The SpaceX IPO, Fable 5, AI Capex Update & Market Check w/ Gavin Baker, Andrew Fox & Clark Tang | BG2

Brad Gerstner sits down with Gavin Baker and Andrew Fox of Atreides Management, alongside Altimeter partner Clark Tang, to break down one of the biggest questions in tech and markets: how should investors think about the SpaceX IPO? They unpack the major levers behind SpaceX’s next phase: Starship rapid reusability, Starlink broadband and direct-to-cell, Elon’s emerging AI compute business, xAI’s model ambitions, the Cursor acquisition, and the long-term promise of orbital data centers. The group also debates whether SpaceX is becoming a new kind of AI hyperscaler — “Elon Web Services” — and what that means for the future of compute, cloud, and frontier intelligence. Then they dive into the latest model race: Fable 5, Mythos, ChatGPT 5.5, long-running agents, open source vs. frontier models, Nvidia vs. ASICs, the AI CapEx boom, and why the market may still be underestimating the scale of AI demand. Enjoy another episode of BG2! Timestamps: (00:40) Intro — SpaceX IPO in Two Days, Mythos Launches, Taiwan Takeaways (03:05) xAI's Google & Anthropic Deals: Highest Operating Profit Per Gigawatt (13:28) "Elon Web Services" — Nobody Had AI Compute in the SpaceX Model (19:08) Data Centers Are Not Commodities: First-Principles Design (26:01) Orbital Compute Economics: $5B Per Gigawatt in Space vs. $25B on the Ground (29:25) The Most Underrated Variable: What Cursor Does for xAI's Model (35:05) Bull & Bear Case — Can SpaceX Really 8X Revenue in 4 Years? (37:00) Post-IPO Drawdowns, Lock-Ups & How to Size the Position (43:56) Fable 5, Mythos & Why Snapshot Benchmarks Are Broken (51:00) Frontier vs. Open Source: 90% of Revenue Accrues at the Frontier (01:04:11) $1.5T in CapEx vs. $300B in AI Revenue — Does the Math Math? (01:18:13) The Next $1 Trillion: Three Companies, Half the Time Produced by Edward Schmidt & Dan Shevchuk Music by Yung Spielberg Available on Apple, Spotify, www.bg2pod.com Follow: Brad Gerstner @altcap https://x.com/altcap Gavin Baker @GavinSBaker https://x.com/GavinSBaker Clark Tang @_clarktang https://x.com/_clarktang BG2 Pod @bg2pod https://x.com/BG2Pod

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Brad Gerstner?

  I think we're all pretty AI-pilled. And if you're AI-pilled, that means we've got to build a lot more compute than the world thinks. And that these models are going to be a lot more valuable than people think. You combine that with their core business. I don't know another entrepreneur or another business that's a better bet on the future, right? Than SpaceX. And so I think for most institutional investors, it's a must-buy, a must-own. It's set it and forget it, right? In order to have a real bet on both the space and the AI future.

• 0:40Brad Gerstner?

  All right, here we go. Early morning, Silicon Valley. BG2 is back. We're chopping it up on all things tech and markets. To do that, I have none other than GB in the house. Gavin Baker from Atreides. He's brought his main guy, Andrew Fox. And of course, I had to draft Clark Tang into the mix, my partner, to talk about some of the big questions of the day. You know, how should we be thinking about the SpaceX IPO? You know, what are the big levers? There are big numbers out there for what's going to happen over the course of the next few years. So let's break that down a bit, help simplify it for folks. Mythos launched yesterday. I want to talk a little bit about like who's up, who's down in the race for superintelligence. Where are we? What did we learn with the Mythos launch?

• 1:22Brad Gerstner?

  And Clark was in Taiwan last week with Jensen at Computex and GTC. So what was our takeaway there? What's going on with GPUs, memory? Where are the bottlenecks? And where do we go from here? To start everything off, you know, maybe just kick it over to you, Gavin. Talking about the SpaceX IPO, the IPO is in two days. You're a big shareholder. Congratulations. We're also a shareholder. You know, we also expect to be buying in the IPO. The Wall Street Journal is reporting, you know, the Goldman Sachs are both saying 160 billion in revenue in 2028. We know that the IPO is $135 a share, 1.77 trillion.

• 2:05Brad Gerstner?

  When we think about kind of what the big levers are, there's so many moving parts in this IPO, nobody's better than you at just breaking it down, simplifying it. What are the key levers that we ought to be thinking about that you're thinking about over the course of the next few years?

• 2:19Speaker 01

  Sure, so great to be here, thank you for having me. I thought we were gonna call it BGGB, but we can stick with BG2.

• 2:27Brad Gerstner?

  Hey, all subject to revision.

• 2:29Speaker 01

  That's okay, that's okay. So I think there's two big levers or variables that I think people should focus on. And you know, I'm not gonna comment on where I think those variables go. But one is, you guys have this chart, did you post this on X?

• 2:46Clark Tang?

  I did, I did before, and then we also included a new addition with XAI's new deals as well.

• 2:53Speaker 01

  Yeah, so Clark, who I've known for many years, made a, did a great analysis here. And he shows that XAI's deal with Google for cloud computing generates more operating profit per gigawatt than Anthropic, than Meta, than Google, than OpenAI. Their deal, actually with Anthropic, also generates probably more operating profit than anyone but Anthropic. And so, you know, your colleague at Altimeter, Freda, also, she calculated a 55% ARR on Clausus 1.

• 3:32Speaker 01

  You know, if you can borrow money at 6%, 7%, 8%, and invest in something with a 55% ARR, I'm not the most sophisticated thinker, but that math maths.

• 3:41Brad Gerstner?

  Right.

• 3:42Speaker 01

  And so I think the most important variable, one of the two most important, is how quickly they bring on terrestrial data centers. We do know from Jensen that Elon brings data centers up faster than anyone, 122 days. Speed is literally cost, because every day you're paying electricians and plumbers, that's cost. And they're now monetizing them at arguably the highest rate. And so I think, you know, everybody should run their own math on that, but that is a massive variable.

• 4:11Brad Gerstner?

  Yes.

• 4:12Speaker 01

  Truly massive variable. The second thing is, you know, we have a chart, and it's wildly out of date now, it's kind of freaking amazing. This chart is, I think, is this chart from 10 days ago? But in like the 10 or 12 days since this chart, since we made this chart, which shows the Pareto curves for Opus 4.7 for coding. For Codex from OpenAI. And now we've had Opus 4.8, it was already out of date. And now we have Fable.

• 4:39Brad Gerstner?

  Totally.

• 4:40Speaker 01

  And Mythos, which is freaking wild, in 10 days. Like, we would have had to update the chart twice. But what the Pareto curve shows is how much intelligence you can get for a given amount of cost. And I do think being all revenue will accrue to the Pareto curve. All, at least, kind of frontier model revenue will accrue to the Pareto curve. And this is Pareto curve for coding. And what I think is so impressive is that you could see in the chart that Composer 2 was Pareto dominant. You know, at the lowest level of intelligence with very little training. And this just reflects, and I know you know Cursor well, I think you know Cursor a shitload better than I do. A vast amount better than I do.

• 5:21Speaker 01

  But my understanding is that Cursor and Anthropic have more tokens of proprietary coding data than anyone else. And they each have more tokens of proprietary coding data than exist on the public internet. And so they fed, Cursor fed, used Kimi K. 2.5, used their own private data, did some RL, some supervised fine tuning. And they got a really good model. And then they spent three weeks in the Colossus 2 cluster. And they got a model that 12 days ago was Pareto dominant with Composer 2.5. Now, it's on their own benchmark, Cursor bench, so maybe take it with a grain of salt. But I think this just suggests that the Cursor data is very valuable for coding.

• 6:04Speaker 01

  And when it is trained, you know, Chinchilla optimal or beyond Chinchilla optimal with reinforcement learning. You know, I think it suggests that XAI and SpaceX AI has a shot of being a real player in coding.

• 6:17Brad Gerstner?

  I mean, I think one of the interesting things is, you know, we, the way you answered the question, right? We didn't talk about launch. Right, we didn't talk about Starlink or Communications. Those, up until really six months ago, were the business.

• 6:31Speaker 01

  Yeah.

• 6:31Brad Gerstner?

  Right? And, you know, and then we merged in X.ai. And we merged in Cursor. And then we announced these deals where it was very clear he was kind of building AWS right under our nose, you know, in terms of this. But what I want to do is go to Fox. Give us the breakdown, three big lines of business, right? We've got the Communication-Starlink Launch Business. We've got the, you know, AI Compute Business. And then I want to come back to X.ai that you were just clicking on. But if we just go to the core business, what do we have to assume goes right in the core business, both with Launch and with Starlink, in order to achieve the numbers that are out there?

• 7:08Speaker 03

  Yeah, sure. So, look, I think the thing that's foundational to everything is the Launch Business. Right? This is the kind of crown jewel of SpaceX. It's something that no one else really has, notably Reusability.

• 7:21Brad Gerstner?

  Right.

• 7:21Speaker 03

  And soon, Rapid Reusability. Right? This is, I think, what you need to believe in to get to the economics in AI that make Orbital Compute something that's very economically attractive. Outside of the idea that we are in shortage for power, shortage for chips, right? So I think Rapid Reusability is the main thing that we're watching for, and I think most people should watch for. You know, Elon talks about it a lot, but getting these rockets to fly at a cadence that's comparable to an airline, right? And Gavin has used this analogy before, but the old rocket industry was kind of like, imagine boarding a plane, flying to California, getting off the plane, the plane explodes.

• 8:04Speaker 03

  So I think what SpaceX are ultimately trying to achieve is have a starship fly both stages, not just the booster, 30, 40, 50 times before you have to retrofit that ship. And when you do that, you're amortizing the cost of the vehicle over many flights, right? And that's what brings the cost down significantly.

• 8:29Speaker 01

  But that's a really hard problem to solve.

