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

27:56There's no land in space, but there is space in space. 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 has, 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.

28:44Yeah, 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. 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 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.

29:22And 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 months, which is they bought Cursor, right? 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.

29:57But 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 core weave type business that they have. 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? Right now, so Composer 2.5 was Pareto dominant 12 days ago.

30:36It 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. You know, that to me is, if I had to say what the one piece that's being lost in the story, right?

31:20Like 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're 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 Michael and the team at Cursor as well. This is an extraordinary team that he just downloaded into SpaceX. SpaceX was already building good models.

31:52And what they have is they have this way to monetize compute that gives you this call option that you can pull all that compute in-house 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? I would say what the last few weeks have proven is that Elon, 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.

32:37They 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 Verirubin, from a lot of my conversations, it looks like they've secured maybe up to 20 % of Verirubin capacity, especially in the early days of when these chips are very scarce, that they're 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.

33:36The irony is, you and I have been doing this long enough to know, that's why Bezos built AWS. He had to build capacity for Black Friday. Yeah. 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. Of course. Because he was consuming all that free cash flow. Meanwhile, he was digging the biggest gold mine in the history of the world. One of the biggest. One of the biggest. Among them. Among them.

34:08At the time was probably the biggest. 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 Anthropica and OpenAge. But they were on the Pareto frontier, and now we'll see what they do with Cursor.

34:46I want to come back to that in a second. By the way, man, I want to ask you some questions. What do you think? So you think the biggest source of potential upside is the model? Yes. I think that's the thing that's least talked about. Least talked about. And so, listen, when I look at the bull bear case on the IPO, 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?

35:20So that's where, you know, I think people get nervous about the valuation. 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. 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?

36:01Even though today, all of these things have risk associated. Back to where we started. 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? And 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 than SpaceX.

36:38And so I think for most institutional investors, it's a must-buy, a must-own. It's set it and forget it in order to have a real bet on both the space and the AI future. From your lips to God's ears. I mean, listen, again, I think that you're going to have to wait. But we had this chart last week that came out. everybody was sending around Twitter, conveniently timed. And, you know, it's like 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've been doing this a long time.

37:18We 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 kind of and forget it? I would say from an altimeter perspective, what we tend to do is we take 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. it. But any thoughts on this chart or how people, you guys are thinking about it in particular? You obviously own a lot going into it. First, agree with absolutely everything you said. And I actually think about it the same way.

37:58Set 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, we 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. We've never had an IPO this big. We've never had an IPO that's going to go into an index this quickly.

38:32We 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? 50 %-ish. 50 % of the company. And by the way, he's locked up for 365 days. Yes, exactly. So we know he's not selling. 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.

39:13We 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. That can change fast. So they added$29 billion in a month. Yes. By the way, have you ever seen that happen? 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. They understand what needs to be done to raise the capital, to get to the next phase.

39:55They have a long-term mission in the business. And so to me, again, what we saw over the course of the last few weeks with Cursor, what we saw with these deals that they cut, But I don't know that any of the Mag-7 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. Can I give you a hug, Brad? 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 I may raise, what SpaceX may raise, let's call it$250 billion.

40:35That's 1 % of the Mag-7. Okay, it's 1 % of the Mag-7. 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 Mag-7 market cap. and then you referenced it, the amount of selling. I've got a chart we'll post here. This is, you know, the Dribbble's share release for SpaceX shareholders. You know, so there's not a lot that can be released up until after the first earnings.

41:13We saw this in the Cerebris 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 that won't trade down. Like, there's a 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. 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.

41:49Exactly. For like the last 10 years. 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. 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.

42:34Absolutely. 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. Let's keep going on models. You know, Anthropic Launch 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 Kaparthi tweet about this yesterday. He said, you know, it's SOTA on all the benchmarks, but what really makes it special is long-running tasks, okay? You retweeted our good friend, you know, Noam Brown.

43:16You 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-axis 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 and model, right? Fable 5, ChatGPT 5.5. What does it mean for the race and superintelligence? Who's up? Who's down? Who's still on the frontier? Give us your thoughts. I mean, it's hard to say that Anthropik's not up. Yeah. Like after the revenue numbers they put up, after the Fable 5 release, and Mythos is evidently even better.

