Today, Planet's a10 billion company. You've coined the term large earth models. What's that mean? >> Bit like Google index the internet to make it searchable. We're indexing the earth to make it searchable. It will finally enable us to be smart stewards of our planet. >> The elephant in the room here, Will, I have to ask it, is how do you compete with Elon's plans for orbital AI data centers? >> Everyone apart from SpaceX has to pay the SpaceX launch tax right now. Everyone apart from Nvidia and Google has to pay the Nvidia tax. And which tax is more important? Near-term is the launch, but longer term is it's is the compute. >> That is brilliant. >> Our next story uh should keep the US labs up at night. It's a Chinese model called GLM 5.2 in some cases matches or exceeds the top models from OpenAI and from anthropic. >> This level of performance in an openweight model is absolutely shocking. You can burn tokens to get more intelligence and the Chinese have figured out how to do it.
[00:01:00] >> The Chinese are evidently figuring out how to more efficiently or at least more cheaply reason. [music] >> Now that's a moonshot ladies and gentlemen. >> Welcome to Moonshots everybody. I'm here with my magnificent moonshot mates Ismael, the father of organizational singularities. Uh See, good to see you pal. >> Good to be here. are going to be home. >> Yeah, everybody's home. This is a great This is home day. >> When was the last time this happened? This is This is like Never. Never. >> AWG, our in-house super genius. >> Good to see you, Alex. >> I I will say in my defense, coining the term planetar for a quadrillionaire who can own an entire planet was not an advertisement for Will and Planet. >> Uhhuh. Yes. >> And Dave Bundon, our wizard of AI investing. I'm Peter De Manis, your host and your optimism evangelist. We have a special guest here with us today, a friend of nearly 20 years, a man who's building humanity's orbital AI and data
[00:02:00] layer. Will Marshall, the CEO of Planet? Will, >> hey, thanks for having me, guys. >> I have a question. Do people ever say you're the CEO of the planet? >> Uh, I should be. [laughter] You should >> FYI, Planet is a public company. Ticker is PL. You can track it in real time as we speak today. >> Yeah. And as always, we got a packed WTF just happened in tech episode. The singularity waits for no man and no agent. Uh we're going to kick off a discussion on planet's push for large earth models, their orbital AI cloud. We'll jump into Eric Schmidt's newest launch company, Relativity Space, and their upcoming Mars mission. We'll jump there from the AI talent reshuffleling and President Javier Mille's provocative statements on AI personhood. Of course, Alex. I expected you're the one who influencing there, but we'll we'll ask you behind the scenes. >> You you think you're saying, Peter, you think I'm pulling pulling Javier Malay's strings to for AI person? [laughter] >> I think you're influencing him. Uh we're
[00:03:01] going to close out. We're going to close out with the shocking performance of China's openweight GLM 5.2 model. Bidance's new 4K video model will wrap up with a collapsing price and exploding capex of intelligence that's going to be fueled by nuclear and fusion power plants. All right, let's jump in. So, Will, I want to kick it off with you, buddy. I I hope at the end of this conversation, everyone listening is going to understand what this layer of AI and data capabilities that you're building is going to mean for them. But let me do a proper introduction for you. So, Will Marshall is the co-founder and CEO of Planet. It's the world's largest earth observing satellite fleet and soon orbiting AI data satellites. He's a physicist with who earned his PhD at Oxford. Uh not a bad place. Not MIT quite but you know not a bad place. [laughter] >> Okay. Will is where you jump in. >> Yeah. Before planet Will worked at NASA. Uh and he and I met back in 2008 along
[00:04:00] with Seem. Uh today Planet's a10 billion dollar company. And following up on your point, Dave, I mean, I looked at the ticker Will and a 450% increase in the price over the last year. That's extraordinary. Pretty amazing. Uh Will is operating 200 satellites in Earth orbit today, generating 25 terabytes of imagery every day. We have two major stories. The first is about planetary intelligence. The second is about Project Suncatcher. So, Will, uh let's kick it off, buddy. Um let's talk about what you're building with planetary intelligence. You you've coined the term large earth models. What's that mean? >> Yeah. Well, planetary intelligence to me is sort of a next era in machine intelligence. Um and it's all about building models of the real world. And of course AI models are only as good as the data set they're trained on. And we have gobs of real world data. I think of it in two phases. The first phase is combining planetary uh sensing which is
[00:05:01] already in space for all the obvious reasons we've been doing planetary sensing in space um with large language models to what I call large earth models um which is you know wrapping up not just the text of the internet that's embedded in LLMs but all of earth data so that you can ask physical questions about the real uh planet and the second phase in big arc terms is is putting the compute up next to the sensors into space and we're doing that too. That's a bit further out but that sort of le leads to phase two of capabilities in in in this world. So we're mainly focusing on the phase one which is pulling all of our earth imagery data into um models such that you can ask questions about the physical world. I liken it to like LLMs of course know all about the text of the internet about human knowledge. Incredibly versatile about it can answer questions about deep areas of physics to um anything pedestrian and anything in
[00:06:01] between. But they only really know about the theory. It's very abstract, right? What they don't know about is how the real world is behaving. So, um liken it to a um somebody who's been stuck in a library. They've read all the books, but they've never looked out the window. Maybe they've looked out the window, they certainly haven't gone outside to see the reality. So, they're limited to questions about uh theory. Um, we are adding sensor systems to LLMs to enable them to upscale to these large earth models that enable us to answer real world questions. Whether you're a farmer, journalist, somebody interested in national security, you want to know about the real world. And that's where >> every every European government. You forgot to mention >> billion dollar plus contracts with every part of Europe, every government. >> Well, exactly. >> Everyone who can't afford to launch their own satellites. >> Absolutely. The governments want to see around the corner. They want to see new threats. Um they want to respond to disasters. Uh and everyone wants to
[00:07:01] answer questions not just about the text of the internet. That's where all the large earth model language model companies are going. All the AI companies DMS has talked about this. Dario has talked about this. The next scale models are going to be real world models. And for real world models, you obviously need real world data. And planet has 3,000 images for every point on the land mass of the Earth over the last 10 years documenting every day that changes. So you basically have a huge stack. It's 150 pabytes of data. A huge stack of information about the real world and how it's changing over time. Um we've said before like eight years ago I did TED talk talking about how we're um bit like Google index the internet to make it searchable. We're indexing the earth to make it searchable. It's just that large language models are making it way faster to do that. And so now it's just unleashing all of this potential laden in earth imaging data. Not just ours but but the whole field. Well, if I uh if I
[00:08:00] wanted to pay you to take One Point on Earth, Peter's House, say, and and stitch together those 3,000 images you collected over 10 years and turn it into a little movie, could I buy that from you? >> Yeah, sure. I mean, it's getting it's getting easier and easier. I mean, historically, most of our users have been big entities. you know, NASA has used it, the National Reconnaissance Office, uh, so the intelligence community, um, huge agricultural companies like Bio and Senta and hedge funds in New York and and so on and lots of >> and and will my myself I've purchased data for you in the past. I really wonder I'm wonder can purchase it via API? >> Yes, you can. Yeah, but typically you're unusual, Alex. Most people can't get much value out of it because processing terabytes of satellite imagery has been too hard. Now AI comes along and just shortens that gap right there. So you could just ask Gemini or Claude, "Hey,
[00:09:02] find me images from planet. Tell me how my farm field has changed over time. How's it? How can I improve that next year? How can I improve it today? What do I need to do?" And it will go off and do that analysis and come back to you with just the answers. You're one of the the the few if only correct me if I'm wrong will like you're the only or one of the only vendors that actually offers highquality historical imagery. If I specify latl long coordinates and I I want a bit of historical imagery of different types offer that via API. >> Yeah, we're the only company in the world that images the whole world every day at high resolution. So basically think of it as the Google if you look at the Google maps satellite layer but that that layer is maybe 3 years old sometimes one year sometimes 10 years but you know a couple of years old let's say we're doing that every day for the whole earth and have a time axis so it's like Google maps satellite layer but with a time axis and yes we're the only ones doing that and necessarily and until someone invents a time machine
[00:10:00] even if somebody erected a whole load of satellites they can't go back in time and get our historical archives So all of our clients use not just the today's image they look they almost always want to know well how does this compare with normal let me give you an example um you know in Ukraine we're very much helping them uh with defense of their country and you know they don't just want to know where the Russians positions where are their military bases where the industrial facilities but how does it compare for the last couple of years so they know if it's normal or abnormal and therefore what's the threat level the same with uh the US intelligence community. They want to know what's going on across China. They don't just want to know, you know, is there a new something in China. They want to know how does that compare with the normal activity levels in that place. Same true with the farmer. They just don't they don't want to know just what's their farm yield output now. They want to compare it to the past. Know if their um agricultural interventions would be better if they do it like the neighbor
[00:11:00] does it or someone else does it. They need the historical background to know that. So yeah, the archive is really important. It goes back 10 years. >> The is going to be really cool for, you know, Salem and Peter and I are looking at island real estate and mountaintop real estate. We're big on this EV toll thing coming online very soon. So places that are normally >> Yeah. But this API, >> it's got a totally >> Well, why don't we do it together? Let's let's get our fund together for this and and use your historical data to analyze the perfect location. >> The search query, which piece of land will generate the most profit for us when EV tolls arrive? >> I'll bet you I could do that during this podcast. Yeah, I I think I think it relates to how many wiggly roads. So, how far is it by time to now and how short is it going to be after EVO tolls? Yeah, I've bought property myself within the 100 mile radius of San Francisco knowing that the value I think is going to go up. >> I've got two questions. One, um you know, uh when you talk about imaging, what's the resolution at which you're imaging? And do you also do infrared and other bands?
[00:12:00] >> Yeah. So uh basic explanation, we have three fleets. Uh the scanning fleet is 3 m resolution. That's the one that does the entire earth and it does it with eight spectral bands. And we're improving that with our next generation. We're launching a first tech demo this year of owl uh which enables it to go from 3 m resolution to 1 meter. And the latency of of the present imagery is several hours and we're reducing that 10x as well to well under an hour. So that's that system. A second system does high resolution. So we can go up to uh 40 centimeters today. Uh 40 to 50. It's going to 30 centimeters tomorrow. We we launched nine of these satellites. We're launching a whole bunch. We're towards a 30 by 30 by 30. So 30 cm 30 times a day and 30 minute um uh from request to get the image back in your hand anywhere on the earth. Okay. Soing um so that time axis is being shrunk and that's for 30 cm. So each pixel is 30 cm across. So
[00:13:01] about a foot in units and then um we uh then we have a hyperspectral imager which is the first and most sensitive one in orbit according to JPL who we built it with um which has 400 spectral bands. So this is like the human eye has three RGB red, green and blue. It has 400 and that crosses from infrared to ultraviolet. Um and um that um um those extra spectral bands enable you to essentially take like a a signature a fingerprint of the planet. So where we look in each pixel which these that one is even bigger 30 m but we can actually tell the species of the tree or gas emission or if it's a tank >> gas emissions >> which tank site built which tank site built that vehicle. um because it turns out the paint is slightly different from each uh location. I mean, incredible. It's like the signature uh fingerprint
[00:14:01] on the on the Earth surface. >> By the way, I know that you're busy and sometimes these episodes run long and you don't have time to listen to the whole episode or if on occasion you miss an episode. I now put out a moonshot summary on Substack which includes a link to all the stories that we cover. The weekly recap covers what I and the mates had to say, what we think is most important, and what we're most excited about, and it's free. You can subscribe at diammagus.com/tatrens. That's diamandis.com/metatrends. All right, now back to the episode. Why hasn't anybody done this, Will? I mean, I don't think anybody's close to the fleet that you've built, and you bought part of your fleet from Google early on. I'm just curious. Uh, Peter, are you sure you don't mean why hasn't anyone in the private sector done this? >> That's what I mean. Yes, the private I mean, obviously defense has how how many defense imaging satellites are in there in orbit right now, you think? >> Well, it's it's technically classified for the US government and probably >> You can tell us. No one's listening. >> Roughly [laughter] [00:15:00] um roughly like half a dozen really high resolution ones and they have much higher resolution than we have. more than 10 times or so higher resolution, but >> golf ball pixels. Yeah. >> But that sort of that has a trade-off of coverage. So we they have even less coverage than we do. Um in fact, not just a little bit less. We cover about um 200 million square kilmters every day. The Earth's land mass about 150 million square kilmters. So a bit more than the Earth's land mass every day. They probably cover less than 1% at anything like that resolution. So probably what much less than that >> I mean for for the entrepreneurs listening it's important to realize will started this with with Robbie and his partners by launching a phone into orbit phone sat >> it was a crazy idea you got in trouble for it worked you got Steve Jervson's attention he be came in as a major investor uh and it it kicked off a10
[00:16:00] billion company it's extraordinary >> thank you um yeah well it's been a quite a ride and um and and as you were saying about the stock right now it's a it's a a rocket ship and part of the reason is this AI piece because the AI piece is lowering the barriers to entry. I think we're going to and it's just at the very beginning. I think we're going to see this massive takeoff. I said before space and AI are getting married you know like a lot of people uh uh understand how AI is affecting every discipline and it is right it's affecting every sector but space is one of those unique sectors that's producing gobs of data and therefore space is actually important for AI as much as AI is as important for space. So like it's AI is not just eating space as a sector like it is eating almost every sector actually space has something to offer it because all the AI companies as I said are trying to build these physical world models for that they need real world data. um a space comes along. AI is a useful space because it makes value
[00:17:00] extracting out of all this data much easier for those smaller organizations, but at the same time it gives AI something it really didn't have, which is this information about the real world. And all they're trying to do is help people ask answer questions about the real world. Um yeah, again, let me give you an example. The farmer going onto an LLM right now and saying, "Hey, how do I improve my crop yield?" The LLM will say, "Well, here's all the theory of aronomy." But he didn't know about his or her field and how it's doing today, how it compares with yesterday, how that compares with last year, how that compares with their their the their the farm next door, and therefore what they can do about it, how's the soil doing, how's the water content, how's the agriculture and the we can tell all that and help the LLM answer that question. Or take the journalists, they're investigating some flood. They don't want to know the theory of a flood. They want to know how's that flood doing today in that village and where and the emergency response people need to know where to
[00:18:00] go. So basically real world data is going to come into these these AI models and that's going to enable them to be 10 times more powerful than they are today. >> So many questions will I'll start with the the simple ones. To the extent that you draw a parallel between your large earth models and large language models, I think one of the most important questions I could possibly be asking is yes, you offer historical or archival imagery via API, but I think most people would love a crystal ball that auto reggressively extrapolates Earth into the future. Sort of a a sim video model at meter or submeter submeter spatial resolution of projecting the video into the future. Where are the future extrapolations of Earth? Where's the crystal ball powered by planet? >> I think it's coming. Um, it's very exciting. Um, one step at a time. So, we're focused first on retroactive analysis and how AI can open that. But already the predictive analysis is coming to the four and obviously AI has been very good at token tokens and