• 8:32Speaker 03

  Extremely difficult. And, look, I think the company, you know, have... Been loud and clear, they're going to attempt to bring back the second stage

• 8:40Speaker 01

  Right.

• 8:41Speaker 03

  of Starship later this year.

• 8:42Speaker 01

  Right.

• 8:43Speaker 03

  And then make it reusable, you know, re-fly the second stage next year. And from there, ramp up the cadence. But at the end of the day, driving down the cost of launch is what enables all of these other businesses and is what makes them so attractive relative to incumbent.

• 9:03Brad Gerstner?

  So how many, how many times are Starship just, Starship 3, you know, just launched? How many launches are, you know, do you think kind of the consensus out there is assuming, you know, two or three years from now? Like, what is the launch cadence? Are we launching one of these every day? Are we launching one of these every week or every month? Like, where are we in terms of expectations?

• 9:22Speaker 03

  Yeah, so look, I think expectations for now, you know, we're going from, you know, call it 160, 165 launches last year, up into the high hundreds of launches in the next several years. And, you know, getting into the thousands of launches probably in the next three years thereafter.

• 9:38Brad Gerstner?

  Okay.

• 9:39Speaker 03

  I think the company of aspirations.

• 9:41Brad Gerstner?

  Thousands of launches, you're launching, you're doing two or three launches a day.

• 9:45Speaker 03

  Right.

• 9:45Brad Gerstner?

  Right. And then, talk to us a little bit, what is this enable? Obviously, you know, I'm here in Silicon Valley, I can't even, I can't even keep a call on Sand Hill Road, two decades into the mobile revolution.

• 9:58Speaker 01

  It is actually wild.

• 9:59Brad Gerstner?

  I mean, it's the craziest thing, it's like a third world.

• 10:00Speaker 01

  It's, it's a major business problem when you're freaking out here.

• 10:04Brad Gerstner?

  It's crazy, it's crazy. Right by the Starwood, den zone, and I'm like, how can this possibly be? It's almost like it's a joke. It's the epicenter of technology in America and you can't maintain a call. Okay, so, we're all gonna switch to Starlink Mobile when it comes along, because I don't want to lose that call on Sand Hill Road. So, walk me through a little bit, just again, high level, it's a big portion of the revenue growth expected in the business over the course of the next two to three years. My hunch is a lot of this is driven by, by direct-to-cell connectivity. Walk me through a little bit those economics.

• 10:37Speaker 03

  Yeah, so look, it's actually interesting, the broadband business is still very early stage when you think about the percent of households that have actually been penetrated to date. You look at the percent of global households with Starlink, it's less than 1%. And that's the broadband, you know, you kind of have a base terminal at your house, on your car, and your boat, and in airlines now as well. So I actually think broadband can scale to hundreds of millions of terminals.

• 11:06Speaker 01

  Okay.

• 11:06Speaker 03

  Hundreds of millions of users. And today, the subscriber base-

• 11:09Speaker 01

  Hundreds of millions if, if they get rapid reusability of Starship, which is really hard. You know, if, if there's not competition, hundreds of millions, it's possible. But maybe-

• 11:25Brad Gerstner?

  I always say around here, it's funny, I love seeing PM and kind of analysts in this situation. It's exactly what I do with Clark. Clark will say something, I'll say, "The future's a distribution of unknown probabilities." It's either more likely or less likely, so give me the distribution. Are we talking 20%, 30%? It's hilarious.

• 11:41Speaker 01

  Well, no, 100% the same thing. And like, I've watched Elon do many hard things, and this is a really hard thing. So I think it's reasonable to think that they're going to succeed with rapid reusability. But just, I just think it's important to acknowledge that like, Orbital Compute, you know, Starlink, you know, Starlink V3, Starlink Direct-to-Cell, we need, we need first reusability for Starship V3, and then rapid reusability unlocks a lot of this.

• 12:07Brad Gerstner?

  Right. When I see, when I see the models that the banks are putting out there, right, in Wall Street Journal, everybody's reported on these. These things have been widely leaked. They, they largely have the revenue on connectivity, so let's call it Starlink, Direct-to-Cell, et cetera, going from, you know, let's call it 10 billion to 50 billion by 2028. And so I'm not asking you guys to react to, you know, to tell me your specific numbers. But when I'm talking to Clark, all I'm trying to size up is order of magnitude. Do we think we can 5X the business over the course of the next three years? Is there enough TAM, both in terms of broadband and direct to consumer? And I think the answer to that is yes.

• 12:46Speaker 01

  Yeah, here's what I'd just say very simply, is I have- I travel with Starlink.

• 12:51Brad Gerstner?

  Yes.

• 12:52Speaker 01

  I'm a big video gamer and very consistently, wherever I am in the world, Starlink is the best connection.

• 12:58Brad Gerstner?

  Yes.

• 12:58Speaker 01

  It's the fastest, it's lowest latency. And I do think, once they get to rapid reusability, it's also going to be, they're going to have the cheapest cost per gigabyte or megabyte delivered. And better, faster, cheaper has been a winning formula. And so 50 billion, that's, you know, 0.3% penetration of the global telecom market. Now, maybe there's some deflation with Starlink pricing. But that's the way I'd frame it up.

• 13:26Brad Gerstner?

  Yeah.

• 13:26Speaker 01

  I like betting on better, faster, cheaper.

• 13:28Brad Gerstner?

  Clark, I would say probably the biggest surprise of the last six weeks is that Elon, you know, we talked about it on All In Podcast, we call it EWS, Elon Web Services, right? That he struck these huge deals with Anthropic and Google. I don't even think people were thinking about SpaceX in the AI compute game, right? If you looked at the models as of a few months ago, it was connectivity, so Starlink. And then it was X.ai, the model. But this whole category of taking all of this compute, which he's uniquely good at standing up, right, and then reselling it in a way that's highly profitable, was not in a lot of people's forecasts.

• 14:10Brad Gerstner?

  Now it's a major component of the forecast. You know, you and I did this podcast with Jensen, where Jensen said Elon is an N of one.

• 14:19Speaker 04

  What they achieved is singular, never been done before. Just to put in perspective, 100,000 GPUs, that's, you know, easily the fastest supercomputer on the planet. That's one cluster. A supercomputer that you would build would take normally three years to plan.

• 14:38Brad Gerstner?

  Right.

• 14:39Speaker 04

  And then they deliver the equipment and it takes one year. To get it all working. Yes. We're talking about 19 days. Wow. N of one is right. Elon is an N of one.

• 14:52Brad Gerstner?

  And his ability to secure supply, stand up the supply, you know, deploy it in a way that's, you know, coherent and effective for both himself and I guess now for others. So walk us through kind of that. It looks to me again like this is a major component of the revenue story.

• 15:10Clark Tang?

  Totally. I mean, so we were all at the macro hard data center. And it was just very evident, the amount of engineering that was, that had gone into building these sites. You know, people always talk about Google and their ability to, to build a TPU and sell the TPU to Anthropic to generate revenues for AI. I think it's a pretty similar dynamic here with Elon able to secure power, build these sites faster than anyone else. And also be able now to monetize it to, to this massive AI market that's ahead of us. If you look at the, the relationships that he's forged with a lot of his suppliers,

• 15:53Clark Tang?

  you know, be it Jensen, be it, you know, all of these different, different sites that actually want XAI as a tenant. His, his ability to finance these deals at, at very attractive financing rates relative to a lot of the other players in, in the space. You know, these are advantages that compound over time. And when you've built the credibility to stand up these sites and monetize at these levels, you know, it's, it's actually a very attractive, very attractive proposition for, for a lot of folks involved. And actually, you know, if you look at these deals in particular, Gavin, you, you pointed out, but you know, they're, they're actually monetizing, you know, perhaps better than

• 16:36Clark Tang?

  other players in the space by selling this infrastructure.

• 16:39Brad Gerstner?

  A lot higher. Google is obviously paying SpaceX a huge premium for this compute. Fox, you said something that I thought was really important, which is, you know, it may very well be that in order to get, you know, first in line on space compute, which Google certainly wants to do, that they're willing to pay a premium for their terrestrial compute. And so to me, that's how you kind of square the circle as to why the premium. Any thoughts?

• 17:05Speaker 03

  Yeah, look, I think there's some of that embedded there. But look, at the end of the day, SpaceX can stand up compute quickly. They can stand it up coherently and they can stand up a lot of it in one place and have it readily available. So, look, I think that's most of the premium, but outside of that, certainly, people are going to space over time.

• 17:22Brad Gerstner?

  Pay a little call option to get first in line for space.

• 17:25Speaker 03

  There you go.

• 17:25Brad Gerstner?

  We've all been investing in the Neo cloud space. So, like, there's a fundamental belief around this table that we lack the compute needed to continue to push the frontier on intelligence. So, we have to build a lot of compute, okay? Now, there's competition going on. On one end, you have the hyperscalers who are building out that capability. Then, we have AI-dedicated clouds that are building out that capability. And now, literally, in a matter of weeks, right, we have a, you know, a giant that's emerged in this category, which is SpaceX. The question to you, Gavin, is can they consolidate this market, right? Because if I think about a marketplace, Elon has a unique ability to get the supply.

• 18:07Brad Gerstner?

  He has a unique ability to cut deals on the other side, and nobody can stand it up like he can stand it up. So, I think there might be a real consolidation in the AI compute market, where you have the hyperscalers on the one hand, and on the other hand, you know, he may emerge as the largest, strongest player in the AI compute market.

• 18:26Speaker 01

  Yeah. So, I think they're, are they the number four, number five hyperscaler today after the Google deal?

• 18:32Speaker 03

  It will be number four.

• 18:34Speaker 01

  Kind of wild. In 30 days, we went from not being an AI hyperscaler to being number four. And we passed a lot of companies, including Oracle.

• 18:45Brad Gerstner?

