SpaceX 的两万亿估值论证、英伟达抛售惊魂、美国人对 AI 转冷、特朗普撤回 AI 行政令、债券危机？

Jason Calacanis (00:00:00): Alright, everybody. Welcome back to the number one podcast in the world. It's the All In podcast episode two seventy four. Saxe is out today, but we're very lucky to have Gavin Baker from Atriti's management joining us. The spicy takes must flow. Welcome back to the program, bestie Gavin. Gavin Baker (00:00:19): Thanks for having me. Always love it. Jason Calacanis (00:00:21): It's been a huge week in tech. We can start with the SpaceX and OpenAI IPOs. We've got Andrej Karpathy joining Anthropic, Nvidia crushing it. So many different places to go, but I think we'll start with Andrej Karpathy joining Anthropic. Karpathy is only 39 years old. He's already a legend in the tech industry if you don't know him. I believe he's also coming to liquidity. Yeah, Chima? Chamath Palihapitiya (00:00:42): He's gonna keynote on Monday morning. Jason Calacanis (00:00:44): Oh, fantastic. Chamath Palihapitiya (00:00:45): I have to know Tuesday. Tuesday. Tuesday. Day two. Think he's keynote on Okay. Jason Calacanis (00:00:49): As is Gavin. Gavin will be there. Founding member Chamath Palihapitiya (00:00:52): Gavin anchoring day two Jason Calacanis (00:00:54): as well. Excellent. Yeah. This is Gavin's second appearance. Chamath Palihapitiya (00:00:56): Hi. Look at those two bookmarks, Jason Calacanis (00:00:58): Andre Carpathi and Gavin Baker. Oh, yeah. You know? Liquidity pulls in the stars. Obviously, Andre was a founding member of OpenAI. He led the self driving team. Chamath Palihapitiya (00:01:08): Also, hold on. Gavin is gonna help us judge the best ideas section as well. Excellent. I don't know if you know that, Gavin, but you're a judge. You're gonna Jason Calacanis (00:01:15): have to judge. Gavin Baker (00:01:15): I'm up for anything, man. I'm easy. Jason Calacanis (00:01:18): Yes. Karpathy also coined the term vibe coding. He recently built auto research. I think we talked about that here a bit. That's an open source training tool. It helps AI models improve themselves by running five minute experiments. That got over 82,000 stars in GitHub. He did that, like, as a weekend experiment, and all these civilians started building their own recursive LLMs. Really inspiring. And the Andre Karpathy skills is a tool based on his set of principles for Claude code, and somebody just released that. And so that's just pretty crazy when you think about it. He's gonna be in charge of a new pre training team at Anthropic. The focus obviously being recursive self improvement. In other words, they're going to have Claude improve itself, and they've already talked a little bit about AI, improving AI over at Anthropic. Shmoop, what's your take on this? Is this, super important in 2026? Obviously, Carpathi is super well respected. He's obviously, you know, one of the true talents in the space. But, hey, we're in a we're in a different inning than we were, say, ten years ago when he was at Tesla or five years ago when he cofounded OpenAI. Chamath Palihapitiya (00:02:25): You know what's interesting? If you go back to, like, Google, the culture of Google, which they got right, was the singular technical talents there. They were singled out, they were called Google Fellows. I don't know if you guys remember this. Like, Amit Singhal, Shrida Ramaswamy, Jeff Dean. These guys are stars. And what's interesting is if you track what folks, particularly Jeff Dean, I guess, now because the other two aren't there anymore, but what they did inside of Google, it's like wave upon wave, they were at the foot of those waves. What's interesting about Andres, he's been at the wave upon wave of AI. He was probably the first person that really commercialized the Richard Sutton bitter lesson essay when he was leading FSD at Tesla, which was really about the brute force computation. And I remember him telling me Jason Calacanis (00:03:18): this story. Don't if he Chamath Palihapitiya (00:03:18): said this publicly or not, where he spent a portion of his time, I wanna say a quarter of his time labeling data. Could you imagine, like, 2016, 17, like, hand labeling video data from Teslas? So he did that, then he's cofounder of OpenAI. He's a star, and he's an exceptional human being, and he's super curious. And then what he's done as a kind of a free agent is also quite impressive. So I think that this is a really important deal. I think he's one of these really curious people that can be sent off, and they'll just go and invent new things. And I think this idea of recursive self learning puts these models on a combination of overdrive and autopilot. And so if you put those two things together, I think that you start to you could potentially live out this idea that there's an order of magnitude improvement on a yearly basis. So, like, this new form of Moore's law. So then the model quality just goes absolutely parabolically just like this straight up. David Friedberg (00:04:22): I think a Jason Calacanis (00:04:22): bunch of compute at the problem, and these things learn really quick. I think is the high owner bit there. Gavin, what are you what's your take on Anthropic's recent success and their massive hiring binge? Gavin Baker (00:04:35): The success is extraordinary. It's undeniable. I think the fact that they are now they were EBIT positive per the Wall Street Journal in the most recent quarter is a really important fact for kind of the whole AI narrative because now there's you know, you could talk about circular funding, you could talk about ROI, and we could go look at the ROIC of the hyperscalers. But if OpenAI and Anthropic are at, call it, a $100,000,000,000 of ARR now with 80% ish gross margins on inference, like, the returns are there. And then if we add in and they're growing really fast. If we add in Gemini, we add in Cursor, we add in XAI, we add in open source, you know, it's it's not hard to see $203,104 $100,000,000,000 of ARR at the end of this year at high margin. Jason Calacanis (00:05:28): Across all of the Across all of language models. So you're talking specifically about the private language model companies, maybe not Google, which is No. Gavin Baker (00:05:36): Was including Google. You're including Google. Okay. But I was excluding, you know, a lot of the returns to this GPU spend have come from, you know, better recommender systems at Facebook and Google, Amazon, better ad targeting, better ad measurement. Chamath Palihapitiya (00:05:51): Sure. Gavin Baker (00:05:52): So excluding that and just narrowing it to LLMs, which are the tokens. Quickest possible definition. And it seems like there's going to be a really strong ROI this year, even excluding what are still some of the most economically important and profitable use cases for GPUs and AI infrastructure. I do think what Karpathy is working on, recursive self improvement is really important, and unlocking that in continual learning, you know, maybe the two final frontiers for for AI. And just the idea of recursive self improvement that the model, while it is training, you know, during a forward pass, has input into its training or another model has input into the training, that could be really powerful. And I think Chamath's statistics of of, you know, 10 x ing every year, you know, might seem conservative if that comes to pass. And then, of course, continual learning is the holy grail where the model learns from experiences the way humans do. Yeah. And that's something we haven't unlocked yet. And those those those two combined, I think, would, they might pull the future forward in a very real way. Jason Calacanis (00:07:13): Yeah. And we have right now, Anthropic has a decent lead on everybody else, whether it's three months or six months. Obviously, they're probably six, twelve months ahead of open source. Maybe they're three, six, nine months ahead of their contemporaries. But they have a lead. You put Carpathi in there, Friedberg. Now you have Karpathy. He does recursive. And at some point, and it may have even occurred at Anthropic, the AI is going to be improving the language model more than the humans in the loop are doing it. Obviously, they're orchestrating at Freeburg. But at what point do we think this, let's call it, super recursiveness occurs? When will we cross the recursive valley and AI is doing more to build a language model than humans are? David Friedberg (00:08:02): I'm not sure when this idea that you feed the whole model into a context window to train itself and build a new model is gonna happen. But I think there's probably a lot of different architectural paths that could be walked here. One of which is this idea that you could make much smaller models and then create networks of smaller models that work together where you ultimately have less energy or less cost per token produced out of a aggregation of models than you did with one single large model. I've said this probably three or four times now. There's a lot of work and a lot of opportunity ahead in kind of rearchitecting models and rearchitecting how models work together to solve problems. My guess is a lot of leadership that that he can bring to exploring those paths. And all it takes is a minor breakthrough and your cost per token drops in half. That's a tremendous efficiency gain that it seems very much on the horizon because some of the early papers, I think I shared one from MIT a few weeks ago, indicate that there's a lot of room to run here in terms of rearchitecting models and deployment of models. Jason Calacanis (00:09:06): These very small, models, small language models, and then verticalized ones are the future. We've got a company, Avocas, that's doing it for corporations crushing it. Everybody's got an interest in doing this. And I don't know if you saw the news this week, Chamath. It happened about two weeks ago, very quietly. Chrome included Gemini or Google included in their Chrome browser the Gemini Nano model without telling anybody, four gigabytes on your computer. And that's the one that does, like, proofreading, spelling, autocomplete, all that. So now we have Google covertly installing this on everybody's operating system. Speaker 5 (00:09:46): Hold on. Chamath Palihapitiya (00:09:46): Hold on. Yeah. Covert's a strong word, so let's not use that word. Jason Calacanis (00:09:49): Without telling people, without giving people a heads up. Chamath Palihapitiya (00:09:52): Let's say it in a way that we can both agree. We're in the phase now where I think breathlessly talking about every model improvement is a waste of time. There's no ROI in it. We are on a path of accelerated learning, and we're gonna start to see end user achievements that were heretofore impossible that should be the focus. So for example, we were able to solve I'm just collectively saying we. In this case, it was specifically OpenAI. By the use of a human, and this is important, a math problem that stood outstanding, not been solved for decades and decades. I can tell you in a different example, there are drug candidates that are about to enter clinical trials and INDs that were sitting on the shelf and people didn't think were very viable at all. So we're at the phase now where these things are front and center. They're useful to people. They're increasingly valuable. I think what we should do now is focus on these end user use cases because the way that you say it, in my opinion, is part of the problem because it starts to create this boogeyman us versus them thing. And I'm not saying you're doing it on purpose, but I'm saying this is exactly why I think so many people are becoming sort of like, it's a four letter word now when you mention AI because it's presented as this thing. And I think we have to present the other side of it at least so that people have the data. So I don't think Google is in the business of doing or shady things. I don't they're not that company. There are other companies that would. You're referring to your alma mater? I'm not gonna say which ones. Okay? But Google is not that company. So I I think that the reason they did it was probably because there's user utility. And my point is we should focus on the user utility because I think that's the story worth telling from now on because I think we collectively, the four of us, can responsibly tell both sides of the story in a well balanced way because I think nobody wins if we become Luddites and go back in time. Yeah. And I think that if we don't if we're not careful with our words, that's what will happen. Jason Calacanis (00:11:59): Yeah. And by the way, obviously not a Luddite, but this is what, you know, has been reported by a lot of folks that people were surprised, shocked when they saw the size of the model being done in the background, and it has triggered some people looking at it around privacy. And I do agree that Google is not a bad actor in the space. So probably a speed hour more than anything, I would say. Gavin Baker (00:12:27): Maybe just add two things. There's attorney maxing, and then there's attorney maxed, and Google is probably attorney maxed. Chamath Palihapitiya (00:12:37): Yes. I mean, Gavin Baker (00:12:38): it happened for a long time. And the second second thing I would say is I do think it's incumbent on all of us as Americans who are involved in the technology industry in one way or another to be advocates for a, the positive, optimistic possibilities that AI introduces to, to everyone in this world, because it it is starting to feel or seem like there may be a CCP funded