Yeah. >> Yeah. >> Yeah. >> If you were if they were to put an open >> If you were if they were to put an open >> If you were if they were to put an open source model out there, how does that source model out there, how does that source model out there, how does that impact the ASIC landscape? impact the ASIC landscape? impact the ASIC landscape? >> Well, >> Well, >> Well, you might not have the revenue you might not have the revenue you might not have the revenue to fund [laughter] to fund [laughter] to fund [laughter] to fund that the revenue of the margins to fund that the revenue of the margins to fund that the revenue of the margins to fund that ASIC. to fund that ASIC. to fund that ASIC. And I do think Nvidia is highly likely And I do think Nvidia is highly likely And I do think Nvidia is highly likely to be the world's dominant provider of to be the world's dominant provider of to be the world's dominant provider of open source AI. And I do think Jensen open source AI. And I do think Jensen open source AI. And I do think Jensen will bring open source, will bring open source, will bring open source, you know, right now it's whatever, 6 you know, right now it's whatever, 6 you know, right now it's whatever, 6 months behind the frontier. months behind the frontier. months behind the frontier. >> Yeah. >> Yeah. >> Yeah. >> We might see it >> We might see it >> We might see it creep closer and closer and closer. creep closer and closer and closer. creep closer and closer and closer. And I do think Jensen has a big business And I do think Jensen has a big business And I do think Jensen has a big business decision. I see this, you know, chart decision. I see this, you know, chart decision. I see this, you know, chart here, so let's, you know, chop it up here, so let's, you know, chop it up here, so let's, you know, chop it up about Nvidia, as you say. about Nvidia, as you say. about Nvidia, as you say. But if all of his customers But if all of his customers But if all of his customers are going to compete with him, are going to compete with him, are going to compete with him, >> Yes. >> Yes. >> Yes. >> then >> then >> then why not compete with his customers? And why not compete with his customers? And why not compete with his customers? And we have all these neo clouds. we have all these neo clouds. we have all these neo clouds. >> Right. >> Right. >> Right. >> So that's a cloud computing business >> So that's a cloud computing business >> So that's a cloud computing business that can compete with all these cloud that can compete with all these cloud that can compete with all these cloud computing businesses. computing businesses. computing businesses. He has his own models that are really, He has his own models that are really, He has his own models that are really, really good. Nematron 3 or 3.1 was really good. Nematron 3 or 3.1 was really good. Nematron 3 or 3.1 was actually really, really cool from a actually really, really cool from a actually really, really cool from a computer efficiency perspective. And computer efficiency perspective. And computer efficiency perspective. And he's always careful to release small he's always careful to release small he's always careful to release small models so as to not tread on Anthropic models so as to not tread on Anthropic models so as to not tread on Anthropic and OpenAI, and OpenAI, and OpenAI, >> Right. >> Right. >> Right. >> Google's toes. >> Google's toes. >> Google's toes. But I do think that is a choice he is But I do think that is a choice he is But I do think that is a choice he is making. making. making. And just, you know, at if if the And just, you know, at if if the And just, you know, at if if the economics change, economics change, economics change, >> Right. >> Right. >> Right. >> I think Nvidia can join the frontier and >> I think Nvidia can join the frontier and >> I think Nvidia can join the frontier and become one of the world's largest cloud become one of the world's largest cloud become one of the world's largest cloud computing companies much faster than computing companies much faster than computing companies much faster than people think. people think. people think. >> Interesting. Interesting. Clark, walk us >> Interesting. Interesting. Clark, walk us >> Interesting. Interesting. Clark, walk us through this through this through this this chart. this chart. this chart. >> Yeah, so so I think one of the takeaways >> Yeah, so so I think one of the takeaways >> Yeah, so so I think one of the takeaways from spending time in Taiwan was there from spending time in Taiwan was there from spending time in Taiwan was there there is certainly a lot of excitement there is certainly a lot of excitement there is certainly a lot of excitement around the next wave of ASICs. around the next wave of ASICs. around the next wave of ASICs. Um, but I think I think it's like a very Um, but I think I think it's like a very Um, but I think I think it's like a very clear moment now where Nvidia clear moment now where Nvidia clear moment now where Nvidia it used to be an argument of Nvidia it used to be an argument of Nvidia it used to be an argument of Nvidia versus ASICs one or the other and, you versus ASICs one or the other and, you versus ASICs one or the other and, you know, total domination one or the other. know, total domination one or the other. know, total domination one or the other. Now I think it increasingly every year Now I think it increasingly every year Now I think it increasingly every year every every one assumed that Nvidia was every every one assumed that Nvidia was every every one assumed that Nvidia was going to lose share dramatically on a going to lose share dramatically on a going to lose share dramatically on a revenue scale, on a gigawatt scale, on a revenue scale, on a gigawatt scale, on a revenue scale, on a gigawatt scale, on a unit scale. And actually, if you unit scale. And actually, if you unit scale. And actually, if you actually look at the last few years, you actually look at the last few years, you actually look at the last few years, you know, they've actually maintained their know, they've actually maintained their know, they've actually maintained their share very, very handsomely. share very, very handsomely. share very, very handsomely. Um, actually, Um, actually, Um, actually, um, if you accounted for the fact that um, if you accounted for the fact that um, if you accounted for the fact that Anthropic Anthropic Anthropic was not really using Nvidia. They was not really using Nvidia. They was not really using Nvidia. They probably actually gain share against probably actually gain share against probably actually gain share against if not for in 25 26. So, I think I think if not for in 25 26. So, I think I think if not for in 25 26. So, I think I think what was very interesting though was a what was very interesting though was a what was very interesting though was a new class of new class of new class of accelerators or ASICs. accelerators or ASICs. accelerators or ASICs. MediaTek with their MediaTek with their MediaTek with their with their new V8T with their new V8T with their new V8T versus, you know, Broadcom's V8I for versus, you know, Broadcom's V8I for versus, you know, Broadcom's V8I for TPUs TPUs TPUs actually was a big topic of discussion. actually was a big topic of discussion. actually was a big topic of discussion. And, you know, I I think for ASICs And, you know, I I think for ASICs And, you know, I I think for ASICs the argument now is that more and more the argument now is that more and more the argument now is that more and more will look custom to the actual workload will look custom to the actual workload will look custom to the actual workload and that is like one vector that people and that is like one vector that people and that is like one vector that people are moving in versus Nvidia now is are moving in versus Nvidia now is are moving in versus Nvidia now is has kind of shown itself as the the has kind of shown itself as the the has kind of shown itself as the the predominant provider of compute to predominant provider of compute to predominant provider of compute to a lot of the world and for, you know, a lot of the world and for, you know, a lot of the world and for, you know, internal internal workloads, internal internal workloads, internal internal workloads, perhaps they will go more and more perhaps they will go more and more perhaps they will go more and more custom and more and more down the stack. custom and more and more down the stack. custom and more and more down the stack. And I I remember just, you know, 1 year And I I remember just, you know, 1 year And I I remember just, you know, 1 year ago when it was kind of a Broadcom or ago when it was kind of a Broadcom or ago when it was kind of a Broadcom or Nvidia battle. It seems there's a lot Nvidia