  CoreWeave is a huge business, right? We're investors in, you know, and have been investors in, right? But there are a lot of other players, the Nebius's of the world, the Irons of the world, and I would say that there are probably 50 Neo Labs being funded in Silicon Valley right now, as we speak, because of the shortage in compute.

• 19:01Speaker 01

  Absolutely. So, that's kind of crazy in 30 days. That's just extraordinary.

• 19:08Speaker 01

  What I would say is that I think there is a belief that these data centers are commodities. And I do not share that belief. I don't think anybody around this table shares that belief. And in the same way that Elon was able to re-engineer a rocket from first principles and make it reusable, he engineered an electric car from first principles. You know, everyone else was trying to, you know, make an electric car, like an internal combustion engine car, and he thought about it differently. And I think he looked at data center design from first principles, and he designed something fundamentally different. And I did actually ask the team, I said, hey, guys, maybe it be a little less public

• 19:49Speaker 01

  about things that are very obvious to you about how to design a data center, but are revelations to other people. Because I think what you're doing is maybe more differentiated than you perhaps realize, because what you're doing is so logical to you, but maybe not logical to everyone else. And that's how he was able to do it 122 days.

• 20:10Clark Tang?

  Yeah, I mean, to that point, Brad, yesterday we were meeting one of our portfolio companies, and we were talking about behind the meter, and we're, you know, really thinking about it. There's only maybe two or three, two or three players now that can actually reliably engineer a behind-the-meter data center. And, you know, there's real engineering work that goes into all of this.

• 20:28Brad Gerstner?

  So if you think about this, if you're a gas combustion, if you're Vernova, and you say, we only have a certain number of gas combustion engines. Now, we can sell them to X dot AI, or we can sell them to one of these startup Neo clouds. Who are you going to sell them to?

• 20:42Speaker 01

  Well, and there's another dynamic. Everyone starts making more money when the GPUs get energized and sold faster. So literally, speed is money for all of the suppliers. Power, land, turbines. So I think it's, we'll see.

• 20:57Brad Gerstner?

  Right.

• 20:58Speaker 01

  Hey, Brad, man.

• 20:59Brad Gerstner?

  But this is just, we're just talking terrestrial.

• 21:01Speaker 01

  Terrestrial.

• 21:02Brad Gerstner?

  I do want to hit on, and then you can flip it back on me. Talk to me, okay, so let's assume, right, that they continue to build out the terrestrial landscape. They continue to find buyers for that. Walk us through what this unlocks and how this is related to space data centers. Because I think, once you start talking terafab capacity and beyond, so we're talking 1,000 gigs, right? And this year, what, we're doing 25 or 30 gigs, just to put it all in perspective?

• 21:31Clark Tang?

  Yeah, 2025 gigs.

• 21:33Brad Gerstner?

  Okay, so, so, once we start scaling up, walk us through, do we have to have space data centers in order to get excited about buying the IPO, right? And then, there's obviously this debate in the world. I heard Jeff Bezos say, "I think it's more like six years, but Elon's gonna say three, because if he says six, then it will take even longer. So say three, and we may get it in four or five." But are space data centers integral and essential to the IPO? And what do you think the timeline is on, to either of you guys?

• 22:05Speaker 01

  So I don't think, I think if you think about those variables around what cursor could mean for XAI. And we do have an existence proof that once you really get on that Pareto frontier, revenue can scale rapidly, and it's called anthropic. And there does seem to be an exhaust, there seems to be a lot of demand for coding. And I do think Amjad Masad posted something very interesting.

• 22:28Brad Gerstner?

  The founder of Replit.

• 22:30Speaker 01

  The founder of Replit. He called a bitter lesson adjacent, that coding may be the fastest path to AGI and ASI. Because if you're really good at coding, you can write code, if a model's good at coding, to do anything.

• 22:43Brad Gerstner?

  Correct.

• 22:44Speaker 01

  So I think that's a profound point, and I think coding is gonna continue to be very important. So I think if you think about that variable, if you think about Starlink direct-to-sale, enabled by Starlink V3, and you think about how quickly they can or cannot bring on terrestrial compute, I don't think orbital compute is necessary for the IPO valuation, but it's certainly important, and it's...

• 23:08Brad Gerstner?

  Well, maybe another way to say it, is you think, you may think we're gonna get to ASI... Faster than we're going to get to Orbital Compute. That may take us from 300 IQ to 400 IQ, 500 IQ and beyond. And the ability to scale it up to consume 10% of global GDP. But maybe that's where we should move next.

• 23:30Speaker 01

  No, no, I think out of Orbital Compute, I think Foxy would be great, Clark, to lay out the math. From first principles, Clark has this great chart on the gigawatts it costs, the dollars per gigawatt. Walk us through the economic case.

• 23:45Speaker 03

  Yeah, so, I mean, on this point of is Orbital key to investing here? I don't think it is, and the first point I'll make is, what are the implied monetization rates based on expectations today for the AI business? I think you threw out the $160 billion number that's been leaked out there that people are talking about. The implied monetization rate on that number is something like $14 billion per gigawatt per year for the AI business. They just signed Anthropic at 22 to 23, they just signed Google at 50. So I think you can invest behind the AI business terrestrially and still be excited about it. But with Orbital-

• 24:22Brad Gerstner?

  I think it's an important point.

• 24:23Speaker 01

  Excited about it if they can get the land and the power.

• 24:26Brad Gerstner?

  Right, but I think for most investors, they have an easier time getting their head around how SpaceX wins terrestrially. Like, can they go get land, power and chips? The answer to that is high probability yes. And what we're saying is, at the rate they're monetizing that, that gets you to the numbers that are being leaked out there. Before you even have to take the leap of faith that they're going to extend the lead with Orbital data centers. But take us there on that, too.

• 24:52Speaker 03

  Yeah, so, look, with Orbital, I think the key thing is two-stage reusability. And beyond that, rapid two-stage reusability. So today with Starship, they've shown that they can successfully reland the booster. The second stage, we'll see what happens later this year. I think they're attempting to bring that back and then make it reusable by next year.

• 25:17Speaker 03

  But the thing that's important about two-stage reusability when it comes to the economics for Orbital Compute, is the cost per kg comes down significantly. We're talking about going from $1,500 per kg on Falcon, somewhere in that range, to 250 per kg, something lower. And the more that you can reuse the rocket- The more that price comes down.

• 25:39Brad Gerstner?

  Right.

• 25:40Speaker 03

  Right, because you're just depreciating the cost of the launch. And eventually, you asymptote to the cost of the fuel.

• 25:46Brad Gerstner?

  Right.

• 25:46Speaker 03

  Right, assuming you can use a rocket for forever.

• 25:49Brad Gerstner?

  Yes.

• 25:49Speaker 03

  Right, which will take a very long time for us to really achieve that. But and at that point, we're talking about something well south of 250 per kg. So then you look at the specs of these AI satellites. You know, Elon did a great.

• 26:03Brad Gerstner?

  Yeah, that that pod was incredible that he laid out the other day, the specs on the satellites.

• 26:07Speaker 03

  It was really great because I think they are finally showing people, here's how you could viably design one of these satellites. And how heavy is the satellite? How many could you fit into a starship launch? And when you back into the numbers, you get to something like five megawatts of capacity per starship launch.

• 26:28Brad Gerstner?

  Right.

• 26:29Speaker 03

  There's 100 metric tons in one of those starships. So you can back into the math of how much will it cost?

• 26:35Brad Gerstner?

  Right.

• 26:35Speaker 03

  Per gigawatt to launch these satellites into space.

• 26:39Brad Gerstner?

  Right.

• 26:40Speaker 03

  Launch this compute into space. And the math that you get to before you account for things like bad GPUs, bad satellites, right, these will all be things that happen. But the math you get to is it's about $5 billion per gigawatt of CapEx to put these in space.

• 26:59Brad Gerstner?

  Right.

• 27:00Speaker 03

  For comparison, terrestrially, talk about the switch gears, the generators, the transformers, the shell, getting the power, that today is about 25, 20 to 25 billion per gigawatt. So we're talking about a 5X reduction in cost on half of your bill of materials.

• 27:19Brad Gerstner?

  Right.

• 27:20Speaker 03

  For the data center.

• 27:21Brad Gerstner?

  Right.

• 27:21Speaker 03

  Which is a huge number.

• 27:22Speaker 01

  Yeah, just just very simply, I mean, just to say it, put it costs $60 billion to put a gigawatt on the ground today.

• 27:29Speaker 03

  Yeah.

• 27:29Speaker 01

  And we'll call it 35 of that is are the GPUs and the silicon that's doing the training in the inference. And 25 billion is the land, the shell, the power and the cooling. I would hypothesize that those elements are probably going to be inflationary, so that 25 billion may not go down. And because space, power, cooling are effectively free in space, and when I say space, I mean land.

• 27:56Brad Gerstner?

  Yes.

• 27:56Speaker 01

  You know, there's no land in space, but there is.

• 27:58Brad Gerstner?

  But there's a lot of space.

• 27:59Speaker 01

  There's a lot of space in space. You're talking about putting a gigawatt into space for 30 billion and having lower operating costs. Now, the first 60 billion, that's inflationary, and that 30 billion, that five, may be deflationary over time. But what we need to consider is the reliability and the maintenance. And so, as long as, you know, everybody can do the math, but as long as these satellites in space aren't failing at an astronomical rate, the math maths. And by the way, we know GPUs melt and lasers fail. We know this happens in data centers, particularly during big training runs.

• 28:44Speaker 01

  Yeah, I mean, GPUs melt. So as long as the reliability and maintenance is not dramatically lower, the math is there once we have reusability and then rapid reusability for Starship V3.

• 28:57Brad Gerstner?

  When you look at this, okay, so we went through Starlink, and we said, okay, like, it just stands to reason that we're going to have direct to sell on Starlink. Like, the assumptions there are, you know, again, seem like you can get your head around. Then when it comes to building terrestrial data centers, again, not a hard one to think that, based on these couple of deals, that Elon's going to build a much bigger, Starlink's going to build, or SpaceX's going to build a much bigger business there. And then you have this call option on space that would drop the price even further. The one thing we haven't talked about is their model, right? And I find this surprising, right? Six months ago, X.ai was competing, they were doing pretty well, but they've done something dramatic over the course of the past couple of months, which is they bought Cursor, right?