campaign against AI and data centers in America. And that's very logical for China, but it is not good for America. And so I just I think it's it we all have responsibility is what I would say. Jason Calacanis (00:13:22): Yeah. Who do you think is doing a poor job at that and responsible for this? Is it Dario with his constant, hey. Everybody's gonna lose their job? Who's responsible for this? Is it the CEO is blaming AI for their layoffs? What what's your take on this, Gavin? Chamath Palihapitiya (00:13:38): Look. Hold on a second. Everybody is trading their own book. It makes enormous sense for Dario to try to create the boundary conditions for a regulatory moat because he will be inside of the tent pissing up. He's big enough now. And if you notice that a lot of the breathlessness has ramped up, and, Jason, we've talked about this, you can annotate successive rounds of fundraising and successive scale with the volume. So I think that it's a reasonable business strategy, and I think that he's quite clever. And I think that look. If you actually and I and I do this. If you actually just have a Nash bot, a Nash agent inside of Quad, and you ask it what it would do, it would come up with this strategy. And meanwhile, there are other versions of other counter strategies and counter exploitative strategies. The point is that each CEO has a clear incentive. They're operating at such a level of scale that they're just reading their own book. So it's up to us to take a step back and actually see the forest from the trees. I think, Nick, can you find this? There was a clip of Shyam Sankar, friend of the pod, fabulous guy, the CTO of Palantir. And he was, I think he was on Fox News. And he said, stop breathlessly asking these model makers what they think. Go to the end user and ask the person in the factory that's using the model and ask him what he or she thinks. Ask what the doctor thinks. Ask what the scientists think. And start to tell those stories. That's what we should be talking about. Jason Calacanis (00:15:08): Yeah. And Gavin, you were I was sort of asking you your opinion on what's what's who's causing this, and then what's the solution? Like, do do you have folks you think in the industry who are representing it particularly well? We can point out, hey. Yuan has said we're gonna move to a world of incredible abundance, and working will be optional. I think that's on the margin a little scary for people to hear because they hear no job. But he does say, hey. Universal basic income is probably gonna have to come into, you know, place. And he said that multiple times. You have Dario, according to Chamath, talking his own book, scaring the bejesus out of people in order to get regulatory capture. What do you think is going on here, and how do we do better as an industry? Gavin Baker (00:15:52): I think Chamath outlined, like, a very viable and positive path forward We're just, you know, real people who are not, you know, at the tip of the spear. These are the positive impacts AI has had on my life. I was at a I was at I was at an event maybe ten days ago, and someone who runs a hedge fund, his daughter was born with a very rare genetic mutation that effectively would have normally condemned her to a life devoid of joy, meaning everything. The neurons in her brain were not firing. So she wouldn't you know, who knows what her life expectancy would have been or what the quality of her life would would have been? And it's it's a tragic disease. He said he didn't accept that as an answer. He found he did an enormous amount of research with LLMs and found an existing safe drug on the market that they thought would have a meaningful impact on his daughter's condition. And it did. It took, I think the percentage of times the neurons were firing was 30 40%, and it took it up to eighty or ninety percent. And that means that she can live a normal life. She may not be as smart as she would have been, but she can live a normal life. And he's now figured out how to use AI, how to to further tailor that drug. And, you know, there've been all sorts of advances in protein design, etcetera, etcetera. And he's reasonably confident he's going to have a drug in months that is a complete cure. And that's just one person, one dad who was unwilling to accept defeat for his daughter and who changed her life and the life of everyone else with that disease. We tell those stories. So I think Elon's doing a good job, a future where work is optional. I think that sounds great to some people, you know, scary to others, you know, four day work week, you know, I think is is probably something that sounds sounds good to a lot of people. I think Jensen is doing a good job of being an effective advocate. Gavin Baker (00:17:56): And I do think anyone who is trying to drive I just we need to stay focused on the positives is what I Yeah. Jason Calacanis (00:18:05): Freiberg, what's your take here on the AI PR crisis, if we'll call it that? We had three different commencement speeches that were booed. Eric Schmidt being one of them, two other ones by maybe less notable folks. When you hear young people booing AI, vociferously, why are they doing that, Freiberg? And what's your take on the overall PR problem and how to turn it around? David Friedberg (00:18:35): Oh, that's a that's a long answer to that question. Jason Calacanis (00:18:42): It relates in some ways to your concerns about socialism and polarization. Answer. Chamath Palihapitiya (00:18:47): Yeah. What's the long answer? David Friedberg (00:18:48): I mean, that's like like, why do people hate technology? The greatest technological Chamath Palihapitiya (00:18:56): why do they Jason Calacanis (00:18:57): this technology, they love their phones. They love the Internet. This technology, they hate. David Friedberg (00:19:02): I think that there's, like, an underlying view that technology creates leverage for a small group of people, which creates power imbalances. And nothing represents that more than AI, That a small number of people that control and profit from and benefit from AI are gonna end up getting outsized returns relative to the broader population, that the time to diffusion of the technology because ultimately, all technologies, like, commoditize and diffuse. But the time to diffusion here is such that it's gonna be, like, extremely asymmetric for society. And I think that there is something fundamental about that. It's like, you know, nuclear bombs, I think, really created this this moment in people's minds in the mid twentieth century that by the back half of the twentieth century gave everyone a high degree of skepticism about technology and science generally, that those who have the knowledge and those who engineer solutions with the knowledge can create outsized advantages for themselves, and it puts the rest of us at risk, the rest of the world, the rest of the population at risk. And because those questions about when does this benefit me, how does it benefit me can't be answered today, the economic benefit that's accruing to the few today becomes the narrative. It becomes the story, and it becomes this, like, power system that a few people take from the many. And so there's something deeply disturbing for the average person about that. They don't understand how it works, why it works, what it'll do for them, when it will do it, and all that they're being told is that some people are making trillions of dollars. So I think that it's pretty obvious why this has got such a backlash. Secondly, I think that there's a deep amount of external energy that's fueling this anti technology sentiment in The United States and has been for decades. I think to Gavin's point, I don't think it's just China with NGOs today. I think that there is a long history of state actors intervening in media activities in foreign nations to try and create the sentiment and fuel a sentiment that reduces progress in that competitive state. I think this goes all the way back to KGB design during the Cold War, and it's been refined and honed and improved over time. This is not just some conspiracy theory. David Friedberg (00:21:28): There are plenty of great books about this. The techniques of what's going on specifically today, I don't know enough. I don't have any great details on that. But I don't think that there's no foreign interest in seeing technology advancements slow in competitive nations. The United States probably does similar things to other nations, and I think that that's probably a a key part of this. And then I think this this, like, third piece is, like, when the Copernican revolution happened, it was a mind You know? Like, heliocentricity was a totally new way of thinking for humans, and it was deeply disruptive to the church. And it was deeply disruptive to the power centers, which were the centers that could tell people Earth is at the center of the universe. We're in control. We're the direct channel to God. And the idea that the sun is at the center of the solar system and we spin around it and we're a tiny speck in the universe was very hard for people to grasp. There's something about AI that's very, like, not human centric, and it kind of shifts and with the ego of the human. It's it's it's almost antihumanist. And I think that that's, like, a deep psychological current. A lot of people and their disdain for this technology, it it fuels it. It's not the cause, but I think it fuels it. So I think there's a lot of complicated aspects to this, J Cal. You know, I don't think there's, like, a simple put put Sham on on a podcast, and he'll solve the the problems with AI right now. I think that there's a real set of shifts happening, and there's a real set of global competition underway where, you know, various state actors and interests are competing with each other. Chamath Palihapitiya (00:23:02): Yeah. Friedberg, do you think that we should slow down? David Friedberg (00:23:06): I don't think you can. Chamath Palihapitiya (00:23:07): No. No. No. Do you think we should slow down? David Friedberg (00:23:10): No. I I think I was just talking to some people on Zoom right before this, but I think after the Manhattan Project, the research labs were stood up to maintain our scientists that worked on the the Manhattan Project from effectively leaching back or leaking back to Russia and Germany and and and other places that that were adversary to The United States. And they all were against the nuclear bomb. They worked on it because it was necessary for The United States security. But then when Russia got ahold of the secrets, they were leaked because people were worried that if The US had all the power, there would be no counterbalance to the power. And so the the nuclear secrets were leaked to Russia for that purpose. Then when Russia had the nuclear secrets and they began developing hydrogen fusion bombs, and it it was clear that they were gonna race ahead, The United States raced ahead with developing nuclear bombs as a counterbalance to Russia. When the proliferation began, there was no stopping it. It began, and you had to have this balance in the world. Otherwise, you have effectively an asymmetric power that can do whatever it wants globally. I think there's that moment in the world right now where if The United States does not advance its AI technology, the availability of it, TBD industry, taxation, all these all these things that we're talking about doing, there will be someone else that will. And if someone else does, we can go through what would happen. There's a there's a complicated game theory on this, but what would happen if China had sufficiently advanced models and sufficiently advanced scaled deployment of those models relative to The United States, as you do that analysis, you realize, wait a second. That's probably not a healthy place for the world to be. It's also probably not a healthy place for The United States to be the only one with AI. David Friedberg (00:24:53): And so I I think what we end up seeing is if we do try and slow down AI, we kinda lose this moment of balance that's necessary when you have a technology proliferation like we saw with the arms race after World War two that, you know, Jason Calacanis (00:25:09): we're gonna see again here. Chamathi, I think bring up a good point. You know? Should we slow it down, or could we slow it down? There actually have been some discussions about ways to do this. One of them would be, hey. With self driving, people are scared that all these cab drivers are gonna lose their jobs, Uber drivers, cab drivers, bus drivers, truck drivers. This is, you know, over 10,000,000 people in The United States driving things for a living. Would you be in favor of some of the announcements that that will be a paced rollout? It won't happen all at once. In other words, those people will be giving some amount of job security to stay behind the wheel with it. Another example that's been given is if you put Optimus into Amazon factories or the Figure Robot just did like a week of just sorting packages. I'm sure everybody saw that video. We'll insert it here. That Figure Robot sorting things. Hey. If Amazon deploys those, there'll be a tax on those per hour, and we'll tax humanoid robots in some ways and then use that for, say, retraining people. Those are two very specific conditions and approaches that people have been promoting. Do either of those resonate with you in any way? Chamath Palihapitiya (00:26:19): I think it's interesting that in all of those discussions, I've yet to