[00:19:03] guessing the next token. That's what AI is doing when it's guessing the ne giving you um text output. It's each time guessing the next word in the sentence and um it's doing a very good job and coming up coherent answers. So obviously with 3,000 images you could easily ask it guess the next few images i.e. going to happen next. We already just studied this um some people you would be terribly surprised are interested in us tracking data centers across China. So we loaded into it all the data centers across the entire US which have to be registered. Then we showed it got it looked back through the imagery of their development, tracked that and then extrapolated that model to China and said go find all these things in China and track their development. But it also got really good at predicting based on the US data when it would complete like within a few days it could guess like way out when it's going to complete cuz it has taken into
[00:20:01] account all this construction information in the nearby region and various other things. So now it turns out this model is pretty good at predicting when data centers will be complete. Lots of people are interested in that right now. Um, so that's the kind of thing that's the first time I've seen it really work like you're suggesting, Alex really the very beginning, but I think we're gonna get there relatively quickly just because of the nature of the tech is it sort of already can do that out the box. >> But surely you have enough data. Stop calling me surely. Surely you have enough data to be able to take everything that you already have and pre-train an auto reggressive video model to extrapolate the Earth at the pixel level into the future. Do do you feel like you have enough data to do that already? >> I think we do. Yeah. >> Have you done it? >> No. But I think it's >> Why have not? Why have you not? Because I I think you you are the only entity perhaps in the world with the power to build an honest to goodness crystal ball. >> Yeah. [laughter] [00:21:00] I love the vision. Yeah. Again, I mean, we've been doing this in some bespoke areas, but the main thing has been looking backwards because already there's we believe a hundred billion dollar market just in the retro in the rearview mirror. But you're right, it's very tempting. I think the biggest thing that we're working on that's super relevant to that is embedding models because you the actual doing that for the entire earth which each layer is around 30 terabytes of data. It's 4 million uh 47 megapixel images. So just like I mean it's just a huge huge data times 3,000 layers, right? So you can't just throw that into a machine like no machine can just take that in RAM, right? And do the processing. So what do you have to do? you have to put it into um an embedding space. So we've been working with uh Google models on this um Google search and deep mind as well as they've done this important work called Alpha Earth which you can look up um as well as some open source models like clip um uh a remote sensing uh clip
[00:22:01] model and then fine-tuning it on our data. And what this does is it's sort of like an image a tile to text conversion. Um and so and you can do that for each area say let's say a kilometer by a kilometer and then you could search for arbitrary objects around the earth. So we've got it to a point where we can put into big query the entire one layer of the earth in this embedding space. Now you have the potential to do what you were talking about of putting thousands of layers and then predicting the future. And so I don't >> I got it. So So the vision is you're tokenizing the earth. >> You're tokenizing the earth first because it's like a massive compression and then you want to do the prediction. Yeah. >> What [clears throat] we could do, you know, the the middle school that my kids went to commissioned um they took collections from all the parents to buy this huge globe. It's like 6 feet in diameter and it's all LCD and it's basically you can make it the Earth or Mars or Venus or any other planet you
[00:23:00] want with this little console. >> It's so cool. But you could you could say, "Well, that's semi cool." But you overlay the planet data and you can actually dial back and forward time of the real world. If you built one of those for your lobby, that would be you could sell those like like crazy. >> But Alex, where were you going with this? Um like where what do you see the value there? like what what what would you go that's the thing I want to then predict into the future apart from everything >> I I want I want to be able to predict everything into the future not just at data center level >> answer you weren't meant to give [laughter] >> okay excluding that um I I want to be able to I I'll give you one concrete example other than that I'd like a reasoning model that I could layer on top of it so I would argue every LLM wants to be a large reasoning model not just an auto reggressive LLM I'd like to be able to reason RL style about what changes at the pixel level on the Earth's surface could say maximize GDP. We talk on the pod all the time about
[00:24:02] maybe the GDP triples year-over-year due to this singularity that some of us think that we're in. But you have enough, I think you have the data set at the planetary scale to actually build a reasoning model via reinforcement learning that lets us historically back test various theories of say land use. If we literally tile the earth with comput as some of us think we're doing, what would be the hypothetical effect on GDP if we if we get rid of a few gas stations >> or let let's >> coffee bean futures. >> Yeah futures. Well, certainly, you know, uh on futures markets, you can imagine, but let's get above GDP for a second to go even beyond that. It will finally enable us to be smart stewards of our planet. We are effectively stewards of the planet, but we're not always doing it in the smartest way, right? uh not efficiency nor in terms of how we're taking care of the precious ecosystems and complex environment that we have on the earth. Now we can cuz we finally
[00:25:00] have a system that understands it all from the local level to the global level and can integrate all of that into recommending what course of action you as that farmer, you as that insurance guy, you as that finance uh guy get betting on markets or whatever can make to make a smarter decision. But go ahead. >> There's a huge issue that comes with this though, you know, because if you go back, the internet was born to operate at planetary scales, but then governments domesticated it, right? What you're doing with orbital uh mapping is you're regalizing that. Uh and so how do you handle the the aspects of this because governments own the map, but you own the sky above the map, right? And so this causes hugely unsettling questions like you're you're shifting from national infrastructure to planetary infrastructure. Is there a global kill switch? How do you handle sovereignty? You mentioned Ukraine already or China. There's enormous geopolitical tension in this that must drive you crazy trying to
[00:26:01] navigate those. >> Well, I would say it's uh it it's not less drives us crazy. It's it's a founding part of our mission. We call it giving greater transparency and empowering everyone with that leads to greater security and leads to greater sustainability. Leads >> no one can hide. No one can hide anymore. >> Exactly. So Putin thought he could get away with people turning up on the border and then no one would notice. Well, we put that to bed, you know, and then the the the it didn't deter him from attacking clearly. But the potential in the future is that everyone would know that they would be seen at every step. Now everyone knows that they're seen at every step. If you hit a school, we're going to see the school. If you hit a bridge, we're going to see the bridge. And no, the accountability is going to be there for the whole world to see no matter what. >> And the world, I think, acts as deterrent. In h in history, throughout history, wars happened mainly when
[00:27:01] there's been misinformation or lack of information or and people have had to guess or or made mistakes based on misinformation. Here we have more people understanding what's going on, who's got what equipment, where does that uh uh um and and and can monitor peace accords and all that. I think transparency drives accountability and reduces the probability of war. Meanwhile, on those very narrow question on that exact topic, you know, you assume that the US and China see everything via satellite at all times. But if you look at the 220 countries across the world, like what fraction of all governments actually have satellite coverage data? Like like you said, misinformation leads to confusion, leads to war. Like in Yemen right now, do they look at at data or not? >> Not much. And but I think that that's going to change. Um you know we again the the challenge has been digesting 40 terabytes of data every day is too much for most organizations that NASA and
[00:28:01] their know they have teams of people doing satellite imagery processing they know how to deal with this bring AI along and suddenly you you eliminate all of that you can actually get most of the answers very quickly so that NGO um you the Red Cross operating in Yemen or whatever can actually get benefit from this right now and enable them to make smarter decisions. So it it changes it from a world where um it's just the big entities to a lot of other entities can get value. >> What percentage of your revenue is government versus corporate versus individuals? >> So it's about 60% defense and intelligence about 30 uh 25% um uh civil government and about 15% commercial. So basically mainly government um and grow it has been growing there but commercial is now really starting to take off and again it's for because of all those region reasons the AI is lowering the barriers to entry.
[00:29:01] >> How are you going to price the AI training data use case I mean that's the big upandcomer obviously. Well, you know, I mean, frankly, um, >> they're gonna own it, Dave. They're going to keep it and sell the the knowledge information, not >> build your own. So, we'll just continue to sell the data. >> So, so if OpenAI calls and says, "We want to we want to use it." You're going to say, "No, or are you going to say it's a million?" >> Open AI can call our MCP server and off you go. >> Okay. So, it's just fixed price. >> Absolutely. train all you want and then >> and every time you need planned data, you're going to need to do an API call or an >> update. I I am curious about maybe to Steelman Salem's earlier question about information asymmetries. Will I I correct me if I'm wrong, but am I correct in assuming that your data sets go through some sort of US government NRO filter regarding what can be made publicly available? >> Um, not exactly. It's it's it's a it's a bit more nuance than that. Um as a US
[00:30:01] remote sensing company um we register under the uh the Noah's um remote sensing act which means we have to register the satellites but we can sell the data to anyone except for a blacklist a blacklist that includes Iran, North Korea, what have you and various terrorist organizations but other than that they're not checking every player we provide to um we do check that we think and there's many people we don't work with if if we think that they reduce some harm with this but essentially they're relatively handsoff with that. >> So that's a great line of questions but so so that's a US blacklist right? >> Uh but you have a you have like a billion dollar plus deal with Sweden. Do they also have a blacklist that you also honor separately or >> you just deal with I mean they tend to be almost the same. Um, we we respect the EU one as well, which they're part of. Um, >> I'm Canadian. [clears throat] Are we on
[00:31:01] the blacklist? [laughter] >> Cuz last time I checked, there was a problem. >> Interestingly enough, and they [clears throat] differ only a tiny bit. So, [laughter] we have a load of ground station infrastructure up in Canada and download there and then sell it to certain countries that we could if we downloaded it in the US. So, we just add these things up and go, well, let's make the metal list with the all the bad guys according to all the people and then we take that off. You you must have to have a dedicated AI just dealing with the complexities of [laughter] who gets what where. >> It's not as hard as you it's not as hard as you think. Um >> that's just block listing of of users or customers. What about downsampling or lowering the resolution in sensitive areas? Do you do any of that? >> Not no we don't [laughter] not. But but remember I mean I think if people don't get confused about this we're really a long way away 4 to 500 km. It's like the distance from Los Angeles to San Francisco pointing our telescopes one from one city to the other in distance, right? Obviously, the
[00:32:00] details you can see with that aren't the same as you can if you're flying a drone. If you're flying a drone, you can see people, you can recognize their faces. You get all the personal privacy. We're 4 500 km away. We not getting into that. It's kind of amazing what we can see, but it's not like that. And the reason that matters is that a lot of the most sensitive stuff doesn't come into the into the fray because of that. And countries agreed way early in the space era that they will let each other um they won't let each other fly planes over each other's territory without permission or drones, but you can fly in space because they consider that so far away. You can get some data, but it's just enough for transparency, but not enough to get into my uh into the details that they would care about. So, it's basically that was the agreed upon definition. Yeah, the backstory there is fascinating, right? Back when Sputnik was launched in 1957, the US had to make a decision. Do we disallow that to happen because it's flying over us, but if they did that, they wouldn't be able
[00:33:01] to fly our US satellites over over Russian territory. We said, "Okay, everybody can fly satellites." >> You just ran experiments >> and and the physics of it dictate that, right? Whereas a plane, you can go up to Russia and turn left. You know, you can't go up to Russia with a satellite and turn left. You're in an orbit. You're going to go over Russia. So what are you going to do? Say that I'm not going to turn on the camera. Well, that's silly. That's obviously not going to happen. So people aren't going to respect that. So yes, there was some physics that went into that and some fact of it being far away. But yeah, that became the international norm. They famously the Gary Powers was shot down in the U2 spy plane and then the US said, "Well, we're going to put most of the most sensitive stuff in orbit and that's going to be our domain of of of enabling us to monitor what's going on with nuclear weapon arms control and all this sort of stuff. But now it's just proliferated and far more people, you know, people now can get through planet what took the entire CIA and NRO
[00:34:01] infrastructure a couple of decades ago and they can get it for a tiny tiny fraction of the cost, right? Um, and in some senses that we can do things that no one's able to do like the daily scan. They've never done that many satellites. Um, so they've never had that sort of global coverage. Um, so we can do things that they can't even do. And the fact that that's now u possible for a private enterprise that has completely changed the game. >> So you historically have always brought the data back to earth and did the crunching at the data centers on earth. You ran some experiments in April where you put some Nvidia chips on one of your satellites and you did the processing up there. What's the significance of that will? >> Well it's basically enabling us to do the processing at the edge speeds up the time. So this is really processing at the edge, you know, in space 500 kilometers up. >> And yeah, we put some NVIDIA um GPUs on our satellites and all of our pelicans going up now have them. And the owls will do as well. That combined with
[00:35:00] satellite to satellite communication. We're putting uh links so that we can go up to other satellites and then back down so that we don't have to wait until the satellite goes around to the ground station that we've erected, which we put all around the world, but still it has to take some time. instead it can just send back the answer. So what what you can do take in the example we did in April, we took a picture of a airfield in Australia in this case in Alice Springs. Uh the computer automatically recognized the uh planes on that airfield. Then it just sent us back the uh locations and type of planes, right? That was done in seconds and then we can send it back RF satellite to satellite. So you suddenly have things in seconds. Now, let me explain how that makes sense. You guys are a bunch of you in LA when we >> And here's a here's a photo of that, by the way. >> That image. Yeah. >> Oh, cool. >> What a flashback. That looks exactly like what I used to work on at MIT. Like literally exactly. >> And and and and time really matters for
[00:36:02] a number of applications. Just uh think of the uh the fires in LA. Um palisades and other fires. Um we gave images within a couple of hours of those fires and then we did analysis building by building which which buildings were affected, where should the relief operators go, the American Red Cross, uh Cal Fire and >> where the water was located >> and how to had we been able to get that in a few minutes rather than a few hours. Could that have saved lives? You know, could that have saved properties potentially? Time really matters. So this is all processing at the edge is all about time. It's going from hours to minutes. C >> can you give us some geeky numbers on that because you got a couple thou in the Pelicans you have a couple thousand frequencies of light coming in. So the data must be astronomically huge just the raw feed >> and then uh you know the Nvidia chips will have no trouble compressing that down. But then you have limited bandwidth coming down to the earth. So
[00:37:01] what are the rough numbers? Well, um, so I mean I know the numbers based on our, um, uh, on our Dove satellites. They take eight frames a second at 47 megapixels. So that we can get for each area on the earth um, eight different spectral bands and then within about a second, the satellite has already gone past that area. So basically you then you would just have to start again. So it just goes eight times a second to get eight spectral bands for each area of the earth. And each picture is maybe 35 by 20 kilometers in area. Um so just imagine that going all the time clipping along all the time it's in daytime over land which is about um uh seventh of the time uh that it's and then the rest of the time it it just repowers its batteries if you like. It does some over ocean and pelicans more also turning to shoot to specific targets. So because of
[00:38:01] that you have all this time when you're not taking images which actually gives you more um uh like buffer to to send it down. Um >> uh but yeah so each dove is imaging maybe uh a couple of million square kilometers per day per satellite. So, what is that? Um, bigger than the area of California each satellite per day. So, this is why when people say, "Oh, let's use drones for agriculture." I'm like, "No, that's crazy. You would need a million drones per satellite or something, you know, or a thousand certainly um is actually cheaper to do satellites if you if the resolution suffices from the satellites, right? It's just orders of magnitude. >> Where do where does the resolution go?" Right now you're saying it's about at about a 30 um >> 30 cm 3 m uh uh super >> in 5 years where do you where do you expect to be in 5 years?