• 29:41Brad Gerstner?

  Cursor is 700, 800 people, was already doing incredibly well from a revenue perspective. Our own projections were that they could exit this year at up to $10 billion of revenue. So they were growing very fast, one of the leading coding agents. But they also had this incredible team with the potential, right, to really build a frontier level model, but they were compute constrained. So all of a sudden, they get bought by X. X has massive compute that they can now train on. And when I think about the revenue in AI, like if I look at that line item in the models, having it go from $10 billion to 150 billion, yes, a lot of that will be the CoreWeave-type business that they have.

• 30:23Brad Gerstner?

  But the question is, how much of that is going to be the core X.ai business that's really powered by the new team from Cursor? So any thoughts on that, Kevin?

• 30:32Speaker 01

  Right now, so Composer 2.5 was Pareto dominant 12 days ago. It was trained on the KIMI K2.5 base model. Now, what's happening is the Grok 4.3 1.5 trillion parameter model is training. One would hypothesize, based on scaling laws, that that might be a better base model. And then the Cursor data is being injected into the pre-training process, not just reinforcement learning. And we'll see, and I think that is going to be a very important data point when that comes out. And I just think everyone should keep in mind that once you are at multiple places on that Pareto curve, if you have compute, you can scale really rapidly.

• 31:14Brad Gerstner?

  You know, that to me is, if I had to say the one piece that's being lost in the story, right, like it's easy for everybody to get excited about the deals with Anthropic because you can put your hands around that, you know how much revenue it is. I see debate about, you know, the 90-day termination and how long they'll last and what multiple do you put on those revenues? But I think the thing that's getting lost is, I think they've dramatically advanced their capability when it comes to building a frontier model. People outside Silicon Valley may not know, you know, Michael and the team at Cursor as well. This is an extraordinary team that he just downloaded, right, into SpaceX. SpaceX was already building good models. And what they have is, they have this way to monetize compute

• 31:56Brad Gerstner?

  that gives you this call option, that you can pull all that compute in-house, right, to train a model and then to run the model. I suspect if there's an upside surprise, if we went around the table, I'd say this is the place that's getting the least amount of attention and could have the biggest upside surprise. Any thoughts, Clark, on what you think is being overlooked or areas that you think are misunderstood about the business today?

• 32:20Clark Tang?

  I would say, I would say what the last few weeks have proven is that Elon...

• 32:28Clark Tang?

  Their team can stand up all this compute. Actually, if you just went back one and a half years, they were behind in the race to stand up compute. They didn't have that many H100s. They brought in Colossus. Then they brought in Colossus 2 at a scale much larger than anyone else. And now, as we gear for Vera Rubin, from a lot of my conversations, it looks like they've secured maybe up to 20% of Vera Rubin capacity.

• 32:56Brad Gerstner?

  It's extraordinary.

• 32:57Clark Tang?

  Especially in the early days of when these chips are very scarce, that they are going to have a lead on all of this because people think that they can stand up this compute better. So I think what the last few weeks have actually shown is that Elon will take a shot at hitting the frontier. But if for whatever reason, they have over procured some capacity, this is a very scarce asset that they have shown that they can monetize at actually best-in-class margins and payback periods.

• 33:36Brad Gerstner?

  The irony is, you and I have been doing this long enough to know. I mean, that's why Bezos built AWS, right? He had to build capacity for Black Friday.

• 33:45Clark Tang?

  Yeah.

• 33:46Brad Gerstner?

  Right? But then the rest of the year, he sat on all this capacity they had to build and he figured out a really incredible way to monetize this. And by the way, investors at the time, 2009, 2010, when he was building out the capability around AWS, hated it.

• 33:59Clark Tang?

  Of course.

• 33:59Brad Gerstner?

  Because he was consuming all that free cash flow. Meanwhile, he was digging the biggest gold mine in the history of the world.

• 34:06Speaker 01

  One of the biggest.

• 34:06Brad Gerstner?

  Among them. Among them. At the time was probably the biggest.

• 34:10Speaker 01

  Yeah, Google search might want to have a discussion. By the way, I do think it is important. Grok 4.3, I think the cursor, if they acquire it, that may end up being very important. But Grok 4.3 was on the Pareto frontier and as of 10 or 12 days ago, and these things move fast, most intelligent 500 billion parameter model in the world. And they were on the frontier and there are four companies on the frontier. XAI, SpaceX AI, Google One with Gemini 3.1 Pro. And then the rest of it was dominated by Anthropic and OpenAI. But they were on the parade of frontier and now we'll see what they do with Cursor.

• 34:47Brad Gerstner?

  I want to come back to that in a second.

• 34:49Speaker 01

  By the way, man, I want to ask you some questions.

• 34:50Brad Gerstner?

  Okay, go, go, go.

• 34:51Speaker 01

  What do you think? So you think the biggest source of potential upside is the model?

• 34:54Brad Gerstner?

  Yes.

• 34:55Speaker 01

  What do you think?

• 34:56Brad Gerstner?

  I think that's the thing that's least talked about.

• 34:58Speaker 01

  Least talked about.

• 34:59Brad Gerstner?

  Right, and so, listen. When I look at the Bull Bear case on the IPO, right? The bears are looking at last year's revenue. Say it was $18 billion. And they're looking at the forecast from the banks of $160 billion, you know, three years from now. And they're saying, listen, not many companies in the history of the world have basically 8x their revenue over three to four years, right? So that's where, you know, I think, and people get nervous about devaluation. When I look at this, again, when you break it down as an analyst, first principles, part by part, which is what I tried to do here, right? When you look at Starlink, it looks totally doable. When I look at what they're building in AI compute terrestrially, looks totally doable over the course of the next three years.

• 35:41Brad Gerstner?

  When I look at the model itself, after the acquisition of Cursor, you know, combining those things around the compute they have, that looks to me like it could be an upside surprise. So I would say that I think that, you know, in the IPO, but I think when you look back three years from now, there's a decent chance that everybody's like, oh my God, that was super obvious, right? Even though today, all of these things have risk associated. Back to where we started, I'm not, you know, none of us are here to pump the IPO at 1.77 trillion. It's really to just break it down as we do inside our shop and to say, what is that distribution of future probabilities? What's the probability that is higher from here? What's the prob? And I think we're all pretty AI-pilled. And if you're AI-pilled, that means we got to build a lot more compute than the world thinks.

• 36:26Brad Gerstner?

  And that these models are going to be a lot more valuable than people think. You combine that with their core business, I don't know another entrepreneur or another business that's a better bet on the future. Right, than SpaceX. And so I think for most institutional investors, it's a must buy, a must own. It's set it and forget it, right, in order to have a real bet on both the space and the AI future.

• 36:49Speaker 01

  From your lips to God's ears.

• 36:50Brad Gerstner?

  I mean, listen, again, I think that you're gonna have to wait, but you know, we had this chart last week, right, that came out, everybody was sending around Twitter, conveniently timed, and you know, it's like, it shows the average max drawdown post-IPO for like 20 companies, from Facebook, Twitter, Alibaba, Shopify, is, you know, over 50%. And so maybe that, again, we'll end this section here, you know, Gavin, you and I have been doing this a long time. We know it's going to be bouncy around the IPO. You know, how do you, as a manager, try to manage that? Do you try to trade around the IPO? Do you set it and forget it? I would say from an altimeter perspective, what we tend to do is we take

• 37:34Brad Gerstner?

  a base position that we set and forget, right, and then we may size up or size down, depending upon how the market reacts in, you know, in a particular moment. But any thoughts on this chart, or, you know, how people, you guys are thinking about it in particular? You obviously own a lot going into it.

• 37:54Speaker 01

  First, agree with absolutely everything you said, and I actually think about it the same way, set it and forget it. You've talked about you have ballast, you move around, and you move the ballast to one side of the ship, when you want the ship to lean into the wind to go faster, and you move it to the other side, when you don't want the ship to tip over. I think that's a great analogy. Think about all important companies in the portfolio the same way, so 100% agree. I mean, this chart is a bummer. What I would say is, you know, there's data on IPOs, but what I would just say is, this is a really unprecedented situation.

• 38:24Brad Gerstner?

  Yes.

• 38:24Speaker 01

  We've never had an IPO this big. We've never had an IPO that's gonna go into an index this quickly. We simply do not know how much selling there will be from investors. I would hazard a guess, I mean, I don't know, but Elon, I don't think he needs liquidity, and I think he owns, what does he own, Foxy?

• 38:50Brad Gerstner?

  Fifty percentish.

• 38:51Speaker 01

  Fifty percent of the company.

• 38:52Brad Gerstner?

  And by the way, he's locked up for 365 days.

• 38:54Speaker 01

  Yes, exactly.

• 38:56Brad Gerstner?

  So we know he's not selling, right?

• 38:57Speaker 01

  So I just think it's an unprecedented situation. And the right answer is, I don't know what's going to happen in the short term. And the right answer that I would just encourage every investor making their own decision is to just think exactly the way you articulated it. We have these different levers. We have these different variables. Think about each one of them from first principles. Make your own decision. Do your own due diligence. Be thoughtful. But there are a lot of variables here. And then it is a little funny to me that it was 100 times trailing TTM revenue. Well, after the deals they signed, I think it's at 39 times.

• 39:32Brad Gerstner?

  That can change fast.

• 39:33Speaker 01

  So they added $29 billion in a month.

• 39:36Brad Gerstner?

  Yes, no.

• 39:36Speaker 01

  By the way, have you ever seen that happen?

• 39:39Brad Gerstner?

  Never, never. And it just goes to show, first, Elon is not only a great engineer. He and Gwen and the team are great at business.