see an actual survey of only the truck drivers and only the package sorters. The question that I would have is, do the people that do these jobs want these jobs? And if they do, then there's a reasonable claim to make to keep those jobs the way that they are. If you're saying this is the job that I do, I love it, I'm able to provide for my family, great. That's a very different argument than, well, you know what? Amazon has 35 or 40% churn inside of their warehouses. And we should probably ask the question, why is that? Because if it was such a great job, I suspect the churn would be 3% or 4%. So what exactly is it that we want to protect? And have you asked them? And I think that this is just, again, a bunch of people in the peanut gallery who wanna take a moral high ground and try to make some other group of people feel guilty or feel bad. At no point are we actually asking the conversation that that we should be having, which is it's interesting to me that the the there was supposed to be an EO, an ex presidential executive order that was announced today, and then it was pulled. It was scrubbed at the last minute. Did you guys notice that? Yep. And yesterday, what was leaked was everybody that was attending. It was all the big Neolabs CEOs, and it was all the big hyperscaler CEOs, including friend of the pod, Nikesh Arora. Shout out to Nikesh. And then it was scrubbed an hour ago. Why was it scrubbed? And the president said that there were aspects of the bill that he didn't agree with. And as far as we can tell, the aspects would have required some amount of supervision, insight, review from the federal government. Jason Calacanis (00:28:01): Of language models, specifically, of these frontier models is what I read. Chamath Palihapitiya (00:28:05): Not language models because I think just of AI because there's gonna be many different kinds. They're not always gonna be language models but of AI. So, look, I think Friedberg is is right. We are in a proliferation with China. I think it's actually good that China is less than nine months behind us. I think it allows us to find a detente where we have a certain magnitude of capability that they also have, and that allows all of us to then seek peace and abundance. And the fact that we are orthogonal societies, we are organized differently, increases the probability of finding peace using the Rene Girard kind of framework of memetic theory than if if it was like us and another country that was exactly similar to us. So I think what we need to do, we probably need KYC. I think that that should be something that us and China get together and say, you don't want it to get into the hands of people you can't control. You probably already KYC those models anyway inside of China. You already review those training runs before you allow these models to get released. We already know that that's happening. Speaker 5 (00:29:08): Yes. Chamath Palihapitiya (00:29:08): So we should probably do some sort of KYC so some crazy person doesn't create some biological weapon. I think that those are, like, some reasonable ground rules. But otherwise, Friedberg is right. You have to take a a little bit of a deterministic view here, which is that we are in this existential race, and we need to get to the place where each of us, meaning us and China, can look each other in the eye and say, alright. Weapons down, so to speak. Jason Calacanis (00:29:31): Kevin, I'm gonna hold you to answer two questions. One, should we run frontier models? Because that's specifically what was mentioned in the leak about the EO frontier models, the powerful ones. Should they be run through some sort of testing before they're released, and should there be some regulatory framework for that? That's my first question to you. Yes or no question, then you can explain your answer. Gavin Baker (00:29:55): Jeez. Like, I just think it's such a complicated topic. It feels we're a little early for that. I don't love the idea of The United States, doing it and no one else doing it. I like, I think in a world where we hold hands with China, look, I think that's that's much more palatable, and we are aligned, we trust each other and have kind of verification, capabilities. Do think Jason Calacanis (00:30:27): Yeah. Let let me then rephrase it. Should China and The US come up with a simple battery of things that have to be tested before these go out, including bioweapons, terrorism, and and that genre of in that vertical of just really known dangerous things, just like the FDA might test for poisons or contaminants in a food or a drug. Would you be in favor of that? I'm curious. Gavin Baker (00:30:51): So a few things. Like, the one one thing that's great about America is there is or one thing that's just we have other forms of regulation. Self regulation. Sure. We have self one, we have self regulation. Also, we have the courts. And if an AI model company behaves irresponsibly, they know that there are ways that people who have been harmed can seek recourse. And so we already have a system that encourages responsible behavior on the part of the model makers. Jason Calacanis (00:31:27): That's a great point because OpenAI is being sued right now by a kid who sue who killed themselves after talking to OpenAI's model. So you're you're correct in that. Yes. After the fact. Yeah. Gavin Baker (00:31:36): And we will see we'll soon we'll see more of that. I just you know, to me, once you give something, give a power to the government, it's almost never taken back. Jason Calacanis (00:31:46): Yes. Gavin Baker (00:31:46): And it's to grow, and it's kind of a one way a one way path. Chamath Palihapitiya (00:31:51): One way ratchet. Jason Calacanis (00:31:52): Yeah. And then that's a second yeah. Second question then. Shmoth was saying, hey. Nobody listens to the, you know, these cab drivers or maybe the people sorting the packages. Do they want the jobs or not? Actually, The UK, there was just a sixty minute special. And UK and also Boston and New York are pretty adamant that they want humans to stay and they wanna ban self driving in those locations or severely limit it or maybe limit it in some way to let those people keep their jobs. How do you feel about that possibility? Is that something you think society should be open to? Some gradual licensing? Chamath Palihapitiya (00:32:27): They're gonna get sued for wrongful death. When somebody runs over somebody else and you could have implemented a solution that has a zero death rate, that's very different from package sorting. Jason Calacanis (00:32:36): Okay. Chamath Palihapitiya (00:32:36): Go talk to the package sorters is what I say. Go talk to the people inside the Amazon warehouse. Ask them what they would rather do at Amazon. Ask them. Jason Calacanis (00:32:44): Yeah. Sure. But, Gavin, what are what are your thoughts here on either one of those examples here? Gavin Baker (00:32:48): I think going to a city where you can't get in a Waymo or a cyber cab is gonna feel barbaric and unsafe until you Jason Calacanis (00:32:56): Agree. Strongly agree. Gavin Baker (00:32:57): I don't know if you remember, like, but the early days of Uber, sometimes you go to a city where there was no Uber. Jason Calacanis (00:33:02): Yeah. It'd be incredibly frustrating. Well, Gavin Baker (00:33:04): I'm not gonna come back until they have Uber. It's so inconvenient. And I think so whatever individual municipalities decide, I do think one, Shama's point is really powerful. There's fifty thousand automotive deaths per year in The United States, if I recall correctly, and a million globally. And you know, that's not tolerable. And there will for sure be wrongful death lawsuits. And then just from a convenience and quality of life perspective, I just don't think it's gonna persist. And that's another great thing about America is, you know, you have this patchwork of different states and municipalities and each one doing things in a different way. And I'm not suggesting that's good for AI, but it does tend to, you know, has historically, the curly effect aside, led to, you know, I think more positive outcomes where cities and states compete with the curly effect being It is fair. Jason Calacanis (00:34:00): Yeah. This is a really important point you're making, Gavin. With flock safety, as but one example, we had a an AI there's an AI tool called flock safety. It's cameras that use AI, monitor people who are committing crimes. There's a privacy issue around it. It is bottom up. You just do it by town. It's not top down, and states can regulate it. Same thing will probably happen with self driving, and states will probably have some say in how AI is deployed even if maybe some centralized governments don't want to do that. I really think this only comes down to The Vlog Gavin Baker (00:34:31): safety thing, I think it's so good, Jason. Crime is now a choice. Jason Calacanis (00:34:34): Yeah. Gavin Baker (00:34:35): You know, I think the the Cambridge Council voted to turn off gunshot detectors two days ago. Jason Calacanis (00:34:43): Which city did that? Which means that's about Cambridge. Chamath Palihapitiya (00:34:45): Cambridge. Cambridge, Mass. Jason Calacanis (00:34:46): That's the place where Harvard is. Gavin Baker (00:34:48): That's the place where Harvard is. Jason Calacanis (00:34:49): So the geniuses coming out of Harvard in that town decided gunshot detection shouldn't occur. We you don't want gunshot detection to sort Gavin Baker (00:34:59): of It's wild because, you know, there's there's a theory that it disadvantages, you know, that it might lead to an illegal migrant who's shooting a gun being apprehended, and we don't want that. David Friedberg (00:35:10): Got Gavin Baker (00:35:10): it. And a 16 z had a great essay on flock. We can really, really solve crime, and it's just a choice. And different states and municipalities will make different choices to be pro crime or anti crime. And I'm sure they don't cast it as pro crime. There's an, you know, some sort of, you know, moral or ethical reason they're making that choice, but people will vote with their feet over time, and then voters will vote with their votes. And we'll see what works. Chamath Palihapitiya (00:35:40): Have you guys been to Vegas recently? My wife and I went to visit Vegas, and we spent the the afternoon with Ben Horowitz and his wife, Felicia. She has done this incredible job with the Las Vegas police department. It is one of the most impressive things I've ever seen. And to your point, crime is an option, and they've said no. So what happens there is they have gunshot detection. They have drones that get deployed off the roof of the police building. We were sitting inside of mission control where you see it happening, Jason. If something happens, they have eyes on-site within minutes. They can track offenders and bad guys all the way to wherever they're hiding. And you walk out of it, and you feel incredibly safe. Like, they're really on top of it. And when you understand the level of investment, it's not. It doesn't take billions of dollars. Jason Calacanis (00:36:27): Compared to the Chamath Palihapitiya (00:36:28): cost of crimes. It's de minimis. It's de minimis. Jason Calacanis (00:36:31): Compared to the cost of the crime occurring. Chamath Palihapitiya (00:36:33): Exactly. If you gave the Las Vegas police department $3,040,000,000 dollars a year, it would be the safest city in America, and that's all it would take. Jason Calacanis (00:36:41): Yep. Exactly. And the for the privacy concerns, Friberg, there are very simple solutions to this. I I am a privacy advocate myself. Of course, we all want some level of privacy. I I had the Flock CEO on this weekend service twice in the past ten years. He's very considered. And the way they do it with Flock is they allow you to have a rolling database, and I think there's a maximum you can save the license plates for, and they don't do facial recognition. I I don't see why not. But let's put that aside. You can only keep it for two or three years. And then they insist on having an audit trail in it. So there are all little things you can do on the back end to protect privacy with audit trails, etcetera. We got a lot more to get to in the docket. I just wanted to give my final thoughts on what we're talking about here in terms of the AI problem and the PR problem. I think we have to recognize that the layoffs that are occurring in big tech and in a lot of these places are not just the bloating issue anymore. And I'm just gonna point to two factors that I think are scaring the bejesus out of people, and we just have to admit that this is occurring as opposed to we've been debating it here. Is it occurring? Is this just cover? And are we AI washing? The first one I wanna give you an example of is Matthew Prince, who is the CEO of Cloudflare, incredible company, public company. Two weeks ago, I laid off more than 20% of my workforce. I didn't do it because Cloudflare is struggling. We posted record revenue growth, have strong free cash flow, and are adding an unprecedented number of customers, yada yada yada. And he says, basically, he's getting rid of measures. Measures are the people who manage people and who measure data. And he just says, we're getting rid of all those people. They're unnecessary because of AI, and we'll be adding people in other positions. At the same time, Zuckerberg did another round of layoffs, and they were done in a way that people felt was not considered. And a bit what's the word? Jason Calacanis (00:38:34): Dystopian? Dystopian. Thank you, sir. He did them in pretty dystopian way. Here's Zuckerberg for thirty seconds. Gavin Baker (00:38:42): In general, the average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks if you're working through the contract through through these contractors. So if we're trying to teach the models coding, for example, then having people internally build tools that or or solve tasks that that help teach the model how to code, we think is going to dramatically increase our model's coding ability faster than what others in the industry have the capability to do who don't have thousands and thousands of extremely strong engineers at their company. Jason Calacanis (00:39:18): Okay. So what Zuckerberg did at the same time, concurrently, he told everybody we're laying off these 8,000 people. A lot of those people are incredibly talented. Some of them are on h one b visas, creates all kinds of chaos for them in their personal lives. And obviously, they're having record profits there as well. At the same time, he was laying off those 8,000 people. This is after tens of thousands of layoffs before, which were obviously because of bloating. He said we're putting recording software on every single person in the company's computers to study and train our model. And people were like, oh. And previous people said, I built during the AI hackathons they had months ago. I built all these AI tools to make my job, more efficient, and then Zuckerberg laid me off. So the now the perception people have now, and it's quite correct, I think, is the most you can hope for here is you keep this job for some amount of time and train your way out of it. And hopefully, there's some more work for you, but they're studying you. And Zuckerberg just said it plainly there. Hey. We're gonna study everybody here, and that's gonna lead to more replacements. This is scaring the bejesus out of people, and we need to have an answer for it. Yeah. Chamath Palihapitiya (00:40:23): I thought the Matthew Prince note was horrible. Jason Calacanis (00:40:27): Okay. Explain. Chamath Palihapitiya (00:40:28): This was, like, from the PR school of retards. Jason Calacanis (00:40:32): Okay. Here we go. Chamath Palihapitiya (00:40:34): You could not have written a worse memo. It's like you reduce humans to a label called the measurer, and then you're like, I'm gonna lay off all the measures. I mean, I just think that part of this again, I'll go back to the and maybe the Sham Sankar quote that I'm thinking about should be extended beyond the model makers. Can you just play this for one second, and I'll tell you why I think this is just so Jason Calacanis (00:41:00): And then we'll go on to the s one from SpaceX. Speaker 6 (00:41:02): We're listening too much to the inventors of AI. I know that's appealing. They're geniuses. They're smart. We need to be listening to the frontline factory workers who are using AI saying, wow. I was able to add a third shift. I was able to hire more workers. Or the ICU nurse who says, I have more time to spend with my patients. I'm able to ensure they don't code during a shift David Friedberg (00:41:20): change now. Chamath Palihapitiya (00:41:20): So look. My my point is, like, the first part of what he said applies here, which is who cares what Matthew Prince thinks? Because the reality is that if this is the way that you're going to message something as critical as this, I think you did a horrible job. And now you label these people and you put a scarlet letter on their back. So now when they try to get a different job, they're like, oh, you're one of the Cloudflare measures. How does that help anybody? It didn't needed to be done this way. There's enough of these tech CEOs that are now public. You can hear them. You can understand them. And I think what we're learning is, man, They're really good at one thing, and they're not necessarily as good at all the other things. Yeah. Jason Calacanis (00:41:57): Okay. Chamath Palihapitiya (00:41:57): And so I would say, shut the up. Get behind the keyboard. Just do your job. And if you need to manage something, just manage it, but don't write these misives. You're terrible at it. All of you. You're all terrible. You suck at Jason Calacanis (00:42:11): this. Alright. Chamath Palihapitiya (00:42:14): End of my TED Talk. Thank you for coming to my TED Talk. Jason Calacanis (00:42:16): Tisha Mott's eighteen minute TED Talk. We'll we'll keep moving on. Chamath Palihapitiya (00:42:22): Sorry. And sorry. When everybody gets upset, this will be why. Jason Calacanis (00:42:25): Yes. I mean, I I do think the Zuckerberg, this, and Jack saying, you know, hey. We're gonna have half as many people. Everybody reports directly to me. This is all building this fear in society, and I think people are right rightfully scared if the people building it tell you be scared. Your job's going away. Chamath Palihapitiya (00:42:40): Wait till the next regulatory filing comes out from these companies, and they authorize a massive share buyback and an increase in their dividend. Jason Calacanis (00:42:49): Yeah. And their cash pile grows. Chamath Palihapitiya (00:42:51): All I'm saying is there's a right way to do this, make these decisions, and then there's a wrong way to do it, which is to message it in the way that they're doing it. Mhmm. So whoever is running PR and comms and approves and reviews these things are really at their job. They don't understand the moment. Jason Calacanis (00:43:08): Oh, some breaking news here. It looks like Anthropic has hired three more people. Here we go. Chamath Palihapitiya (00:43:14): Let's see. Jason Calacanis (00:43:14): Oh, here we go. Personal job news from Sam Altman. He'll be joining Anthropic. I see. That's pretty good. Chamath Palihapitiya (00:43:20): K. Jason Calacanis (00:43:20): Who else is joining Anthropic? Let's see. We checked everybody's socials. Oh, Tucker. Tucker Carlson is also joining Anthropic. They'll be doing their PR and podcast from Anthropic headquarters. And who else? Oh, Shamash. Looking good. Well, it looks like you put five pounds on. And using the pants Chamath Palihapitiya (00:43:38): First of all, this is not this is not what I look like. And Jason Calacanis (00:43:41): Did you get five pounds? Looks like Gavin Baker (00:43:44): the six pack's not. Are you not wearing are you Jason Calacanis (00:43:46): not wearing underwear? He's wearing underwear, and he's wearing his khakis, but I think it's just he trying to not show off those scrawny those scrawny scents, those those little slats he calls legs. He's covering them up now. Chamath Palihapitiya (00:43:57): Hold on. I'm gonna send Jason Calacanis (00:43:58): I don't wanna leg shame you. Chamath Palihapitiya (00:43:59): I I I knew that this was gonna come up. I'm gonna send Nick an updated picture of my legs, and you you can deal with this. Jason Calacanis (00:44:05): You can Photoshop your legs all you want. Chamath Palihapitiya (00:44:07): I didn't I didn't Photoshop. I didn't brother. Those are not Jewish. I have hundreds adults today. Male legs. Working on it. Gavin Baker (00:44:14): They may not be photoshopping. He may have been leg maxing. He could be leg maxing. Are you leg maxing? David Friedberg (00:44:19): Are you Jason Calacanis (00:44:20): BP one five seven in your legs? What are Chamath Palihapitiya (00:44:22): you doing to do? First of all, I'm six foot two, you goons. Okay? Jason Calacanis (00:44:26): So Gosh. Legs. Chamath Palihapitiya (00:44:27): All you little people, you know, Jason, you're five foot all. So, you know, your ability to a Jason Calacanis (00:44:32): good day with my platforms. You know? Chamath Palihapitiya (00:44:33): Your ability to have legs is is different because, like, my muscle mass won't show on my legs. The weight shows on your leg. Jason Calacanis (00:44:40): Oh, I don't think you're helping. I look at you know what he's doing there? Friedberg, you see it. Right? Look at how sprung the calves are, and then look at how he's doing Chamath Palihapitiya (00:44:47): a Oh my god. Jason Calacanis (00:44:48): It's a push up bra for your quads. Chamath Palihapitiya (00:44:50): Oh my god. Jason Calacanis (00:44:51): It's the equivalent of a push up bra. He has those Bonsu balls. He put Bonsu balls under his hammies to you did it. You're using fillets to pull those up. Chamath Palihapitiya (00:45:00): My legs and I flat okay. You know what? Move on. Does that sense? Gavin Baker (00:45:03): I was just saying I think we should give credit where credit is due. Yeah. Jamoth's legs. Better. Moth has been doing a lot of work on his legs. Thanks. It's little better. Chamath Palihapitiya (00:45:11): Thank you. Jason Calacanis (00:45:12): It's better. Chamath Palihapitiya (00:45:12): Thank you. Jason Calacanis (00:45:13): I'll I'll give him better, but I do think that he's pumping them up here. Okay. Let's keep going here. Alright. Topic two, SpaceX just filed their s one on Wednesday. They are aiming to raise 75,000,000,000 at a 1,750,000,000,000 with the t valuations to be the largest IPO ever by more than double Saudi's Aramco $29,000,000,000 IPO a couple years ago. Listing is expected mid June, likely June 12. Ticker will be SPCX. We got a lot of interesting information in the s one teardown. Obviously, SpaceX has three main business units. Starlink is the money printer right now, but there is a second one that's emerging. Starlink did 11,400,000,000 in revenue last year on 50% growth with 4,400,000,000 in operating income. Over 10,000,000 people are now subscribing to Starlink. That business could easily be hundreds of millions of paying subscribers, so that that's a lot of growth potentially there. The space business is but 4,000,000,000 in revenue. It's growing 17% growth, which should still be strong growth, but had 650,000,000 in operating losses. AI did 3,200,000,000 in revenue. That's more than double year over year growth, but it had 6,400,000,000 in operating losses. SpaceX had 20,000,000,000 in CapEx spend last year. Over 60% was for the AI compute build out. And, obviously, they were trailing Anthropic and OpenAI and Gemini in terms of x AI playing catch up, and he did a a big reboot of that as we saw on the Twitter. But here's the big one. EWS, Elon Web Services, as we call it here on the all in pod, has exploded. Anthropic is paying SpaceX, wait for it, 1,250,000,000 a month to rent out Colossus one and parts of Colossus two. It's a $45,000,000,000 deal over three years, 15,000,000,000 a year. In other words, they added a Starlink in terms of revenue to the party. Plus, if they buy Kursar, that's gonna add another 2 or 3,000,000,000. Chamath Palihapitiya (00:47:15): Not not if. I already told you they already did. Jason Calacanis (00:47:17): I'm just trying to, you know, dot the i's and t's here. But when they buy, Kursar, that adds 2 or 3,000,000,000. That's not in the s one. Chamath Palihapitiya (00:47:26): That's also growing and doubling. Jason Calacanis (00:47:28): Yeah. That's probably growing two x year over year, and who knows how much faster it will grow. Polymarket, 71% chance SpaceX closes its first day of trading with a market cap above 2,000,000,000,000. Thank you to our partner, Polymarket. I'll stop here. Gavin, you've been involved in the company for a long time. Shamaq, you, I think were a big investor in the satellite company that became part of Starlink, which is the revenue driver there. So you both have, a lot to say about this, Gavin. Your take on the s one, and I think specifically Elon web services. Gavin Baker (00:48:02): Well, I think what's important about, Elon web services does make me laugh. But 15,000,000,000, that means the AI business right there is going to quadruple. It it has already effectively quadrupled. I think what's important about that is there's a stat in it that for I think the their first data center was 122. The second one, it took them ninety one days. The third one was, I think, sixty six days. They build data centers dramatically faster than anyone else at a lower cost. And now that you have a clear off take partner, and I would expect partner to become partners, there is no reason they can't start stamping these data centers out really fast. And having watched Jensen for a long time, it is important to Jensen that his GPUs be used. And so GPUs will be allocated to who can plug them in, turn them on, and start converting electrons into tokens. And so I think this business can grow dramatically faster than I think, you know, maybe what anyone could have contemplated three, you know, three three months ago. But $15,000,000,000 from Anthropic is is extraordinary. Jason Calacanis (00:49:22): Important note, it can be canceled by either party with ninety days notice. Just wanna make sure we also have that in there. So that means Elon might want his compute back or Anthropic may find another solution. So they do both have an out. Gavin Baker (00:49:34): And I think that's that's a you know, I think that's that's that's probably an important provision Jason Calacanis (00:49:39): Yeah. Gavin Baker (00:49:39): For everyone. But I think the other thing that came out this week, which was not in the s one, Nick, can you throw up the Pareto frontier and maybe don't, you know, include the email and the names and everything, but the Composer 2.5 stat. I think this is really extraordinary. So Cursor's Composer 2.5 model came out this week. And I mean, this is Pareto dominant. And this is just, you know, three, four weeks of