IPO 回归：科技巨头为何终于要上市 | All-In 流动性 IPO 专题

Jason Calacanis (00:00:00): Hey. 2026 could be an all time record for IPOs. Andrew Feldman (00:00:04): The AI IPO of the year so far. That company is Cerebras. Cerebras Systems founder and CEO Andrew Povnan. We are participating in something extraordinary. On everything we do, we are the fastest bar none. Well Marshall is the co founder and CEO of Planet Labs. Will Marshall (00:00:20): Space and AI are really a match made in heaven. They're getting married, in fact. Just like Google figured out how to index the Internet and make it searchable. We are indexing the Earth and making it searchable. Jason Calacanis (00:00:33): He's got his glasses, the famous red glasses. Brad Gerstner's here, founder and CEO of Altimeter Capital, a leading tech investment firm. Brad Gerstner (00:00:40): I believe that the wave is the biggest wave in the history of technology, will be incredibly beneficial for America. I'm rooting for all of them because I'm rooting for America. Andrew Feldman (00:00:50): Ladies and gentlemen, please welcome Brad Gerstner, Will Marshall, and Andrew Feldman. Jason Calacanis (00:01:01): Welcome on the couch. We switched it up. Go on. Good couch. Nice you. Nice to you, my friend. Hey. Chamath Palihapitiya (00:01:08): Lovely, Blake. Will Marshall (00:01:09): Nice to see you. Jason Calacanis (00:01:09): Last last time I saw you were Davos? Yes. We were in Brad Gerstner (00:01:13): Davos causing J. Trouble. Cal. You know that little Davos yacht? Chamath Palihapitiya (00:01:17): I would just Andrew Feldman (00:01:18): you know, Jason Calacanis (00:01:18): it was pre IPO. We're chopping it up. Davos. We're in Davos. Brad Gerstner (00:01:22): Hanging out at Davos. Jason Calacanis (00:01:23): Well, no. Listen. I was everybody knows the story. I'm supposed to go on my yearly Japan ski trip. Sax calls me. With Tucker? Yeah. Well, anyway, we we don't drop that name, but I'll pick it up for you. Put it over here. So talk anyway, so I cancel on Tucker. I cancel the ski trip because Sax calls me. He says, listen, POTUS needs you, the world's greatest moderator in Davos. No problem. Chamath Palihapitiya (00:01:44): I said Brad Gerstner (00:01:45): Sachs, POTUS, and Davos. Jason Calacanis (00:01:47): I said, when? He says, in three days. I say, you got it. I go and they give me a badge and it's like the special green badge and they buzz you through the security. And I look at the monitor and it says, Jason McCabe Kalacanis with Donald J. Trump. Chamath Palihapitiya (00:02:05): Oh, wow. How did you feel? I thought Andrew Feldman (00:02:08): it was hilarious. Jason Calacanis (00:02:10): So then I went and we did a great interview there, and we did like six or seven of these great all in interviews, and it was fun. Chamath Palihapitiya (00:02:16): Let's start this because the two of you guys run two of the most interesting and consequential newly public companies in the stock market. Andrew Feldman is the founder and CEO of Cerebras. Will Marshall is the founder and CEO of Planet Labs. But you are also the insight and a gateway for all of us to understand these two big trends. One is in AI silicon. The other one is in space data centers. I think it would be a really interesting thing to Will Marshall (00:02:40): And they're emerging. Chamath Palihapitiya (00:02:42): And emerging, yeah. But let's just take one step back. You just heard the last conversation about being public, going public early. Let's just talk about that because I'm just very curious. How's it been? It's been three weeks or so for you. It's been about a year and a half or two years for you. It's more fresh for you. Was everything that you thought it would be? Andrew Feldman (00:03:04): What's clear so far is I need to upgrade my name drop game. I mean, that that was a tour de force. Yeah. But by the Jason Calacanis (00:03:11): way, you were you were in Davos with J Andrew Feldman (00:03:13): I I was. I was there, but that Will Marshall (00:03:17): Tour de force. Andrew Feldman (00:03:21): Look, I I I think you do all this work and I I think it's really difficult to to overestimate the amount of garbage that's involved in in going public. The number of meetings where you look on the Zoom and there are 130 attendees, and the amount of times you review these documents and the commas move and just no values added. You go there and you have this enormous event and the next morning you've sold no more stuff. Your engineering projects have made no progress since the day you weren't public and you go back to work And you have some new constituents that you have to address and communicate with. But the core parts of your business, you have more money in the bank, but not a damn thing changes in the important parts of your business. If you need new supply or if your relationships with your vendors are bad, they're still bad. If they're good, they're still good. And so I think what we've seen is your employees have a party, everybody's really excited, put your head back down, you high five and you go back Brad Gerstner (00:04:38): to work. Can I just give a little context and then I want to hear from Will? You know, if I can, Andrew, you know, we were investors in Cerebras. I was on the board a year earlier where we were trying to go public. And, you know, aside from just being a warrior who weathered a decade worth of storms that would have taken