• 39:50Speaker 01

  And Brett?

• 39:50Brad Gerstner?

  They understand what needs to be done to raise the capital, to get to the next phase. They have a long-term mission in the business. And so to me, again, what we saw in the course of the last few weeks with Cursor, what we saw with these deals that they cut, I don't know that any of the Mag7 could have moved that quickly to adjust the business that they did. It's exceptionally entrepreneurial at scale, which we very rarely see in businesses. Two other things I would just say.

• 40:17Speaker 01

  Can I give you a hug, Brett?

• 40:19Brad Gerstner?

  Two other things I would just say. Number one is, people talk a lot about the total amount of capital being raised. If you add up the capital here, right, for Anthropic, what they may raise, what OpenAI may raise, what SpaceX may raise, let's call it $250 billion. That's 1% of the Mag7. Okay, it's 1% of the Mag7. And we will as well. You know, like that to me is like a bet on the future that we all believe in. And so if I said, where are we out of consensus? What is our variant perception? We actually think it's going to be bigger, faster, and we've thought that for a couple of years. So first, it's only 1% of the Mag7 market cap. And then you referenced it. The amount of selling, I've got a chart we'll post here.

• 41:02Brad Gerstner?

  This is, you know, the dribbles share release for SpaceX shareholders. You know, so there's not a lot that can be released up until after the first earnings. This, we saw this in the Cerebras IPO. There's a version of it here in this IPO. And so again, I think the banks have been thoughtful here, knowing that this is a very large IPO. And I'm not saying they won't trade down. Like, there's possibility, you know, these things trade down. But again, for me, telescope out. Is there any company better positioned as a bet on the future? I think what they've shown over the course of the last five weeks, they're probably number one, but let's move on.

• 41:39Speaker 01

  No, no, can I just say one thing about the employees? I think another thing that's unprecedented here is the employees and, to a large degree, the investors here have had liquidity every six months.

• 41:49Brad Gerstner?

  Exactly, exactly.

• 41:50Speaker 01

  For like the last 10 years.

• 41:51Brad Gerstner?

  Yes.

• 41:52Speaker 01

  So if you're a SpaceX employee or former employee and you wanted to sell, you've had, whatever that is, close to 20 chances. And it is a matter of historical record that large investors have been able to sell. So I would think a lot of the people, they've chosen to own it. Now there's a new valuation and we'll see what they do, but just, this is utterly unprecedented and we'll see.

• 42:17Brad Gerstner?

  Yeah, no, it's a great point. We've in fact called these companies quasi-public. You and I both know that SpaceX and I put Anthropic in this category as well, Databricks in this category, these things in many ways have been more liquid over the course of the past three years than some public biotech companies we know.

• 42:34Speaker 01

  Absolutely.

• 42:35Brad Gerstner?

  And so there's a continuum of liquidity here. We treat it as a binary, private versus public, but it's really about this continuum. You know, let's keep going on models, you know. Anthropic launched Fable 5, which you referenced yesterday, which is basically Mythos with some classifiers and safeguards around cyber and biology, chemistry and distillation. When those things get triggered, it fails back to Opus 4.8. You know, there's a Kparthy tweet about this yesterday. He said, you know, it's SODA and all the benchmarks, but what really makes... It's special is long running tasks, okay? You retweeted our good friend, you know, Noam Brown.

• 43:17Brad Gerstner?

  You know, ChatGPT 5.5 also exhibited these capabilities. You know, it led Noam right to suggest that it's not very relevant to do these snapshot benchmarks anymore. Like the X axes has to be time or tokens or compute. Because we can solve most problems now if we just let these frontier models for a very long point in time. So Gavin, what is this new class of model, right? Fable, Fable 5, ChatGPT 5.5. What does it mean for the race in superintelligence? Who's up, who's down, who's still on the frontier? Give us your thoughts.

• 43:56Speaker 01

  I mean, it's hard to say that Anthropic's not up.

• 43:59Brad Gerstner?

  Yeah.

• 43:59Speaker 01

  Like after the revenue numbers they put up after the Fable 5 release and Mythos is evidently even better. But I just think that Noam Brown post from yesterday. Polnomial is so profound. And just the idea that we do not know how smart these models are. And we made.

• 44:19Brad Gerstner?

  Say more about that. Why don't we know how smart they are?

• 44:22Speaker 01

  Because nobody has run Mythos for a year continuously. And we may never know how smart each generation of models actually is or was. But because we don't have time to appropriately evaluate their intelligence before the next model comes out. I mean, this is a profound statement. And just, just imagine, okay? So I always say, like, when you think about FSD, just imagine a human being who never gets distracted, never gets tired, never talks on the phone in the car, never drinks and drives, never yells at their kids, never has to go to the backseat to give their baby a bottle. And like, of course, you would think that over time that is superior to humans who are distracted.

• 45:04Speaker 01

  I don't know how long, how long can you think deeply about one topic, Brad?

• 45:08Brad Gerstner?

  It's like an hour.

• 45:11Speaker 01

  Oh man, that makes me feel terrible. Because I think I could think deeply about one topic continuously before having a stray thought enter my mind for like maybe five minutes. Now I could come back to that. Imagine if Albert Einstein had been able, instead of, you know, maybe that, maybe, maybe he could think for three hours at a time. Clearly an exceptional intellect. But imagine Albert Einstein had just thought about fundamental physics 24 hours a day. He doesn't have to eat, he doesn't have to sleep, he doesn't have to relax, he doesn't drink.

• 45:44Brad Gerstner?

  And never gets old.

• 45:45Speaker 01

  Never gets old.

• 45:46Brad Gerstner?

  Never has diminution of intelligence.

• 45:48Speaker 01

  And he thought for one year. I mean, we might already, you know.

• 45:52Brad Gerstner?

  Have solved a lot of these intriguing problems.

• 45:55Speaker 01

  So, I just think that's an extraordinary thought and just my takeaway was, however bullish I was on compute before then, I'm just a lot more bullish.

• 46:06Brad Gerstner?

  Right, right, right. So, so, so that is a, you know, we saw when, that was probably what really unlocked Opus 4.6. It was the first really long-running model that could maintain that context, maintain that memory, solve some of these longer-running problems, right? For us, the signal was in January. We knew, we felt like that was a big moment, but then when you started to see the revenue go up, we knew that lots of people were voting independently, that that was a profound moment, that they became much, much more useful. So, but one of the things that, the consensus going into this year, right? So, the big question going into this year was, was the AI revenue going to show up?

• 46:50Brad Gerstner?

  Were we going to get to these thresholds of intelligence that caused enterprises and consumers to use them more? And I think the consensus at the time, at least on this podcast, the, the debate with my, with, with Bill, was that open source models, cheap tokens, were catching up on the frontier, that perhaps these models were beginning to asymptote, that people wouldn't really pay for premium tokens. And it seems to me that the evidence on the field, six months into the year, is just the opposite, right? That frontier tokens are capturing the vast majority of all the revenues. And that in fact, if you believe in the long-running capabilities and more compute

• 47:33Brad Gerstner?

  allows you to do that, they may actually be extending their lead, right, on some of these models that were built on distillation. So I just open it up to anyone around the table. What are your thoughts on whether or not, you know, have we challenged this thesis that cheap open-source tokens are going to always, you know, close the gap on these frontier models? Or are they extending their lead?

• 47:54Clark Tang?

  I think this debate, like this same debate has existed since the beginning of, since we started training these models to begin with, which was, hey, we're always kind of three, six months behind the frontier. But empirically, like, you can just see all of the revenue has actually just accrued at the frontier. And I think that's because every time we release the frontier, a whole new, like, slew of use cases

• 48:21Brad Gerstner?

  Right.

• 48:22Clark Tang?

  That previously we could have never tackled before.

• 48:25Brad Gerstner?

  Like coding.

• 48:26Clark Tang?

  Like coding. But also just, you know, we've just been locked at our desks for the last day, just, you know, hammering Claude, because, you know, it's just fascinating the things that now we can do with Fable 5 that we just couldn't do with Opus 48 just a day before.

• 48:41Brad Gerstner?

  So what are some of those things, man? I'm curious.

• 48:43Clark Tang?

  So I think it's really, really good at multi-agent orchestration now. So Anthropic released a blog post about like different agent, six different agent, like, orchestration patterns that, you know, they've talked about. But really, like, once you start being able to manage all these agents, the harness and the model itself is being arched with one another. They're actually being, you know, fused closer and closer together, but the model can understand the extent of your work. So, you know, one of the things, for instance, is I just threw in, like, seven of our models and just said, okay, like, I want to create a master view of, like, my beliefs, given

• 49:26Clark Tang?

  all of these assumptions of all these companies' TSMC capacity, like, and then produce me a report on all this stuff. And, you know, the model is able to reason through all of our assumptions. Like actually, if you believe this, this thing is inconsistent with this.

• 49:41Brad Gerstner?

  What are the contradictions exactly?

• 49:42Clark Tang?

  Yeah, it was fascinating. And, you know, before, we'd never do that. But now, you know, I think we're just step one into multi-agent orchestration. We're gonna do this even further. And that's one example. I've also dumped all my notes into it. And it's reason across all my notes from the last three years and said, you know, here are some of your ideas that were consistent. Here are like, you know, the sources that were actually the highest signal to what actually played out. You know, and then it was actually just super fascinating what you could do. And we've just blown through our limits.

• 50:16Brad Gerstner?

  It's unlocking all this. I mean, like, they gave examples yesterday in the release. Anthropic did, you know, 50 million line Ruby code base at Stripe that was, you know, refactored in a day versus many weeks with many people. You think about where this is impacting biology and life sciences, just across the spectrum. And to me, it really gets back to this fundamental point. Number one, if you believe this to be true about long-running agents, then we're going to produce and consume more tokens in the future as far as the eye can see. So the world, this gets me back to, you know, terra fab and space orbital and all this. Because we may, in fact, unlock real thresholds of intelligence, but we're going to have to let these horses run for a long time in order to get there.