doing reinforcement learning on Colossus two with Cursor's data. And Cursor has we will never know, but but CRRSR allegedly has more tokens of coding data than exist on the public Internet. And that is a stat from, I think, more than a year ago, so I would imagine it's grown significantly. And I think Cursor and Anthropic probably have the most proprietary tokens of coding data. And what this this jump from Composer two to Composer 2.5 showed us is that when you do an appropriate amount of reinforcement learning using that data, let alone injecting it into the pre training of a new base model, because Composer 2.5 is the same base model as Composer two, which is Kimi k k point two five. Like, this is amazing. This is three or four weeks, and it is pareto dominant. The pareto frontier, if you draw a curve of the blue dots, you can see composer 2.5 is literally well outside the Pareto frontier, and that's after three weeks. And what's gonna happen next is you're gonna have a new base model with a cursor model in it. Yep. Then the cursor model are held using the biggest coherent, compute cluster in the world. And I think this is I I think this may Jason Calacanis (00:51:37): It's significant. Yeah. Gavin Baker (00:51:39): It's extremely significant for XAI and cursor. Jason Calacanis (00:51:42): And Cursor was dead in the water in terms of access to compute, and they were falling very far behind Codex, Google, Anthropic, and then Elon let them on Colossus, and boom. Instantly, their models are growing faster. And this could be we could be sitting here a year from now, and they're the dominant player. And could we be sitting here, Gavin, in a year, and Elon is selling compute to Google and OpenAI? Is that a possibility or not? Gavin Baker (00:52:18): Well, I think it's much easier to see him selling compute to Google than I think That's gonna happen. Been there have already been posts about that. Yeah. And for sure, Google is gonna wanna be part of orbital compute. You know, it's it's very funny. The only people who are skeptical of orbital compute are those people who are not involved in in data centers or space. Google, Anthropic, Amazon, NVIDIA, David Friedberg (00:52:45): they Gavin Baker (00:52:45): are all very convinced that orbital compute is gonna be reality, and, obviously, SpaceX is extraordinarily well positioned for that. But I do think that Composer 2.5 data point is really powerful. Jason Calacanis (00:52:55): Keep an eye on it. Yep. Gavin Baker (00:52:57): And and the other thing that's come out is Grok build. So what Grok lacked that a lot of other models had and I do think it's important to remember that the newest version of Groc 4.3 is on the Pareto frontier for all frontier models. And you're either on the frontier or you're not. And the the companies on the frontier are x AI with one build of Grok 4.3, which is a 500,000,000,000 parameter model, Google 3.1 Pro, and then OpenAI and Anthropic. And that's it. Those are the companies on the frontier. Jason Calacanis (00:53:28): And the four horsemen. Gavin Baker (00:53:29): Yeah. Google today each have one dot on the Pareto frontier. And obviously, you want as many dots as possible, but Grok lacked a harness. So Claude had Claude code, OpenAI had codex, and now with Grok Build, there is a harness that is available to to Grok. And, you know, as I'm as I'm sure Jason Calacanis (00:53:51): A downloadable app to translate into English that has integrations to all your favorite stuff, whether it's Notion, Gmail, Slack, etcetera. And if you don't have that, it's just like using a a basic chatbot from a year ago. So now they have their downloadable. It's in market, and they are cooking with oil on it, they're playing catch up, but they're moving fast. Gavin Baker (00:54:10): And and it's it's more than just an app. It's it's a runtime. It's an environment. It manages state. It manages memory. It makes these models dramatically more useful to the extent that I think the people at the frontier all agree that the harness is essentially as important as the model, especially in an agentic world, and the harness and the model need to be developed together. So the release of Grok Build and the pace at which they're iterating is, I think, also really encouraging. So now you have cursor, you have the cursor data, you have a clear existence proof that the cursor data is really important because Composer 2.5 is now Pareto dominant and the most selected model on cursor. And that's also important because, you know, these evals don't capture everything. You know, this is why people on Axe talk about the vibes. And the vibes on cursor 2.5 are also really good. Jason Calacanis (00:55:04): They're immaculate. Gavin Baker (00:55:05): Together Yeah. With Grok build, I think these are really important developments. Jason Calacanis (00:55:10): Yeah. There there was Elon was incredibly frustrated by the state of affairs at XAI. He was very public about that. And he's less frustrated now, and he's shipping a lot faster. And so I think that says something, and he has been very focused on it. Freiberg, your thoughts on the SpaceX IPO and what this collection of companies might look like a year or two from now, especially if, like many people believe, Tesla and SpaceX merge. What do you think of dollar sign e l o e l o n as an entity and what impact it might have as if those two were put together in the market cap would put them in the fourth largest company in the world? David Friedberg (00:55:53): We can revisit our earlier conversation about an anti tech, anti AI, anti progress world and society ahead. And if there is an effort, a concerted effort, an organized effort by governments to stop or block access to information, restrict freedom of speech, restrict freedom of purchasing or buying things to control more things. And I think there's a trend line in this direction right now globally. The Internet has always been lauded at this kind of system that provides an open alternative to physical commerce that you could create digital commerce, digital information, digital media that you could share. And it's almost this digital representation of society, but the Internet has to sit physically somewhere. And the assault on data center builds outs in The United States right now, I think may indicate the importance of having an alternative Internet. From the ground layer up, if you have a communication network that isn't restricted and controlled by a government on Earth, it's almost like a backup for civilization, but it's a backup for progress. And I don't own any SpaceX shares, and I'm not trying to sell the book of SpaceX. But I think that there's, like, an important aspect of can you create a system that's not under the control of governments as a way to ensure humanity's progress, to ensure civilizational continuity if things go south, if things aren't good, if things are restricted, and if, you know, fundamental forms of tyranny start to restrict speech, restrict commerce, restrict information flow, and whatnot. And I think having, like, a space based communication network, space based data centers, and space based communication back down to Earth wireless, I think is generally a good thing. It's good to have a backup. Yeah. So put all the economics aside and the multiples and evaluations and whatnot and whether it's SpaceX or not, I think the idea that you could have data centers store information, transmit information, route information, and access information through space based systems that can't be controlled, manipulated, or destroyed by governments is is important. And I I just I like that. Jason Calacanis (00:58:17): Yeah. If you most people don't remember this, but when Elon was starting SpaceX, the original idea when he was running around with Adeyo and they were looking at some rockets and getting carriage from Russian rockets was to back up the biosphere. And he came back from that trip, and I remember talking to him about it. And he said, I think I just have to make my own rockets because that's actually where the problem is. And it would be easier just to make my own rocket to back up the biosphere. And he wanted to put geodomes, like geodesic domes in space with all the plants and wildlife and and creatures. And what incredible vision. And then it you know, there was the necessity of actually getting that up into space, and that's that's the unknown origin story. I will say this, Chamath, the idea of putting data centers in space seems completely doable even though there are a bunch of people who are saying it's not when you compare it to what happened with SpaceX, with Starlink, which people said also wouldn't work. And now he's got 10,000 Starlinks up there. The difference between a Starlink satellite and a data center satellite is really not that different. And Chamath Palihapitiya (00:59:25): No. They're they're pretty different. Jason Calacanis (00:59:27): Well, conceptually yeah. Of course, they're physically different. But conceptually, Elon put 10,000 Starlinks up. Is he capable of putting 10,000 Chamath Palihapitiya (00:59:37): No. Look. The size center of rockets. The size is much bigger, Jason. The foils are much bigger. The wings are Jason Calacanis (00:59:42): much bigger. But it's But Chamath Palihapitiya (00:59:43): it's My Yeah. Jason Calacanis (00:59:44): My point is it's not different if he has the new Starship because that's 10 times bigger. You know? Chamath Palihapitiya (00:59:50): You can't just scale like this. That being said, it's technically possible. I think he will be the first one to figure it out. But I'll just take a much more pedestrian take, which is, okay. You're sitting here. And if I'm asking myself, Jamath, how do I underwrite SpaceX at 2,000,000,000,000? Here's the basic math that I would do. Well, last year, did $1,819,000,000,000 dollars. It'll probably do $25 to $30 this year. Okay. So I'm buying this thing at a fairly costly premium. So what am I buying? Well, I'm buying probably the most important Internet infrastructure project that's happened since the Internet itself. That's gonna scale to hundreds of millions of users. And the reason that's gonna scale to hundreds of millions of users is it's just very useful, it's just going to become cheaper and cheaper and cheaper. So that's number one. I'm buying a delivery infrastructure, but I think over time GDP plus 10, GDP plus 15, kind of a grower. So good business, valuable business, but it's the underlying platform that allows everything else to happen. And then I'm buying an AI business, which will be at the top level, the apps, but at the bottom layer, all the compute capability. And I think when you scale that out, like, why is Colossus so valuable to Anthropic? Maybe that's like a good question to ask. It's because if you look at who's actually capable of delivering a gigawatt data center, these guys are the closest, like an actual gigawatt. And and the reason is is that this stuff is very complicated and very, very hard. I think you've probably heard this famous story where Jensen was like, yeah. He was the one that figured out this one thing that we that nobody else could figure out so that you could strip a bunch of racks and drive a bunch of East West traffic and make the whole thing work together. Chamath Palihapitiya (01:01:43): So I suspect what happens is next year, it's probably $4,045,000,000,000. And then the year after that, it probably doubles again. So now I'm buying it at 20 times revenue. And you would say, well, why can you buy a company like this on revenue versus earnings and cash flow? And I think the reason is because what the revenue does is it gives him the operating leverage to go and invest in all of these other businesses that ultimately consolidate his differentiation and his competitive moat. Because what he creates is a capital moat that then accelerates a technology moat that then accelerates an execution and a learning moat. And that flywheel, when it starts to spin very quickly, and you would say, hey, hold on a second. It's probably spinning quickly now. I would say we're at the beginning of the beginning. Because he's, again, he still has all these disparate assets. I still don't like the fact that Tesla's over here. And as I've told you, that will get merged in. And now you have this incredible corpus of physical capability, movement of all kinds, x, y, and z, right? You have learning capability. You have infrastructure. You have all the connectivity. That thing will look very cheap, I think, in a few years. And he has this one thing that nobody else if you look at the big CEOs, who steps on stage where you're always curious, okay, what has he got up his sleeve? You know, the Steve Jobs, oh, and one more thing. This is the only guy at the scale of civilizational out of left field He's he's the guy. Whether you like him or you hate him, he's the guy. And there's a premium that is well deserved that comes with that. So if you had to pick an underwriting case, Jason, I would flex the revenue and realize that terrestrial data centers alone are 100 or $200,000,000,000 of revenue by 2030, 2032. And that means just building it. So already you're buying it at 20 times revenue just for that business. Chamath Palihapitiya (01:03:46): And that's just Jason Calacanis (01:03:47): the Colossus on the ground earth based. Chamath Palihapitiya (01:03:49): It'll be not even three no. No. Forget space for a second. It's like Colossus three, Colossus four. Jason Calacanis (01:03:55): It pencils out