battle. It seems there's a lot Nvidia battle. It seems there's a lot more nuance now to, you know, what type more nuance now to, you know, what type more nuance now to, you know, what type of accelerators will fit which workloads of accelerators will fit which workloads of accelerators will fit which workloads and fit which customers and fit which and fit which customers and fit which and fit which customers and fit which business models. Um business models. Um business models. Um and yeah, and yeah, and yeah, I thought I thought that was a I thought I thought that was a I thought I thought that was a a new topic. a new topic. a new topic. >> It's actually >> It's actually >> It's actually >> New realization though, I think we all >> New realization though, I think we all >> New realization though, I think we all kind of shared this view for a long kind of shared this view for a long kind of shared this view for a long time. time. time. >> Yeah, I was just shocked. I mean, I'm >> Yeah, I was just shocked. I mean, I'm >> Yeah, I was just shocked. I mean, I'm I'm out here. I did a board meeting with I'm out here. I did a board meeting with I'm out here. I did a board meeting with one of our companies one of our companies one of our companies and just, you know, their biggest one and just, you know, their biggest one and just, you know, their biggest one thing they emphasized is thing they emphasized is thing they emphasized is we thought the world would be have be we thought the world would be have be we thought the world would be have be consuming less Nvidia than it is and if consuming less Nvidia than it is and if consuming less Nvidia than it is and if anything, Nvidia is accelerating and anything, Nvidia is accelerating and anything, Nvidia is accelerating and they just continue to out execute their they just continue to out execute their they just continue to out execute their competitors. And I think a lot of people competitors. And I think a lot of people competitors. And I think a lot of people are indexing to this are indexing to this are indexing to this OpenAI gigawatt and you know, Nvidia has OpenAI gigawatt and you know, Nvidia has OpenAI gigawatt and you know, Nvidia has 10. 10. 10. Broadcom has 10. Broadcom has 10. Broadcom has 10. Um Um Um who has six? who has six? who has six? AMD AMD has six and they have warrants. AMD AMD has six and they have warrants. AMD AMD has six and they have warrants. And then Cerebras has And then Cerebras has And then Cerebras has our shared portfolio company our shared portfolio company our shared portfolio company has a gigawatt. has a gigawatt. has a gigawatt. And I just that is what's on paper. And I just that is what's on paper. And I just that is what's on paper. >> Right. >> Right. >> Right. >> What actually gets deployed, let's see. >> What actually gets deployed, let's see. >> What actually gets deployed, let's see. I will be very surprised if you know I will be very surprised if you know I will be very surprised if you know that 10 out of 27, what's that math? that 10 out of 27, what's that math? that 10 out of 27, what's that math? Let's see who's best at math. What Let's see who's best at math. What Let's see who's best at math. What percentage market share is that? percentage market share is that? percentage market share is that? >> 30% yeah. >> 30% yeah. >> 30% yeah. >> Yeah. I'll be very surprised if that is >> Yeah. I'll be very surprised if that is >> Yeah. I'll be very surprised if that is where they land. I think that is an where they land. I think that is an where they land. I think that is an extremely unlikely outcome. extremely unlikely outcome. extremely unlikely outcome. And especially as long as we're in a And especially as long as we're in a And especially as long as we're in a watt constrained world, if you can get watt constrained world, if you can get watt constrained world, if you can get more tokens per watt, which is literally more tokens per watt, which is literally more tokens per watt, which is literally revenue with Nvidia revenue with Nvidia revenue with Nvidia than a lot of alternatives just if you than a lot of alternatives just if you than a lot of alternatives just if you build your factory with another chip build your factory with another chip build your factory with another chip you may save some money, but you're you may save some money, but you're you may save some money, but you're going to have less revenue and the going to have less revenue and the going to have less revenue and the margins may be lower and that's a point margins may be lower and that's a point margins may be lower and that's a point that Jensen keeps hammering and I think that Jensen keeps hammering and I think that Jensen keeps hammering and I think is a really important. And by the way, is a really important. And by the way, is a really important. And by the way, credit where credit is due credit where credit is due credit where credit is due the most important the most one of the the most important the most one of the the most important the most one of the most surprising things to me in this most surprising things to me in this most surprising things to me in this ASIC landscape ASIC landscape ASIC landscape >> I'd say Meta and Microsoft have been >> I'd say Meta and Microsoft have been >> I'd say Meta and Microsoft have been probably disappointing. probably disappointing. probably disappointing. >> Yes. >> Yes. >> Yes. >> You know who made a good ASIC? >> You know who made a good ASIC? >> You know who made a good ASIC? >> Yes. >> Yes. >> Yes. >> Well, I know you know. >> Well, I know you know. >> Well, I know you know. >> Yes. >> Yes. >> Yes. >> Jalapeno >> Jalapeno >> Jalapeno >> Yeah, exactly. >> Yeah, exactly. >> Yeah, exactly. >> from Open AI. They made a great chip. >> from Open AI. They made a great chip. >> from Open AI. They made a great chip. >> Yes. >> Yes. >> Yes. >> Now, unfortunately needs to run at a >> Now, unfortunately needs to run at a >> Now, unfortunately needs to run at a much lower temperature than the Nvidia much lower temperature than the Nvidia much lower temperature than the Nvidia GPUs, which means you need to spend more GPUs, which means you need to spend more GPUs, which means you need to spend more money on cooling and that consumes more money on cooling and that consumes more money on cooling and that consumes more power. They made a great chip. power. They made a great chip. power. They made a great chip. >> Well, we can I mean I think the question >> Well, we can I mean I think the question >> Well, we can I mean I think the question there and the question for everybody is there and the question for everybody is there and the question for everybody is going to be is that the highest and best going to be is that the highest and best going to be is that the highest and best use of your time? Right? Like I you use of your time? Right? Like I you use of your time? Right? Like I you know, I tend to think that the frontier know, I tend to think that the frontier know, I tend to think that the frontier companies like there's this belief that companies like there's this belief that companies like there's this belief that they got to be vertical vertically they got to be vertical vertically they got to be vertical vertically integrated. But if you believe like I do integrated. But if you believe like I do integrated. But if you believe like I do that the race to super intelligence that the race to super intelligence that the race to super intelligence particularly as we get these recursive particularly as we get these recursive particularly as we get these recursive loops working may be over in the next loops working may be over in the next loops working may be over in the next two to three years, then I think focus two to three years, then I think focus two to three years, then I think focus focus focus focus. You exist to build focus focus focus. You exist to build focus focus focus. You exist to build the best intelligence in the world and the best intelligence in the world and the best intelligence in the world and to deliver the best intelligence in the to deliver the best intelligence in the to deliver the best intelligence in the world and you that means you have to world and you that means you have to world and you that means you have to have all the revenue. Because if you have all the revenue. Because if you have all the revenue. Because if you want to build out the compute that's want to build out the compute that's want to build out the compute that's going to be required to continue to push going to be required to continue to push going to be required to continue to push the frontier, you have to have the the frontier, you have to have the the frontier, you have to have the revenue in order to support it. So I revenue in order to support it. So I revenue in order to support