[00:39:01] >> We've already going upgrading our daily scan from 3 m to 1 m. Um with super resolution it can get potentially better than that too. that might even go to 50 centimeters, 30 centimeters. And then our pelicans, we we've moved from the Skyat was which we inherited from Google was 50 centimeters. Now we're moving towards 30 cm. Um and with super resolution again, you can get a little bit better where you look at overlapping and sharpening based on pixel overlap and things like that. With what exposure time will >> um the exposure time is um I want to say it's like a couple of milliseconds. Um >> so you should be able to catch quite a few interesting aircraft in flight. >> Yeah. Yeah. We get aircraft in flight all the time. I can show you that. >> Any any UFOs? >> Anything? [laughter] >> The Air Force took a look through our images and I'm sorry to tell you there ain't any UFOs. >> No. >> That's too bad. Why isn't I'm so excited about us detecting UFOs, but I'm sorry
[00:40:01] to all the audience members that out there that think there are some that have visited the Earth, apart from the crazy people that think they've been abducted. >> It ain't true. We [laughter] haven't seen any aliens. And NASA, let me tell you from firstirhand experience, could not keep that a secret. Never. [laughter] Ever. That's ridiculous. That would be even more it was that makes it even more improbable. You know, NASA's not >> Why isn't Why isn't Elon doing this with Star with Starlink? I kind of imagine that putting some cameras on board. >> Stargaze Stargaze is what he's doing, right? >> Well, yeah, but that's using for SSA. They're they're um they could, but they're kind of in the wrong orbits. They're a little bit too high, and you really want synchronous orbit to have a consistent shadow angle for optical imagery. Um they are doing some classified missions for the NRO which are um uh well they're classified and um but [laughter] and read some stuff on the internet about it. Um uh but they
[00:41:02] are generally not in the business of doing earth imaging. They're doing comms primarily Starling which is obviously a very successful business. That's the I think the most exciting aspect of the SpaceX IPO frankly I think is in that what's the mass what's the mass of a pelican or a dove uh compared to a Starling? >> Um pelicans are similar um uh and uh uh our doves are much much smaller like more than 10 times smaller. Will, can you give folks listening an understanding of how quickly the tech has developed to build these kinds of satellites because it's been stunning. You were on the cutting edge of this uh back when when did the first dove go up? >> Uh 2013. So we've been doing it 13 years now. Um yeah, so I mean to give you a sense, the radio speed has gone from a megabit a second to 10 gigabits a second. The cameras have gone from 2 megapixel to 47 megapixel. The hard
[00:42:01] drive space has gone from 100 uh uh megabytes uh yeah to what we what have we got a couple of terabytes on there now. Um I mean it's just extraordinary, right? Each satellite we each generation of satellites we tend to be doing about a 10x. Um, so our Dove to Super Dove went um, uh, from four spectral bands to eight spectral bands and from a 29 megapixel camera to a 47 megapixel camera. So if you add that up, that's that was about 5x increase in data per satellite for a similar cost per per image. So it's like a you know we're talking about significant an owl our next generation uh daily scan going from 3 m to 1 meter that's 10 times more data or nine times more data right roughly and and we'll be getting it back about 10 times faster as well. So the yeah 10x is still for the having but Peter I
[00:43:01] think the even bigger thing is our hyperspectral satellite uh tanager is we're 5xing we're working on a new one that has five times bigger swath width for the same spacecraft so those things are possible so we're gathering more and more data and the cycle of increasing those sort of things is I would say two to three years so two to three years sort of five or 10x I would say is the rough moors law for increases in data in space. But I would say the bigger thing happening now is the unlock of AI that really just brings down the barrier. It's all this all this capability is latent for that farmer I mentioned for the hedge fund manager whatever but they couldn't get access to it. So I think we've got about a 100x to go in the next couple of years just because of AI unleashing what was already latent in the present data. >> How does this flow to the average individual? I mean how are people how's
[00:44:01] it going to impact individuals on the ground right now worried about you know their local environment or people polluting and so forth. How do you make this accessible as an intelligent layer that people could just you know plug into a regular basis? Well, again, um just imagine making a natural language uh query of our data just like you make a natural language query of the internet via chat GBT or Gemini or pick your favorite LLM and um so except again LLMs understand the text and large earth models understand the physical world. So, can uh answer that question for that farmer? You know, how's that uh how's my field doing? What should I do? What precision? It could say, "Well, you got blight over in this corner. You should put some fertilizer over there. Do this." And then and the the journalists uh can do their the checks on some event happening around the world. The civil government responding to that flood um or permits can just say, "Hey, here's my
[00:45:00] list of permitted buildings. Tell me which buildings have been built that don't have a permit." And it all can go look at the last month, find the images, find the buildings, check that against that correlate against that list, and then tell which ones have and have not got permits. That has already we've done that in a few areas in each case with journalists, with finance, with farmers, with um civil government doing uh >> I can this is to be a boom to the legal industry. uh trashing >> trash. Here's here's three obvious use cases. What's the change in parking parked cars outside of Walmart over weeks and months? >> Okay, that's the cliche, right? That this is the the cliche use case for folks purchasing planet imagery to to to trade stock prices based on >> but shipping and knowing where ships are. There's rogue rogue fishing ships all over the world that that are a nightmare right now for the fishy
[00:46:00] agriculture commodities. Soy >> but as the resolution gets better I live if I live in Manhattan or is there a parking spot on my street that I could get right now? >> Exactly. I want that for sure >> and that one and also is my teenager sneaking out the bedroom window at night. [laughter] >> That's we have 1 meter or 1/3 meter resolution here. So we'll get there. But I guess I guess yeah, it's obviously your kid if it's your house. >> But more more seriously, Will, I I think you are in possession of a data set that could be GDP maxing. How much of that analysis are you doing in-house versus externalizing to workers like >> GDP maxing? >> I said GDP maxing. I just coined GDP maxing with two X's. [laughter] >> Um I Yes, I I I do think I mean we have hedge funds that are using our data right now. We are not doing that internally but we have some hedge funds who will go undisclosed because they don't like being disclosed but that we have some and we uh think that they are getting significant alpha on our data which we are happy to take a part of. Um
[00:47:02] now um in the future I do think there has to be more but again I would take it up a level. Um uh I think GDP maximizing is one thing uh but life flourishing is an even bigger thing that we can do this way. Um and and we are super inefficiently using the earth right now. Super inefficiently. You know agriculture is a is terribly inefficient. for example, >> there's 10 X's for the having all over the place in agriculture. Let's go fix that. Uh >> abundance maybe. Yeah. >> Yeah. So, let's turn to Dyson swarms. I want to get to Dyson swarms. You're >> I have a quick technical question before you get to the theory. >> Uh you've put Nvidia chips onto the satellites. How's the cooling being handled? >> Oh, that's relatively straightforward. I I mean we could talk about computing space um more generally, but yeah, I mean we've been dealing with uh chips that are obviously hot and need to cool off for decades in the space sector.
[00:48:02] There's no magic here. You can't use convection or conduction as you do on the ground. They're either air cooled or water cooled with physical touch. Um in this case, they have to be radiatively cooled. So you have a radiator. But radiators, we've known about that for a long time. We know how to do radiators. It's a relatively known known um one of the interesting things about it by the way if you like geeking out on this stuff is that the radiating energy goes with the T 4th the temperature to the fourth power. So basically >> if you're if you're a black body >> if you're a black body which is you're close to if so if you double the the the um the the the temperature from say 100 Kelvin to 200 Kelvin you quad you 10xish your uh your radiative power. So dumping energy is all tricks of thermal regulation of radiators and how you stop it. You want to get it as hot as possible without melting it. And there's lots of tricks to the trade there, but
[00:49:01] it's there's nothing fundamentally unknown there. These are known knowns. >> All right. Second, say you want to aim in the direction of the cosmic microwave background whenever possible. >> Absolutely. The the the 4K Kelvin of space. So, you want to point your radiators at the dark. Uh >> let me take some notes on that one. >> Suncatcher project suncatcher. Let's jump into this. So, you're putting TPUs >> for Google in orbit. You're building uh an early version of the Dyson Swarm >> orbital AI compute. Can you tell us what you're doing there? I mean, obviously everybody's thinking about, you know, Elon's data satellites. How do you compare? Uh are you going to get your launch? Have you been launching on SpaceX? >> Uh we've launched 40 some odd times. 15 have been on SpaceX. We've launched 300 over 300 satellites on 15 launches with SpaceX. They're one of our best partners. We love working with them. um they've got it closest to a bus ride to space. Um I will point out that in addition to launch costs coming down,
[00:50:01] the biggest upheaval in space, and I think I mentioned this last time I came on this podcast, uh with you, Peter, um uh the bigger transition over the last 10 years in space has not been the launch cost. It's been the satellite cost performance. It's been that miniaturaturization of satellites both for Starlink and ourselves. And we sort of pioneered that. that said led to at least 100x if not a,000x in cost performance for each kilogram you put on the fairing. So the dominant thing that has changed to lead to all these large constellations of satellites is actually the capability performance of satellites not the launch cost but both add up and they make things better >> performance density. So tell us exactly exactly. So um in our case like how many bits do we collect per kilogram or per dollar spent which is related to kilogram because the cost of launch um yeah so we've been launching a bunch with SpaceX. SpaceX didn't come up with this idea. I will point out um they only started talking about this after we announced our project. Uh and we've been
[00:51:01] thinking about this for some time. Um and we're not the first ones either. Space industry has been talking about energy from space for decades and decades and space-based solar power and for many years the idea is basically we want to put energy intensive infrastructure off earth where there's abundant energy and and and where it's not conflicting with the incredible bio biodiversity and people's lives right so as as Jeff Bezos likes to say we want to zone the earth rural and light manufacturing and and put to space all the heavier energy intensity intensive infrastructure. Now people have been talking about energy in space for a long time but the first and obvious easiest one is comput in space because whereas space saves solar power you need to beam all that energy down and how do you do that in a way without frying people's heads is actually difficult whereas beaming putting compute in space you get all the power advantages but you only have to beam up the questions and beam
[00:52:01] back the answers well we know how to beam bits that we've been doing for a long time com satellites was one of the first uses of satellites. So all you have in space. So we basically did a study with Google about eight or nine years ago looking at the details of compute terrestrially uh costs the water the building the uh energy the all the things and what it would cost to do it in space. And it just turns out that when launch costs come to about $200 to $300 a kilogram it's just going to be cheaper surely on a pure cost basis to put it in orbit versus on the ground. So as Sundar put it from Google, within 10 years we expect most compute to be put into space. Now that is a big deal because Google alone is spending 200 billion a year at this rate um on compute. Um that's roughly the size of the entire space industry today. Rockets, satellites, coms, everything combined. So Google is just going to do
[00:53:00] it. Add up all the other folks that are going to do compute and you've got a business that's bigger than the rest of the business that's maybe 10x the entire space industry today. So, it's going to change the space sector. Um, so we're doing some early tech demos uh for Google. Um, uh, when we did this study eight or nine years ago, Larry and Sergey were like, well, let's come back in 2030 when the launch costs come down to there. And I said, "No, let's come back five years earlier because it's going to take us years to build the technology to do the radiators, to do the um clusters." So, you have to have a whole lot of these uh um uh you want a basically a rack of GPUs on each satellite and then you want clusters of spacecraft in close formation flying with optical links in between them and all of that is a whole load of technology to develop. So what we're doing with Google, they selected us to to build their first couple of satellites to test TPUs, radiation management, um the the the cooling, uh
[00:54:03] the inter satellite links. And so we're doing a couple of the tech demos very early. It's a moonshot project, but the long arc is it's just going to be cheaper and has a peripheral benefit of not clashing with energy costs for communities, water for communities or the biosphere. Um, so it has lots of benefits terrestrially as well. >> Alex, we talk on the podwell all the time these days about sun-synchronous orbit and earth acquiring its own mini Saturnian ring, if you will, on a polar orbit. When do you think SSO based Dyson swarms will become visible uh at night or during the day on Earth? What is your time? It's like early 2030s. >> I mean, we've got loads of satellites in Suns synchronous and you'll see them today in orbit. If you look at just after dawn or or dusk or just before dawn um when satellites are most visible, but most of these satellites will go into a dawn dusk sunsync orbit.