with that. Yes. Chamath Palihapitiya (01:03:57): Getting a nameplate one gigawatt look. It is freaking hard, man. Getting a gigawatt nameplate working is almost and then, by the way, there's all the stuff that he can do on land that he's the best positioned to do. I'll give you one example. There's a great push that Jensen's making, which he needs a partner, and I think Elon becomes a natural partner, to do DC to DC. Forget all this DC to AC to DC nonsense that goes inside of a data center. All the lossiness. Jason Calacanis (01:04:22): Explain what that is in English. Yeah. Chamath Palihapitiya (01:04:23): For everybody. Just like, look. You go through a bunch of power transformations to to actually deliver the electrons into the rack so that, as Gavin said, you can generate the token on the other end. Today, it's it's very inefficient. It's very costly. It requires a lot more power. It requires a lot of cooling. It requires complexity. And what people have said is, wow. If we could just do DC to DC, like, comes in as DC, direct current, it it goes right to the rack as DC, but it requires a fundamental rearchitecture. Jensen needs a design partner and a thought partner to get that done. He's probably the only one. I just think there's a lot of reasons where you can underwrite this to a multiple of revenue plus the x factor, which is just the creativity and the the one more thing. Jason Calacanis (01:05:07): Love it. And then here's two, charts, and we'll have you comment on these, Gavin, after it. Here's the rocket sizes just in terms of scale, and I most people have not actually seen a Starship in person. When you see this thing in person, and I I've been inside that rocket. I think you were we were together, Gavin, when we were in the first build. And, like, inside of that, you can fit 300 people. It's basically like a giant if you thought of a commercial air aircraft, that's what it feels like when you're inside, right, like a seven forty seven in terms of the amount of space in it, especially when you compare with the Falcon Heavy, which is their workhorse. Correct, Gavin? Gavin Baker (01:05:48): Yeah. And Starship's gonna get bigger based on their road map. It's gonna get a lot bigger. Jason Calacanis (01:05:53): A lot bigger. And then this one is the most interesting that this started trending last week. This is cumulative payloads launch 1957 to today. SpaceX is basically about to, in just that and and this is really what exponential growth is about, and this is what disruptive technologies are about. Just from 2012 to today, SpaceX is about to dwarf the rest of the world's cumulative payloads into space. So, Gavin, maybe take the other side of it. When do these data centers in space happen? What has to happen for those to be a reality? When does that hit SpaceX's bottom line? We've heard from Chamath, Here's all the things that hit the bottom line in the short term and midterm. But I think data centers in space would be a midterm to long term play. Three years is what I'm hearing. So tell us about that business in relation to the two charts I just shared. Gavin Baker (01:06:49): Well, the one thing I would just say well, first, all those charts about launch are before Starship was operational, and most most of that master orbit was done by Falcon. Yes. And Starship, Falcon is reusable. Starship is designed to be rapidly reusable, and this is a critical difference. Like, let's say Blue Origin successfully solves reusability. They're where SpaceX was 10 ago. Let's say China solves it ten years ago. Rapid re the reusability means that you extensively refurbished the rocket, you know, the engines, everything, the fairing. It takes a lot of time. You know, maybe you can fly that rocket again in thirty days, sixty days. Rapid reusability means that you can fly the same fly and land the same rocket multiple times per day. So if SpaceX and and it's really hard to do rapid reusability. I think it would have been it would have been not trivial, but much easier to have Starships working if it was just designed to be reusable. That's not enough for what Elon wants to achieve of, you know, a a moon base, a colony on the moon, a colony on Mars, mass drivers on the moon. You need rapid reusability, and that is why Starship is such an engineering challenge and will be such an impressive achievement when they have rapid reusability. But I do think that master orbit, rapid reusability of Starship means if they When do Jason Calacanis (01:08:25): you when do you predict they'll have that? Rapid reusability to space, You think Gavin Baker (01:08:28): I mean, find out. We're gonna find out. You know, we find out I'm I'm I'm I'm gonna be at Starbase today for the launch. Yeah. So, you know, we we we turn over cards, and, you know, it's important for everyone to remember. Like, let's just let's just say it's a fireball. SpaceX will still learn from this. Yes. They they learn from failure. If you don't fail, you're not learning. Same way if you're not wrong, you didn't learn anything in that day. And this is a brand new rocket, a brand new booster, a lot of new technology. There's a lot of instrumentation on it. So whatever happens today, SpaceX is gonna learn and rapidly iterate. I don't know when. I don't wanna make a prediction. I would guess a year or two, maybe sooner. Jason Calacanis (01:09:14): I think that's most consensus. A year or two is I think perfect consensus. Yeah. Gavin Baker (01:09:17): We'll see. So like the, even if everybody else solves reusability, master orbit from everyone else will quickly asymptote to a very small number. As far as when will orbital compute be a reality, I would say, well, it is important to realize there is a working h 100, an NVIDIA h 100 GPU in space today. Yeah. Andre Karpathy both trained a model on and used for inference. So this is you know, it's it there's a working GPU in space today. Jason Calacanis (01:09:51): And NVIDIA's making a space designed version of this, which will be different because the heat sink has to be different. There's a bunch of weight that you put on it when it's in a data center that you don't need in space. And you also have to reinforce it for the journey to space because these things are gonna shake and break apart. The data center ones are not made to have that many g's put on them. So you're gonna need an an an an an industry an industrial strength one that gets to space that has a different profile. Yeah, Gavin? Gavin Baker (01:10:20): Well, one of the things that's been so magical about SpaceX is they're very good at injure engineering the rocket and payload. Yeah. So that you can use semiconductors that are not designed be in space or satellites in space, and those semiconductors are a lot cheaper. We have a cup my my firm, Atreides, is an investor in a company called XSight Labs that it's a matter of public record, is gonna be in essentially every Starlink. And the chips were not designed to go to space. They're not radiation hardened. You know, everyone, you know, SpaceX really liked a lot of the the specifications on the chips. And then it's like, well, we'll see how they do with rad testing. And they just, you know, happened to pass. And so that is one of, like, one of something that's very underappreciated, I think, about SpaceX. Jason Calacanis (01:11:08): One of their specialties. Gavin Baker (01:11:09): One of their specialties. Jason Calacanis (01:11:11): Yeah. Gavin Baker (01:11:11): But I Jason Calacanis (01:11:11): think Gavin Baker (01:11:11): we'll second half of twenty eight to first half of twenty thirty would be my point prediction. Jason Calacanis (01:11:17): Alright. Let's do NVIDIA and then the the market recap since we have here, Gavin, since Freiberg, you wanted to get in on that. NVIDIA blew out its earnings again. Q one performance is just mind boggling. 81,600,000,000 in revenue, up 85% year over year, 20% quarter over quarter. High growth in the stock market for those people who don't participate. 20% would be a high growth company Year over year, they did that quarter over quarter 58,000,000,000 of net income, and 48,000,000,000 in free cash flow. They're doing all this at 75% gross margins. They're growing massively. And they're obviously the most valuable company in the world at a $5,300,000,000,000 market cap. Stocks up but 16% year this year with all that growth. That's a magnitude of that 16%. And, they've announced another 80,000,000,000 in additional buybacks on top of the 100,000,000,000 in buybacks, they did at the start of 2023. So they're buying back about 4% of the company. They raised the quarterly dividend 25 x from 1¢ a share to twenty 5 percent cents per share. And their CFO said they're gonna return 50% of the free cash flow to shareholders. Never been a company like this, Freiburg. The the scale of this is just extraordinary. David Friedberg (01:12:35): Mhmm. Yep. Don't don't see Gavin Baker (01:12:38): it. Jason Calacanis (01:12:39): There you have first, there's your market report from Freiberg. Chamath Palihapitiya (01:12:42): Don't seem so enthused. Jason Calacanis (01:12:43): Yeah. It's a mhmm. Mhmm. He's potatoes in the oven. Chamath Palihapitiya (01:12:46): I have a question for Gavin. He did a really interesting talk with Patrick O'Shaughnessy, and there was this one thing that I wanted to ask you about because I thought it was so interesting. You said when you look at multiples of the chip companies and you look at the revenue multiples of the DRAM companies, both cannot be true. In the context of NVIDIA's earnings, can you just explain maybe in plain language for folks? I just thought it was so fascinating because it explains Yeah. It explains, I think, just to set it up, where is value over the next five years? Like, I think if you looked at Leo Aschenbrenner, his fund has gone from, like, 0 to $5,000,000,000 overnight, and it looks like he's just got massive puts on the chip sector, and he's kind of rotated. So just give us context, Gavin. Where where's the puck going? Gavin Baker (01:13:30): Well, so maybe take the questions in reverse orders. For Leopold, who's clearly a brilliant man, I think he's a Rhodes scholar at, like, 19, and I think my understanding is he's putting up pretty extraordinary numbers. I I have yet to meet him. He actually shares an office in San Francisco with a friend of mine, so I think I'll probably meet him sometime soon. But it's gotta for that 13 f that he filed was at the end of the first quarter when, you know, I would say we were in the, you know, the the thick of geopolitical fears. And I think you saw a lot of puts on a lot of 13 f's, and I don't know that those puts are still there. Chamath Palihapitiya (01:14:08): Okay. Gavin Baker (01:14:08): You know, I think a lot of people wanted to be hedged for Iran. And, you know, now I think it's a little more clear. So I wouldn't read I wouldn't read Leopold's 13 f as being super negative on on semi. Chamath Palihapitiya (01:14:21): Chips. Okay. Gavin Baker (01:14:22): On chips. Second thing, think cross sectionally, if you look at the valuations for all these AI names, they just they can't all be accurate. You have memory makers that, you know, three to five times PE. You have NVIDIA at a really low PE. You actually have, you know, some other accelerator companies at reasonable multiples, and then you have everything else. Everything in power, everything in cooling. And when I say power, I don't mean utilities. So IPPs are actually quite reasonably valued. But power, cooling, even probably some of some of the optical names, these are discounting very different things. If the multiples on the power, cooling, optical names are correct, NVIDIA memory, they're going up a lot. If the multiples on NVIDIA memory are are correct, everything else is probably gonna underperform. The AI market is cross sectionally inefficient right now, which is what I was trying to say. As far as the NVIDIA quarter, I do, they went to a new reporting structure, data center and AI, and there was no. Data center and edge. And then within a within AI, they have hyperscalers, And then I think they call it AI clouds Chamath Palihapitiya (01:15:37): AI clouds. Gavin Baker (01:15:38): Industrial and enterprise. What I believe is if we were to make a true apples to apples comparison, and Broadcom, you know, there's a narrative that NVIDIA is losing share to the TPU. And Broadcom guided for a 143% year over year growth in their AI semiconductor revenue in the quarter that they will report, that's comparable to the one NVIDIA just reported. I think if you were to and I just I so wish they had reported slightly differently. I wish they'd done hyperscalers, AI clouds, and then industrial and enterprise. Because I think the segment that is comparable is the sum of hyperscalers plus AI clouds stripping out China because Broadcom just did not have the China business that NVIDIA did. And I think on that basis, in other words, in within the western AI world, within data centers that are being built, whether they're being built by Coraweave, x AI, Amazon, Google Chamath Palihapitiya (01:16:39): Mhmm. Gavin Baker (01:16:39): NVIDIA's AI business is growing faster than broadcom's and faster than a lot of other companies that are, you know, seen as part of this ASIC share gains story. And, you know, I think Jitsu has become you could you can hear it increasingly frustrated and rightfully so with two things. I would say, what is the performance of stock to to You've Jason Calacanis (01:17:06): been vocal