• 51:00Speaker 01

  Yeah, I would just say two things can be true. The majority of economic value may continue to accrue to the frontier. And man, has it ever accrued to the frontier thus far. And for sure, the first six months this year. But the majority of tokens consumed to the world may be open source.

• 51:17Brad Gerstner?

  And they are today.

• 51:18Speaker 01

  Yes. And I think that this current state is likely to persist. Harvey had a great blog post that they put out on X. And they used, and it's just amazing how everything gets out of date like in five days, you know? But they use their own proprietary legal data to do reinforcement learning and supervised fine tuning with fireworks on an open source model. And then they use a router, and a router being something that picks which model you send which query to and which model you use to check which model. And they got better outcomes than OPUS 4, either 4.7 or 4.8 at a lower cost.

• 51:57Brad Gerstner?

  Yes.

• 51:57Speaker 01

  And I think that is the future. And the reality is, they were still consuming a lot of OPUS, but a majority of the tokens they were processing probably were in their own open source.

• 52:07Brad Gerstner?

  We hear the same thing. We did, we did, we did an enterprise survey that we'll post of 300 companies. Which ones were optimizing? So these are folks who are kind of looking at model routing and saying, we're going to send certain tokens over here. Which ones are thinking about optimizing? Which ones aren't optimizing yet? And then what is their expected use of frontier model tokens? Right? And they're all expecting to consume a lot more, even though they're already in the process of optimizing. Think of it in the context of JP Morgan. If they're doing some back of the house stuff, right, on customer service or whatever, they may very well use an open source model. Now, I think they're loathe to use Chinese open source models, so they're waiting on kind of US open source models to, you know, be able to really deliver the bang that they

• 52:52Brad Gerstner?

  need. But my hunch is for these enterprises, a lot of that back of the house stuff will get rooted there. That will probably be a majority of the tokens, but I think the really high value stuff, you know, coding as an example, they don't want to write second tier code. I think the vast majority of that will continue to be on the frontier.

• 53:08Speaker 01

  You don't need Albert Einstein to book you a trip. You don't need Albert Einstein to do KYC.

• 53:14Brad Gerstner?

  But this is the debate we had literally at this table two years ago. However, if you just look at the revenue curves, right, what folks concluded when they said that, they said, therefore, the frontier models will not accrue most of the revenue. And what we're seeing right now, it's 90% of the revenue.

• 53:32Speaker 01

  That has been decisively wrong. Probably more than 90%, and it may continue to be decisively wrong. Frontier might be 90% of the economic value.

• 53:39Brad Gerstner?

  Right.

• 53:40Speaker 01

  Open source might be 80% of tokens. Something that I think is very important on open source is that, you know, I think there's this belief that it's bearish for AI.

• 53:50Brad Gerstner?

  It's actually, it may be bearish for the frontier models. There's that bear case you talked about. It's actually really bullish for compute and hardware. Because if the frontier models are capturing less of the margin, then you're going to spend more on compute. So the better open source does, the better it is for compute providers.

• 54:08Clark Tang?

  I will say, there is a very, I would say between spending time in the heart of like the West, Silicon Valley, and also spending time in Asia, there is like a very big, like a deep-seated belief in one versus the other, which is like if you spend a lot of time here, it's like all closed source, cloud, every, all traffic is going to go by way of this direction. And then you spend time in Asia, you know, the overwhelming belief is that we're going to find the right model to the right workload and we're not going to overspend. And I think, you know, I would say, I would say the next year is probably going to be the most indicative of which way this falls.

• 54:53Clark Tang?

  Because I think, I think the reason why closed source models have captured so much of the value is because the models actually get the intention and actually carry through the work. And this was the first year where we actually had agents that actually carried out user intention from just answering a chatbot request to actually producing useful work.

• 55:16Brad Gerstner?

  Right.

• 55:17Clark Tang?

  Now, the level of this intelligence has scaled so rapidly and we continue to push against like the most economically valuable tasks, which are coding and finance and all these like knowledge work tasks. But like for the long tail of tasks, if open source continues to maintain a six-month lag, we might actually see a lot more open source used for, you know, our everyday tasks that we might actually.

• 55:42Brad Gerstner?

  And that's basically Jensen's argument, right? Jensen's argument is you're going to have model routing and we're just in a moment in time where the frontier models gained the advantage, can do long-running tasks, and the open source models couldn't do it very well. And so they're accruing all of the value. But as soon as the open source models can do the long-running tasks as well, which is not far away, that they too will grab a bunch, a bunch of this revenue. Are you investors in reflection?

• 56:06Speaker 01

  I'm not.

• 56:07Brad Gerstner?

  Okay, nor are we, but I'm very impressed by Misha and the team and what they're doing. I very much want a Frontier Open Source US lab to win. We know that, you know, I heard you say recently, and I believe it to be true, Nvidia, any day that they really wanted to, right? They already have some great open source models. They could absolutely build a Frontier Open Source model whenever they chose to do it. And so it's not a question in my mind as to whether or not the US is going to have a Frontier Open Source model, it's just a question about timing. And then, like, at that point in time, is the, you know, let's assume they get these long-running capabilities. Have the Frontier labs now achieved something yet again

• 56:49Brad Gerstner?

  that allows them to keep the stranglehold on the revenues?

• 56:53Speaker 01

  Yeah, and I just think it's, if you're, wow, that's a cute ASIC you've built there. That is so cute.

• 57:00Brad Gerstner?

  Right.

• 57:00Speaker 01

  How would you like open source to join the Frontier?

• 57:04Brad Gerstner?

  Right.

• 57:04Speaker 01

  How would you like that? How do you like the apples? So, I mean, I'm not sure that's the explicit calculation, but I do think Jensen is...

• 57:11Brad Gerstner?

  Say more, just double-click on that for everybody at home.

• 57:13Speaker 01

  Yeah.

• 57:14Brad Gerstner?

  If you were, if they were to put an open source model out there, how does that impact the ASIC landscape?

• 57:19Speaker 01

  Well, you might not have the revenue to fund, to fund that, the revenue or the margins to fund that ASIC. And I do think Nvidia is highly likely to be the world's dominant provider of open source AI. And I do think Jensen will bring open source. You know, right now, it's, whatever, six months behind the Frontier.

• 57:41Brad Gerstner?

  Yeah.

• 57:42Speaker 01

  We might see it creep closer and closer and closer. And I do think Jensen has a big business decision. I see this, you know, chart here, so let's, you know, chop it up about Nvidia, as you say. But if all of his customers are going to compete with him.

• 57:58Brad Gerstner?

  Yes.

• 57:59Speaker 01

  Then why not compete with his customers? And we have all these Neo clouds, so that's a cloud computing business that can compete with all these cloud computing businesses. He has his own models that are really, really good. Nematron 3 or 3.1 was actually really, really cool from a compute efficiency perspective. And he's always careful to release small models.

• 58:18Brad Gerstner?

  Right.

• 58:19Speaker 01

  So as to not tread on Anthropic, OpenAI, Google's toes. But I do think that is a choice he is making. And just, you know, if, if the economics change.

• 58:30Brad Gerstner?

  Right.

• 58:31Speaker 01

  I think Nvidia can join the frontier and become one of the world's largest cloud computing companies much faster than people think.

• 58:37Brad Gerstner?

  Interesting, interesting. Clark, walk us through this, this chart.

• 58:41Clark Tang?

  Yeah, so, so I think one of the takeaways from spending time in Taiwan was, there, there is certainly a lot of excitement around the, the next wave of ASICs. But I think, I think it's like a very clear moment now where Nvidia, it used to be an argument of Nvidia versus ASICs, one or the other, and, you know, total domination or one or the other. Now, I think it increasingly, every year, every, everyone assumed that Nvidia was going to lose share dramatically on a revenue scale, on a gigawatt scale, on a unit scale. And actually, if you actually look at the last few years, you know, they've actually maintained their share very, very handsomely.

• 59:21Clark Tang?

  Actually, if you accounted for the fact that Anthropic was not really using Nvidia, they probably actually gained share again, if not for in 25, 26. So I think, I think what was very interesting, though, was a new class of accelerators or ASICs, MediaTek with their, with their new V8T versus, you know, Broadcom's V8I for TPUs, actually was a big topic of discussion. And, you know, I, I think for ASICs, the argument now is that more and more will look custom to the actual workload. And that is like one vector that people are moving in versus Nvidia now is.

• 1:00:06Clark Tang?

  Has kind of shown itself as the predominant provider of compute to a lot of the world. And for internal workloads, perhaps it will go more and more custom and more and more down the stack. And I remember just one year ago, when it was kind of a Broadcom or Nvidia battle. It seems there's a lot more nuance now to what type of accelerators will fit which workloads and fit which customers and fit which business models. And yeah, I thought that was a new topic.

• 1:00:43Speaker 01

  This is actually

• 1:00:44Clark Tang?

  New realization, though. I think we all kind of shared this view for a long time.

• 1:00:47Speaker 01

  Yeah, I was just shocked. I mean, I'm out here. I did a board meeting with one of our companies. And just, you know, their biggest, one thing they emphasized is we thought the world would be have be consuming less Nvidia than it is. And if anything, Nvidia is accelerating and they just continue to out execute their competitors. And I think a lot of people are indexing to this OpenAI gigawatt. And Nvidia has 10, Broadcom has 10.

• 1:01:17Speaker 01

  Who has 6? AMD. AMD has 6 and they have warrants. And then Cerebras, our shared portfolio company, has a gigawatt. And I just, that is what's on paper.

• 1:01:29Clark Tang?

  Right.

• 1:01:30Speaker 01

  What actually gets deployed? Let's see. I will be very surprised if, you know, that 10 out of 27, what's that math? Let's see who's best at math. What percentage market share is that?

• 1:01:40Clark Tang?

  30%.