out any normal human being, the path to going public for cerebris was a particularly challenging one. One of their investors was The UAE. So, was questions about CFIUS, you know, in the prior under the Biden administration challenging to get public. My observation outside looking in is everything was really hard until it got really easy. Like nine nine and a half years of really hard and then twelve months, you know, of of of really easy where everybody wanted to get in. They priced the IPO at $1.85, which was up the range was taken up two times. Okay. The stock opened at $320 a share, I think. Today it's at $230 a share, $50.60000000000 in market cap for a business like, you know, and Andrew is just one of these people, let's get back to work and build shit. But my just add on question to that is from an employee morale perspective, like distraction perspective, etcetera, has the last three weeks, you got a lot more capital, you got a lot more profile, presumably it's easier to sell to enterprise customers today. Net net, if you were advising me, if I was in a similar position, would you say go public? Andrew Feldman (00:06:17): I think the first thing is a lot of people asked us about how we got the timing right. Brad Gerstner (00:06:23): Right. Andrew Feldman (00:06:23): And I think the answer is by getting it wrong for a decade. I mean, that's really the right way to get timing right. Think first, we've been at this for more than a decade and we brought everybody who'd been with the company more than nine years to share and we brought their families. And first I learned that engineers own ties. I didn't actually know that. And they didn't die when they wore them. And second, I was surprised at how big a deal it was for them and their family. They were really proud in a way that sort of their parents might have heard of it or that somehow this was like a bar mitzvah or quinceanera or something. Then you had these sort of the children of immigrants. One of our leaders, her father, a Chinese immigrant, said, I thought it would have happened faster. That's Yeah. Right? And but I I think we are sort of by nature sort of in the the trenches, people. And so we we love solving hard problems. And so when when we had this excitement, everybody went, and they were so excited, we had a party. And I I think it it gave external validation and then everybody turned around and said, now what are we now back to work? And and so I I I think Brad Gerstner (00:07:59): And so you you started off kind of bang right out of the gates. Will, you had a little bit different experience in terms of, you know, the entry to the public markets, but over the last twelve months, your stock has gone from $5 a share to $50 a share, some 10x move in the public markets. So talk us through the other side of this where you come public, nobody really notices until they notice. Well, we were one of Will Marshall (00:08:23): the first space stocks and I think people just had no idea Andrew Feldman (00:08:26): No idea. Will Marshall (00:08:27): Earth is going on in space, how it was changing everything. And they were just like, What the heck is that? But, you know, I have similar opinions. Mean, the end, you've just got to get on with executing the business. Going public gives you access to liquidity for early shareholders, whether that's the early employees or early investors, and that's great. It gives you cash for the company, that's great. I do think it helps your business as well because the maturing event gives you more credibility to various customers. Oh, true. For us, we work with biggest agricultural customers, big governments, civil governments, defense and intelligence, all of those sort of actors. They want to know you're going to be around Brad Gerstner (00:09:10): Exactly. Will Marshall (00:09:11): And not going to disappear. I mean, have countries that are fully dependent on us giving them information. They don't wanna just disappear, so they really care that we're gonna be around. And being a public company gives you the kind of force in the world that people go, okay, you're here to stay and you have access to capital if you need it and so on. Right? It's legitimizing that. And, you know, you you know, where the stock is any one day, you know, we're not focused on that day to day. We're focused on how we build long term value for our shareholders. Right? And, you know, the market is, I think, starting to really understand where space is going, why it is changing the the the world. You know, people forget how space is part of your everyday life. Every time you use a phone, you're either using communications communications using using satellites satellites or or GPS GPS using using satellites satellites or or satellite data in some way or another that's sort of integrated in your lives, you may not realize it. But it's just booming now. Jason Calacanis (00:10:06): There's a the story's changed as well, obviously with SpaceX going public, but has the framing of Planet gone from like a data source for people who need data from space and maps to, hey, this is a tool to accomplish tasks and military. Post Andril's success, you probably would have been bucketed into Andril as a military tech company. So is that framing what's driving a lot Will Marshall (00:10:33): of Well, I think it's a bit more nuanced than that. I mean, firstly, for the audience's benefit, what