about that. Like, what is going on here? You're putting up record numbers, and we're getting no, like, credit. Gavin Baker (01:17:12): Yeah. I get it. It it just how could there be a share loss narrative if I am gaining share? And it is indisputably true that he is growing faster than hyperscaler CapEx even without these adjustments. Chamath Palihapitiya (01:17:26): Yeah. Gavin Baker (01:17:26): And I think the other thing that's so frustrating to him is these other ASICs are not being submitted for benchmarks. They're not in the seminalysis infer inference max. They're not in MLPerf. I think the reason they're not being submitted is they will lose. And you can't fight shadows. And until we see a clean benchmark of whether it's GB 300 versus TPU, V7s are, you know, Chamath Palihapitiya (01:17:54): very Versus Inferentia. Yeah. Gavin Baker (01:17:55): Yeah. Versus Tradium. Yeah. Versus Tradium. Versus And that's why a lot of these other chips I think Tradium's in a great spot, are being submitted. But nonetheless, NVIDIA is doing well. Once you become the largest company in the world, you kind of you tend to trade, my observation, would be at stair step patterns where you kind of the multiple compresses, compresses, compresses because people are skeptical of the size. Chamath Palihapitiya (01:18:19): Then you have a rerating. Yeah. Gavin Baker (01:18:20): And then you rerate. Jason Calacanis (01:18:21): New floors established at a higher rate. Yeah. Gavin Baker (01:18:24): I think there was one other really important thing in the NVIDIA quarter. It's they said that they thought their CPU business was gonna be $20,000,000,000 this year. Chamath Palihapitiya (01:18:32): Yeah. Yeah. Yeah. Gavin Baker (01:18:32): That's extraordinary. I mean, the overnight for one of the world's largest CPU manufacturers. And I think that is a testament to NVIDIA has a unique position. They're the only company that works with every lab. And so that puts them in the best position to architect their chips, they call it co design, for where the models are going. And I think that $20,000,000,000 CPU figure is pretty extraordinary. Chamath Palihapitiya (01:19:00): This is the thing. Like, at the end of this Grok transaction last year, my kind of prevailing thought on this is we're gonna move to these domain specific architectures. I thought that was like a We're just now waiting for which models. But the reality is that that DSA market evolution is actually happening inside of NVIDIA. That's what's so insane to me. That was my takeaway from the quarter as well, which is like, holy These guys actually have domain specific architectures because they're doing these design programs with every this is why back to sort of the you know, when he does DC to DC with Elon and Colossus three or whatever. It's just this. It's another game changer for everybody. Gavin Baker (01:19:35): He doesn't take a month. He makes nine. And then I don't think the cost at which you can finance these chips and these useful lives is really important. Chamath Palihapitiya (01:19:44): You had an incredible insight, which is the amortization schedule for Corweave and all these guys. They got saved. You may wanna just explain what that is and what why you said it. I thought that was a great insight. Gavin Baker (01:19:53): No. Thank thank you, Travath. I appreciate it. So when Coraweave and all these Neo Clouds came public and by the way, this goes for the hyperscalers too. There was a big bear case that, hey. These guys are amortizing their GPUs and CPUs over four, five, six years, and that's way too short of a lifespan. The true lifespan of a GPU is more like two years, and therefore, you know, the profits of all these businesses are overstated. The reality is Jason Calacanis (01:20:16): This was Michael Burry who put this out there. Gavin Baker (01:20:17): Yes. Jason Calacanis (01:20:18): Just to be clear. Yeah. Gavin Baker (01:20:19): Yeah. And, you know, thank you, Michael Burry. We need bears. Yeah. Thank you. Chamath Palihapitiya (01:20:23): Yeah. That's like it's like asking Gerardo about modern music. Gavin Baker (01:20:28): Well, I don't I don't wanna cast this person on Michael Berry. He's a he's a brilliant man, but we need bears. Chamath Palihapitiya (01:20:33): Love it. Hey. Somebody call Vanilla Ice and ask him what he thinks. Jason Calacanis (01:20:36): Oh my god. Millie Vanilla, check your name. Chamath Palihapitiya (01:20:41): Of time. What a waste of time. Jason Calacanis (01:20:43): Oh, poor come on the program anytime, Michael Murray. Go ahead, Gavin. Well, let's keep us Gavin Baker (01:20:47): on track. Happy to chat. But now that we've disaggregated inference, we have these different domain specific accelerators. You can mix and match them. And I think the GPU stays in a lot of ways at the center of this constellation for a while. And you can put whether it's a Grok accelerator, whether, you know, it's a Cerebras accelerator in front of old GPUs, use Grok or Cerebras for decode. And then those older GPUs, they have a useful life for ten or fifteen years, and this means that you can finance GPUs. I think Corey's lowest financing, I can't forget if it's six or 7%. Chamath Palihapitiya (01:21:20): 6%. Gavin Baker (01:21:20): It's gonna come down. Chamath Palihapitiya (01:21:22): Yeah. And if you Gavin Baker (01:21:22): can get an asset backed loan and asset backed financing for GPUs at a lower rate than other chips Chamath Palihapitiya (01:21:29): No. Gavin Baker (01:21:29): That that a profound advantage. Chamath Palihapitiya (01:21:31): That quarter single handedly saved the neo Neos clouds this quarter. I mean, they single handedly saved them all. I think they they should all they should all say an incredible thanks to Jensen because Jason Calacanis (01:21:43): I interviewed the CEO of CoreWeave, Michael in Trader. Michael in Trader. Yeah. And he was saying, hey. Listen. People have no problem buying and financing these over a six year period, and people are asking for things that are coming off and that he thinks they're gonna have year seven, eight, nine. They'll have some useful life, you know, in addition to that. So, yeah, so he's like, I I I don't know what anybody's talking about here. Like, this is just not informed analysis was his point. Right. Gavin Baker (01:22:11): Like, I Jason Calacanis (01:22:12): the game on the field, and people are betting with their dollars with him. He has them pay in advance and sign six year contracts. If they didn't think it has six year lifespan, they wouldn't be signing a six year contract. Pretty straightforward, and they can't get enough of them. Okay. Let's end on this market update. Macro picture, not great. Oil remains elevated, although there might be a settlement. Every week we have there is a settlement coming. Maybe this time, sixteenth time, it's a charm in the Iran war is gonna wrap up, but we're in week 12 of it. And this was supposed to be four to six weeks, so wars never get resolved quickly. That's one thing we've learned in our lifetimes. Oil is driving inflation massively higher. Polymarket says 99% chance May inflation comes in at 4.2% or higher. Survey of professional forecast is projecting CPI hits 6%. You heard that right, folks. We weren't just talking about a 3% handle, which we just hit. Now people are saying four, five, and 6% in q two, and, obviously, that's a huge revision. And, the narrative was, hey. More fed rate cuts coming. Now we're talking about fed rate increases. Inflation is causing, obviously, bond yields to rise. Ten year hit 4.6%. You remember we've had Besson on the pod multiple times, and his goal was to get that under 4%. Now it's significantly above that number. And also if we go around internationally, Japan's thirty year is at a high of 5.1%, highest ever recorded. UK yields highest since the great financial crisis. Germany highest since 2011. And in Korea, retail investors are borrowing borrowing record amounts of money to trade in AI chip stocks. They also had a incredible run-in Korea betting on crypto at the peak. So that's some sort of an interesting signal. Friedberg, is your alt personality gonna come out right now? Are you concerned? Is doctor Doom making an appearance here, or do you think this is manageable? How concerned are you? Jason Calacanis (01:24:07): What is the David Friedberg (01:24:08): point of being concerned when you have ridden the roller coaster to the top and Speaker 6 (01:24:12): it is beginning its descent? I I don't know what there is David Friedberg (01:24:15): to be concerned about. The the the force of gravity is inevitable. The roller coaster will roll down. Will throw our hands in the air, and we will scream whee as we go for the ride. Global debt to GDP is 310%. Reserve currency status to the side, the spending problem at the federal, state, local level, the spending problem at every country to basically keep economies growing to support existing leverage ultimately creates a cascading effect. It ultimately breaks. And as it starts to break, you have massive inflation because the value of your underlying, currency collapses, and then you have money printing and all this other sort of stuff, which inflates the value of assets, which allows you to keep servicing your debt, and the spiral takes off. And so we will just, enjoy the ride. This is the moment. You know? Thirty year treasury, 5.2%. This Japanese yield, some argue might you know, you should talk to more active market participants than me, but and probably some economists who trade the market, but I would think that this is one of those things that could be a catalyst for a for a a credit crisis because there's a lot of people that are in this carry trade. And we'll see. You know? This is Okay. This is water leaking out of the bucket. There it Jason Calacanis (01:25:39): is. Doctor. Dumasier, Chamath, your thoughts on Doctor. Dumas' panic attack of the month. Is this is this time real? Is this the seventeenth prediction of the next six recessions? What do we got here, Chamatha? Are you concerned? How concerned are you about these signals that are flashing? Chamath Palihapitiya (01:25:58): I I think that's exactly what that is, Jason. There are signals that are flashing. I think there's pockets of the market that still make sense that you can underwrite if you want to buy businesses that represent the future. And if you can find a few of those and you can get comfortable with that and you can own it for ten years, I think you buy those companies. And generally, everything else I think you should not speculate and you should generally avoid, not just because it's an upmarket, but in every market. I've learned this the hard way. We've all kind of gone through this. As I get older, it's just not worth it. The vicissitudes of the market don't give me anywhere near the sugar high it used to on the way up, and it makes me feel horrible on the way down. So how I manage myself is I have a few companies that I really believe in. I have extremely concentrated large holdings in those. Large for me doesn't mean large for everybody else. And then otherwise, I just kinda stick to my eating and keep my head down. It's a much more rational way to behave. Jason Calacanis (01:26:56): So How many public stocks can you keep in your brain and still sleep at night holding for the long term? What's the is there a number for you? Is it five? Is it 10? Chamath Palihapitiya (01:27:05): Seven. It's five or less. Jason Calacanis (01:27:08): Five or less. Chamath Palihapitiya (01:27:09): Five or less. Jason Calacanis (01:27:09): And so what your largest holding right now is what percentage of your net worth would you say? Chamath Palihapitiya (01:27:14): I don't know. Jason Calacanis (01:27:15): Or like top two maybe. Chamath Palihapitiya (01:27:16): Yep. Oh, top two? Jason Calacanis (01:27:19): Yeah. Like one and two. One is 22 is 15, or one is 42 is 20. Chamath Palihapitiya (01:27:24): I don't know. Again, it depends on the data. I don't know. But it's it's Jason Calacanis (01:27:27): Just curious. But I think that's I think that's really important. Chamath Palihapitiya (01:27:30): My point is there's no 30 things that I'm tracking. I don't I don't have the time. I'm not smart enough. There's too much information. There's, like, four things that I stay on top of. Jason Calacanis (01:27:39): Gavin, you you do this for a living. How many positions do you manage, and what's your take on some of the flashing signs that are saying, hey. Slow down, or maybe there might be a wreck around the corner here, you know, when they do the checkered flag in the f one or whatever the metaphor you wanna use is? Gavin Baker (01:27:54): Well, so one, I I manage more than a 100 positions at my firm, and I do that with a team. We're over 30 people now. So it's not just me, and I work with some some great people. And then three things can be true. Rates going up is very concerning. What is happening with AI right now with Anthropic growing faster than any country, any company in history at massive scale. Jason Calacanis (01:28:20): And certainly any country. Gavin Baker (01:28:21): Absolutely unprecedented. Yes. They're actually yeah. They're they're now the size of, you know, Yeah. Yeah. They're we pull up where Chamath Palihapitiya (01:28:29): they bigger than a Jason Calacanis (01:28:30): 100 different countries for sure. Gavin Baker (01:28:32): Exactly. Compounding really fast and now profitable. Yeah. Which I think really changes the moment. Which is