it. So I think you know, subject to the focus think you know, subject to the focus think you know, subject to the focus question, I think they certainly did. question, I think they certainly did. question, I think they certainly did. This all brings me back to kind of a This all brings me back to kind of a This all brings me back to kind of a reality check, though. reality check, though. reality check, though. Um Um Um you know, we just got done talking about you know, we just got done talking about you know, we just got done talking about test time compute, inference time test time compute, inference time test time compute, inference time compute, long-running agents. This is compute, long-running agents. This is compute, long-running agents. This is really the thing that's unlocked the really the thing that's unlocked the really the thing that's unlocked the revenue this year. Um it all pushes us revenue this year. Um it all pushes us revenue this year. Um it all pushes us in the direction of more CapEx. Google in the direction of more CapEx. Google in the direction of more CapEx. Google just raised $80 billion, just raised $80 billion, just raised $80 billion, right? We've now taken the Mag 5 or Mag right? We've now taken the Mag 5 or Mag right? We've now taken the Mag 5 or Mag 7 free cash flow, you know, down 7 free cash flow, you know, down 7 free cash flow, you know, down dramatically, 80% um from just a few dramatically, 80% um from just a few dramatically, 80% um from just a few years ago. Um and Morgan Stanley, you've years ago. Um and Morgan Stanley, you've years ago. Um and Morgan Stanley, you've got this chart in front of you, up to got this chart in front of you, up to got this chart in front of you, up to their 2027 CapEx forecast from 950 their 2027 CapEx forecast from 950 their 2027 CapEx forecast from 950 billion to 1.1 trillion. I mean, we were billion to 1.1 trillion. I mean, we were billion to 1.1 trillion. I mean, we were talking about this with Jensen. That was talking about this with Jensen. That was talking about this with Jensen. That was his forecast 2 years ago. You know, his forecast 2 years ago. You know, his forecast 2 years ago. You know, obviously, this doesn't even include obviously, this doesn't even include obviously, this doesn't even include SpaceX, CoreWeave, etc. So, I think the SpaceX, CoreWeave, etc. So, I think the SpaceX, CoreWeave, etc. So, I think the number on 2027 is likely closer to 1.5 number on 2027 is likely closer to 1.5 number on 2027 is likely closer to 1.5 trillion. trillion. trillion. And if we compare this to the total And if we compare this to the total And if we compare this to the total incremental inference revenue, so the incremental inference revenue, so the incremental inference revenue, so the thing that the market gets worried thing that the market gets worried thing that the market gets worried about, you know, back to my Sam Altman about, you know, back to my Sam Altman about, you know, back to my Sam Altman podcast, you know, in October of last podcast, you know, in October of last podcast, you know, in October of last year, can we really afford to spend 1.5 year, can we really afford to spend 1.5 year, can we really afford to spend 1.5 trillion of CapEx a year if we're only trillion of CapEx a year if we're only trillion of CapEx a year if we're only generating X amount in inference generating X amount in inference generating X amount in inference revenue? The thing I think that lit the revenue? The thing I think that lit the revenue? The thing I think that lit the fuse this year was Anthropic showed up fuse this year was Anthropic showed up fuse this year was Anthropic showed up in a major way with revenue, right? And in a major way with revenue, right? And in a major way with revenue, right? And so, we have, you know, the AI lab so, we have, you know, the AI lab so, we have, you know, the AI lab revenue everybody combined at around revenue everybody combined at around revenue everybody combined at around $300 billion next year, right? So, can't $300 billion next year, right? So, can't $300 billion next year, right? So, can't you know, and go roll that out to 2027 you know, and go roll that out to 2027 you know, and go roll that out to 2027 uh or that is 2027, 300 billion. So, uh or that is 2027, 300 billion. So, uh or that is 2027, 300 billion. So, we're spending 1.5 trillion of CapEx on we're spending 1.5 trillion of CapEx on we're spending 1.5 trillion of CapEx on 300 billion of inference revenue. Does 300 billion of inference revenue. Does 300 billion of inference revenue. Does that math math for you? And what would that math math for you? And what would that math math for you? And what would cause you, you know, to to get more cause you, you know, to to get more cause you, you know, to to get more nervous again about our ability to nervous again about our ability to nervous again about our ability to continue to make these investments? continue to make these investments? continue to make these investments? Because the second we get nervous about Because the second we get nervous about Because the second we get nervous about it, the entire semi complex is going to it, the entire semi complex is going to it, the entire semi complex is going to come down a lot. Well, what do you think come down a lot. Well, what do you think come down a lot. Well, what do you think the gross margins are on that 300 the gross margins are on that 300 the gross margins are on that 300 billion? Yeah, let's call it 50%. billion? Yeah, let's call it 50%. billion? Yeah, let's call it 50%. >> I I would guess they're probably a >> I I would guess they're probably a >> I I would guess they're probably a little bit higher than that. I might say little bit higher than that. I might say little bit higher than that. I might say 60 or 70. 60 or 70. 60 or 70. But, I mean, that math starts to math, But, I mean, that math starts to math, But, I mean, that math starts to math, and what I would just say is I think and what I would just say is I think and what I would just say is I think that 300 billion is low, man. that 300 billion is low, man. that 300 billion is low, man. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. >> I just think it's low. >> I just think it's low. >> I just think it's low. >> From your mouth to God's >> From your mouth to God's >> From your mouth to God's >> Yeah, yeah, exactly. I think I think we >> Yeah, yeah, exactly. I think I think we >> Yeah, yeah, exactly. I think I think we end this year well over 200 billion in end this year well over 200 billion in end this year well over 200 billion in inference revenue, well over. inference revenue, well over. inference revenue, well over. And so, I think the math really maths, And so, I think the math really maths, And so, I think the math really maths, and I do think we have to give uh Jensen and I do think we have to give uh Jensen and I do think we have to give uh Jensen >> Yeah, our friend. >> Yeah, our friend. >> Yeah, our friend. >> some credit because he said some things >> some credit because he said some things >> some credit because he said some things that seemed outlandish. that seemed outlandish. that seemed outlandish. >> Right. >> Right. >> Right. >> And he was conservative. He was low. He >> And he was conservative. He was low. He >> And he was conservative. He was low. He said a trillion 2 years ago. said a trillion 2 years ago. said a trillion 2 years ago. And I mean, he was really low. And I mean, he was really low. And I mean, he was really low. >> Right. >> Right. >> Right. >> And so, like, let's give the guy some >> And so, like, let's give the guy some >> And so, like, let's give the guy some credit and think about what he is saying credit and think about what he is saying credit and think about what he is saying right now. right now. right now. >> For sure, for sure. And and listen, >> For sure, for sure. And and listen, >> For sure, for sure. And and listen, I would say consistently, I would say consistently, I would say consistently, Elon's been taking the over. Elon's been taking the over. Elon's been taking the over. Sundar's been taking the over. Sundar's been taking the over. Sundar's been taking the over. Sam, Dario, you know, Dario did the Sam, Dario, you know, Dario did the Sam, Dario, you know, Dario did the podcast with Dwarkesh when he was podcast with Dwarkesh when he was podcast with Dwarkesh when he was talking about country geniuses in the talking about country geniuses in the talking about country geniuses in the data center. He said that will be here data center. He said that will be here data center. He said that will be here by 2028. He said revenues will go into by 2028. He said revenues will go into by 2028. He said revenues will go into the low hundreds of billions by 2028. the low hundreds of billions by 2028. the