SpaceX filed for what's expected to be the largest IPO ever. $75 billion being raised at a valuation probably north of $1.75 trillion. He's about to have a currency to go on a shopping spree. That sentence is so out of band with any point in human history. So anyone out there doing a startup, there is capacity for a thousand unicorn transactions. Remember unicorn is called unicorn cuz it's supposed to be extremely rare. GPT 5.5 is now beating prediction markets on forecasting the future. It beat poly market crowd predictions for the Super Bowl. This is the worst psycho history models will ever be. >> The concentration of wealth effects from this would be insane. >> The financial singularity. So this is a problem posed by the famous Hungarian mathematician Paul Erdish about 80 years ago. Now AI has just solved it. Not only was it faster, not only was it able to brute force, but it was also smarter. This is like a much bigger moment in history than just solving a math problem. #solve
[00:01:00] everything, Peter. We're seeing it. >> Now that's a moonshot, ladies and gentlemen. >> Welcome everybody to another episode of Moonshot. I'm here with my extraordinary moonshot mates, Seem, the father of organizational singularities. You're spawning singularities everywhere, buddy. Alex Weezner Gross, our in-house polymath. Dave Blondon, our wizard of AI investing. I'm Peter D. Mandis, your host, and hopefully your abundance whisperer for an optimistic future. You know, we've loaded the show today with extraordinary stories. Uh hopefully stories that get you excited about being a builder, literally starting to build before this episode is out. Gentlemen, good to see you all. I have to ask uh our normal where's Waldo question. So today, Dave, where are you, buddy? Uh, Becca Stanford had a whole bunch of really fun meetings with AI founders here. >> Awesome. And Seem, I I know you're not
[00:02:00] home, but you're never home. Where are you? >> No, I'm in Brazil like we did yesterday. So, I'm still here till tomorrow and then I fly back. >> Okay. Well, and Alex, you and I are in our normal haunts. Um, >> that's right. >> Yeah. Uh, amazing. So, uh, let's begin. You know, we've stacked the show today. Here's a quick look and preview of what we're going to be covering. SpaceX just filed their biggest IPO in human history. Uh it's extraordinary. And later today after we record this episode, we've got Starship V3 scheduled to launch. OpenAI uh is just disproved a conjecture in the mathematics real. We'll be talking to Alex about what that all means. GPT 5.5 is now beating prediction markets on forecasting the future as well. Uh Chad GPT just became your financial adviser. Uh a lot to cover you know our mission keep you optimistic informed and ready for the supersonic tsunami heading our way. So with that uh let's get started. Our
[00:03:02] first story here is in fact the SpaceX IPO. SpaceX filed for what's expected to be the largest IPO ever. 75 billion being raised at a valuation probably north of 1.75 trillion. Uh the biggest in history. you know, over 2.5 times that of Saudi Aramco. Uh Elon is maintaining his super voting rights with insiders controlling 86% of the voting power. Uh and I love this. SpaceX's IPO perspectus says it expects an addressable market of $28.5 trillion. I mean, that's quite a tam. Uh Dave, let's go to you first on this one. you know, the the TAM is just under the size of the entire US GDP, and I I'm sure they landed it, you know, we don't want to claim to be bigger than the entire US, so we'll be just one notch below. But Eric Bolton hosted Mor and I
[00:04:03] for dinner last night at his house. Eric is a professor of economics and AI at Stanford HAI. That's the AI lab of Stanford. And he was the first guy to mention this to me. And I was like, come on. 25 28.5 trillion dollar tan. So he was going off on Yeah. But um there's no way to disprove it. Uh there's no reason it shouldn't be true. You know, Elon, remember Elon's core thesis is that we can 10x the global economy in 10 years. Most people think it's possible, but it's longer than 10 years. But regardless, if the if the global economy 10xes, then his TAM should easily fit within 25 or 28.5 trillion. I looked at the breakdown of how he got to 28.5 trillion. And it's interesting. So 870 billion, you know, just a mere bit under a trillion is Starlink's uh business. Uh 740 billion is uh Starlink's mobile unit. 600 billion is their digital