Planet does, we have satellites doing earth imaging. We have the largest earth imaging fleet, about 200 satellites. They image the entire earth every day. So think of it like the Google satellite layout on Google Maps that you can look at, except it's today's date rather than three years old, and we have every day going back. So it's a time series analysis of everything going on on the earth. That's useful for farmers. It's useful for energy companies. It's useful for civil governments, flooding and fires. It's useful for security applications like you're getting at, and it's a wide variety of use cases. I think that where we're seeing this is that AI is now enabling the it's basically reducing the barrier to entry so that more people can get access to this, right? And, know, there's a lot more to say on that, but AI is only as good as the data Chamath Palihapitiya (00:11:27): it's What trained Jason Calacanis (00:11:28): percentage is military? I'm curious. Sorry? What percentage of revenue customer base It's Will Marshall (00:11:32): about 60% of our revenue today. Security is part of the initial thing that we said we would do out of the gate, but it's true there's a bigger fraction today than perhaps we would have guessed. But the needs of the geopolitical situation right now demand what we're doing. You know, just as an example, what this does is enable them to see threats around the corner. Yes. And then, you know, give them weeks or months advance warning of things and then that enables them to more likely do things that stop conflict. So, believe this is really better for the world. Jason Calacanis (00:12:06): Are you reticent to be perceived as a military company? Will Marshall (00:12:10): Not really, but I wouldn't say we're limited to being perceived like that, right? We are helping farmers, we are helping energy companies, civil governments. Work with NASA, we work with what have you. So, it's a bigger play than that. But back to the space piece of it, what has changed, obviously, rocket costs have come down about four or five x over the last ten years, which has helped tremendously. But a thing that people don't know that is actually perhaps more important is that we've had a miniaturization of satellites so that the same satellite that used to cost a billion dollars and weigh 20 tons now costs a few kilograms or a few tens or hundreds of kilograms and can do just as much stuff, if not more. It's it's the same as the sort of mainframe computer to desktop revolution for space and it's unlocking just like mainframes to desktops unlock loads of applications. This is unlocking loads of applications and it's so both go in combo. The launch cost coming down and this. Chamath Palihapitiya (00:13:16): Let's build let's build on this. So I think I I'd like first, you maybe take a few minutes and then I I wanna talk to Andrew the same question. Both of you guys are at the foot of what are probably huge secular trends in technology. How I would frame this is we are rebuilding the data processing infrastructure that has existed on the earth in the sky. And first you do the satellites, but I would love for you to explain space based data centers because I think everybody's hearing about that. Are they really viable? What are they? How will they work, etcetera? And then Andrew, this is the rebirth of silicon. We're going to find the next version of Moore's Law, which I think is more time bounded, not transistor density bounded. We now hear a lot about domain specific architectures. We hear I mean, your chip was just a complete transformation in terms of the design principles that Like at Grok, we took a very different approach. NVIDIA has taken a very different approach. You took a big pizza shaped die and said, fuck it, YOLO, this is it. And you were right. Just explain where we're going in silicon. So maybe Will, you start and then I Andrew, Will Marshall (00:14:26): mean, what we're seeing firstly in space is all these new applications based on data and AI. So, we're collecting vastly more data about the planet and with SpaceX and Starlink and OneWeb, they're transporting far more data around the planet. As you say, we're sort of changing the nature of data using satellites and that's basically doing what was once the province of governments only and giving everyone else access to satellite capabilities. I estimate there's a $75 to $100,000,000,000 market just on Earth observation, this data we collect, and AI on top of that, unleashing all the applications. So that's the near term thing, applying large language models to Earth imagery data, unlocking agriculture, energy, civil government applications, permitting, you name it. This is going to make everything more efficient. And then, where we're going is indeed space is is we did a study with our partners at Google about eight or nine years ago looking at what are the costs of data centers on the ground, what are the costs that it would take to put them in space, and when might it make sense to do it non terrestrially. And we figured out that when launch costs come down to about $200 to $300 a kilogram, it would be cheaper, just simply cheaper to put the data centers in space. Now, we're about $1,000 a kilogram just over that in right today, but that's come down about 10 x in the last ten years. On the current trajectory with Starship in particular, I would expect the launch