• 1:01:41Speaker 01

  Yeah. I'll be very surprised if that is where they land. I think that is an extremely unlikely outcome. And especially as long as we are in a watt constrained world. If you can get more tokens per watt, which is literally revenue with Nvidia than a lot of alternatives. Just if you build your factory with another chip, you may save some money, but you're going to have less revenue and the margins may be lower. And that's a point that Jensen keeps hammering and I think is really important. And by the way, credit where credit is due, the most important, the most, one of the most surprising things to me in this ASIC landscape. I would say Meta and Microsoft have been probably disappointing.

• 1:02:21Brad Gerstner?

  Yes.

• 1:02:22Speaker 01

  You know who made a good ASIC?

• 1:02:23Brad Gerstner?

  Yes.

• 1:02:24Speaker 01

  Well, I know you know.

• 1:02:24Brad Gerstner?

  Yes.

• 1:02:25Speaker 01

  Jalapeno.

• 1:02:26Brad Gerstner?

  Yeah, exactly.

• 1:02:26Speaker 01

  From OpenAI.

• 1:02:27Brad Gerstner?

  Yeah.

• 1:02:27Speaker 01

  They made a great chip.

• 1:02:28Brad Gerstner?

  Yes.

• 1:02:29Speaker 01

  Now, unfortunately, it needs to run at a much lower temperature than the Nvidia GPUs, which means you need to spend more money on cooling and that consumes more power. They made a great chip.

• 1:02:37Brad Gerstner?

  We can, I mean, I think the question there and the question for everybody is going to be, is that the highest and best use of your time? Right? Like, I, you know, I tend to think that the frontier companies, like, there's this belief that they gotta be vertically integrated. But if you believe like I do that the race to superintelligence, particularly as we get these recursive loops working, may be over in the next two to three years, then I think focus, focus, focus, focus. You exist to build the best intelligence in the world and to deliver the best intelligence in the world, and you, that means you have to have all the revenue. Because if you want to build out the compute that's going to be required to continue to push the frontier, you have to have the revenue in order to support it. So I think that, you know, subject to the focus question, I think they certainly did.

• 1:03:21Brad Gerstner?

  This all brings me back to kind of a reality check, though. You know, we just got done talking about test time compute, inference time compute, long-running agents. This is really the thing that's unlocked the revenue this year. It all pushes us in the direction of more CapEx. Google just raised $80 billion, right? We've now taken the Mag 5 or Mag 7 free cash flow, you know, down dramatically, 80% from just a few years ago. And Morgan Stanley, you've got this chart in front of you, upped their 2027 CapEx forecast from $950 billion to 1.1 trillion. I mean, we were talking about this with Jensen. That was his forecast two years ago. You know, obviously, this doesn't even include SpaceX, CoreWeave, et cetera.

• 1:04:05Brad Gerstner?

  So I think the number on 2027 is likely closer to 1.5 trillion. And if we compare this to the total incremental inference revenue. So the thing that the market gets worried about, you know, back to my Sam Altman podcast, you know, in October of last year. Can we really afford to spend 1.5 trillion of CapEx a year? If we're only generating X amount in inference revenue? The thing I think that lit the fuse this year was Anthropic showed up in a major way with revenue. Right? And so we have, you know, the AI lab revenue, everybody combined at around $300 billion next year. Right? So can't, you know, and go roll that out to 2027. Or that is 2027, 300 billion.

• 1:04:50Brad Gerstner?

  So we're spending 1.5 trillion of CapEx on 300 billion of inference revenue. Does that math math for you? And what would cause you, you know, to get more nervous again about our ability to continue to make these investments? Because the second we get nervous about it, the entire semi complex is going to come down a lot.

• 1:05:08Speaker 01

  Well, what do you think the gross margins are on that 300 billion?

• 1:05:11Brad Gerstner?

  Yeah, let's call it 50%.

• 1:05:13Speaker 01

  I would guess they're probably a little bit higher than that. I might say 60 or 70. But I mean, that math starts to math. And what I would just say is I think that 300 billion is low, man.

• 1:05:24Brad Gerstner?

  Yeah.

• 1:05:24Speaker 01

  I just think it's low.

• 1:05:25Brad Gerstner?

  From your mouth.

• 1:05:26Speaker 01

  Yeah, exactly. I think, I think we end this year well over 200 billion in inference revenue, well over. And so I think the math really maths. And I do think we have to give Jensen, our friend, some credit because he said some things that seemed outlandish.

• 1:05:43Brad Gerstner?

  Right.

• 1:05:44Speaker 01

  And he was conservative. He was low. He said a trillion two years ago. And I mean, he was really low.

• 1:05:50Brad Gerstner?

  Right.

• 1:05:51Speaker 01

  And so like, let's give the guy some credit and think about what he is saying right now.

• 1:05:55Brad Gerstner?

  For sure, for sure. And listen, I would say consistently, Elon's been taking the over.

• 1:06:02Brad Gerstner?

  Sundar's been taking the over. Sam, Dario, you know, Dario did the podcast with Dwarkish. When he was talking about country geniuses in the data center, he said that will be here by 2028. He said revenues will go into the low hundreds of billions by 2028. So let's call that, you know, 300, 400 billion of revenue by 2028. And he said that a while ago now, so he may even be revising up his number. And he said, it's hard for me to see that there won't be trillions of dollars in revenue before 2030. And if you're on that revenue trajectory, if we're on a trajectory to 200 by the end of this year, let's call it 400, 500 by next year, and a path to trillion plus by 2029, then the math maths.

• 1:06:46Speaker 01

  And we got to keep in mind that half of the spending

• 1:06:48Brad Gerstner?

  Right.

• 1:06:49Speaker 01

  is there to, you know, for training, maybe a little less than half. What is it, Foxy?

• 1:06:53Speaker 03

  It's probably, it depends on the lab, but it's, I would say it's increasingly less than half.

• 1:06:56Speaker 01

  Yes. Okay, so we'll call it 35% is spending that's not revenue generating, that is going to kind of make the next model. So I think the math maths.

• 1:07:04Brad Gerstner?

  Right.

• 1:07:04Speaker 01

  And there's still this prisoner's dilemma where if you opt out, that may be an existential decision.

• 1:07:09Brad Gerstner?

  For sure.

• 1:07:09Speaker 03

  And I think like coming into this year, going back to this kind of what narratives were violated, you know, I think into this year, everyone expected token pricing, the price of compute, it's all deflationary. And it will be kind of a smooth line deflationary over time. But I think this year what we've seen is the opposite. And, you know, it's all comes back to supply and demand. The demand side of the equation seems to be far outstripping the supply. Right. And I think you look at the deals signed by SpaceX and others, the monetization rates per watt are increasing. And look, that is on a pretty nascent, small base of users, right?

• 1:07:50Speaker 03

  Like Alex at WellRock, he has this great way to frame it. Less than 0.2% of people on earth are actually using AI in an agentic way.

• 1:08:01Speaker 01

  Right.

• 1:08:01Speaker 03

  Right? Like, I'm not a technical person, but I'm consuming 500 CPU cores in a VM instance, five GPUs 24/7. I mean, if you draw that out to any meaningful percentage of the population, I mean, we're going to be in, you know, this kind of shortage environment, maybe for some time. So I think that is all positive for this ROI question.

• 1:08:23Speaker 01

  Man, Foxy, 100 to 1 CPU to GPU ratio? Kind of a strategic workflow. Of course. You said course. Fine, fine.

• 1:08:34Speaker 03

  I'm being smart with my spend.

• 1:08:35Speaker 01

  Good, good, good. Excellent.

• 1:08:37Clark Tang?

  I will say also that ratio of 300 to 1 point, you know, call it 1.2, 1.5. There is also a rate that now physically we can only expand how much we can produce and how much we can actually increase that spend by. Whereas we're seeing the opposite right now on the willingness to pay for these tokens. And actually, like when the willingness to pay for these, when the monetization per gigawatt is actually increasing from, you know, call it like 20, 20 billion in the best of cases for, at the beginning of the year to now like 30 to even pushing 40.

• 1:09:17Speaker 03

  Per gigawatt.

• 1:09:18Clark Tang?

  Per gigawatt. All of that is, it's a very heavy fixed cost base, but all of that is like pure margin flow through now. And you're actually, you know, as we scale, like the willingness to pay for all of this and now that all of this stipulated by like, you know, everything we're talking about of like how much is open source versus not and all of these different flows. But really like as we're climbing this curve, you know, the revenue is, might actually outstrip our fixed cost base by a significant amount. And I think that's why all the labs are pushing, you know, the gas to the pedals because they all, they all see like within, if we continue this curve within like three years, you know, we're just going to be so short on all the compute.

• 1:10:01Speaker 01

  This is a great, I'm sorry, but I mean, like, it's a great point. Like you thought you were getting, when you made these decisions. In November of 2025, you thought you were getting a certain return. You may be getting triple that return today.

• 1:10:16Brad Gerstner?

  Atropik, no way, no way did they think they were going to be anywhere close to break even.

• 1:10:21Speaker 01

  Yeah.

• 1:10:21Brad Gerstner?

  Right? In this part of the curve. And the reason, like I called it accidental profitability that, you know, people have been talking about that because they want to spend a lot more money on compute. They've just had a hard time doing it. Now maybe with SpaceX, you know, they could take some of those dollars and go spend them other places. But that to me is, you know, a fundamental change. The first argument against the Frontier Labs was they'll never generate revenue. Okay, and then we that got blown up. Then it was like, even if they generate revenue, it'll be really shitty gross margins, and they'll never be able to get make money. And then kind of that that's blown up. And and you know, I think now, you know, people are falling back and they're saying, well, they're overcharging. This is token maxing. My good friend, you know, Chamath said, there's no ROI on any of this spend.

• 1:11:05Brad Gerstner?