[00:05:00] advertising market through X. 2.4 trillion is their AI infrastructure. And get this 22.7 trillion is kind of come from Macro hard. If you remember macrohard is uh their their partnership with Tesla where they want to emulate all digital work and create an AI run software company. >> So um pretty spiffy. I mean a trillion here, a trillion there adds up to a lot a lot of money. Alex, what do you think of it? You almost have to ask did SpaceX acquire XAI or did XAI acquire or reverse acquire SpaceX because certainly based on the TAM analysis it looks a little bit more like the latter rather than the former with enterprise applications dominating the addressable market. I think the most interesting part in the entire prospectus was actually the bit about what Anthropic is now paying SpaceX $15 billion per year for data center access. And by the way, in the past 48 hours, it's not just
[00:06:00] Colossus one that Anthropic is paying for. It's Colossus 2 as well. So in the past couple of pods, I made remarks to the effect that Grock is on life support. And bunch of folks I saw in the comments said, "Oh no, he's he's against Elon. How outrageous is this plan?" No, actually I I think far from being outrageous, I I think SpaceX's prospectus arguably supports that entire thesis that SpaceX is basically taking now not just Colossus 1, which maybe charitably one could have argued is sort of a relic older slightly older GPUs and heterogeneous set of GPUs at that as at that and giving it to Anthropic. But Anthropic now also using compute from Colossus 2. No, this to my eye looks like SpaceX basically abandoning the foundation model space, handing it over to anthropic, focusing on the infra layer, building out the Dyson swarm,
[00:07:00] record Dyson swarm, >> record Dyson swarm into the episode. uh >> and focusing on becoming the the infra layer and they can I I think SpaceX I'm just reading the tea leaves here with the prospectus SpaceX AI seems relatively uninterested at this point in owning their own foundation model. They're they've also as part of this announced that they're they are going through with the purchase of Curser 30 days after their planned IPO and Curser is based on Kimmy at this point. So SpaceX's foundation model lineage is seemingly switching over to a derivative of Chinese openweight models with a lot of fine-tuning based on probably American reasoning traces. But SpaceX in this weird climate wants to seemingly own the layer below and above. It wants to to own the infra layer. That's the Dyson swarm and all the data centers. And it wants to own the layer above. That's macro hard. Which other hyperscaler does that start to look like? Microsoft with its strategy of
[00:08:01] enveloping open AI. >> Are they still playing? >> Meet Me meet the old boss same as the new boss. >> SpaceX is trying to transform itself, I think, into a Dyson swarm version of Microsoft. >> I was on CNBC this morning uh doing an episode about the SpaceX IPO. And one of the things I was sort of like just shouting from the hilltops is listen, you you can't think about SpaceX just from their Starlink revenue or even their launch revenue. What SpaceX is doing is opening up the space frontier and everything we hold of value. You know, metals, minerals, energy, real estate is in near infinite quantities in space. This is not just, you know, the first ships from Europe to the US. It's the gallions. It's the railroad. And SpaceX as they open up this transportation infrastructure, they're going to own the businesses all along the route. And at the end, what we're building there on the moon, in Earth orbit, in the inner solar system and
[00:09:00] beyond, you know, if you look at this, Alex, I know we've talked about this before, uh, Starship, uh, compared to all the other launch vehicles, right? We can compare it to New Glenn or to Relativity Space, what they're building or what, um, Rocket Labs is building with Neptune. Doesn't come close. I mean, Starship is planning to launch on the average of once an hour. Today, you know, Falcon 9 is launching every 2.5 days. And when they get to an hourly launch rate, airline operations, they open up the most extraordinary wealth in the universe. >> But the interesting thing to me is that they're they're barely even selling it. Like, yes, it is in the prospectus that they intend to provide earthto-earth transport to humans, cargo transport type things. Obviously, they're still a space heavy lift company, but if you just look at the prospectus, they look like Microsoft in space. That's the story that they're selling to to retail right now >> because that's what the investors understand