44:06But I just think that Noam Brown post from yesterday, polynomial, is so profound. And just the idea that we do not know how smart these models are. Say more about that. Why don't we know how smart they are? Because nobody has run Mythos for a year continuously. And we may never know how smart each generation of models actually is or was, 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 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 back seat to give their baby a bottle.

44:59And like, of course, you would think that over time that is superior to humans who are distracted. I don't know how long, how long can you think deeply about one topic, Brad? It's like an hour. It's like an hour. Oh, man. Yeah. 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 can come back to that. Imagine if Albert Einstein had been able instead of, you know, and maybe that, maybe, Maybe he could think for three hours at a time. Right, exactly. Clearly an exceptional intellect.

45:33Yes. But imagine Albert Einstein had just thought about fundamental physics 24 hours a day. Yeah. He doesn't have to eat. He doesn't have to sleep. He doesn't have to relax. He doesn't drink. And never gets old. Never gets old. Never has diminutive intelligence. And he thought for one year. Yeah. I mean, we might already, you know. Have solved a lot of these intractable problems. 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. Right, right, right. So that is a, you know, we saw when, that was probably what really unlocked Opus 4.6.

46:15It 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? Where are we going to get to these thresholds of intelligence that cause enterprises and consumers to use them more?

46:57And I think the consensus at the time, at least on this podcast, the debate 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 allows you to do that, they may actually be extending their lead, right, on some of these models that were built on distillation.

47:39So I just opened 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 leads? 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 slew of use cases that previously we could have never tackled before.

48:25Like 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 4.8 just a day before. So what are some of those things, man? I'm curious. 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 RL'd with one another.

49:06they're actually being you know fused closer and closer together but the model can understand the you know the extent of your work so you know one of the things for instance is um 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 all of these assumptions of all these companies tsmc capacity like and then and then produced me a report on all this stuff. And, you know, the model was able to reason through all of our assumptions. Like, actually, if you believe this, this thing is inconsistent with this. What are the contradictions?

49:42Yeah, it was fascinating. And, you know, before we never do that. But now, you know, I think we're just step one into multi-agent orchestration. We're going to do this even further. And that's one example I've also dumped all my notes into. And it's reasoned 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. But it's unlocking all this stuff.

50:18I 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, TerraFab 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.

51:00Yeah, I'll 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 in the world may be open source. And they are today. 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.

51:44And then they used 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 an Opus 4, either 4.7 or 4.8 at a lower cost. 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 model. We hear the same thing. We did an enterprise survey that we'll post of 300 companies, which ones we're optimizing. So these are folks who are kind of looking at model routing and saying we're going to send certain tokens over here.

52:22Which ones are thinking about optimizing? Which ones aren't optimizing yet? And then what is their expected use of frontier model tokens? 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 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 need. But my hunch is for these enterprises, a lot of that back of the house stuff will get rooted there.

52:57That 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. You don't need Albert Einstein to book you a trip. You don't need Albert Einstein to do KYC. 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.

53:32That has been decisively wrong. Probably more than 90%, and it may continue to be decisively wrong. Frontier might be 90 % of the economic value. Open source might be 80 % of tokens. Something that I think is very important on open source is that I think there's this belief that it's bearish for AI. 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.

54:08I 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, you know, by way of this direction. and then you spend time in Asia, 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 the next year is probably going to be the most indicative of which way this falls because 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.

55:05And 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. Right. Now the level of this intelligence has scaled so rapidly and we continue to push against the most economically valuable tasks, which are coding and finance and all these 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 our everyday tasks that we might actually - And that's basically Jensen's argument, right?

55:43Jensen's argument is you're gonna 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 of this revenue. Are you investors in reflection? I'm not. 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 U.S. 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?

56:24They 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 U.S. 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 that, you know, let's assume they get these long-running capabilities. Have the frontier labs now achieve something yet again that allows them to keep the stranglehold on the revenues? Yeah, and I just think it's if you're, wow, that's a cute ASIC you've built there. That is so cute.

57:00Right. How would you like open source to join the frontier? Right. How would you like that? How do you like the baffles? So, I mean, I'm not sure that's the explicit calculation, but I do think Jensen is - Say more. Just double click on that for everybody at home. Yeah. If they were to put an open source model out there, how does that impact the ASIC landscape? Well, you might not have the revenue 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.