bizarre. Yeah. Jason Calacanis (01:28:39): Yeah. How did that happen? Yeah. Gavin Baker (01:28:40): So those things can both be true. And I think we should all remember the tech bubble happened with, you know, the ten year and the thirty year much higher than they are today. And, you know, these the NVIDIA of of of the tech bubble was Cisco, and it traded a 100 times forward earnings. And, you know, I think NVIDIA is probably at a low teens multiple of kind of low to mid teens multiple of real earnings, you know, if you that that'd a buy side consensus, not in a trading's number. And then the third thing that I think is true is the straight up formulas being closed. While it's terrible for everyone, it is relatively the best for America. Because we are self sufficient in energy. We are self sufficient in food. We have become a massive exporter of oil. We're now the world's largest, not only oil and gas producer, but oil and gas exporter. And, you know, those three things can be true. What do they mean? I think it's probably hard for me to see with America being the most advantaged by what is going on. Jason Calacanis (01:29:38): We're still the best currency. Yes. Still the best economy. We still have the best public market companies, and we have the best private market companies. Yeah. And we are one of the greatest producers of oil in the world. So we're in good shape despite this international chaos. Gavin Baker (01:29:54): I don't think a dollar crisis is around the world. Now listen. Like if, you know, the Bundesbank was still in charge and you had the Deutsche Mark and there was a currency with better fundamentals, we would be at very high risk. Probably. But just because we're the best house in what is globally a bad neighborhood of high debt levels, and we have AI in our corner, and we have energy self sufficiency, and every day the straight up form was disclosed, I think is relatively good for the reindustrialization of America. Like, I think, you know, you have to you have to Jason Calacanis (01:30:30): all these You're saying it's a forcing function. It makes us, just like COVID did, be more resilient and, yeah, more self reliant. Gavin Baker (01:30:38): Yes. And electricity is a base input to every manufacturing or industrial process, essentially all of them. And what we make electricity with in America overwhelmingly is natural gas. And you can look it up. NG one. It is down this year. The input cost for electricity in the rest of the world, you know, lots of different things, but LNG is a very important one, and it's up 100, 200%. And so the street from us is absolutely bad for everyone, but relatively good for America and relatively good for Trump's policy goals. And that's why I think he's in no hurry. Every day the strait is closed. And for whatever he does seem like a relative thinker. Every day the strait of almost is closed is relatively good for America. It's terrible for Europe. It is terrible for Asia. Jason Calacanis (01:31:33): Japan and China need that oil. Philippines needs it. India needs it. Yeah. Gavin Baker (01:31:37): Yeah. And so all these things can be true, but the one thing I do just wanna say is rates going up and inflation going up is never good. But we have to hold what's happening with AI where the fundamentals are getting a lot stronger in our mind. And then the one thing I would just add is AI has been seasonal. The market's seasonal. You know, it often, you know, sell in May, go away. And AI fundamentals also appear a little seasonal. In the past, that's been you know, because college students, they they use a lot less chatty PT and Claude in the summer. And, generally, people maybe work a little less hard when the weather's nice. Now with the JYPIC AI, will the fundamentals still be seasonal? We will see. Jason Calacanis (01:32:23): Oh, that's a really interesting point. Right? We see that ecommerce, apps, subscriptions. As investors in a lot of these companies, we would always have these board meetings, Chamath. Oh, q three. Yeah. People are out gallivanting and they're not playing Candy Crush or buying comm or whatever. But, oh, hey, Uber and DoorDash went up. People are traveling. Etcetera. Okay. Final story of the week. We had a forty eight hour jaunt by a bunch of tech CEOs and the president to hang out with she. Lot of high fives, lot of handshakes, a lot of great vibes. But coming out of it, we haven't seen anything definitive, Friedberg, in terms of policy. This was supposed to be some big breakthrough. It would have downstream effects on tariffs, on us selling chips to China. We did see a little movement there. And the Strait Of Hormuz, we would become, you know, wonder twins with Xi and Trump reopening it, but nothing really definitive other than some soybeans being sold and some h one hundreds, h two hundreds getting sold to Baidu and some of the top folks. So what do and maybe some planes got sold too. So other than a little BD, a little business development, yes, Frabar, what's the outcome here, or was it just a bit performative in your mind? David Friedberg (01:33:40): There was a a question. Would the administration leave China with a grand deal that made everyone feel like there's a long range view on a partnership, but I don't think that that's what happened. There were a few announcements, obviously, around an intention to continue to work together in a cooperative way and find a path to partnership, an intention to establish additional trade deals, and and, you know, there were some purchases of aircraft and and some agricultural product commitments. But fundamentally, the grand deal, the big deal that I would say reduces, deescalates tension probably didn't manifest as some had hoped it would. And I don't think it's any surprise that Putin is with Xi today. And this is also performative that following The US visit, there is now a relationship bonding moment happening between China and Russia. So the story continues. You know, there is no happy ending, and there is no rainbow colored chapter three in this book. It's gonna continue to be a dramatic arc as this rising power continues to challenge The United States. Gavin Baker (01:34:50): And I David Friedberg (01:34:50): think the story continues. Jason Calacanis (01:34:51): Were you expecting a happy ending? Chamath Palihapitiya (01:34:54): I can't answer that question. I mean, Jason Calacanis (01:34:59): to the story of the China visit. Not not I'm not talking about any other things going on in your life. But did seem like some planes and soybeans got sold, some h four hundreds perhaps. But to me, it wasn't like there was some grand deal that occurred, but it's nice to see them together. Right? I mean, that is nice. Chamath Palihapitiya (01:35:16): I think it was successful. I think there's what you see on the surface, and then there's what happens behind closed doors. And Yeah. Without speculating too much, I think that it was a useful and productive trip. I think the the biggest thing that they probably got alignment on is just geopolitically the tic tac toe of what has to happen next. And and and I think that there's some amount of agreement there. I'm just guessing. Jason Calacanis (01:35:37): Yeah. And so that guess, if I was gonna unpack it, hey. We get, we have Venezuela. We have Iran and Taiwan. You have one I would just say, look. Chamath Palihapitiya (01:35:47): Here's our Jason Calacanis (01:35:47): house here. Chessboard. And Chamath Palihapitiya (01:35:50): Here's what I would say just very generally. Like, it's just I think that there's a that there's a a way to divide up the game board in a way that helps them and helps us. Jason Calacanis (01:36:04): Gavin, any thoughts on specifically NVIDIA being able to sell more chips into China material for the company? Good for America? I mean, it's obviously a pretty debatable issue. Gavin Baker (01:36:16): I think I disagree with Jamath on this. I think selling and deprecated NVIDIA GPUs to China lowers the odds of them developing their own alternative ecosystem, which would be a lot power hungrier, because you do use optical you bring in optical a lot earlier for scale up fabrics. I think there's sound arguments that this is stabilizing for the world and is the best highest probability path for keeping America ahead in AI and kind of keeping control of AI. And it's almost a shame we've had to have this debate because now people like me have said this many times and try to if they didn't understand it, they probably do really understand it. By the way, that's not to say we shouldn't have had the debate. But, that is what I believe, you know, reasonable minds can disagree. Chamath Palihapitiya (01:37:11): By the way, what where do you think we disagree? I'm not sure. I agree with you. Gavin Baker (01:37:14): Oh, good. I'm glad we No. You guys yeah. Chamath Palihapitiya (01:37:15): I was gonna turn You it Jason Calacanis (01:37:16): start you start with the on No. Chamath Palihapitiya (01:37:19): No. No. I'm, like, sell I'm, like, sell everything to them. No. What I was just saying is that there was what you see on the surface of what they can speak to the press, but I think the most important thing was the negotiation of, hey. Listen. Like, we're gonna do these things. You do these other things. And that's never gonna get put out in a multi you know, memoed press release. That's my point. That's all I'm saying. Gavin Baker (01:37:39): Yeah. And I I would just say, listen. It like, American China talking is only good. Want to avoid the I agree. The disease trap that has been discussed. And I talks about a lot. They're very aware of it. Speaker 6 (01:37:52): And They brought it up. Yes. She brought it up by name. Gavin Baker (01:37:56): Yeah. Yeah. Talking is a integral step of avoiding a dissident Jason Calacanis (01:38:00): I have to say. Gavin Baker (01:38:01): Having a nice resolution. I do say Jason Calacanis (01:38:03): perspective, like, the greatest superpower Trump has is his ability to bond with dictators, monarchs, royal families, Gulf monarchies. He's just great at it. They see eye to eye. They vibe. He has no problem going to see them. He has no problem inviting him to UFC fights. Like, this is like if if she comes to The United States and he's sitting courtside with Dana White, like, that's when we know things are gonna be okay. I do think he's probably given them the green light on like, hey. Taiwan's yours. Just let's not have it during my administration. Maybe like we do a thirty year deal or a twenty year handoff deal. I wouldn't be surprised if something like that happens. Gavin Baker (01:38:43): Or a one hundred year deal or a two hundred year deal. But the one thing I would just say that I'm sure was communicated is, hey. Wars consume a vast amount of oil. You buy your oil from Iran, Venezuela, and Russia. Russia alone can supply a fraction of what you need. And now it should be clear to the world. Chamath Palihapitiya (01:39:03): Two out of three are off the chessboard. Gavin Baker (01:39:05): Yeah. If you two or three are off the chessboard. If you do something we don't like, Venezuelan oil, gone for you. American oil, gone. Brazilian oil, gone. And we'll say to all of our good friends in the GCC, we're so sorry, but we have to close the Strait Of Formos again. So Iran, all of Middle Eastern oil, gone for China. Now you just got Russia. And good luck fighting a war with just Russian oil against us, Japan, South Korea Jason Calacanis (01:39:38): Australia, UK, France, Germany Good luck. The world. Good luck. Gavin Baker (01:39:42): I mean, who knows about Europe? Jason Calacanis (01:39:43): But for sure They'll come. Gavin Baker (01:39:44): They'll come. Would be there. For sure, Japan would be there. Jason Calacanis (01:39:47): Oh, and Australia, for sure. Korea, for sure. Gavin Baker (01:39:49): Yeah. And so I think it's gonna be a more stable world on the other side of Iran, however it resolves. And I think that's a static but good. Chamath Palihapitiya (01:39:59): Yeah. I think that's a good insight. Jason Calacanis (01:40:00): I think it's a great insight. Alright. Listen. We missed you, Saks. Come back soon. And Gavin Baker. Chamath Palihapitiya (01:40:07): Shout out to bestie, Gavin. Thank you. Jason Calacanis (01:40:09): Yeah. You're so great. Thanks for coming. Appreciate it. And your father-in-law, what's his name again? Gavin Baker (01:40:15): Jeff Painter. Jeff. Jason Calacanis (01:40:17): Jeff Painter, we love you. Thank you so much for all the kind words. We'd love to invite you to come to Liquidity or the Summit. I know you're a big fan of the show. Wanted to give you a shout out here on the show. Thanks. I used your fandom of the show to leverage Gavin who was like, I can't make it today. I was like, tell your father-in-law. We're gonna get him backstage VIP tickets to the next two events if you show up today. And if you don't, I'm gonna backchannel it to him. And all of a sudden, Gavin made it to the show. Chamath Palihapitiya (01:40:48): Love you, boys. Jason Calacanis (01:40:49): A little bit of pressure. That's my way of the that's my homo straight. Gavin Baker (01:40:53): Yes. I'm always there for my father-in-law. Jason Calacanis (01:40:56): Absolutely. Alright, We'll see you next time. Chamath Palihapitiya (01:40:58): Bye bye. Gavin Baker (01:40:59): Take care. Speaker 5 (01:41:31): We should all just get a room and just have one big huge orgy because they're all just useless. It's like this, like, sexual tension that they just need to release somehow.