low hundreds of billions by 2028. So, let's call that, you know, 3 400 So, let's call that, you know, 3 400 So, let's call that, you know, 3 400 billion of revenue by 2028. And he said billion of revenue by 2028. And he said billion of revenue by 2028. And he said that a while ago now, so he may even be that a while ago now, so he may even be that a while ago now, so he may even be revising up his number. And he said it's revising up his number. And he said it's revising up his number. And he said it's hard for me to see that there won't be hard for me to see that there won't be hard for me to see that there won't be trillions of dollars in revenue before trillions of dollars in revenue before trillions of dollars in revenue before 2030. And if you're on that revenue 2030. And if you're on that revenue 2030. And if you're on that revenue trajectory, if we're on a trajectory to trajectory, if we're on a trajectory to trajectory, if we're on a trajectory to 200 by the end of this year, let's call 200 by the end of this year, let's call 200 by the end of this year, let's call it 4 or 500 by next year, and a path to it 4 or 500 by next year, and a path to it 4 or 500 by next year, and a path to trillion plus by 2029, then the math trillion plus by 2029, then the math trillion plus by 2029, then the math maths. maths. maths. >> And we got to keep in mind that half of >> And we got to keep in mind that half of >> And we got to keep in mind that half of the spending is there to the spending is there to the spending is there to you know, for training, maybe a little you know, for training, maybe a little you know, for training, maybe a little less than half. What is it, Foxy? less than half. What is it, Foxy? less than half. What is it, Foxy? >> It's probably that depends on the lab, >> It's probably that depends on the lab, >> It's probably that depends on the lab, but it's I would say it's increasingly but it's I would say it's increasingly but it's I would say it's increasingly less than half. less than half. less than half. >> Yes. >> Yes. >> Yes. >> Okay. So, we'll call it 35% is spending >> Okay. So, we'll call it 35% is spending >> Okay. So, we'll call it 35% is spending that's not revenue generating, but it's that's not revenue generating, but it's that's not revenue generating, but it's going to kind of make the next model. going to kind of make the next model. going to kind of make the next model. So, I think the math maths. So, I think the math maths. So, I think the math maths. >> Right. >> Right. >> Right. >> And there's still this prisoner's >> And there's still this prisoner's >> And there's still this prisoner's dilemma where if you opted out, that may dilemma where if you opted out, that may dilemma where if you opted out, that may be an existential decision. be an existential decision. be an existential decision. >> And I think like coming into this year, >> And I think like coming into this year, >> And I think like coming into this year, going back to this kind of what going back to this kind of what going back to this kind of what narratives were violated, you know, narratives were violated, you know, narratives were violated, you know, I think into this year everyone expected I think into this year everyone expected I think into this year everyone expected token pricing, uh the price of compute, token pricing, uh the price of compute, token pricing, uh the price of compute, it's all deflationary. And it will be it's all deflationary. And it will be it's all deflationary. And it will be kind of a smooth line deflationary over kind of a smooth line deflationary over kind of a smooth line deflationary over time. But, I think this year what we've time. But, I think this year what we've time. But, I think this year what we've seen is the opposite. And you know, it's seen is the opposite. And you know, it's seen is the opposite. And you know, it's all comes back to supply-demand. The all comes back to supply-demand. The all comes back to supply-demand. The demand side of the equation seems to be demand side of the equation seems to be demand side of the equation seems to be far outstripping the supply. Right? And far outstripping the supply. Right? And far outstripping the supply. Right? And I think I think I think you look at the deals signed by SpaceX you look at the deals signed by SpaceX you look at the deals signed by SpaceX and others, the monetization rates per and others, the monetization rates per and others, the monetization rates per watt are increasing. watt are increasing. watt are increasing. Um Um Um and and and look, that is on a a pretty nascent look, that is on a a pretty nascent look, that is on a a pretty nascent small base of users, right? Like Alex at small base of users, right? Like Alex at small base of users, right? Like Alex at Well Rock, he has this great um Well Rock, he has this great um Well Rock, he has this great um way to frame it. way to frame it. way to frame it. Less than 0.2% of people on Earth are Less than 0.2% of people on Earth are Less than 0.2% of people on Earth are actually using AI in an agentic way. actually using AI in an agentic way. actually using AI in an agentic way. >> Right. >> Right. >> Right. >> Right? Like I'm not a technical person, >> Right? Like I'm not a technical person, >> Right? Like I'm not a technical person, but I'm consuming 500 CPU cores in a VM but I'm consuming 500 CPU cores in a VM but I'm consuming 500 CPU cores in a VM instance, five GPUs 24/7. instance, five GPUs 24/7. instance, five GPUs 24/7. >> Yeah. >> Yeah. >> Yeah. >> I mean, if you draw that out to any >> I mean, if you draw that out to any >> I mean, if you draw that out to any meaningful percentage of the population, meaningful percentage of the population, meaningful percentage of the population, I mean, we're going to be in, you know, I mean, we're going to be in, you know, I mean, we're going to be in, you know, this kind of shortage environment maybe this kind of shortage environment maybe this kind of shortage environment maybe for some time. So, for some time. So, for some time. So, I think that is all positive for this I think that is all positive for this I think that is all positive for this ROI question. ROI question. ROI question. >> Man, foxy, 100 to one CPU to GPU ratio. >> Man, foxy, 100 to one CPU to GPU ratio. >> Man, foxy, 100 to one CPU to GPU ratio. >> [laughter] >> [laughter] >> [laughter] >> Kind of agentic workflow. >> Kind of agentic workflow. >> Kind of agentic workflow. >> He said of course. >> He said of course. >> He said of course. >> [laughter] >> [laughter] >> [laughter] >> Five. >> Five. >> Five. >> Five, yes. >> Five, yes. >> Five, yes. >> Yeah. >> Yeah. >> Yeah. >> I'm being smart with my phone. >> I'm being smart with my phone. >> I'm being smart with my phone. >> Good, good, good. Excellent. >> Good, good, good. Excellent. >> Good, good, good. Excellent. >> I I will say also that ratio of 300 to >> I I will say also that ratio of 300 to >> I I will say also that ratio of 300 to 1. You know, call it 1.2, 1.5. 1. You know, call it 1.2, 1.5. 1. You know, call it 1.2, 1.5. Um there there is also a rate that now Um there there is also a rate that now Um there there is also a rate that now physically we can only expand physically we can only expand physically we can only expand how much we can produce and how much we how much we can produce and how much we how much we can produce and how much we can actually increase that spend by, can actually increase that spend by, can actually increase that spend by, whereas we're seeing the opposite right whereas we're seeing the opposite right whereas we're seeing the opposite right now on the on the the willingness to pay now on the on the the willingness to pay now on the on the the willingness to pay for these tokens. And actually like when for these tokens. And actually like when for these tokens. And actually like when the willingness to pay for these when the willingness to pay for these when the willingness to pay for these when the monetization per gigawatt is the monetization per gigawatt is the monetization per gigawatt is actually increasing from, you know, call actually increasing from, you know, call actually increasing from, you know, call it like 20 20 billion um in the in the it like 20 20 billion um in the in the it like 20 20 billion um in the in the best best of cases for at the beginning best best of cases for at the beginning best best of cases for at the beginning of the year to now like 30 to even of the year to now like 30 to even of the year to now like 30 to even pushing 40 pushing 40 pushing 40 >> per gigawatt >> per gigawatt >> per gigawatt >> per gigawatt. >> per gigawatt. >> per gigawatt. Um all of that is is a very heavy fixed Um all of that is is a very heavy fixed Um all of that is is a very heavy fixed cost base, but all of that is