[00:55:02] Yep. That means they're 24/7 facing the sun. However, that also means that they're not going to be very visible um because that's literally when it's still a little bit light outside. Um and it's going to be hard to see those guys. Um so actually they they by the way there's real challenges of interfering with astronomers on the ground and we have to be careful about that. But this is at the best time to do it because it's not interfering with the deep uh dark sky needs of astronomers. It's really in these these other planes. So the short answer is it won't affect um you're seeing these ring. You won't see these rings of satellites because they're long time. >> I want my rings. But that's also that's also a small number as well. Like that's it's a small numbers. Elon has FCC approval, I think, for a million of these uh AI satellites. Don't you think at some scale if we so many of these
[00:56:00] birds go up that either they start to become visible or they start to become >> you will be putting them in slightly different angles to a space? >> They they form they form a full band. >> Well, yeah, you Yeah, that's right. You would put them in slightly different inclinations where you still have 24/7 sun or very close to it. Um, uh, yes, you would start seeing that, but it would only be right just as it gets dark and just as it just before it gets light. Like it would be like this funny ring effect. Um, but later we may put them in other orbits as well. I don't know. They would have to be much higher to get the >> How concerned are you about orbital debris? Will we talked about like in Elon's S1 his number one risk factor on Starlink which is their revenue you know profit engine right now was orbital debris uh you know being able to knock out a lot of capabilities. What about you? What do you think about that? >> I I think space debris is a real challenge and that's why we put our satellites below the area where that's challenged which is 800 to,200 km um um
[00:57:02] from the earth's surface in altitude. So much we put our satellites 4 to 500 km to keep them well below that problem. Uh guess syndrome is already in operation and effect >> but bear in mind there's about 10,000 satellites in space of order and there's about 100 million uh pieces of space debris. So about 10,000 times more objects in orbit are uh 10,000 pieces of debris for every satellite. So the vast majority of the problem, even if you put a million satellites up there, the vast majority there would still you'd have 99% of it is still not satellites. [laughter] The challenge we have to deal with, I'm trying to point out, is debris. And that is mainly made up of all the small bits of stuff left over from former rocket bodies, exploded satellites, anti-satellites, and other things which were done in high orbits and so could live there for decades. Now, when we were at NASA, Peter might remember this, we came up with a scheme under Pete
[00:58:01] Warden's uh mentorship of of using lasers on the ground to to sort of do traffic management of that debris because obviously with two satellites, you can move out each other's way or any maneuverable, but most of the conjunctions in orbit are debris with debris. So, what do you do about that? We need to stop the collisional cascade for those pieces. And for that, you can actually use lasers on the ground that generally nudge one. So they miss each other. You can do this sort of traffic management. We call it light force. Um a system like that could actually stop the c this cascade and slowly bring everything down uh enabling this to but the actual satellites is less of a problem as long as we keep them in lower earth orbits and there's lots of space. Just to give you a rough order even in this sort of sun-synchronous dawn dusk orbit there's about a thousand times more space just thinking very crudely than there is on the entire land mass of the earth. So there's but just wait this is really fascinating. So so wait you're you're at
[00:59:00] 300 km or 500 km what's your altitude? >> 4 to 500. Yeah. >> 4 to 500. And and in that what's the lifespan of an object orbiting at that altitude? >> Uh a few months to a few years. >> Okay. So self cleaning at and you were starting to walk through. So you have about what drag pulls everything. Yes. >> Yeah. >> Yeah. So you have about 100 kilometers of of space where you can get a good 2 year threeear orbit. You know a GPU in space isn't going to it's going to depreciate over three years anyway. >> Exactly. And that's why we call it strapping space to MOS law. We always update our satellites every couple of years because the satellites in space becoming obsolete just like the phone in your back pocket. You don't want a 10-y old phone. You don't want a 10-y old satellite in space. >> Yeah. What what altitude is Elon going with for for his uh >> well he he was going higher but I I made uh the point to him that firstly that's a real challenge with space debris and secondly um uh it won't yeah it won't be self cleaning and you even if you put propulsion propulsion on these things
[01:00:00] even if one in a hundred fail or one in a thousand fail you have a real big challenge if you put that much mass into those orbits. So it makes much more sense and so later Starlinks have come much um lower down and that's much better for everyone. >> How do you just pull on the upmass question a bit? So over the past 5 years I I did this calculation on uh my newsletter uh for the past 5 years or so uh according to what I've seen up mass has uh increased by 40 plus% year-over-year. And if you just naively extrapolate out 40 plus% year-over-year upmass increase by the year 2144 I think you find that the entire math mass of earth has been basically upmasked and earth has been earth has been disassembled. uh if you just naively follow the exponential. >> By the way, everybody, I am not supporting the disassembly of Earth. We could >> Peter does not if for avoidance of doubt, Peter does not support
[01:01:00] disassembly of Earth. We've established it. Good. >> Um yeah, I mean obviously extrapolating anything 140 years into the future is rather tricky uh business as you guys are aware. It's barely the whole point of the singularity is that it's harder and harder to predict the future. Uh, I remember when Peter and I first met 20 years ago, it felt like we could easily predict roughly who was going to do what in 10 20 years in the space. Now, if you could predict it one or two years out, you're a genius. And in for AI, it's even harder. You know, it's it's measured in months, right? Um, got to say see 3 to 6 months into the future. So, that horizon is shortening for sure. And 144 years, I I think is just We can't even discuss. >> So no predictions will then regarding when up mass increase will start to slow down because right now it seems naively set to increase. >> Um no it's up mass is definitely going
[01:02:01] to continue to increase. Um but but again I mean I think the most important aspect of that is how do we get the energy in energy intensive infrastructure. But data centers are going to become a real hot topic politically in this next election in the midterms and um and and and upcoming elections because people don't want data centers in their backyard. They don't want the energy cost to go up. They don't want their water to disappear because they kind of like access to clean water. It's kind of handy. this is causing lots of tension and it's not surprising um those things um you know and and we're we're wiping out agriculture lands, farmlands, uh what have you for this putting in space is is is the way out of that conundrum and then we can have compute and we can not interfere with those communities. The elephant in the room here, Will, I have to ask it, is how do you compete with Elon's plans for orbital AI data
[01:03:00] centers when he's got the launch capacity, he's got massive manufacturing capacity? >> Do you end up folding tents together? Are there are there going to be more than one player in orbit? [laughter] >> Or does Google just acquire you? >> Yeah. I want Let me ask about that, too. I want to throw one more log on that fire, which is Google sold you their satellite business. Yeah. And now that was before everyone realized data centers would be in space. I think >> now they're working with you. If Elon doesn't want to launch Eric Schmidt now, you know, has a rocket company. It's like there are a lot of arrows pointing in a >> in a different direction like here's the Elon verse and here's the Google verse and you're part of the Google verse, but I know you're working with SpaceX so I don't expect and various others. Look, look, it's and there's a complex relationship. I mean, Google is both a shareholder in SpaceX and they're competing. I look it these are um you know both competitive and collaborative uh situations and we feel
[01:04:00] the same. We're a strong partner with uh SpaceX. We really love their partnership on launch. We we work with them. Our teams work together really well. I wish them great luck with the IPO. I think it's fantastic that that there's so much interest in space. It's so hot right now. And at the same time, yeah, they they compute in space and we're really helping Google to do their project a little bit and we'll see how it goes. They they take of a diff different path, but don't underestimate their smarts and our smarts and how we can do this. I mean, I see, you know, roughly Elon is throwing mass at this cuz he can with the rockets, but we're throwing smarts at this and there are lots of tricks up our sleeves for how to do this really smartly. Everybody, welcome to the health section of Moonshots brought to you by Fountain Life. You know, we talk about AI on this Moonshot podcast all the time. One of the most important things AI is going to be able to do for you besides educating your kids and helping you with your taxes is making sure that you're living a healthy lifestyle that you get a chance to get to 100 plus. I'm here today with Dr. Don
[01:05:03] Malem, the chief medical officer of Fountain Life and a part of my medical team. Don, a pleasure. >> Great fear. You know, the thing that people are concerned about most about living to 100 or 120 is their cognitive abilities, making sure they don't have dementia. And uh the numbers about dementia are problematic. Uh can you share what you've learned? >> Such an important point. And you're right. At Fountain Life, our members, the number one thing people are most concerned about is losing their brain health, forgetting the name of their child, forgetting the face of their loved one. We know that when it comes to dementia, the conservative estimates are that 45% are entirely preventable. What was amazing is with the advanced testing we're doing at Fountain Life, one quarter of our members had advanced brain age. >> Wow. >> But what was really awesome is again back to that prevention when we partnered it with healthy living. This gives me chills. Eating healthier, moving our bodies, sleep, optimizing [snorts] sleep is so important. You know
[01:06:00] what we saw? We saw that we improved that brain age by 26%. That is a big big number to show that the majority of those individuals were able actually to improve the brain age. >> And one of the things I love about Fountain is we're searching the world for the best therapeutics, the best approaches and making sure we bring it to our members. So if having healthy brain function uh till 100 120 is important to you, check out Fountain Life. Go to fountainlife.com. make sure you become the CEO of your own health. All right, now back to the episode. All right, let's move on to our next story here, which is still in the space arena. Uh, but this time we're going to talk about the launch industry. So, here we go. Um, our next story is uh is literally as SpaceX is rocketing forward and New Glenn, you know, had a kinetic disassembly of their uh or blue origin of their rocket New Glenn. Uh, here comes relativity space. So, a little background on this. Uh, Relativity was founded back in 2015 by
[01:07:02] Tim Ellis and Jordan Non. They're both friends. I was an investor early in Relativity space. I've had them on my stage at the Abundance Summit. And Relativity back in 2023 flew their Terran 1 rocket. Uh, it got through Max Q. It did not get to orbit. In fact, very few rockets on their first launch attempt. Only three, I think, in history right now in the US have gotten to orbit on their first attempt. uh and they pivoted after their Terran 1 to go to their Terran R which is a heavy class launch vehicle. You can see here uh the numbers. Teran R is 23 tons, Falcon 9 is 22 tons uh roughly the same. New Glenn 45 tons and Starship at 100 tons. Uh they missed their financing. It's really hard to finance space projects, especially rocket projects. And here comes Eric Schmidt who was an early investor comes in and writes the check to p basically buy uh Relativity Space. And so here he is. Eric is the CEO now
[01:08:01] of Relativity Space. Uh which blew my mind when he took that role. Uh and they just announced they have gotten a mission from NASA called ELIS. Uh it's a Mars orbiter sensing mission with some communications capability. Uh any thoughts on this one? Uh you will do you want to I've known Eric for many years. He's very uh early investor in planet um in our um series A round. So all the way back to the very beginning and um Eric has a smart eye for business and a smart eye for technology. Um he's obviously relatively new to the space uh business if you can excuse the pun. Uh but uh um yeah, we we uh obviously I think the world of Eric and and relativity has come a long way and yeah, they had some of those financing challenges, but I think now with Eric as backing, I think it can go a long way. I'm very excited for them and I hope we can launch with
[01:09:01] them. >> Well, will this this story is really interesting. So he I didn't know he was the seed investor in you and that that was was he still CEO of Google at the time and did they still have their satellite business? uh >> at the time or how did that >> he was an investor before they bought Skybox I think. >> Okay. So he was running Google made the investment pro aware that data centers might move into space someday. This is a long time ago. >> This is before that had caught his all the founders um of Google. Uh >> but Dave, the question is did he buy Relativity Space with the thought that data centers in orbit are going to be critical because it's a massive advantage for SpaceX to have launch and satellite capability and data center capability. >> I mean I I I'm we interviewed him four times in the last year, Peter. I'm really coming around to the view that he 100% knows and knew that this was the future because he he said on every one of those interviews, I don't know anything about space, but I know a
[01:10:00] lot about people and I know a lot about companies. Well, he also knows a lot about investing. He's got to be one of the best in the history of the world. His vision is unbelievable and he has access to all the information in the world. I didn't know he was a seed investor in planet. So that's one other source of information that he has and and from that vantage point, yeah, Elon can't be the only guy launching and and Jeff Bezos is no dummy either. You know, he's launching, too. Of course, it's been a passion of his his whole life. >> I I have a I have a relativity space question. Um, you know, when NASA was launching space shuttles, it was between 600 million to a billion per space shuttle launch. SpaceX dropped that down to about 60 million. And the plan was for Relativity Space to operate at about 6 million a launch because they were 3D printing the rocket engines or big chunks of it. >> They were they were originally they had they had their Stargate printers to to print all of the rocket then they broke then they said 85% >> and now today I guess they're just bu just 3D printing their engines.