cost come down there in two to three years. Elon might say it's next week, but at least realistically a couple of years. So we're not far away from it literally just being cheaper. Then in addition and and the intuition there that is that helps people understand that is you would naturally use solar panels for doing the the data centers are a power problem, and it's a power game. And you would normally use solar panels. That's the cheapest way to get a watt today by far, but you you don't want intermittent power. Will Marshall (00:16:34): So then you have to have batteries or you then you have to have gas or then you have to have nuclear, and then it gets really expensive. In space, you can put a solar panel in a sun synchronous dawn dusk orbit where you're twenty four seven looking at the sun. So you can have a solar panel that collects and gathers five times more energy per solar panel than on the ground, and you don't have to have batteries or anything else. So the infrastructure for the computing space is literally just solar panels and the chips and then the RF signals up and down. So it's actually really quite simple. It was just a question of when it's gonna be cheaper to launch all those solar panels and chips in the space than putting it on the ground. And it turns out that's gonna be in a few years. So we're partnering with Google to launch some of their TPUs into space. We've already launched NVIDIA's some of NVIDIA's GPUs into space. We're launching Google's TPUs into space on an early test. There's lots of technology They Chamath Palihapitiya (00:17:33): want to launch engines. Will Marshall (00:17:33): To figure out percent. Let's have a conversation. But it's an early it's early days, but I think no question within ten years, most compute will be put in space, which to give you a sense is a lot of money, trillions, and will be bigger than any of the other space businesses today, Calms, earth imaging, this is why we're getting into And this you're dealing game early Andrew Feldman (00:17:59): with this. Brad Gerstner (00:17:59): Do you believe Jason Calacanis (00:18:00): sending data centers to space makes more sense? Or is it just the regular Can Chamath Palihapitiya (00:18:05): you have him explain the business first and then? Jason Calacanis (00:18:07): Oh yeah, of course. Andrew Feldman (00:18:08): Yeah. So I think there, know, with all due respect, one or two hard problems still left beyond putting putting GPUs in in space right now. I I think we we we're not super good yet at building the clusters in space necessary for the communication. Between. Between. Exactly. Between. We're not good Brad Gerstner (00:18:33): at doing it on the ground. Andrew Feldman (00:18:34): We're not good at doing it on the ground. We're really not good at doing it in space. I I think this is an extraordinarily important and interesting problem and one we should be spending money and attacking. I've got it in a slightly different time frame, but one that certainly will occur. And the the hard part is this. Is it is it one of those problems where the last 10% is 80% of the time? Mhmm. Now self driving was a problem like that. Right? Where the last 10% proved to be a decade's worth of work. And just now we're over the hump. And we don't know yet. But I think the interesting work they're doing at Planet is really important. I think the fundamental driver to experiment, to even get insight into whether I'm right or not, is to get down the cost of launch vehicles. Then you can start doing experiments and getting it wrong and fixing it and figuring it out. And until then, it was For mostly on Chamath Palihapitiya (00:19:27): the foreseeable future, you're going to be terrestrial. Explain your business and how you made these critical decisions that kind of took you on a different path and you versus NVIDIA versus AMD and what you think the future of AI silicon looks like? Andrew Feldman (00:19:41): I think there were two parts. Your first question was around sort of the rise of silicon in general. And I think what AI did, and it's rarely sort of framed this way, but it allowed computers to address a class of problems that before AI, computers were bad at. We were bad at images for almost the entire history of compute. We could store them and that's about it. We were bad at language. We could store it, but that's about it. We could transform numbers. We were magical with numbers. And what AI did starting in about 2015, 'sixteen is it opened the door, the aperture, to say, maybe we could use computers on images. All right? Maybe we could find insight in images. Maybe not only could we store language, but we could generate it. All right? Maybe we could understand it rather than storing it and regurgitating it. And what this did is it it opened up sort of to compute huge areas that were previously foreclosed. And at the same time, we were adding to those areas. We were taking vastly more images. All right. Terrestrially in satellites. And what this did is it it simultaneously opened up this entire area and allowed compute to attack it. And this is what's underpinning both NVIDIA's growth and and sort of all the growth you're hearing about in AI compute is as a processor builder, as a hardware builder, suddenly our tools could attack more in different parts of knowledge. And that was sort of the first part to answer your question. Now, how you do