[00:10:00] >> that that's legible to capital markets. >> I think it would freak people out. I think it would freak people out. What I I go down the same path you did, Peter. This is planetary infrastructure. It's like Christopher Columbus sailing off to kind of trying to colonize a whole new world here. >> Yeah. Well, just some confirmation for what Alex was saying, too. Uh, the the Stanford PhD I was just meeting with before this podcast uh is tracking all of the talent and uh confirmed that yeah, the great people have left XAI uh by the droves actually, but they're all going to Anthropic. >> Yeah. >> And you know, Carpathy, I mean, he's got to be the ultimate coup. We had that on the last pod pod. Uh Shane Longpre from MIT who's phenomenal. He's joining Anthropic now, too. So, a duopoly between anthropic and SpaceX AI is is pretty daunting for everybody else. You know, you think about the best researchers all want to join Daario because they trust him and because the models are phenomenal and then you put
[00:11:00] all that compute onto onto Elon's, you know, space empire and that that's a heck of a duopoly. I don't know though if Elon plays duopoly forever. You know, it doesn't seem like his his >> playbook. Yeah, he doesn't play well with others. Uh it's it's fascinating. Um so >> you know the uh the other thing the terapab you know where's the terapab in the perspectus. >> Maybe it's the same story. We we don't need to you know promote things that are still hypothetical. This is 1.7 trillion is enough for now. Maybe >> yeah there I mean there were a bunch of assets that were shared. Macroheart itself was if I remember correctly characterized as in part shared with Tesla in part being informed by Tesla AI and I also think Optimus there's there's a a curious split that I I don't know how it's going to play out between the Optimus AI and the macro hardxai/cursor AI where right now SpaceX in this bizarre uh sort of uh incestuous
[00:12:02] ecosystem of of Elon affiliated companies SpaceX is seeming to get uh in in some sense the the digital optimist if you will the the macro hard worker for knowledge work labor and Tesla is seeming to get the embodied optimist and not obvious to me exactly how from a governance perspective all of this IP is supposed to flow back and forth between them. a lot of predictions, Alex, about merging obviously uh Space XAI and Tesla uh into you know Musk Corp. I don't I wonder if there's a poly market on the under over there but you know a lot of >> there is a prediction market for it. >> Yeah. Do you remember what it might be? >> Maybe you can maybe you can check in the background mode. >> Yeah, I'll check. But honestly, I think within a year, we're going to see that, you know, when we've got two publicly traded markets, the ability to value them and merge them uh becomes a lot easier. Well, the the bullet two is really important in that the super voting control. Remember, we mentioned
[00:13:00] on a podcast a while ago that Elon has never had what Sergey and Mark Zuckerberg have, which is a public entity that can raise, you know, many billions of dollars in an overnight where he's the controlling shareholder. So, this will be the first time that he's in that position. Remember, he doesn't control Tesla. That's why he's constantly, you know, going to Delaware court over his package. And >> he will. >> Yeah. This will be this will be a new thing in the Elon verse. And remember, Peter, you know, you were an early investor in XAI. Remember that capital raise when it was getting off the ground? >> Yeah, the very first one. Yeah. >> Remember the amount that he was raising, which seemed like a lot at the time. >> Oh god, I don't what I I recall. I don't remember either, but I recall it being like an $8 billion valuation and maybe a billion dollar raise or something like that, which is just hilarious now because you're, you know, now you're looking at a hundred billion, you know, size 35 billion here raise. >> He's about to have a currency to go on a
[00:14:00] shopping spree. >> Yeah. >> And Joe, my bet is once they go public, they're going to be beginning to acquire a number of companies as part of it. And and one thing we posted the other day, uh once these guys are public, they can easily do a thousand acquisitions or more of a billion dollars or more. The world like that sentence is so out of band with any point in human history. So anyone out there doing a startup, you know, yeah, maybe you'll screw it up, but assuming you don't screw it up, there is capacity for a thousand unicorn transactions. Remember, unicorn is called unicorn because it's supposed to be extremely rare. >> All right, this is just a whole new world. the old days. >> And by the way, the prediction market, uh, I have it for you. Poly market predicts by the end of this year a 20% probability of SpaceX and Tesla merging. >> Okay. All right. Uh, end of this year, but I think within a year, I think that's going to happen. Things settle out. >> Yeah, >> that's right. >> Yeah. All right. Uh, you know, I wish we were recording this later. I'm so