like pure cost base, but all of that is like pure cost base, but all of that is like pure margin flow through now. And you're margin flow through now. And you're margin flow through now. And you're actually, you know, as we scale like the actually, you know, as we scale like the actually, you know, as we scale like the willingness to pay for for all of this willingness to pay for for all of this willingness to pay for for all of this and and now the all of this stipulated and and now the all of this stipulated and and now the all of this stipulated by like, you know, everything we're by like, you know, everything we're by like, you know, everything we're talking about of like how much is open talking about of like how much is open talking about of like how much is open source versus not and all of these source versus not and all of these source versus not and all of these different flows, but really like as different flows, but really like as different flows, but really like as we're climbing this curve, you know, the we're climbing this curve, you know, the we're climbing this curve, you know, the the the revenue is might actually the the revenue is might actually the the revenue is might actually outstrip our fixed cost base by by outstrip our fixed cost base by by outstrip our fixed cost base by by significant amount. And I think that's significant amount. And I think that's significant amount. And I think that's why all the labs are pushing, you know, why all the labs are pushing, you know, why all the labs are pushing, you know, the the the gas to the pedals because the the the gas to the pedals because the the the gas to the pedals because they all they all see like within if we they all they all see like within if we they all they all see like within if we continue this curve within like 3 years, continue this curve within like 3 years, continue this curve within like 3 years, you know, we're just going to be so you know, we're just going to be so you know, we're just going to be so short on all the computer short on all the computer short on all the computer >> It's a great I'm sorry, but I mean I >> It's a great I'm sorry, but I mean I >> It's a great I'm sorry, but I mean I like it's a great point. Like if you like it's a great point. Like if you like it's a great point. Like if you thought you were getting a when you made thought you were getting a when you made thought you were getting a when you made these decisions these decisions these decisions >> Yes. >> Yes. >> Yes. >> in November of 2025, >> in November of 2025, >> in November of 2025, you thought you were getting a certain you thought you were getting a certain you thought you were getting a certain return. return. return. >> Yeah. >> Yeah. >> Yeah. >> You may be getting triple that return >> You may be getting triple that return >> You may be getting triple that return today. today. today. >> At Tropic, no way no way did they think >> At Tropic, no way no way did they think >> At Tropic, no way no way did they think they were going to be anywhere close to they were going to be anywhere close to they were going to be anywhere close to break even. break even. break even. >> Yeah. >> Yeah. >> Yeah. >> Right? And and and and in this part of >> Right? And and and and in this part of >> Right? And and and and in this part of the curve, and the reason like I I I the curve, and the reason like I I I the curve, and the reason like I I I called it accidental profitability that, called it accidental profitability that, called it accidental profitability that, you know, people have been talking about you know, people have been talking about you know, people have been talking about that because they want to spend a lot that because they want to spend a lot that because they want to spend a lot more money on computer. They just had a more money on computer. They just had a more money on computer. They just had a hard time doing it. Now maybe with hard time doing it. Now maybe with hard time doing it. Now maybe with SpaceX, you know, they could take some SpaceX, you know, they could take some SpaceX, you know, they could take some of those dollars and and and go spend of those dollars and and and go spend of those dollars and and and go spend them other places. But that to me is, them other places. But that to me is, them other places. But that to me is, you know, a a fundamental change. Um the you know, a a fundamental change. Um the you know, a a fundamental change. Um the first argument against the frontier labs first argument against the frontier labs first argument against the frontier labs was they'll never generate revenue. was they'll never generate revenue. was they'll never generate revenue. Okay? And then we that got blown up. Okay? And then we that got blown up. Okay? And then we that got blown up. Then it was like even if they generate Then it was like even if they generate Then it was like even if they generate revenue, it'll be really shitty gross revenue, it'll be really shitty gross revenue, it'll be really shitty gross margins, and they'll never be able to margins, and they'll never be able to margins, and they'll never be able to get make money. And then kind of that get make money. And then kind of that get make money. And then kind of that that's blown up. And and you know, I that's blown up. And and you know, I that's blown up. And and you know, I think now, you know, people are falling think now, you know, people are falling think now, you know, people are falling back and they're saying, "Well, they're back and they're saying, "Well, they're back and they're saying, "Well, they're overcharging. This is token maxi." My overcharging. This is token maxi." My overcharging. This is token maxi." My good friend, you know, Chamath has said good friend, you know, Chamath has said good friend, you know, Chamath has said there's no ROI on any of this spend. there's no ROI on any of this spend. there's no ROI on any of this spend. It's all this token maxi. My best It's all this token maxi. My best It's all this token maxi. My best evidence for why we all know, of course, evidence for why we all know, of course, evidence for why we all know, of course, when somebody puts on this much spend when somebody puts on this much spend when somebody puts on this much spend like at Altimeter, we're not optimally like at Altimeter, we're not optimally like at Altimeter, we're not optimally spending every single dollar. But, the spending every single dollar. But, the spending every single dollar. But, the question is, why are millions of question is, why are millions of question is, why are millions of independent businesses, small, medium, independent businesses, small, medium, independent businesses, small, medium, and large, why are millions of consumers and large, why are millions of consumers and large, why are millions of consumers all choosing to do the same thing? all choosing to do the same thing? all choosing to do the same thing? They're not dumb. These are, you know, They're not dumb. These are, you know, They're not dumb. These are, you know, rational economic actors that are all rational economic actors that are all rational economic actors that are all simultaneously saying, "I want to do simultaneously saying, "I want to do simultaneously saying, "I want to do this because it makes my life better. It this because it makes my life better. It this because it makes my life better. It makes my business better, etc." To me, makes my business better, etc." To me, makes my business better, etc." To me, that is the best evidence as to why I that is the best evidence as to why I that is the best evidence as to why I think this revenue can continue. think this revenue can continue. think this revenue can continue. >> Yeah. And Clark, I think like the point >> Yeah. And Clark, I think like the point >> Yeah. And Clark, I think like the point you made is dead on cuz I mean, you want you made is dead on cuz I mean, you want you made is dead on cuz I mean, you want to own asset-heavy businesses in to own asset-heavy businesses in to own asset-heavy businesses in inflationary environments, and token inflationary environments, and token inflationary environments, and token pricing is going up, and supply and pricing is going up, and supply and pricing is going up, and supply and demand is tightening, so totally agree. demand is tightening, so totally agree. demand is tightening, so totally agree. >> Um you know, as we begin to >> Um you know, as we begin to >> Um you know, as we begin to uh find our way to the exit ramp and and uh find our way to the exit ramp and and uh find our way to the exit ramp and and [laughter] and wrap here, one of the [laughter] and wrap here, one of the [laughter] and wrap here, one of the things I you know, you and I've been things I you know, you and I've been things I you know, you and