that, there are lots of different strategies, tons of different ways to skin cats. What we saw in 2015 were several things. First, we saw that AI would be an enormous consumer of compute. Alright? And historically for computer architects, new workloads were the opportunity for share to change. Share changed when the rise of graphics emerged and you got the dedicated GPU. That's how NVIDIA was born. Andrew Feldman (00:22:04): Share changed when cell phone compute emerged and Intel and AMD who had fabs and the best architects got zero share, And it all moved to ARM, right? Share changed in the late 90s when Nortel and all these companies we've forgotten about couldn't build chips and couldn't do data networking. And what you got with Cisco and Juniper and a wrist in this collection of new companies. So we knew that this new problem would present an opportunity for massive change. So we saw that. We we made two bets. The the the first was dedicated silicon would be the answer. And the second was it couldn't look like a GPU. And our view as computer architects is if you want to be 20 times better than somebody, right, your architecture can't look like them. Right? It it it can't. They have they have enjoyed and and eaten all the low hanging fruit. So if you build a GPU, the odds that you're better than NVIDIA in our view are approximately zero. That led us to a fundamentally different architecture. Alright? The hard part here, the hard part is moving data from memory to compute. This is the fundamental problem in AI. And we solved it with a way that that very few others had even attempted, which was to build a very big chip and to put memory right next to compute. By building a big chip, a chip the size of a dinner plate, whereas most chips are the size of a postage stamp, we could use a different type of memory. And by using a different type of memory, a memory that was vastly faster, we opened up all sorts of opportunity. So when OpenAI uses us, we're 15 or 18 times faster than a GPU. That means your answers are delivered more quickly. It means your engagement with with the AI is more enjoyable. It means you can use the AI to solve harder problems and not wait. And the way to think about this is sort of to ask yourself the the counter factual question. Andrew Feldman (00:24:21): Big is the market for slow search today? Right? Right? It's zero. How big is the market for dial up? It's zero. How long do you wait for a website to resolve before you click away? Three seconds, five seconds? You will not wait for AI. We have to deliver it to you in real time. And that's what we saw. That's what we built. So the panels on going public, a lot Brad Gerstner (00:24:49): of LPs in the room, they need to get liquid. I'm curious about the journey for your investors. Yeah. Okay. So, well, you guys went public what year? 2021. 2021 by way of a SPAC. Will Marshall (00:25:01): Correct. Brad Gerstner (00:25:02): Okay. And your VCs were who? Draper Professor Jervisin one Will Marshall (00:25:09): of the earliest Capricorn, Peter Thiel's Founders Fund. Okay. Then we got Uri Milner's DST. Great. Okay. Brad Gerstner (00:25:17): So, investors come in, you go public at 2,000,000,000 via a SPAC. Now we're four years later, really it wasn't until year three or '4 that 90% of the value was created. Okay. So did those early investors capture this Yeah. 90% move? Did they stay in it? Will Marshall (00:25:38): Most of them did. Yeah. Most of them did, which is really smart on their part, obviously. Think Great. They should hold on even more. If I didn't think that, Andrew Feldman (00:25:47): you should Well, I'm a little bit self Brad Gerstner (00:25:49): interested. What's interesting about this Will Marshall (00:25:51): is No. But really they did and and I mean, Google hasn't sold a share. They're our largest single investor. Capricorn didn't until very recently. Basically, most of them stayed really well in, and they got all of that upside and good for them. Brad Gerstner (00:26:06): Reason And this is so important is that there are lot of LPs in this room who they're like, when a company goes public, give No, us the Will Marshall (00:26:14): no, no. Yeah. Give us Brad Gerstner (00:26:15): the shares. This is a counter example, right? This happened to us in Mongo ten years ago. We invested pre IPO at a billion dollars. We distributed the shares, I think at 3 or 4,000,000,000. And then it went to 50,000,000,000 over the course of the next twenty four months. And we have people who called us who said, well, why didn't you hold onto the shares? And we're like, because you're pounding on us, right? To distribute the shares. So, you're an example. Now, your case, Andrew, you have an innovation, right? You're just now public. So, all of your investors are still under lockup like altimeter, but you guys have innovated with the banks on what I call a dribble lockup. So, over six months, the shares can be dribbled out according to a bunch of performance hurdles, which SpaceX is going to have a very similar Andrew Feldman (00:27:05): When did we lockup Jason Calacanis (00:27:06): start this process of the dribble? The dribble? Brad Gerstner (00:27:11): Consulate. We started it years ago, but Yes. But all of that age. Respect to the lockup, I think this is the the most innovative and I think SpaceX is gonna have a very similar innovation. But Andrew, for your investors, if you