[00:15:00] excited. Uh what's coming up today is the launch of the Block V3 Starship, 100 ton payload capability to orbit. Uh it's an extraordinary vehicle. Um you know, we're talking about uh a thrust of 18 million pounds using the Raptor 3 engine, the most beautiful, elegant engine ever. This is flight 12. Uh on this flight, they're going to be uh demonstrating the docking ports that are going to enable orbital refueling. And of course, orbital refueling of Starship is required for the lunar missions they're planning uh to win as well as going to Mars. Both stages are going to be splashing down. Uh the Super Heavy uh in the Gulf of Mexico and Starship in the Indian Ocean as it's done before and hopefully they'll have buoys out there to watch both of them. Uh remember the NASA Artemis mission, Artemis 3, uh is a docking test later this uh in 2027. Artemis 4 to the lunar surface, particularly the south pole is taking
[00:16:01] place in 2028. Alex, uh, you'll be watching, of course. >> I'll be watching. Not from the moon. I I would really love to see orbital refueling happen sooner rather than later. That's I I don't know when that's expected, later this year. But I I think being able to get to the point where we can not just do propulsive landing, but also orbital docking and refueling between Starships is going to be such a key moment. It it's also the the hammer that the Bezoses of the world have been using to argue that the starship architecture which is a little bit if I were to play out an analogy a little bit more internet packet oriented. It's the the lunar architecture of Starship colonization of the moon is very launch intensive. It involves dozens of independent launches and refueling steps. Whereas historically if if you look at like the Apollo architecture was a much more monolithic architecture. You go up and then you go over and then you land and then you come back. >> You carry all the fuel from the ground
[00:17:02] for that one mission all the way. >> Exactly. So I I think the the world, myself included, will be watching with quite a bit of optimism that call it the Starship packet type architecture for you send everything up to to LEO in packets and then you do a bunch of refueling steps and then you you send the packets over to the moon or you send them to Mars is a superior solution. I I would view it almost as analogous to the switch uh no pun intended that the internet made uh or I should say that networks made from circuit switched systems where you had in some sense a single contiguous bit of of atoms connecting a transmitter and a receiver on a network to packet-based switching where there was a complete decoupling of bits from atoms. Same idea here with a Starshipbased solar system transport architecture. We're aggressively decoupling cargo from transport. And so fingers crossed that that Starship V3
[00:18:00] and orbital refueling later this year enable us to packet switch the solar system. >> You know, I don't think people realize how unique Starship is in terms of how it was designed, right? This is designed for full reusability. It's designed to land, refuel, and go again. It's you know his vision is airline like operations >> and the amount of throwaway um outstrips everything else. I have a you know the one thing that Elon has done extraordinarily well is his manufacturing approaches and his sort of building for an ultimate capability. um you know New Glenn uh again relativity space now CEO uh friend of pod Eric Schmidt that those vehicles I don't think they can compete uh they're going to have to build new capabilities to to get to this launch frequency uh that enables all of these visions >> well if data centers you heard Sundar at Google IO saying data centers in space