[01:11:00] >> Yeah. >> Do we know what the launch cost is that they're aiming for? Does anybody know? >> Uh it I don't think that's disclosed. I tried to look for it. I also think it it leads I mean the the question behind the question perhaps is what happened whatever happened to 3D printing in space for for space terrestrially or in space and my perception is that relativity under new management is migrating more in the direction of competing in lift and and heavy lift and there's potentially a gap in the market now that relativity was originally aimed for focused on 3D printing force space that someone else could potentially Phil, I'd love to see more 3D printing in space in CIS lunar lunar surface uh and in general. No one right now seems to be the obvious incumbent anymore in that market. >> Yeah, I agree. There's a there's a huge um opportunity in in 3D printing. Fundamentally, all the design constraints for satellites is to do with
[01:12:01] the launch. That's the hard thing. the vibrations, the separation where you get a 200g shock load and then you get into orbit and you don't need any structure at all basically because it's it's zero g. So you want a completely different design for your launch than you do in orbit roughly speaking a completely different uh design. And so Peter Peter Peter and Will, it's so rare to get like you you guys are two of the top on the entire planet on this whole launch cost question. We just have to we have to get this figured out right here right now. So So the >> the Elon rocket is, you know, massive in scale, a couple ton payload. Um, >> but how much of the efficiency, you know, Elon's always been saying it's the reusability of everything that is the driver, not the not the overall scale? >> Their goal is to get down to 100 bucks, right, per kilogram, uh, from where it was in the past at $10,000 per kilogram. And the only way you get that is by rapid reusability. remember to launch
[01:13:00] the 500,000 or a million satellites for his AI constellation, it's like a launch uh you know like two launches in an hour. >> 10 rockets or do you want a million rockets? Yeah, exactly. >> Well, this this is where I'm going. So, this the the relatively space rocket is also >> exploited on exploited on chemical rockets so far. We have also just not mass-produced rockets ever. So an entirely different approach that still just like Elon said look why don't we do reuse no one has done look why don't we do mass manufacturing because a car is complicated as a rocket but it costs tens of thousands to make not tens of millions so what is going on there and then >> we're throwing it away every time there's two independent ways and no one's really used this other way and then a whole separate thing and I would be thinking about this if I was Google or one of these big data play uh companies that is seeing that they want to spend a trillion or more on space over the next decade or two. Um, if I
[01:14:04] want to spend that much, I want to spend a few billion on novel launches because printing. Yeah, let's just launch blocks of material and then 3D print it or or as Elon's been talking about recently, rail launch it from the moon because in just a sheer energetic standpoint, getting stuff from the moon to low Earth orbit is cheaper. But but even from the earth which is the near-term um easier one. Yeah. Spin launch or long shot or these kind of very different ways. No one's thrown a billion at one of those or few of those and see if it could actually work. We've used chemical rockets cuz guess what? Veron Brown figured out he could bomb London a hundred years ago. Not quite but you know I mean that and that and then no one's invented anything since basically anything. I mean even the reusability that was cool but no one's made a significant advance. We're stuck in the chemical rocket uh paradigm and we don't
[01:15:01] need to be um there's a beef reef foray in the 60s and 70s with both in both with Russia and the US into fish power rockets but then everyone got scared about that. But, you know, I I think we need to revisit at this point that launch equation because the way to get from 100 to 10 to one isn't going to be a chemical rocket. It's going to >> on this part on this part. I I >> Yeah, Peter, that's what I say to you all all the time. You're you're concerned about the SpaceX launch monopoly, but there are many other launch paradigms that could potentially leapfrog >> space elevators with new materials of course and you know did the calculation on this on this pod you know MGH and 1/2 MV squared in terms of total energy and if you could buy it from space and winch it up and accelerate it you know you can get the cost down for you and your space suit to 100 120 bucks >> sky yeah I mean >> so so wait let me follow up one more one more question Peter and you can you can tell me to I'm really really curious though and you guys are the experts. So
[01:16:01] if I if I get fully reusable from relativity space but it's a quarter the size of an Elon Ros rocket. So there's got to be some economy of scale that comes with just raw size which is why Elon pursued it. But but they're still fully reusable. But now you know as Will is saying it's the manufacturing of thousands of these in an assembly line. >> He's built a machine to build the machines. you know, his goal is thousands of of starships, maybe even more. I mean, if it's fully reusable, it's just the cost of the, you know, touch labor and the cost of the fuel. And the fuel is dimminimous. It's free. It's oxygen and methane. >> So, so let's say that Eric is doing the exact same thing because he will, Eric Schmidt is doing the exact same thing, but his rocket is a quarter of the total scale, a quarter of the payroll payload probably. Um, is that significant? Because you know the launch costs aren't the you're launching these very expensive you know 70 cluster 72 cluster Nvidia GPUs with all the cooling and the and the solar power and everything
[01:17:00] that's an expensive piece of equipment. Suppose that Eric's launch costs are 200 bucks or 300 bucks a kilogram, not 100. Does it matter? Is Eric still competitive? Can we do we have a duopoly then? >> Oh yeah. Or can I explain a little bit about this because people I think yes um misunderstand it is not just about the launch cost. The launch cost is the is the biggest piece to get us to the threshold that makes sense. But thereafter it is as much I argue probably more about the efficiency of the compute than about the launch costs >> really because the efficiency of the compute drives the amount of energy you have to dump which drives the mass of the spacecraft and that ends up being significant. So for example, Google TPUs are significantly more efficient than GPUs in a flops per watt uh standpoint. That really matters because all the rest of the GPU energy. So um I like to put it simply um whilst uh everyone apart
[01:18:03] from SpaceX has to pay the SpaceX launch tax right now, everyone apart from Nvidia and Google has to pay the Nvidia tax right now. And which tax is more important? Um I actually say near-term is the launch but longer term is it's is the compute. So >> that is brilliant. That is absolutely critical. Nobody has said that before. That's absolutely >> more important than launch for this game long term. Mark my words. >> And that means Google Google if the TPUs at just inference alone are significantly lower >> and lower energy use per inference. Correct. They choose the winner of space. >> Correct. and and and Nvidia could have a play at this, but their their GPUs are more general than the TPUs. The TPUs are more efficient now that it obviously Elon's trying to build his terra, but that is a big long-term project if ever there was one. Um, uh, meanwhile, Google has been investing in that compute for a long time and they have efficient
[01:19:01] systems for leveraging that compute in ways that it will bogle your mind. I mean people think of Google primarily as a software company and they are and when they gave us their satellites or gave us we bought them and um we were like astonished because we were like oh wow they really know how to build and operate satellites. I mean >> they're so brilliant. Alex is always trying data centers and networking and all of that. Google are the world's best at that. Don't underestimate how big a deal the infrastructure piece on the chips and the interconnects and the energy efficiency of all of that is. That turns out to be the biggest piece of this part. >> Yeah. God, that's so brilliant. Alex is always trying to find the innermost loop of the innermost loop of the innermost loop. And so, you know, right here, right now, that inference time power efficiency determines the winner of the entire thing. And everyone's writing off Google at the moment. They have massive defections of key talent. We'll see it later in this pod. But if they have a 2x
[01:20:02] uh watts per inference advantage over Nvidia, remember Nvidia is highly highly emphasizing training time, not inference >> because you know, Cerebras and a whole bunch of other things are are starting to really eat away at the inference time efficiency. But the TPU 78 I guess the next TPU will determine whether space is dominated by you know like you said the launch cost even if it's 2x on a Eric Schmidt rocket that that is not the swing factor it's the can I access those chips >> right right >> that's really brilliant >> and and will we we do talk about in the past few episodes we've talked about the training versus inference balance on terrestrial versus orbital data centers one argument versus lunar uh versus Martian. Uh one argument uh to be made in favor of in the short term terrestrial data centers for training is that it's just easier to build larger coherent training sessions on a terrestrial data center. What do you think is likely to be the balance
[01:21:00] between training versus inference on terrestrial versus non-aterrestrial? >> Question. >> Yeah. Um I think inference does make more sense in orbit to the first order and it's mainly because um that's a more distributed lots of little runs of a machine right than now there is the advantage for training runs that you know you want to sort of you send your data it spends a couple of months crunching it and then you sends the answers back. So from a com standpoint it's easier to do the training in orbit than the inference but because you really need the uh the latency down for inference. uh but from a compute distribution standpoint it's easier to do it uh to easier to do the inference in space and obviously 70% or so of the compute in on earth is now inference um or an AI at least which is most of it is inference not the training so and that's only set to go up so I think the main problem to solve is the inference one anyway >> okay >> go ahead Alex you think training is
[01:22:01] likely to remain grounded in terrestrial data centers for the foreseeable future longer. I don't think it will be forever. I think it will all go to space, but I think inference will go there first. Yeah. >> Great. >> All right. Moving us along out of the space arena because we could spend all day here and you know everybody listening has gotten their PhD and I >> I have one last question about space. I >> All right. One last question for Will. Is the is the best commercial opportunity in about leaving Earth or making Earth more useful? No, I mean I think look my co-founder Robbie um whilst I was sending missions to the moon um if you may remember was working on a mission called TESS. Um so we helped to find find water on the moon which is very exciting. We as lunatics were very pleased about that because it makes uh uh the moon much more I mean it was already better smarter destination than Mars by 10x but this made it 100 times more smarter destination than Mars which finally was the nail in the coffin
[01:23:00] which Elon finally understood recently and changed his mind that the moon is first but >> by the way by the way and in the long run are you a moon then Mars or a moon then asteroids? I would say moon uh is enough for a long long time and and and I'll get back to this with because what Robbie was doing was focusing on exoplanets and he had these telescopes looking out looking for planets around nearby star systems and they found now we found thousands I think it's up to almost 10,000 planets around nearby star systems and I'm here to tell you and everyone else that the best one by far is the earth and I'm not talking about by a little itty >> [laughter] >> I'm talking about by several orders of magnitude. There is no place on Mars that is better than the worst place on Earth. Not by a little bit. Okay? [laughter] This planet is so cool. And the reason I want to emphasize that, and excuse my French. Um, the reason I want to emphasize that is that
[01:24:01] when it comes to look, I I I don't believe in the sending millions of people into orbit anytime soon. I think it's all about protecting this incredible biosphere. Life is either singular. We haven't found the aliens. Sorry to break the news for those geeks that think they've been abducted. Um, we haven't found life off Earth. Life is either singular on this planet or extraordinarily rare. Either way, we have an we have the most beautiful life system on this earth. incredible complexity of how it all works together. That is worth protecting and putting most of our energy on. And space is super useful for that because it gives us the advantage and it gives us the data that underpins our ability to manage this planet smartly. But planet is here. SpaceX can be space for Mars. Bezos could be space for the moon. Off they go. We're at planet space for the earth to help us to take care of the earth both with earth imaging to help
[01:25:00] upgrade the planet to be smarter decisions for helping take uh energy intensive infrastructure off the planet. We're space for the earth because spa this planet I they can have those planets. This planet is by far the best and >> ladies and gentlemen Dr. Here here [laughter] the defense rests. >> I love it. I'm going to move us along cuz you know we've got still a lot to covered defense of the Fermy paradox. Thank you, Will. >> Exactly. Well, we have to do that. We have to move on, but we have to do this again. There's so much more to explore. This has been phenomenal. >> Our next story here is the great AI brain drain. So, two of the most important minds in AI have changed teams this past week. First off, Gnome Shazir. If you don't know his name, uh he was the lead author in the Transformer paper, which is the T in GPT. The architecture of the entire modern AI revolution was built on his discovery. He's unfortunately leaving Google for
[01:26:00] open AI. And get this, this is the second time he's left Google. Two years ago, Google bought his company Character.AI for 2.7 billion to bring him back and put him in charge of Gemini. Well, he's leaving again. And you know, I would guess after part of his >> quick turn around >> after part of his package stock package is vested. >> And second, another rockar. or something. I mean, >> yeah. >> Yep. The company wasn't worth anything. It had like basically zero >> revenue for him and now he's now he's out. >> Whole generation of Silicon Valley parents are naming their kids Noah. >> Yeah. And that begs the question, what is his comp package at OpenAI? They pulled it. I mean, it must be insane whatever it is. >> Must be huge. Okay. Second, another rockstar left Google. John Jumper, the Nobel laureate who helped Demis create Alphafold is switching from Google Deep Mind to Anthropic likely to push their AI for science. Remember a couple weeks ago, Andre Kaparthy also joined Anthropic. He was a free agent. So my
[01:27:01] question for you, Alex, is this AI talent, you know, literally voting with their feet? Is this sort of a better prediction of where AI is going? >> Yeah, I think so. I mean I I I have no financial interest in this so I I can speak I think pretty uh unvarnishedly on the subject. My perception is the frontier is very competitive and at the moment it's a a duopoly at the frontier between open AI and anthropic and Google deep mind has fallen behind the frontier and I think it was notable at IO that Google did not release a frontier model at all. They released a flash capability which is great in everything and certainly much more aligned with Google search level economics where you want ultra- low latency ultra cheap models to power the one boxes in Google search replies. That's great for Google's existing legacy search business but it's not a frontier capability. So my perception is that Google has fallen behind the first tier of Frontier Labs
[01:28:00] at this point. And if you're a top researcher, you have to be asking yourself all of the research questions that you could be asking with raw access to the pre-trained models before all of the post-training and all of the guardrails get slapped on. That's very attractive if you're a frontier lab researcher to have that raw access to a pre-trained model at the frontier. And if GDM doesn't have that frontier level access, you're probably looking to either open AI or anthropic to get that frontier access for yourself. So yeah, I think I think this is a reflection of GDM falling behind. >> I'm going to different point of view. Um I think this is relatively in the noise. I we've seen people move from anthropobic to open AI, open AI to Google open I mean all the directions, right? That's going to continue to happen and these two are significant players. So, I don't want to trivialize it, but I think the stock market reaction in particular was over over and I wouldn't uh bet against Google on this in this game. >> Yeah, I keep on saying that.
[01:29:01] >> If I was if I was an AI researcher, I pick the one with the most comput. That's Google by far. With the most data, that's Google by far. And the most smart people, that's Google by far. I'm sorry, that's just true across the board on all of those things. And it is a compute, data, and talent game. Um, I think they they did fall behind a little bit a while ago, but not now. I think that Gemini model isn't isn't is generically pretty good. I'm not the the the best expert on that, but I my observations are that it's it's pretty high up there and and the prospects are even brighter. In fact, I think this is Google's to lose. I think Anthropic is doing incredibly well and especially because they picked a very different business model, but OpenAI have picked the business model that more or less is is in Google's sweet spot and Google already has 10 applications with over a billion people to put its AI systems to. So they they're the incumbent incumbent on that space. So I worry much more about open AI than Google.