I've been doing this for a long time, Gavin, a doing this for a long time, Gavin, a doing this for a long time, Gavin, a couple decades. Um you may even sketch couple decades. Um you may even sketch couple decades. Um you may even sketch longer than me, even though I'm a little longer than me, even though I'm a little longer than me, even though I'm a little bit older than you. Um bit older than you. Um bit older than you. Um you know, we have uh I always like to do you know, we have uh I always like to do you know, we have uh I always like to do a market check, because I find a lot of a market check, because I find a lot of a market check, because I find a lot of time that analysts come on these things, time that analysts come on these things, time that analysts come on these things, and they talk their, you know, talk and they talk their, you know, talk and they talk their, you know, talk their book, and you know, there are a their book, and you know, there are a their book, and you know, there are a lot of people who listen to these lot of people who listen to these lot of people who listen to these things, retail investors and others. things, retail investors and others. things, retail investors and others. It's just kind of like, what do we It's just kind of like, what do we It's just kind of like, what do we really think? And so, I always really think? And so, I always really think? And so, I always characterize as kind of small, medium, characterize as kind of small, medium, characterize as kind of small, medium, and large. Like, what am I doing? Do I and large. Like, what am I doing? Do I and large. Like, what am I doing? Do I have small exposure on? Do I have medium have small exposure on? Do I have medium have small exposure on? Do I have medium exposure on? Do I have large exposure exposure on? Do I have large exposure exposure on? Do I have large exposure on? You know, and if you look at what's on? You know, and if you look at what's on? You know, and if you look at what's happened in the markets, semis ripped happened in the markets, semis ripped happened in the markets, semis ripped this year. I mean, like uh you've been this year. I mean, like uh you've been this year. I mean, like uh you've been doing this a long time. I don't I've doing this a long time. I don't I've doing this a long time. I don't I've never seen it before, right? I've never never seen it before, right? I've never never seen it before, right? I've never seen, you know, the doubles and the seen, you know, the doubles and the seen, you know, the doubles and the triples across the board like we saw. triples across the board like we saw. triples across the board like we saw. But, there's been huge dispersion, But, there's been huge dispersion, But, there's been huge dispersion, right, in the market. Internet's down right, in the market. Internet's down right, in the market. Internet's down 16%, 16%, 16%, uh software's down 8% on the year. You uh software's down 8% on the year. You uh software's down 8% on the year. You know, spy and and Nasdaq are up, but know, spy and and Nasdaq are up, but know, spy and and Nasdaq are up, but really up because of their components really up because of their components really up because of their components that are related to AI and compute. And that are related to AI and compute. And that are related to AI and compute. And so, the market itself has kind of so, the market itself has kind of so, the market itself has kind of struggled. Meanwhile, if you were in the struggled. Meanwhile, if you were in the struggled. Meanwhile, if you were in the stuff that we were invested in, we've stuff that we were invested in, we've stuff that we were invested in, we've all done pretty well. I think you know, all done pretty well. I think you know, all done pretty well. I think you know, I've said it a couple times. I think if I've said it a couple times. I think if I've said it a couple times. I think if the Anthropic revenue had not shown up the Anthropic revenue had not shown up the Anthropic revenue had not shown up this year, because that was the overhang this year, because that was the overhang this year, because that was the overhang on the market, I think the whole market on the market, I think the whole market on the market, I think the whole market could be down this year. Right? Um but could be down this year. Right? Um but could be down this year. Right? Um but that showed up. You know, we just had that showed up. You know, we just had that showed up. You know, we just had these huge months in in in April and these huge months in in in April and these huge months in in in April and May. Um for us, you know, because prices May. Um for us, you know, because prices May. Um for us, you know, because prices came up so much, because I have some came up so much, because I have some came up so much, because I have some worry about, you know, geopolitics, the worry about, you know, geopolitics, the worry about, you know, geopolitics, the macro backdrop with, you know, with with macro backdrop with, you know, with with macro backdrop with, you know, with with what's going on with inflation in the what's going on with inflation in the what's going on with inflation in the short run, and just like, you know, short run, and just like, you know, short run, and just like, you know, needing a little consolidation in this needing a little consolidation in this needing a little consolidation in this market to answer some of these market to answer some of these market to answer some of these questions, because now expectations are questions, because now expectations are questions, because now expectations are higher. You know, we dialed back from higher. You know, we dialed back from higher. You know, we dialed back from what I would call large for Altimeter to what I would call large for Altimeter to what I would call large for Altimeter to something kind of like medium small. Um something kind of like medium small. Um something kind of like medium small. Um again, it's never all or nothing for us. again, it's never all or nothing for us. again, it's never all or nothing for us. It's like, what is the risk-reward at a It's like, what is the risk-reward at a It's like, what is the risk-reward at a given price? given price? given price? Um and so, we think this is a, you know, Um and so, we think this is a, you know, Um and so, we think this is a, you know, maybe going to be a period of maybe going to be a period of maybe going to be a period of consolidation on way to much higher consolidation on way to much higher consolidation on way to much higher highs. Um curious just how you run the highs. Um curious just how you run the highs. Um curious just how you run the book, how you think about it like a book, how you think about it like a book, how you think about it like a portfolio manager. portfolio manager. portfolio manager. >> Very similarly, man. I always think >> Very similarly, man. I always think >> Very similarly, man. I always think stocks, the markets, I imagine them as stocks, the markets, I imagine them as stocks, the markets, I imagine them as runners. Okay? runners. Okay? runners. Okay? And like in '22, And like in '22, And like in '22, that runner had gone downhill. It had a that runner had gone downhill. It had a that runner had gone downhill. It had a lot of energy, man. lot of energy, man. lot of energy, man. Yeah, it was painful. It wasn't fun. Um Yeah, it was painful. It wasn't fun. Um Yeah, it was painful. It wasn't fun. Um but coming out of that, there was a lot but coming out of that, there was a lot but coming out of that, there was a lot of kind of pent-up upside in the market. of kind of pent-up upside in the market. of kind of pent-up upside in the market. And you know, the market, particularly And you know, the market, particularly And you know, the market, particularly last 2 months, it has run up a very last 2 months, it has run up a very last 2 months, it has run up a very steep hill. steep hill. steep hill. And a lot of companies, semiconductor And a lot of companies, semiconductor And a lot of companies, semiconductor companies in particular, you know, companies in particular, you know, companies in particular, you know, ironically, you know, Nvidia and ironically, you know, Nvidia and ironically, you know, Nvidia and Broadcom, they they have been laggards. Broadcom, they they have been laggards. Broadcom, they they have been laggards. >> Totally. >> Totally. >> Totally. >> And so, but a lot of these, like I do >> And so, but a lot of these, like I do >> And so, but a lot of these, like I do see a lot on X about finding the next see a lot on X about finding the next see a lot on X about finding the next bottleneck. I think that was the last bottleneck. I think that was the last bottleneck. I think that was the last game. That game is over. game. That