  It's all this token maxing. My best evidence for why we all know, of course, when somebody puts on this much spend like at altimeter, we're not optimally spending every single dollar. But the question is, why are millions of independent businesses, small, medium and large, why are millions of consumers all choosing to do the same thing? They're not dumb. These are, you know, rational economic actors that are all simultaneously saying, I want to do this because it makes my life better. It makes my business better, etc. To me, that is the best evidence as to why I think this revenue can continue.

• 1:11:39Speaker 03

  Yeah, and Clark, I think like the point you made is dead on because I mean, you want to own asset heavy businesses in inflationary environments and token pricing is going up and supply demand is tightening. So totally agree.

• 1:11:51Brad Gerstner?

  Um, you know, as we begin to find our way to the exit ramp and rap here, one of the things I, you know, you and I've been doing this for a long time, Gavin, a couple of decades. You may even sketch longer than me, even though I'm a little bit older than you. You know, we have, I always like to do a market check because I find a lot of time that analysts come on these things and they talk their, you know, talk their book and, you know, there are a lot of people who listen to these things, retail investors and others. And it's just kind of like, what do we really think? And so I always characterize as kind of small, medium and large, like what am I doing? Do I have small exposure on? Do I have medium exposure on? Do I have large exposure on? You know, and if you look at what's happened in the markets, semis ripped this year.

• 1:12:34Brad Gerstner?

  I mean, like, you've been doing this a long time. I don't, I've never seen it before, right? I've never seen, you know, the doubles and the triples across the board like we saw. But there's been huge dispersion, right, in the market. Internet's down 16%. Software's down 8% on the year. You know, SPY and and NASDAQ are up, but really up because of their components that are really Related to AI and compute. And so the market itself has kind of struggled. Meanwhile, if you were in the stuff that we were invested in, we've all done pretty well. I think, you know, I've said it a couple of times. I think if the anthropic revenue had not shown up this year, because that was the overhang on the market, I think the whole market could be down this year. Right, but that showed up.

• 1:13:16Brad Gerstner?

  You know, we just had these huge months in April and May. For us, you know, because prices came up so much, because I have some worry about, you know, geopolitics, the macro backdrop with, you know, with what's going on with inflation in the short run. And just like, you know, needing a little consolidation in this market to answer some of these questions, because now expectations are higher. You know, we dialed back from what I would call large for altimeter to something kind of like medium, small. Again, it's never all or nothing for us. It's like, what is the risk reward at a given price? And so we think this is a, you know, maybe going to be a period of consolidation on way to much higher heights. Curious just how you run the book, how you think about it like a portfolio manager.

• 1:14:01Speaker 01

  Very similarly, man, I always think stocks, the markets, I imagine them as runners. Okay. And like in 22, that runner had gone downhill. It had a lot of energy. Yeah, it was painful. It wasn't fun. But coming out of that, there was a lot of kind of pent up upside in the market. And, you know, the market, particularly the last two months, it has run up a very steep hill. And a lot of companies, semiconductor companies in particular, you know, ironically, you know, Nvidia and Broadcom, they have been laggards.

• 1:14:32Brad Gerstner?

  Total.

• 1:14:33Speaker 01

  And so, but a lot of these, like I do see a lot on X about finding the next bottleneck. I think that was the last game.

• 1:14:40Brad Gerstner?

  Yes, yes.

• 1:14:40Speaker 01

  That game is over.

• 1:14:42Brad Gerstner?

  Yes.

• 1:14:42Speaker 01

  You've had a lot of stocks that forget climbing a mountain or a hill.

• 1:14:46Brad Gerstner?

  Yes.

• 1:14:46Speaker 01

  They've gone straight up a cliff.

• 1:14:48Brad Gerstner?

  Yes.

• 1:14:48Speaker 01

  Okay.

• 1:14:49Brad Gerstner?

  Yes.

• 1:14:50Speaker 01

  They're tired.

• 1:14:51Brad Gerstner?

  Yeah.

• 1:14:51Speaker 01

  They need to rest.

• 1:14:52Brad Gerstner?

  Yeah.

• 1:14:52Speaker 01

  And we'll see, do they just rest at the top of that cliff they climbed?

• 1:14:56Brad Gerstner?

  Yeah.

• 1:14:57Speaker 01

  Do they hang out in their harness?

• 1:14:58Brad Gerstner?

  And we've seen in the last week, we've seen, you know,

• 1:15:00Speaker 01

  We've seen some.

• 1:15:01Brad Gerstner?

  Some retracement.

• 1:15:02Speaker 01

  Or do they need to go downhill for a bit?

• 1:15:03Brad Gerstner?

  Yeah.

• 1:15:04Speaker 01

  We'll see, but I'm thinking very similarly to you. But it is, and I think there's, you know, the market is seasonal.

• 1:15:10Brad Gerstner?

  Yes.

• 1:15:10Speaker 01

  I think there's real, real concerns around inflation and rates. What was CPI this morning?

• 1:15:16Brad Gerstner?

  It was 4.2. I think we added, core came in at like 0.2 versus 0.3, so a little bit better. But, you know, clearly we're above 4 again.

• 1:15:26Speaker 01

  Yeah.

• 1:15:26Brad Gerstner?

  And, and there's short-term pressure on, you know, core PCE, et cetera. And we have some unknown unknowns, but the market, I mean, if I had told you the fact pattern for this year, that we're going to be in a war with Iran, that, you know, oil was going to be at 100 bucks, that CPI was going to be creeping back up, that internet was going to be down 15%, software is going to be down 8%, you would have said, I want nothing to do with that market. Right?

• 1:15:50Speaker 01

  Yeah.

• 1:15:50Brad Gerstner?

  And here we are, the market's done pretty good in the stuff that we traffic in because the world underestimated AI revenues and underestimated the amount of compute that was going to be needed.

• 1:16:00Speaker 01

  It's, I'd just say, you know, we're heading into a seasonally weak period with all these fears.

• 1:16:04Brad Gerstner?

  Right.

• 1:16:05Speaker 01

  AI has actually been seasonal for the last three summers.

• 1:16:07Brad Gerstner?

  That's interesting.

• 1:16:08Speaker 01

  Yeah. Token consumption has kind of plateaued, slowed down, and that's because, you know, college kids are big AI consumers and they don't use as much AI. You know, hopefully they're all using it to learn and not cheat. But that may happen, it may not happen because of the gigantic AI.

• 1:16:22Brad Gerstner?

  My 15-year-old is building swarms of agents, building a SpaceX model. He's going to the SpaceX IPO with me at the exchange on Friday. But he had to build an AI model, using AI agents. He had to build a model, a DCF, before we go to the exchange. He is mesmerized. He is absolutely, and it's extraordinary what he's building.

• 1:16:41Speaker 01

  So he's one kid who's not using less compute in the summer.

• 1:16:44Brad Gerstner?

  He's burning it, he's burning it.

• 1:16:45Speaker 01

  Yeah, but you know, if token consumption plateaus, if open source takes some share, there's this Silicon Data Index that is showed, which is an index of kind of consumption and pricing. I think there may have been a little bit of a shift over the last two weeks to open source tokens that are cheaper. I think people looking at that data has bearish or not understanding it. But nonetheless, like, I just think there's reasons, you know, to look around, be careful, be thoughtful. I always assume a bullet is coming for me, head on a swivel. It's the bullet you don't see that gets you, so I'm trying to spin as fast as I can. But yeah, it's, the market may need to take a breather, but man, when I think about what Noam Brown said.

• 1:17:23Brad Gerstner?

  I know.

• 1:17:24Speaker 01

  And when I see the capabilities of Fable. It's just hard for me to get too bearish.

• 1:17:32Brad Gerstner?

  I mean, like, to me, and we got two, I think, of the most extraordinary guys of, you know, the next generation, you know, sitting in the room. We have, at Altimeter, we have deep admiration for the work that you guys do. I always appreciate when you send me a note about the work that we do and we publish. But for the guys who are newer to the business, they might think this is the way that it kind of always was, right? And like, this line, the steepening of the line of creative destruction, the steepening of the line of, you know, scale advantages. I always believed it was going to be true. I never thought it would be true at this rate. I went back last night.

• 1:18:14Brad Gerstner?

  In the last seven years, we've added 1 trillion of revenue to the MAG7 in the last seven years. Okay? To get to a trillion, to get to the first trillion, you know, took over 20 years. In the last seven, we added another trillion, and that added 17 trillion in market cap. That trillion dollars, okay? The forecast now that we're going to add another trillion of revenue in just three companies, SpaceX, Anthropic, and OpenAI, over the next four to five years. Okay? Like, not seven companies, three companies, and in half the time, right? And so, I would say that, you know, we are going to have bumps in the road.

• 1:18:55Brad Gerstner?

  I know that it's going to be like this, but we're going to higher highs because the size of the prize, this is going to transform 5%, 10%, 15% of global GDP, there is no doubt in my mind, and 10% of global GDP is 10 trillion dollars. It's an exciting future to be a part of. It's fun to do it with you guys. I think we're going to have to do our work to do the things to make sure America wins, and that we evolve the social contract, keep everybody, you know, lift the floor, take everybody with us on this ride. But it's a, it's a really exciting time to be doing what we're doing. It's fun to be doing it with you guys.

• 1:19:30Speaker 01

  Yeah, I just want to say, Brad, thanks for having us, and thank you for what you've done with the Trump accounts. I actually think it's super important for America, for the world, to give people an equity stake at a very young age. They will see it compound over their lifetimes. This is a great thing you've done for the world, so thank you. I'd echo all your comments, like, deep admiration for you, your team, gratitude for the collegiality and friendship between our firms. I know Clark and Foxy, they hang out like, all the time.

• 1:19:57Brad Gerstner?

  People think that, and there are people in our business who don't want to share anything. Our view is like, we open source it. But there are very few people who we actually call and ask their opinion. Because there are very few people who do the thousands of hours of work that we do. That are adding to that. And you do it, and we appreciate that. And you do as well, Gavin, we appreciate that. So with that love fest, let's call it a wrap. Thanks for being here.

• 1:20:21Speaker 03

  Thank you.