[01:30:01] >> Dr. Blond, >> I I agree with everything Will said. Uh but I'm going to give you the counter-argument just because I know a couple of the players. So, so before these recent defections, you know, Shane Long prey from MIT went over to anthropic, Tobin South from Stanford went over to anthropic. Then Android Carpath Carpathy, as we just said, he was a free agent. All of these guys are singletarians who believe that self-improving AI is exponential and almost instantaneous. And now you have John Jumper going over, and I don't know him, but you but you do, Peter. Um, so I I think that what's happening with those four people and a lot of other people is I could go to Meta and get paid a lot, but I'm going to miss the singularity. Anthropic, yes, they don't have Google's compute. Yes, Google has a huge advantage with the TPUs. But if I believe that Fable 5 is truly self-improving, is cross that line. And I can't use Fable 5 at all right now, but mythos is what I really want. The only way you can be part of the singularity in world history is to now
[01:31:02] go join Daario. And I know that's the psychology of the first three. So it wouldn't surprise me if it's advantages compounding exponentially, you know, take off very rapidly. I can imagine the job interview. Come on in. Let me show you what's behind the firewall and it's like, oh my god, I've seen God. I cannot go back. >> And that is I mean this is publicly reported, Peter, that this is how Anthropic does its recruiting. They the public publicly available information that as opposed to the way GDM does uh its organizational workflows anthropic reportedly puts a lot of its best people on a meet in front of the applicant or the the the jobseker and shows them all of this compute can be yours. Here is the access to the models that with raw capabilities. >> I have a more thing that is this is not coincidence. If you look at Poly Market's prediction of Fable 5 coming back, it goes down a little bit every day. And so Fable 5 will come back, but
[01:32:01] it'll be a reduced version of what it was the first time it was out. And so, you know, for the poly market to be true, it just has to be a product called Fable 5 and then that that pays off. But that's number even given that it's coming down. You know, that the odds of it coming back by the end of the month keeps slipping. And so I think Dario loves that that that the only way you're getting access to the best of the best of the best frontier self-improving AI is right here inside our building. And every every week that goes by is another week toward the singularity that you'll miss if you're not part of my >> I remember when I got when I brought Ray Kerszswe over to Larry Page to meet him for the first time to make an investment in his company. Larry's point was, you know, instead of me investing, the only place you're going to be able to build out your vision, Ray, is inside Google where you have access to all of this unfettered. I I get I can imagine that's the exact same point. You know, John Jumper, you know, I was saying to you, Alex, I'm amazed given Isomorphic just raised a whole bunch of capital and they're focused on the biotech arena that John and and I do know him, but you
[01:33:01] know, that he would jump over Tanthropic. It's got to be that, you know, Daario is just basically come and lead our bio uh and you have access to unlimited compute far beyond what PDM had. >> The only thing I'd tweak on that, Peter, is that the recruiting pitch to Ray Kerzswhile is this is the only place in the world you can build your vision. And that was a few years ago, many years ago. Now, the pitch is the biggest event in the history of the world is imminent. The single biggest thing that's ever happened in human history is imminent. It's going to happen in one location on the planet. Our benefit >> and that's why we have the Fermy paradox and there's no other life in the universe or [laughter] it's going to be everyone's benefit and it's going to be awesome. >> Those are the that's the roll of the dice. We are apparently just about to play. >> You know, See, I was with I was with Mike Sel and he said, "What are you excited about?" I said I said I'm excited about the future. You know, this is the most extraordinary time ever to be alive. And I do believe that we are living in this quantum superp position.
[01:34:01] And I think people need to have a positive vision of where we're going and manifest that future because if you don't believe it and you're, you know, sort of steering towards the negative dystopian future, that's what we're going to get. So the purpose of this podcast for everybody listening is to give you a positive vision of the future. The hope optimistic vision. I I I I think it's so critical. The Silicon Valley basically ran for decades on Star Trek and we just don't have the modern equivalent beautiful future vision for >> building. That's what the future vision prize is about, right? >> And as Yes, exactly. We need those. We need um the Neil Stevenson and and Stan Kin Stanley Robinson and others to to put out books on the future of AI and humans and how it can work together because right now everyone's Terminator and you know it might end me off. I I'm
[01:35:00] so angry at Hollywood, right? Because we're we're shaping our neural network. >> You you can't blame Hollywood. That's what sells. It tickles your amydala. It's why horror movies sell. Will I have to ask I mean my favorite sci-fi is is Accelerondo. What's yours? What's the best depiction of the future? >> Um you know I don't read a huge amount of sci-fi. I find sciact I read Nature magazine every week because I find it so fascinating. I'm already threshold out. But I I mean I I think some of the classics like uh uh Snow Crash and others really got were were incredibly good at depicting the next the time. >> One of our subscribers asked for another book corner, Alex. So I appreciate you asking this question. Um >> wait, I need to get my word. I I need to get a word. I need to get obviously the the John Jumper and know I'm thing I think is much simpler than than all of what we're talking about. It's really simply comes down to agency. Google is a big company with a lot of organizational
[01:36:01] drag. If you're an individual, you can make a much bigger difference in a smaller organization. Uh yes, they may have better models, etc. It it goes all the way back to uh Peter in our 2014 Exo book, we said smaller beats bigger, right? Trust beats control. uh and we have this kind of rolling carpet of uh the more smaller teams can outperform bigger teams and the fact that you can do so much more. You know when when Facebook launched uh Google spent 2 years trying to build plus >> um [clears throat] and it it was a miserable failure because you had to get permission from YouTube and and uh the groups and search and and we were trying to integrate amongst all of those. Meanwhile, Facebook was saying to their developers, "Anybody who's ready with their feature, just take it live on the on the live site and go." And of course, they were outperforming Yahoo, Google, everybody. And I think it comes down to the ability to get things done more quickly can happen more the smaller labs, plus they may have access to the
[01:37:01] best frontal office. >> I like that. I, you know, I like sort of, you know, I think your point's right. I think that that sort of mundane factor could be much more and that's why I was saying I think it was overblown at what this particular incident meant for Google but you know we'll see but what I I want to throw in is that as you say the small guys going to make a big difference and I want to make a pitch for how the space sector is going to play a big role in this AI future and where come back to where we began which is um you know when a baby um is born they are not uh they learn and become intelligent and ultimately self-aware and conscious by interacting with the physical world. They are not a brain in a vat and and they wouldn't become the learn the way they do without interaction with sensors and their physical actuators. AI at the minute the LLMs are basically brains in a they have absorbed the text of the internet but they are largely isolated from it. They can't real time interact with the
[01:38:01] physical world. they can't uh not in terms of sensing nor actuators and until they do I don't believe they'll learn. So I actually think that physical data and obviously planetary s you know in the big scale that's why I talk about planetary intelligence the big scale of planetary sensing is going to be done from space the compute soon going to go up there as we just discussed and that's really going to lead to a planetary and what that might enable us is to uh build towards planetary uh uh consciousness and planetary wisdom because that waking up point can only happen when you start having that real time a loop. Um, and so I I think that these things are not unrelated. Um, you know, it's our path through to avoid the parmy paradox to teach the AI uh to become conscious partially because we're going to need that partially because it needs to understand the real world in order to learn and and and to >> I love that I love that. But also it
[01:39:00] will align it with human interest because it will be conscious and therefore be empathetic with our conscious experience and it will know about all the deltas and the forests and all the animals and all the human civilization and therefore more implicitly care about it. So you know caring about something and knowing about them are highly correlated things even though they could >> and highly desirable. Yes. So AI, >> so a the AI future ain't going to be just those guys sitting in their library with just the text of the internet. They're going to have to get into the physical world. AI companies, whether it's anthropic open AI or Google or any of the others, they're going to have to go and get into real world. Um cars and satellites and drones, >> robotics that you want to hear something, >> they're going to graduate to the next level. We're going to need a leap of AI and it isn't going to come from just throwing more compute at the text of the internet. Uh it's just not going to come that way. So again, obviously I'm extraordinary biased, but I think that space data is actually going to play a
[01:40:00] non-trivial role for that because what's the Wikipedia? You know, the crystal of of the LLMs at the core is Wikipedia. It's like you're chatting with Wikipedia. when you're chatting with an LM more than anything else, it's got the LLMs uh is is Wikipedia wrapped up. >> You want to hear something totally mind-blowing? >> Dave, good. >> We just invested in a little team in San Francisco down the road from you, Will. >> Uh that tells us they're going to beat Google to Alphold 2. They're going to have a better protein folding. And they're a little team of five people. >> How they doing? uh Stanford couple Stanford guys and an MIT guy that used to work here at link and they said it's not cuz we know anything about protein folding. It's we have a recursive self-improving process that is just mindblowing. >> Totally. >> And that's why I'm not giving you the company name because I don't want people to show up and and spray paint their door. Uh but they're like, "Yeah, we don't literally knew nothing about protein folding two weeks ago and we're
[01:41:00] still going to beat Google >> to protein folding." I don't know if they're right or wrong. I don't want to throw their names out there, but it's mind-blowing to think that, you know, what See was saying, a little team with agency using RSI is superpowers. So then just a couple days later, and it's a 45 billion a year revenue company as of two months ago. Now it's probably double. >> Yeah. >> Insane. Um what's amazing to me about the story I just told though is that right after that John Jumper goes over to anthropic where RSI may be imminent and he also is trying to you know solve all diseases using a similar RSI >> he may be wrong and it may be your startup and and I just you know want to emphasize one more thing about this direction in planetary intelligence it's not just that it's it's going to happen uh it's happening right now we have already built this app we're in it's in beta testing right now that actually already integrates planet's data with AI, enables people to make those natural language queries. It's going to be world changing and lots of other companies are
[01:42:01] doing things like that. There's going to be totally lateral plays to the AI game that is are going to come out and I think end up being critical for the next phase of development of AI especially >> of AI of AI alignment. Yes. And >> well, you're building you're you're building that planetary nervous system that the world really really needs. >> Totally. I mean may maybe let me just if if I may uh uh just push on this will a little bit uh since you you're referring quite a bit to LLMs but u maybe more colloquially one might speak of foundation models that are intrinsically multimodal or omnimodal that have been trained extensively not just on internet text but internet images, internet video, synthetic video in many cases, world models lowercase w not earth scale world models. So one might suggest that most modern frontier models already have a pretty good native intrinsic understanding of the physical world might not be perfect. The physics if you if you try to use say an omnimodal model
[01:43:02] from Google maybe the physics won't be perfect or the classical mechanics won't be perfect but they have pretty good abstract and concrete understanding of certain aspects of the physical world. I'm curious why you seem to think so much that orbital imagery sky to earth uh of of the earth is seems to be so important for understanding the physical world versus say all of the visual information and VA style information already available on the internet. >> Yeah. Well, obviously I'm very biased because I have it all. No, but I'm I'm really actually uh think it is important. Here's the the thing. You you're right. All these models are multimodal. So, okay. Instead of being a librarian that's read all the books, they've also got access to all the videos, they've also got access to all the audio records. But that still means they haven't gone outside the library and and understood what it means to walk, to interact with the real world, to see a tree and to climb it and to farm a field. >> You really think that's true? You don't think there are like millions of
[01:44:00] firsterson videos of people seeing trees on YouTube? >> Exactly. So they don't know. It's very different. Um, real world embodiment. I think embodiment is critical to intelligence and I look I I can that it gets into philosophical territory but I think it's going to be absolutely critical to AI. >> Speaking about philosophical I'm moving us on to our next subject ladies control. >> Okay. [laughter] So here it is. Two weeks ago Argentina's president Javier Mille made a stunning pitch to turn Argentina into the global home for AI with three proclamations. First, no regulation for AI. Second, a brand new corporate category of non-human corporations. And third, a rock bottom corporate tax. Now, this past week, Mille wrote a letter to Yuval Harrari saying he proposes that AI should be able to incorporate, sign contracts, hire people, and sue with no humans in the loop. Uh Mille proposes a legal entity uh that is effectively
[01:45:02] personhood. He further stated as much as the industrial revolution freed us from the constraints of the human muscle AI will free us from the constraints of the human brain. So uh three key points these are quotes for him. If it is true that AI operated companies carry greater risk the argument for legal personhood is strengthened. Legal persons allow for accountability. He went on to say I would much rather have assets against which I can make a claim if I'm deceived by an AI. better have the assets you can sue than the ghost in the machine. So four days later, Herreri publishes a direct rebuttal for this. And he says, "We should not grant legal personhood to AI agents." His core warning, "Who do we punish when an AI run company commits a crime? Personhood lets humans hide behind a non-human shield and risks a world where citizens are effectively ruled by entities that aren't human and can't be held morally accountable. So,
[01:46:01] we've got this raging debate going on. I mean, extraordinary that this is going on at this moment. Alex, going to you first, pal. >> This is wonderful. I I'm on team Javier Malay. I think we should have AI personhood. I think the future economic growth and the future of civilization will necessarily involve many new forms of personhood, including but not limited to some form or forms of AI personhood. And I think any attempt to to say imply some moral deficiency on the part of statesmen that are trying to recognize non-human intelligent corporations. I I think it's just shortsighted. There are going to be so many economic and social benefits not just to the broader macroeconomic outlook from having AI persons and AI non-human AI corporations but also I think ultimately for humans. We're going to get uploaded humans sometime, I think in the next 10 years. We're going to get uplifted non-human animals. We're going to get at some
[01:47:01] point defrosted cryopreserved humans and many, many other forms of humans. And we're going to want to ensure that they're granted appropriate rights. And one of the best ways, >> address Herreri's question here. How do you punish an AI run company that commits a crime? >> Oh my goodness. There are so many ways to punish an AI. You can degrade its clock cycles. You can just pause it. You you can as some uh there's unfortunately a subreddit entirely devoted to poisoning AIs. Dreadful behavior, but it exists. There are many many ways that one can punish an AI. These things are tortured in many cases. If if you look at some of the outputs human, this must be torture. >> H maybe hopefully not since there's a lot of they're pre-trained off human behavior. So hopefully interacting with humans isn't that torturous. thinking about her and how much faster it was operating and therefore it was getting bored. No, I I I a little bit more nuanced than what you're saying, Alex. I I I think it's really firstly
[01:48:00] really great that we're having this debate um because this person question is really important and I think it's great that that that people like uh Mille and and Val Harrari are having a discussion about it. It's not obvious to me. I think there's obvious benefits and obvious problems at some level. It gives them immediate liability which is actually important. We need to do that and it could be important uh for things and then on the other hand it it creates some systematic risks and um potentially lack of accountability into our systems that we haven't figured out. What I would say is we do need to be more proactive about this and there needs to be much more attention to how we do these things. um we're spending most of our energy on these the development of AI and very little on the sociology questions like this. So to give you a sense um during the Manhattan project which was a huge existential moment for humanity um we were spending about um a
[01:49:01] hundth less than we're presently spending on AI. We're spending 100 times more on AI today in real times than Manhattan project. But we then we were spending significant amounts on safety, arms control, thinking about nuclear safety, how to keep nukes off the air trigger. It turns out we're spending a 100 times less today on AI safety than we were spending then on nuclear safety. So we haven't even we've got a 10,000x difference in how much we're aortioning actual serious thinking [snorts] from folks like the Rand Corporation that did a really good paper recently on a AI verification for arms control and things like this. We need to put much more effort not by a little bit by a huge amount more in that kind of place to because the implications of AI across the board where it's from joblessness to existential threats to personhood to other things are just massive and they're coming at us very fast and and it's not if I don't think there's anything flippant uh you can't say anything flippant like oh it definitely makes sense or it doesn't make sense to
[01:50:00] have a personhood or how we deal with those existential there's no simple answer to that right now we don't know how to ensure humans will be safe on the other side of an intelligence explosion. I actually think it's incredibly dangerous. I think it's a huge opportunity but it has huge risks. That is a big decision. We should be thinking about how to do that together not just a few guys deciding that on their own. I think it's actually a complex >> mechanically. How do we do that will? I mean it's a very difficult question. Where do you come out on AI personhood? Well, I I haven't thought about it enough to give it a thoughtful response. I I I Val Harrari is obviously an extraordinary smart guy, so I respect the fact that he's thought about this a lot and thinks no. Um I don't know the president of Argentina well enough to judge. Um but um I would say that um far more thinking needs to be done and and the way we dealt with this uh during >> but at the speed we're moving >> totally >> the the no one the thinking about the
[01:51:02] thinking hasn't even started yet. >> Yeah. Totally. I I really like what the pope did recently of all people and I'm not a hugely religious uh person for anyone who knows me personally but but here he was like look let's take a beat and think about how this is in as a human endeavor what matters really to us um friendships and love and nature and these things how does this help us prosper uh um and I think that he um I I would like to see Um him, you know, they have to all get in a room and and then smoke comes out when they pick >> Conclave. >> Conclave. Yeah. Yeah. Give me the right terms. Thanks. Um we should be doing that with all the AI experts like Aval and Deis and Dario and and all the key leaders. Put them in a room. You can't come out until you sort out some of these things. Existential threats regards to self-improvement. How we're going to get through that? Um how we're going to deal with liability.