game is over. game. That game is over. You've had a lot of stocks that forget You've had a lot of stocks that forget You've had a lot of stocks that forget climbing a mountain or a hill. They've climbing a mountain or a hill. They've climbing a mountain or a hill. They've gone straight up a cliff, okay? gone straight up a cliff, okay? gone straight up a cliff, okay? >> Yes. They're tired. They need to rest. >> Yes. They're tired. They need to rest. >> Yes. They're tired. They need to rest. And we'll see, do they just rest at the And we'll see, do they just rest at the And we'll see, do they just rest at the top of that cliff they climbed? Do they top of that cliff they climbed? Do they top of that cliff they climbed? Do they hang out on the in their harness for a hang out on the in their harness for a hang out on the in their harness for a while? while? while? We've seen some. We've seen some. We've seen some. Or do they need to go downhill for a Or do they need to go downhill for a Or do they need to go downhill for a bit? We'll see, but I'm thinking very bit? We'll see, but I'm thinking very bit? We'll see, but I'm thinking very similarly to you. similarly to you. similarly to you. But it is and I think there's, you know, But it is and I think there's, you know, But it is and I think there's, you know, the market is seasonal. I think there's the market is seasonal. I think there's the market is seasonal. I think there's real real real real concerns around inflation and real concerns around inflation and real concerns around inflation and rates. rates. rates. >> What was CPI this morning? >> What was CPI this morning? >> What was CPI this morning? >> uh 4.2. I think we added core came in at >> uh 4.2. I think we added core came in at >> uh 4.2. I think we added core came in at like 0.2 versus 0.3, so a little bit like 0.2 versus 0.3, so a little bit like 0.2 versus 0.3, so a little bit better. better. better. Um but you know, clearly we're we're Um but you know, clearly we're we're Um but you know, clearly we're we're above four again. above four again. above four again. And um and and there's short-term And um and and there's short-term And um and and there's short-term pressure on, you know, core PCE, etc. pressure on, you know, core PCE, etc. pressure on, you know, core PCE, etc. Um and we have some unknown unknowns, Um and we have some unknown unknowns, Um and we have some unknown unknowns, but the market, I mean, if I had told but the market, I mean, if I had told but the market, I mean, if I had told you the fact pattern for this year, that you the fact pattern for this year, that you the fact pattern for this year, that we're going to be in a war with Iran, we're going to be in a war with Iran, we're going to be in a war with Iran, that, you know, oil was going to be at that, you know, oil was going to be at that, you know, oil was going to be at 100 bucks, that CPI was going to be 100 bucks, that CPI was going to be 100 bucks, that CPI was going to be creeping back up, that internet was creeping back up, that internet was creeping back up, that internet was going to be down 15%. Software is going going to be down 15%. Software is going going to be down 15%. Software is going to be down 8%. You would have said, "I to be down 8%. You would have said, "I to be down 8%. You would have said, "I want nothing to do with that market, want nothing to do with that market, want nothing to do with that market, right?" And here we are. The market's right?" And here we are. The market's right?" And here we are. The market's done pretty good in the stuff that we done pretty good in the stuff that we done pretty good in the stuff that we traffic in because the world traffic in because the world traffic in because the world underestimated AI revenues and underestimated AI revenues and underestimated AI revenues and underestimated the amount of compute underestimated the amount of compute underestimated the amount of compute that was going to be needed. that was going to be needed. that was going to be needed. >> It's odd to say you know, we're heading >> It's odd to say you know, we're heading >> It's odd to say you know, we're heading into a seasonally weak period with all into a seasonally weak period with all into a seasonally weak period with all of these fears. AI has actually been of these fears. AI has actually been of these fears. AI has actually been seasonal for the last three summers. seasonal for the last three summers. seasonal for the last three summers. Token consumption is kind of plateaued, Token consumption is kind of plateaued, Token consumption is kind of plateaued, slowed down, and that's cuz you know, slowed down, and that's cuz you know, slowed down, and that's cuz you know, college kids are big AI consumers and college kids are big AI consumers and college kids are big AI consumers and they don't use as much AI, you know, they don't use as much AI, you know, they don't use as much AI, you know, hopefully they're all using it to learn hopefully they're all using it to learn hopefully they're all using it to learn and not cheat. and not cheat. and not cheat. But that may happen. It may not happen But that may happen. It may not happen But that may happen. It may not happen because of generative AI. because of generative AI. because of generative AI. >> is building swarms of agents, building a >> is building swarms of agents, building a >> is building swarms of agents, building a SpaceX model. He's going to the SpaceX SpaceX model. He's going to the SpaceX SpaceX model. He's going to the SpaceX IPO with me at the exchange on Friday, IPO with me at the exchange on Friday, IPO with me at the exchange on Friday, but I he had to build an AI model using but I he had to build an AI model using but I he had to build an AI model using AI agents. He had to build a model, a AI agents. He had to build a model, a AI agents. He had to build a model, a DCF before we go to the exchange. He is DCF before we go to the exchange. He is DCF before we go to the exchange. He is mesmerized. He is absolutely and it's mesmerized. He is absolutely and it's mesmerized. He is absolutely and it's extraordinary what he's doing. extraordinary what he's doing. extraordinary what he's doing. >> So he's one kid who's not easy to less >> So he's one kid who's not easy to less >> So he's one kid who's not easy to less computer computer computer >> [laughter] >> [laughter] >> [laughter] >> or something. >> or something. >> or something. >> He's burning it. He's burning it. >> He's burning it. He's burning it. >> He's burning it. He's burning it. >> Yeah, but you know, if token consumption >> Yeah, but you know, if token consumption >> Yeah, but you know, if token consumption plateaus, if open source takes some plateaus, if open source takes some plateaus, if open source takes some share, there's a Silicon data index that share, there's a Silicon data index that share, there's a Silicon data index that has showed, which is an index of kind of has showed, which is an index of kind of has showed, which is an index of kind of consumption and pricing. I think there consumption and pricing. I think there consumption and pricing. I think there may have been a little bit of a shift may have been a little bit of a shift may have been a little bit of a shift over the last 2 weeks to open source over the last 2 weeks to open source over the last 2 weeks to open source tokens that are cheaper. Like people tokens that are cheaper. Like people tokens that are cheaper. Like people looking at that data as bearish or not looking at that data as bearish or not looking at that data as bearish or not understanding it. But nonetheless, like understanding it. But nonetheless, like understanding it. But nonetheless, like I just think there's reasons, you know, I just think there's reasons, you know, I just think there's reasons, you know, to look around, be careful, be to look around, be careful, be to look around, be careful, be thoughtful. I always assume a bullet is thoughtful. I always assume a bullet is thoughtful. I always assume a bullet is coming for me. Head on [laughter] a coming for me. Head on [laughter] a coming for me. Head on [laughter] a swivel. It's the bullet you don't see swivel. It's the bullet you don't see swivel. It's the bullet you don't see that gets you. So, I'm trying to spin as that gets you. So, I'm trying to spin as that gets you. So, I'm trying to spin as fast as I can. fast as I can. fast as I can. But yeah, it's the market may need to But yeah, it's the market may need to But yeah, it's the market may need to take a breather. But man, when I think take a breather. But man, when I think take a breather. But man, when I think about what Noam Brown said about what Noam Brown said about what Noam Brown said and when I see the capabilities of and when I see the capabilities of and when I see