57:38Right now it's whatever, six months behind the frontier. We might see it creep closer and closer and closer. And I do think Jensen has a big business decision. I see this chart here, so let's chop it up about NVIDIA, as you say. But if all of his customers are going to compete with him, then why not compete with his customers? And we have all these neoclouds, 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. Nemo Tron 3 or 3.1 was actually really really cool from a compute efficiency perspective And he's always careful to release small models, right?

58:19So as to not tread on anthropic open AI Google's toes, but I do think that is a choice he is making and Just you know at if if the economics change right I give video can join the frontier and become one of the world's largest cloud computing companies Much faster than people think interesting interesting Clark walk us through this chart? Yeah, so I think one of the takeaways from spending time in Taiwan was there is certainly a lot of excitement around the next wave of ASICs. But 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 one or the other.

59:03Now, I think it increasingly, every year, everyone assumed that 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. Actually, if you accounted for the fact that Anthropic was not really using NVIDIA, they probably actually gained share against, if not for in 2526. So I think what was very interesting, though, was a new class of accelerators or ASICs, MediaTek with their new V8T versus Broadcom's V8I for TPUs, actually was a big topic of discussion.

59:52And I think for ASICs, the argument now is that more and more it will look custom to the actual workload, and that is like one vector that people are moving in, versus NVIDIA now 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.

1:00:43There's actually... New realization though. I think we all kind of shared this view for a long time. 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 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, you know, NVIDIA has 10. Broadcom has 10. Who has 6? AMD. AMD has six and they have warrants. And then Cerebris, our shared portfolio company, has a gigawatt.

1:01:26And I just, that is what's on paper. What actually gets deployed, let's see. I will be very surprised if that 10 out of 27, what's that math? Let's see who's best at math. What percentage market share is that? 30%. 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.

1:02:06And 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. Yes, you made a good ASIC. Yes. Well, I know you know Yes, jalapeno exactly from open AI. Yeah, they made a great chip. Yes Now unfortunately 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 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?

1:02:43Right. Like I, you know, I tend to think that the frontier companies like there's this belief that they got to be vertically integrated. But if you believe like I do that the race to superintelligence, particularly as we get these recursive loops working, maybe 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 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.

1:03:16So I think that, you know, subject to the focus question, I think they certainly did. 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.

1:03:57I 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, etc. 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, back to my Sam Altman podcast 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 Anthropics showed up in a major way with revenue, right?

1:04:35And so we have, you know, the AI lab revenue, everybody combined at around$300 billion next year, right? So can't, you know, roll that out to 2027, or that is 2027, 300 billion. 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. Well, what do you think the gross margins are on that$300 billion? Yeah, let's call it 50%. I would guess they're probably a little bit higher than that.

1:05:15I 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. I just think it's low. From your mouth? Yeah, exactly. 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. And he was conservative. He was low. He said a trillion two years ago. And I mean, he was really low. And so, like, let's give the guy some credit and think about what he is saying right now.

1:05:55For sure. For sure. And listen, I would say consistently, Elon's been taking the over. Sundar's been taking the over. Sam, Dario. You know, Dario did the podcast with Dworkish when he was talking about country geniuses in a 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, three, four hundred 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 or 500 by next year, and a path to trillion plus by 2029, then the math, maths.

1:06:46And we got to keep in mind that half of the spending is there for training, maybe a little less than half. What is it, Foxy? It's probably, it depends on the lab, but I would say it's increasingly less than that. Yes. Okay. So we'll call it 35 % is spending that's not revenue generating, but is going to kind of make the next model. So I think the math, maths. Right. And there's still this prisoner's dilemma where if you opt out, that may be an existential decision. For sure. 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.

1:07:21And 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 all comes back to supply-demand. The demand side of the equation seems to be far outstripping the supply. 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. like Alex at WhaleRock, he has this great way to frame it. Less than 0.2 % of people on Earth are actually using AI in an agentic way. I'm not a technical person, but I'm consuming 500 CPU cores in a VM instance, five GPUs 24-7.

1:08:10I mean, if you draw that out to any meaningful percentage of the population, I mean, we're going to be in this kind of shortage environment maybe for some time. So I think that is all positive for this ROI question. Man, Foxy, 100 to Watt CPU to GPU ratio? What kind of a jitter workflow? Of course. You said course. Fine, fine. I'm being smart with my spend. Good, good, good. Excellent. 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.