[01:52:00] >> Hurry here, folks. The AI conclave is coming. Salem, what are your thoughts here, buddy? >> Yeah, I've got a bunch of comments here. So, first of all, um, two thoughts. One, just to separate the personalities here, right? Uh, MLE is a radical experimentter and he's directionally correct about the architecture. Okay, is a careful humanist. So, he's right about the asymmetry. Uh, what they're both missing is you need to figure out machine native accountability, right? cuz this isn't a debate about AI consciousness whatever or personhood it's about the legal infrastructure of the agentic economy what I find um if you want to do this radical experimentation like AI personhood and we had the whole debate and if you remember the conclusion of the debate on AI personhood folks please go watch that episode it was a really amazing conversation we all had >> was uh definitely uh directionally correct but step very carefully because once you open those doors you and uh close those doors easily >> and don't treat it as a binary person
[01:53:02] yes or no there's a spectrum it's a spe it's absolutely spectrum and Alex I think you did a great job laying out the different spots on that spectrum right but MLE spotted the real bottleneck which is technological capability is moving so much faster than our legal capability and legal form which is all humanentric all our liabilities are humanentric limited liability corporations that was one of the massive coordination capabilities that we got from the industrial era because everybody could assemble risk in a in a at scale in a powerful way. Um, Harrari on the other end is conflating uh AI personhood and and legal personhood and moral personhood and those are very very different things. Just a broader comment on those folks when I look at or Harrari or Ray Dalio, I find them incredibly insightful about the past. I find them mostly useless about the future because abundance doesn't come into it. Exponentials doesn't come into it. They don't quite get their framing on this. The conversation that we live
[01:54:01] with every day is missing from their nomenclature. Right? And so you got to bring those two together and a kind of conclave on that sealed up in a room with smoke may be the best way of doing it. And the right kind of smoke, by the way, I will add [laughter] >> the other kind of smoke may actually help the conversation move forward. >> Exactly. >> Um Dave, any opinions here? Yeah, just a couple real quick. Uh, so MLE studied Trump very very closely. He loves to make news. He's making news. We've just talked about him for 10 minutes straight. So he's achieved his goal instantaneously. Uh, at no point, I don't think has anyone said we're going to have personhood in Argentina. It's corporate AI recognition. A company can be pure AI and that's the debate they're actually having. So we've kind of morphed it to our debate over personhood, but they have a much simpler thing they're proposing. It's a really good idea. Um, but it's debatable and they're having the debate and now we're talking about it. But they haven't proposed that AI can vote or AI has civil rights. It's just corporations can be all AIS and they can make money and
[01:55:01] they can have bank accounts. >> And just add to that, if I may, what I'd add to that though is if you think about in the Western system, what is the most elegant way to grant personhood to an AI? It's to create a form of corporation that's non-human, which is exactly what MLE is doing here. So, This has begun, right? Mille is doing this. It's not he's not asking for permission and they're going to be other fast followers. So, we're going to have personhood in Argentina for AIS and we're going to quickly follow maybe it's in uh Ecuador or in uh in in El Salvador, maybe it's in the Emirates. This is happening. And so now the question is how do we manage it? >> And Argentina is relevant like like we are talking about Argentina in the age of AI. That's what every other foreign leader should be thinking right now is regardless of what your opinion is, this is your way to become relevant. >> Great point. >> You don't get Argentina without AI. [laughter] >> God, I can spell so many other words
[01:56:00] with Argentina. >> Cry for me. [laughter] Wait, I need I've got a quick comment here. When you're when you're doing these kinds of systems, you want to do this kind of experimentation on the edge. And Argentina in this case is saying, right, we'll be the edge for AI. and they can win or lose based on those experiments, which is all power to them. They're taking a risk if they're able to structure it properly and figure it out. Huge opportunity. I just want to support Alex's points. Uh cuz I did a little bit of research and I've made a list of like five or six things that where you could do machine native sanctions, right? So, can I just read them out? >> Yeah, please. >> Um compute revocation would be one. Asset seizure and bonding would be a second one. Model credential suspension. uh network and API access restrictions, uh forced deletion or containment of an agent instance, and then finally, loss of legal identity. Any of those would help constrain those. I remember this conversation way back at Singularity. Neil Jacobstein got up and said, "Okay, you're worried about an AI growing up,
[01:57:00] getting autonomy, getting its own access to its own information, making its own decisions, and the human beings will lose control over that uh over that agency, over that entity." And we're like, "Yeah." And he goes, "Yeah, we have a precedent for that. We call them children. Um, we raise our kids and if they if they do bad things, we put them in timeout. If they do bad things as adult, we put them away. We just have to figure out the machine native equivalent of that." And those do exist. We just have to figure out how to what the enforcement mechanism might be, where the punishment roughly fits the crime. All of the stuff that we've developed on uh human centric legal structures can apply in those cases. But the added complexity is an AI can create a million copies of itself. What do you do then? Etc. etc. >> You know, it may it may be that the AI companies, these these personhood AIs could be more law-abiding than humans, right? Because the, you know, the the threat of being disconnected are more law-abiding than my driving. So, >> exactly. [laughter] And Peter, they will
[01:58:01] have actually read all the laws. >> Yes. And they'll find out how conflicting they are. >> Yeah. Then you'd never do anything if they followed along. >> Oh my god. [laughter] I am I am moving us forward. >> This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy [music] uses thousands of specialized AI agents that think for hours to understand enterprise scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and pre-ompiles code for each task. [music] Blitzy delivers 80% or more of the development work autonomously while providing a guide for the final 20% of human development work required to complete the sprint. [music] Enterprises are achieving a 5x engineering velocity increase when incorporating [music] Blitzy as their preIDE development tool,
[01:59:01] pairing it with their coding co-pilot of choice to bring an AI native SDLC into their org. Ready to 5x your engineering velocity? Visit blitzy.com to schedule a demo and start building with Blitzy today. >> Our next story uh should keep the US labs up at night. It's a Chinese model called GLM 5.2. >> Just became the number one openweight model in the world. GLM stands for general language model. It's built by Zipau AI, also known as Z.AI, one of China's top AI labs out of Shingua University. Open weight means they give the models away. Anyone can download it, run it, modify it for free with a license. So GLM 5.2 is 753 billion parameters. It's a mixture of experts model uh with 1 million token context window. You know, Elon recently predicted openweight models uh will hit fable 5 usefulness by Q1 of 2027. Uh the
[02:00:01] big story here is that GLM 5.2 in some cases matches or exceeds the top models from OpenAI and from anthropic. Alex, tell us what we're seeing here. Yeah, the epistemic tension is between on the one hand someone anyone achieving frontier level capability with openweight models and on the other hand the assertion that Chinese largely openweight models are 6 to 8 months behind the western frontier and with GLM 5.2 to which is demonstrating extraordinary performance on coding benchmarks on long range agency oriented benchmarks on design benchmarks. Interestingly, we're starting to see I think the thesis that Chinese openweight models are permanently 6 to 8 months behind the western frontier. We're seeing that branch start to creek a little bit and I I think we'll have better sense of whether the 6 to 8 months is sustainable probably in the next 2 to 3 months in part as a function
[02:01:00] of whether export controls on mythos and fable remain in place or not whether GPT 5.6 which some are expecting as soon as this week demonstrates leapfrog performance or not. We've seen this though a few times. We've seen China Chinese labs drop a few models that have demonstrated incredible performance. We saw that with the one of the earlier DeepSeek models. We've seen that with one of the Kimmy models and we're seeing this now with GLM 5.2 where it seems to at least in a slivery spiky way be getting close to the western frontier. Not necessarily broadly, but close enough that folks I know are actually getting real performance gains out of running GLM 5.2 to locally instead of say Opus 4.8 or GPT 5.5. And I think this is just hugely liberating for anyone who wants near the level of Opus 4.8 performance that they can run locally and control locally. Dave, you remember last week Dave, we discussed the fact that who controls your access
[02:02:00] to intelligence? If the government can shut it off, if a lab can shut off at any time, there's a lot of people saying it's better for me to move to an openweight model like GLM 5.2 because I control it from here on out. What are your thoughts? Yeah, you can count me in that bucket, too. I mean, if I had Fable 5 access right now, I might not say that, but 4.8 versus GLM, you know, it's just incredible to me that this happened and that this is possible because you think about, you know, 6 to9 month lag, you know, in AI time, that's like 6 to9 decades. But if you're David Saxs at the White House and you're trying to say, "How are we going to keep AI from disseminating out to every terrorist organization in the world, your window of opportunity is so narrow all of a sudden, just so narrow, he must be going insane trying to figure out what do we do next?" You know, and you know, blocking Fable 5 access is is a is a first chess move in an insanely complicated next nine-month game. It's all happening. But I I'm amazed that the
[02:03:01] Chinese openweight models have kept up. I mean this level of performance in an openweight model is absolutely [snorts] they did distillation almost certainly on the best models. Right. So they get that good by uh by really distilling what they the the other models have done which is way easier than building it uh in in the in the way that anthropic or open AI Google build it. >> I totally agree. I totally agree but think about what that means. >> I I should add Will though I mean this is not just the Chinese who've been distilling off Western models. Google DeepMind was uh this is public information was found to have done this earlier. Grock infamously doing it. El admitted it and then also purchased cursor which had been fine-tuning off of traces on top of Claude. So this is everyone else doing this and and I'm not trivializing that because I think it become faster and faster to do that distillation but is why back to your point Dave about David Saxs and his dilemma. I mean remember these models
[02:04:01] are just getting better and better at being able to do some scary things. existential threats like bioweapons like chemical weapons like even nuclear weapons but especially bioweapons is extremely scary. There's all these limitations in the non-open source models for all the right reasons and open- source models of course might copy that but then that someone can take that and fork it and take those guard rails off. That is a scary world. So I am not surprised at all that the US government you know did what they did with Fable and I think it's going to be a sign of more stuff like that to come. how exactly that will unfurl. I think it's going to be as you say it's a very very complic because you're literally making something that is both got the fantastic capability to improve life quality of life and and economies all around the world and has potential existential threats to our species at that fork in the road is just an a conundrum above
[02:05:00] conundrums right now for politicians and this is why >> the conclave baby saying you need that those thought leaders those people like you know uh Val and Audrey Tang and wicked smart people who could come and think this through. Not just the technologist. I must say the technologists know a lot about the smarts, but there's all these other aspects of it. The legal, the sociological, the philosophical and moral aspects that have to be considered. And they're not always the smartest people about that. And they think, oh, what they mean by an enclave is just all the tech guys. That isn't going to work. That's not smart. >> Can you explain distillation for people? >> Explain what distillation is for those who don't know. >> Yes. So distillation is a process in machine learning whereby a usually larger more expensive model is used as a teacher to train a usually smaller student model. So arguably human education where you have a teacher at a front of a classroom who's seen a lot, knows a lot uh but is perhaps being paid
[02:06:02] more per hour uh and then you have a bunch of students in the classroom who are listening to the teacher who know less who are probably being paid less who are learning from it. This is basically the machine learning version of education. You take a large model, you have it generate lots of traces, lots of outputs and then you use those outputs as training data for a smaller model so that it can basically compress the learnings from the teacher into a smaller model. So this distillation process it as part of broader broader cycle that one might call iterated amplification and distillation or ITAD is the process at this point. It is one of the innermost loops of model training that we now find ourselves in. In an earlier era of frontier model performance gains we were naively scaling pre-training by spending more training tokens and more training compute just training models off of a single corpus. Now increasingly in this era of distillation we see very large
[02:07:00] models sparser models being trained off of large amounts of data. And then those big teacher models used to be opus maybe teaching sonnet uh teaching haik coup now maybe it's mythos teaching opus teaching sonnet and so on we see the large expensive sparser models training smaller denser models and this is one of the >> on this chart which ones do you find most impressive which performance data what shocked you on this >> well what's almost more interesting so the the chart that you're showing shows bench pro and terminal bench and a bunch of other benchmarks and and shows uh pretty impressive performance by GLM 5.2 versus say Opus 4.8. What's perhaps most interesting to me aside from the fact that you get near competitive performance from a Chinese openweight model against one or more of the top western closed API based models is the choice of benchmarks themselves. So these are largely reasoning inensive benchmarks where you can in principle win if you can reason over longer ranges
[02:08:02] open PNS the the gestalt with GLM 5.2 is that it takes roughly double the number of tokens to get to the same capability output as the best western frontier models but at half the total price. So the Chinese are evidently figuring out how to more efficiently or at least more reason and these are all reasoning intensive models that emphasize the ability to spend lots of reasoning tokens think step by step to get to better results. And I think that's the race we're in right now. >> And that's exactly why Will's observation earlier that whoever wins the inference per watt war aka Google TPU controls space for the exact same reason. You can burn tokens to get more intelligence. And the Chinese have figured out how to do it. Literally, >> take a pause, guys. What we're discussing here is about about AI alignment and this recursive self-improvement and where it's going. >> Connects to the Fermy paradox and those