were talking to my LPs right in the room, should Altimeter be distributing the shares when they come out of lock? How do you think about, you know, your VCs holding onto the shares kind of post lock? Andrew Feldman (00:27:39): I think historically more money's made after IPO than before. Yeah. I think every single study shows that there is more money to be made both in percentage and in what we care about, which is absolute. Yes. And so the amount of money that it's possible to put to work in most venture companies is very modest. I mean, are two or three or five outliers, but for the most part, can only put a relatively little bit of money to work. By the time we get public, there's a lot more money there. Things are going well, and the opportunity to make vastly more is after IPO, not before. Will Marshall (00:28:17): If I could just add on that, one interesting question is what's going to happen with SpaceX on this because, you know, a lot of the value is in the future, right? But, I mean, most of the big tech companies went public at a few billion, not a few trillion. There's a lot of zeros in between those, right? And you've got all this upside afterwards. Now, for the equivalent liftoff, SpaceX would have to be aiming at quadrillion dollar valuations. Now, I know Elon has those sort of ambitions, but you really have to believe in that to get, you know This is kind of the point I'm getting to. Chamath Palihapitiya (00:28:53): Yeah. Brad Gerstner (00:28:54): Right. We have three mega IPOs, know, we keep talking about that are multi trillion. Yeah. All of that value accrued to private market investors. Planet Labs is a great example of venture capital in the public markets where the 10x has occurred in the public markets. We're all advocates of these companies coming public sooner. Had Andrew had his way, he would have been public eighteen months ago, probably at $10,000,000,000 rather than $50,000,000,000 and that 5x over the course of last two years would have gone to public market investors. So, go ahead. Way better to be lucky than good. Yeah. So, think that I hear a lot of people thinking that Anthropic OpenAI and SpaceX are the new normal. Actually think the public markets may And be shifting back in this a lot of the companies in our portfolios are now thinking about going public at a billion or 3,000,000,000 or 5,000,000,000. We had this period of a decade where Andreessen was really pushing stay private forever. And I see the pendulum swinging back to companies are like, man, I wanna be like Planet Labs and get public. Yeah. Right? And and have to play in the big leagues and do it in the public markets. So here's here's Chamath Palihapitiya (00:30:09): what I'll say maybe, just like to the two of you guys. Both of you guys have had enormous pressure because there's visible competition that's always sort of in your periphery. But I do think that getting public sooner, having the scrutiny of public markets, having the scrutiny of having to deliver sharpens the focus. Brad Gerstner (00:30:28): It's For Chamath Palihapitiya (00:30:29): sharpens steel, iron sharpens iron. And I think innovation tends to get better. And so the idea that you allow everybody to participate, but you also put yourself in the spotlight, to me is where great things happen. I agree. And so anyways, I just wanted to say to both of you, just as we wrap, you guys are an incredible testament to entrepreneurship, both of you. I mean, we've been talking literally since day one, me and Andrew, because we went in different paths and then we kind of reconverged. And then Will, same with you. Andrew Feldman (00:30:59): I'm happy it worked out for you, Chemosh. Chamath Palihapitiya (00:31:01): Well, it's worked out for both of us, so it's fine. You guys are incredible testament to entrepreneurship. I And just want to say thank you for everything you guys are doing. The next few years are going be really Yeah. Will Marshall (00:31:11): If I could just spend thirty seconds on the next few years, because I think it's going to be so exciting with, as I mentioned, AI and space merging together, we're going to see a takeoff of applications. I'd say that all the cool stuff that we're doing on the with LLMs now is really based on just the text of the Internet being absorbed into these models, which is incredibly powerful already. But they don't know shit about the real world. I call them blind. They don't know about that farm field, that flood, the security situation around the corner. If you give them real world data, then they can answer real world problems. And that's going to open up gazillions of applications for these AI models. I call them, instead of having large language models, large earth models, or instead of AI, planetary intelligence, where you have planetary sensing systems in space, planetary compute systems in space, and we could disagree or agree on exact time frame, I think it's going to happen. And then that's going to enable a huge economy. So it's an exciting time in the next few years. Chamath Palihapitiya (00:32:16): Andrew, thank you guys very much. Jason Calacanis (00:32:17): Well done. Will Marshall (00:32:18): Thanks, guys. Brad Gerstner (00:32:19): Okay. Thanks, buddy. Chamath Palihapitiya (00:32:22): Thanks for Will Marshall (00:32:23): having me. Appreciate it. Jason Calacanis (00:32:24): Great seeing you, brother. Congrats on everything.