the capabilities of Fable, Fable, Fable, it's just hard for me to get too it's just hard for me to get too it's just hard for me to get too bearish. bearish. bearish. >> I mean, like to me >> I mean, like to me >> I mean, like to me um and we got two, I think, of the most um and we got two, I think, of the most um and we got two, I think, of the most extraordinary guys of, you know, the extraordinary guys of, you know, the extraordinary guys of, you know, the next generation, you know, sitting in next generation, you know, sitting in next generation, you know, sitting in the room. We have at Altimeter, we have the room. We have at Altimeter, we have the room. We have at Altimeter, we have deep admiration for the work that you deep admiration for the work that you deep admiration for the work that you guys do. I always appreciate when you guys do. I always appreciate when you guys do. I always appreciate when you send me a note about the work that we do send me a note about the work that we do send me a note about the work that we do and we publish. Um but for the guys who and we publish. Um but for the guys who and we publish. Um but for the guys who are newer to the business, they might are newer to the business, they might are newer to the business, they might think this is the way that it kind of think this is the way that it kind of think this is the way that it kind of always was, right? And like this line, always was, right? And like this line, always was, right? And like this line, the steepening of the line of creative the steepening of the line of creative the steepening of the line of creative destruction, the steepening of the line destruction, the steepening of the line destruction, the steepening of the line of, you know, scale advantages. Um I of, you know, scale advantages. Um I of, you know, scale advantages. Um I always believed it was to it was going always believed it was to it was going always believed it was to it was going to be true. I never thought it would be to be true. I never thought it would be to be true. I never thought it would be true at this rate. I went back last true at this rate. I went back last true at this rate. I went back last night. In the last 7 years, we've added night. In the last 7 years, we've added night. In the last 7 years, we've added 1 trillion of revenue to the Mag 7 1 trillion of revenue to the Mag 7 1 trillion of revenue to the Mag 7 in the last 7 years, okay? To get to a in the last 7 years, okay? To get to a in the last 7 years, okay? To get to a trillion, to get to the first trillion trillion, to get to the first trillion trillion, to get to the first trillion of, you know, took over 20 years. In the of, you know, took over 20 years. In the of, you know, took over 20 years. In the last 7, we had another tr- trillion and last 7, we had another tr- trillion and last 7, we had another tr- trillion and that added 17 trillion in market cap. that added 17 trillion in market cap. that added 17 trillion in market cap. That trillion dollars, okay? That trillion dollars, okay? That trillion dollars, okay? I The forecast now that we're going to I The forecast now that we're going to I The forecast now that we're going to add another trillion of revenue in just add another trillion of revenue in just add another trillion of revenue in just three companies SpaceX Anthropic and three companies SpaceX Anthropic and three companies SpaceX Anthropic and open AI over the next four to five open AI over the next four to five open AI over the next four to five years. years. years. Okay, like not seven companies three Okay, like not seven companies three Okay, like not seven companies three companies and in half the time right and companies and in half the time right and companies and in half the time right and so I would say that you know, we are so I would say that you know, we are so I would say that you know, we are going to have bumps in the road. I know going to have bumps in the road. I know going to have bumps in the road. I know that it's going to be like this but that it's going to be like this but that it's going to be like this but we're going to higher highs because the we're going to higher highs because the we're going to higher highs because the size of the prize. This is going to size of the prize. This is going to size of the prize. This is going to transform five ten 15% of global GDP. transform five ten 15% of global GDP. transform five ten 15% of global GDP. There is no doubt in my mind and 10% of There is no doubt in my mind and 10% of There is no doubt in my mind and 10% of global GDP is 10 trillion dollars. It's global GDP is 10 trillion dollars. It's global GDP is 10 trillion dollars. It's an exciting future to be a part of it's an exciting future to be a part of it's an exciting future to be a part of it's fun to do it with you guys. I think fun to do it with you guys. I think fun to do it with you guys. I think we're going to have to do our work to do we're going to have to do our work to do we're going to have to do our work to do the things to make sure America wins and the things to make sure America wins and the things to make sure America wins and that we evolve the social contract keep that we evolve the social contract keep that we evolve the social contract keep everybody you know lift the floor take everybody you know lift the floor take everybody you know lift the floor take everybody with us on this ride everybody with us on this ride everybody with us on this ride but it's a it's a it's a really exciting but it's a it's a it's a really exciting but it's a it's a it's a really exciting time to be doing what we're doing it's time to be doing what we're doing it's time to be doing what we're doing it's fun to be doing it with you guys. fun to be doing it with you guys. fun to be doing it with you guys. >> Yeah, I just want to say Brad thanks for >> Yeah, I just want to say Brad thanks for >> Yeah, I just want to say Brad thanks for having us and thank you for what you've having us and thank you for what you've having us and thank you for what you've done with the Trump accounts. I actually done with the Trump accounts. I actually done with the Trump accounts. I actually think it's super important for America think it's super important for America think it's super important for America for the world to give people an equity for the world to give people an equity for the world to give people an equity stake at a very young age. They they stake at a very young age. They they stake at a very young age. They they will see it compound over their will see it compound over their will see it compound over their lifetimes. This is a great thing you've lifetimes. This is a great thing you've lifetimes. This is a great thing you've done for the world. So thank you. I'd done for the world. So thank you. I'd done for the world. So thank you. I'd echo all your comments like deep echo all your comments like deep echo all your comments like deep admiration for you your team gratitude admiration for you your team gratitude admiration for you your team gratitude for the collegiality and friendship for the collegiality and friendship for the collegiality and friendship between our firms. I know Clark and Foxy between our firms. I know Clark and Foxy between our firms. I know Clark and Foxy they hang out like all the time. they hang out like all the time. they hang out like all the time. >> That's a people think that you know and >> That's a people think that you know and >> That's a people think that you know and there are people in our business who there are people in our business who there are people in our business who don't want to share anything. Our view don't want to share anything. Our view don't want to share anything. Our view is like we open source it is like we open source it is like we open source it but there are very few people who we but there are very few people who we but there are very few people who we actually call and ask their opinion actually call and ask their opinion actually call and ask their opinion because there are very few people who do because there are very few people who do because there are very few people who do the thousands of hours of work that we the thousands of hours of work that we the thousands of hours of work that we do do do you know that are adding to that and you you know that are adding to that and you you know that are adding to that and you do it and we appreciate that and you do do it and we appreciate that and you do do it and we appreciate that and you do as well Gavin we appreciate that. So as well Gavin we appreciate that. So as well Gavin we appreciate that. So with that love fest let's call it a with that love fest let's call it a with that love fest let's call it a wrap. wrap. wrap. Thanks for being here. Thanks for being here. Thanks for being here. >> Thank you.