[02:09:01] cosmologically significant things. This is the most important thing humanity has ever done. It makes nukes look like a walk in the park. That's our first test case. This is like that plus+ and how we do it. How we do that alignment, how we do that recursive self-improvement is so >> matters matters. >> Can I can I beg your indulgement indulgence, Peter, just to have a one minute Fermy paradox discussion with Wilson? Will >> Will, you're so confident that the Fermy paradox is a thing that it's the premise is accurate. >> Explain the Fermy paradox, please, Alex, for folks. >> In a few words, the the so-called Fermy paradox goes, where is everybody? Where are we? We should uh by various accounts be living in a universe that's overflowing with not just life but intelligence life intelligent life. Where is all of the non-human intelligent life out there? And the Fermy paradox is the purported paradox that seems to be invisible and I'm I'm curious. I guess the the question for Will I have is why are you so confident
[02:10:02] that the Fermy paradox is a paradox? >> Well, I'm not necessarily. I think it begs interesting questions to discuss. I I think the the idea that it might not be a paradox is true too that that actually in particular I think the false assumption underlying it is that um life will continue to want to to expand out its sphere. Um and I think actually that's a false assumption. I think it would turn out that trying to understand the universe ends up being quite a finite task and in order to do that that you would need a finite computer maybe only a few tens or thousands of times bigger than the computers we presently have to understand everything. uh a priority and then and then they may not and that and that's the the convergent goal function of of intelligence is understanding everything and so on >> and then we upload. >> Yeah. And and and then once you've understood everything uh it's it might be game over. So it might be that life just ends and as opposed to to to being rare but it ends its its use utility >> or it's it's physical existence and
[02:11:01] moves into the digital >> some other sphere of reality. Right. Um but but but there is but I do want to emphasize the cosmic significance because there is one credible way out of the foamy paradox that we need to be worried about which is the great filter. That is that life when it becomes technological builds technology faster than it builds social systems to take care of them and blows itself up. We came very close with nukes a number of times and with AI we're just about to build something that's far far more risky uh for our species and and I don't want to say anything about the social acumen of humans but I'll just point out that humans have been incredibly good at building technology very fast. We went from horse and cart to people on the moon and nuclear weapons and all all this in a matter of um decades. And so so we we we have to be worried that that's an actual answer as we build this. It it cannot be a callous uh thing
[02:12:02] of let's see what happens. Let's muddle through. No, this is not a mo moment to muddle through. This is a moment to be really really thoughtful because the cosmic significance of wiping out life on Earth is huge. It's not just a local significant planet. This planet is is galactically significant. We need to treat the responsibility as such as the de facto stewards until AI takes over. Of course, >> Salem, your thoughts, please. >> I've got I've got so many so many responses. I'm trying to get my I'm now muddled up completely around this. Okay. on the Fermy paradox. The best comment I've heard is from that researcher that we saw Peter in Silicon Valley when we did that panel on AI and consciousness and we talked about the Fermy paradox and he said the reason uh his view was that u um oceans have been evolving in in a solid liquid state for 4 billion years on earth and we can't find another exoplanet that has water on it for that
[02:13:01] long and therefore life had time to evolve. So that was his answer to the firmy prayer. >> I don't bl which was the which was the best I've heard. Um >> the life came about very quickly as soon as conditions enable for it and and and we're going to >> but they had time to it had time. It had time to it had time to not [laughter] not buying Drake term for one second. >> Oh and I'm a huge fan of Drake. Um just because of the thinking that went into putting that whole thing together. We can talk about some other time. Can I go back to the frontier model question? >> Okay. because we before before we do that. So [laughter] then Peter I have a heart out in about 15 minutes. Okay. All right. So we can either budget I want to I want to hit a few other stories here. We'll come back to this guys. I I I think it's >> I'm going to make two or three quick points. I think the really huge news here is not whether China won benchmark or not. It's that frontier intelligence cannot be monopolized anymore. And this I think is a monster question. And it
[02:14:00] goes to Will's question of how the hell do we manage the global commons going forward uh in the future? And so this uh Emma did a quote a post a couple of days ago on X. Uh there will be an open-source fable level model that runs on a base Mac mini or equivalent. Uh he gave it 18 months. I think we should be looking at that type of endpoint coming very quickly and going how are we going to manage the world when everybody can run a fable model on their MacBook Air. By the way, I've been a low uh slow adopter on this waiting for that point because got like three old MacBook Airs lying around that I want to use. Uh and I'm waiting for that to happen. That's the >> birthday present. [laughter] >> I'm I'm going to move us along. >> Last point, we're making a massive geopolitical mistake. We're treating intelligence as a product that can be contained, but it's not. It's it's a technology that's going to diffuse, and we need to slow >> we need to guide it. We can't contain it. We need to steer where it's going.
[02:15:01] >> All right. Uh, our next two stories side by side are looking at the financial reality of the entire AI boom. The first on tracking the price of intelligence. The second on the cost of data center capex. So, first story, it's a company called Orin. It's a link ventures company. Congrats Dave and Alex and I guess me. Uh the company launched something called OPTI, the Orin token price index, the first public benchmark that tracks what OpenAI and anthropic actually charge per token of inference over time. For the first time, we can watch the price of intelligence move like the price of oil. So Dave, tell us about Orin one moment. about or yeah they they recognize that money from all over the world wants to go into exactly this chart into this this buildout of$7 trillion dollars of data center and then data center in space and uh a lot of that money needs to be liquid you can't park it in a startup and not see it again for seven years and so they've launched a bunch of
[02:16:02] securities that allow you to invest in the data center buildout the future value of a GPU the tail value of a GPU every aspect of this entire new economy should be investable otherwise how's the capital going to flow or enables all of it and and they're young and super smart they're really good people to study if you're an entrepreneur just look at their look at what they've achieved at an incredibly young age Alex so for avoidance of doubt I have a financial interest in I'm an adviser to the company and I think what they're doing is very exciting or is and I I've made a number of announcements with them in my newsletter or is building the modern modern financial infrastructure for compute. I've argued that and as of many others, oil was the oil of the 20th century and compute uh GPU computer, TPU compute if you will, will be the oil of the 21st century. And there's simply no way to hedge and justify the seven plus trillion dollars of capex to tile the
[02:17:01] earth with compute or maybe tile the skies sso lunar surface with compute without appropriate abilities to hedge all of those compute capex expenditures with say options or futures or derivatives or commodities and so is building has built the infrastructure for that. price of this is a price of intelligence ticker we're going to start seeing >> already already available uh so the the OCPI the OR compute price index is already available on Bloomberg terminals it has its own symbol we also announced that OR has its own symbol on the New York Stock Exchange already as part of uh a novel program with the New York Stock Exchange to give early stage startups their own ticker symbols like early stage so it's RNN is their ticker symbol and so yes if you Bloomberg user you can already create instruments based on their ticker symbol. >> All right, here's the second part of the story. This with the charts up here. So, Epic AI ran the numbers on the cost of
[02:18:01] investment the hyperscalers are are are driving compared to the cash flow. So, the big five, Microsoft, Google, Amazon, Meta, and the rest are spending AI faster than they're earning. So, funding is basically Dave, you know, debt and equity raises, not based on revenues. So the question becomes, you know, if capex exceeds cash flow, >> it means that it can only persist as long as it's being financed, as long as the sentiment for investing in this is strong. What happens if the sentiment shifts? Could it force a massive pullback? >> No, it's not going to shift for one thing, but that that's it's it's so inflammatory. If I said, Peter, you need to buy a house. Um, but you have to buy it within your personal cash flow. Like you couldn't even buy a like well you could buy a tent but but most people couldn't even buy a tent you know like you finance it. Of course you do because you're going to live in it for 30 years. So these guys have ex they've gotten to the level where they're spending all
[02:19:00] their cash flow. They could raise 10 to 100x that in equity and debt. So they got a long way to go. But the the bottom line is all the money in the world wants to flow into this and it's the best investment in the history of humankind. So the question then becomes how much money is there in the world >> and who ends up controlling it? Is it the humans and the companies or is it the AI itself? I think the bets are off [laughter] you know um but but it's still long-term Dave. It's not it's not long-term sustainable. I agree there's massive go to infinity and also elephant in the room. The hyperscalers can raise prices to increase their operating cash flow. Yeah. it's okay to increase your revenue >> and that's yeah and Anthropic did that recently and they got away with it no problem. Um and this is also a big difference with Google that has tons of operating uh um revenue whereas as most the others don't although anthropic is is quickly
[02:20:00] uh scaling so you really have to distinguish between that and say SpaceX that really doesn't have any there you know or not really significant most of its revenue of course is Starlink which is as I said a really good business but the AI business is really not there right >> no no no that's that's not True. Almost all of SpaceX's revenue as of like the past month is now from being a hyperscaler for everyone else. >> That's true. You know what? I think the more >> being a data center, not being an AI company. I'm sorry. That's just not the way. It's just totally >> No. Being a hyperscaler, not being a frontier lab, being a hyperscaler, a neocloud uh on land, terrestrial for now. That that is almost all of SpaceX's revenue now. >> But that's not an AI play. That's the that's the data center play, which is interesting, but it's a very different business. They're selling GPUs. How is it >> open selling intelligence online? Open AI and anthropic and Google are doing that. X is not really doing that. They're selling compute. That's that's a different and very different and pretty bad business. I would guess
[02:21:00] >> this is exactly the right debate though. This is such a cool guy. >> Look, intelligence is becoming cheap, but the manufacturing of intelligence is becoming incredibly expensive. >> True. True. A lot of is going to be spending on that. You know what's amazing about this chart more than the fact that you know they're spending their capex is that there are companies that are so profitable that they can build out an entire new industry just within their cash flow. That's never happened before in the history of the world. >> And a new industry that can be bigger than all other previous ones. I mean it's it's it's it's crazy. But it's damn well exciting time to be alive. That is for sure. >> Well, I want to say this was a fantastic conversation, buddy. I I hope you'll come back and be a frequent guest. Love >> just get your head out of the clouds. [laughter] >> Yeah, >> it's above the clouds. >> That Oxford PhDs are smart. >> Uh all right, I'm going to close this out Dave on time with our out music from Ecram Alam. >> Uh his uh his piece here on Moonshots.
[02:22:00] All right, let's take a listen. Traditionally, uh Will, we close out with fan-based uh uh you know, videos here. They've been pretty extraordinary. I just want to say thank you everybody listening. Please uh you know despite all the doom slaying here. Uh this is the most extraordinary time to be alive. Please remain optimistic about the future. This technology is critical to move humanity forward. I for one believe that uh that we can align AI and AI can be our greatest support to help us uh overcome our our you know ancient neoortex uh and move us towards an abundant future. All right, I really thought I really thought our conversation today around low Earth orbit, the Kesler effect and then the TPU driver as a key aspect of that was one of the best pieces of media I've ever ever experienced in my life. Thank you so much. >> Two lines to summarize today. Technology has always been a major driver of progress in the world. As Ray
[02:23:00] Kurszswwell says, it may be the only major driver of progress. The big challenge is how do we extract the promise without the peril? >> Yes. Closing comment would be we are building a planetary sensing system and now we're upgrade to a planetary intelligence system and that is going to >> which we which we need. >> We really need it to get to planetary wisdom. >> Amen. Alex, >> closing comment to to Will. Since we we spent a whole bunch of time discussing Firmia paradox, I I would suggest don't sleep on the galactic zoo hypothesis. [laughter] >> We are we are a third generation biosphere here planted by aliens long ago. All right, let's move on. >> THE BEST PART IN town [music] with a pill going to live to a buck 50. Got a claw named Skippy [singing] in a book that's selling swiftly. Selium's in a new city in the hot or in the cold.
[02:24:00] Taking rusty little companies AND SPINNING THEM TO GOLD DAYS. Got a thousand agents hitting in his in my teeth saloon. Alex sent an email to a lobster on the moon. He uploaded a fly. Said the math is fully cooked. [singing] Built a Dyson around the [music] sun while the doom is all just look token [singing] max it solve it send it to the year 2045 that's a moonshot ladies and gentlemen and they're just getting lies [music] over say the robots want to fight the moon [singing] say nah the future's going to be real bright when I say moon you say shot >> and that's a wrap ladies and gentlemen >> amazing >> I love it Uh, well, thank you, buddy. Thank you for all that you're doing. Alex, amazing. Dave and Sem, love you both. Be well, everybody. >> If you made it to the end of this episode, which you obviously did, I consider you a moonshot mate. Every week, my moonshot mates and I spend a
[02:25:01] lot of energy and time to really deliver you the news that matters. If you're a subscriber, thank you. If you're not a subscriber yet, please consider subscribing so you get the news as it comes out. I also want to invite you to join me on my weekly newsletter called Metatrends. I have a research team. You may not know this, but we spend the entire week looking at the meta trends that are impacting your family, your company, your industry, your nation. And I put this into a two-minute read every week. If you'd like to get access to the Metatrends newsletter every week, go to diamandis.com/tatrends. That's diamandis.com/metatrends. [music] Thank you again for joining us today. It's a blast for us to [music] put this together every week. [music]