1:08:54Whereas we're seeing the opposite right now on the willingness to pay for these tokens. And actually when the willingness to pay for these, when the monetization per gigawatt is actually increasing from, call it like 20 billion in the best of cases at the beginning of the year to now like 30 to even pushing 40 per gigawatt. All of that is 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 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.

1:09:39But really like as we're climbing this curve, you know the the the revenue is Might actually outstrip our fixed cost base by by a significant amount And I think that's why all the labs are pushing You know the the gas of the pedals speak because they all they all see like within if we continue this curve within like three years You know we're just gonna be so short on all the compute This is a great I'm sorry Like it's a great point like you you thought you were getting it when you made these decisions Yes. In November of 2025, you thought you were getting a certain return. Yeah. You may be getting triple that return today.

1:10:17At Tropic, no way did they think they were going to be anywhere close to break even, 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 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 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 make money.

1:10:53And then kind of that's blown up. 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, Tamatha said there's no ROI on any of this spend. 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 rational economic actors that are all simultaneously saying, I want to do this because it makes my life better.

1:11:33It makes my business better, etc. To me, that is the best evidence as to why I think this revenue can continue. And Clark, I think the point you made is dead on because you want to own asset-heavy businesses in inflationary environments and token pricing is going up and supply demand is tightening. So, totally agree. As we begin to find our way to the exit ramp and wrap here, one of the things I, you and I have 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.

1:12:20And 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? And if you look at what's happened in the markets, semis ripped this year. I mean, you've been doing this a long time. I've never seen it before. I've never seen the doubles and the triples across the board like we saw. But there's been huge dispersion in the market. Internet's down 16%. Software's down 8 % on the year. SPY and NASDAQ are up, but really up because of their components that are related to AI and compute.

1:12:58And 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. You know, we just had these huge months in April and May. For us, because prices came up so much, because I have some worry about geopolitics, the macro backdrop with what's going on with inflation in the short run, and just needing a little consolidation in this market to answer some of these questions because now expectations are higher.

1:13:39You 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 highs. Curious just how you run the book, how you think about it like a portfolio manager. Very similarly, man. I always think stocks, the markets, I imagine them as runners. Okay. and like in 22, that rudder had gone downhill. It had a lot of energy, man. 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.

1:14:19And, 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. Total. 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. That game is over. You've had a lot of stocks that forget climbing a mountain or a hill. They've gone straight up a cliff. They're tired. They need to rest. And we'll see. Do they just rest at the top of that cliff they climbed? Do they hang out in their harness for a while?

1:14:58And we've seen the last week. We've seen some retracement. Or do they need to go downhill for a bit? 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. Yes. I think there's real concerns around inflation and rates. What was CPI this morning? 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. Yeah. 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 oil was going to be at$100, that CPI was going to be creeping back up, that internet was going to be down 15%, software was going to be down 8%, you would have said, I want nothing to do with that market, right?

1:15:50And 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. I'll just say, you know, we're heading into a seasonally weak period with all these fears. AI has actually been seasonal for the last three summers. That's interesting. 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.

1:16:20It may not happen because of a gigantic AI. 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 doing. So he's one kid who's not using less compute. He's burning it. He's burning it. Yeah, but, you know, if token consumption plateaus, if open source takes some share, there's a silicon data index that has showed, which is an index of kind of consumption and pricing.

1:16:57I 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 as bearish are not understanding it. But nonetheless, I just think there's reasons 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, the market may need to take a breather. But man, when I think about what Noam Brown said, and when I see the capabilities of Fable, it's just hard for me to get too bearish.

1:17:32I mean, like, to me, 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 and the steepening of the line of you know scale advantages I always believed it was to was going to be true I never thought it would be true at this rate I went back last night.

1:18:14In the last seven years, we've added$1 trillion of revenue to the MAG-7 in the last seven years. To get to a trillion, to get to the first trillion, took over 20 years. In the last seven, we added another trillion, and that added$17 trillion in market cap, that trillion dollars. OK, 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. OK, 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. I know that it's going to be like this, but we're going to hire highs because the size of the prize.

1:19:01This 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. 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, lift the floor, take everybody with us on this ride. But it's a really exciting time to be doing what we're doing. It's fun to be doing it with you guys. Yeah, I just want to say, Brad, thanks for having us. And thank you for what you've done with the Trump accounts.

1:19:34I 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. I mean, that's what people think that, you know, 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, you know, that are adding to that.

1:20:13And 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. Thank you.

1:20:28Thank you.