[00:19:01] we're not going to talk about it today but it's it's very clearly on the road app. But if Elon and Anthropic are working in cahoots to to, you know, to to build the Dyson swarm or Dyson sphere, then Google has to react to that with something. And here's Eric Schmidt, our former CEO, with a rocket company. So, they have to play fast followers somehow. I mean, you're right. It's it's starting from pretty far behind. Uh, but something has to has to give because Google can't obviously can't buy SpaceX. I also wouldn't necessarily overindex on the Starship architecture being the definitive final word on how we get to orbit. There are going to be many many future technologies I would predict over the next 5 to 10 years that will leapfrog in terms of the applied physics Starship's launch capabilities and it won't necessarily be SpaceX that's the leader in the field for leaprog capabilities. So I think there there are many ways to orbit. >> The development time takes years, right? We haven't seen a vehicle go from uh from design to operational flight in
[00:20:04] anything. I mean, even even Falcon 9 uh to get it to reusability, get it to the the rate of success, right? 99.99% if you would uh takes a good 5year time. And in the interim, I mean, don't forget Elon's going to be designing whatever follows Starship as well. >> That's true. But there are two things happening concurrent with that. First of all, the fast follower effect. you know once you can see what worked and what didn't work it really helps you a lot in the copying and that varies by technology but then concurrent with that you have like Maccato coming up with mechanical design AI >> you know you remember when we were interviewing Elon he said this is the greatest thing ever built by humanity without AI assistance it'll be the last great thing >> but it was entirely built with people in protractors and crayon like I'm getting >> how how retro it was CAD CAM, you know, hand built
[00:21:01] designed CAD CAM without AI mechanical design assistance that'll never happen again. So that might really accelerate Eric Schmidt. >> So I think that >> the the design space of aerospace lift and heavy lift is vast and we've only scratched the surface of it, I think. And to Dave's point, once it's demonstrated how large the market for heavy lift is in the form of building out that Dyson swarm, I expect many many competitors to to come out of the woodwork. Many of which are probably already known names and some of them or maybe all of them in aggregate I do think will give SpaceX a run for its money. >> All right, we could put a bet on the side on that. I think they will ultimately, but I don't think that's going to happen between now and, you know, 2029. And then you're part of NASA's infrastructure. And then you've built the original the initial Dyson swarm. >> Also, don't sleep on China. China is busy copying everything it can out of
[00:22:00] SpaceX. And I'm sure there will be Chinese heavy launch capabilities and multiple Chinese Dyson swarms as well. >> They've they've tried. No successes yet. No successes yet. All right. Uh let's move ourselves out of this. And by the way, by the time people have watched this episode, uh you know, Schroinger's cat has happened. Either either V3 of Starship has succeeded or it's failed. And if it's failed, hopefully there'll be another one following quickly. This has been 5 months since the last launch. Uh hopefully now that V3 is up and operational, we'll see a lot more frequent launches. And the one thing you got to appreciate about Elon is he's not a fail he's not afraid to fail forward. All right. Uh moving on. Uh I love this article uh this story. GPT 5.5 Codeex is leading at forecasting. So OpenAI built something called Future Sim that replays the internet a day at a time, day by day. It gives AI agents access to the real news starting from January 1, 2026
[00:23:00] onward. then ask them to forecast realworld events over the next 90 days. So GPT 5.5 uh is running codecs and scoring 25% accuracy leading across all the frontier models. Uh it beat poly market crowd predictions for the Super Bowl. Uh and you know the way I think about this this is the beginning of giving AI wisdom. You know, I've said this before. If you think about what wi human wisdom is, is the ability to go to the elder council and say, "Okay, you know, which direction do I take? Which way do I go?" And the elders say, "If you go this way, based on our experience, it's not going to end well. You go this way and it's likely to win." And if AI is able to run high resolution uh simulations a billionfold, you know, a billion times, it's going to say this is the highest probability of success. Um, Alex, what do you think about that?
[00:24:01] >> Remember Isaac Azimov's psycho history from the foundation novels. >> Yes. >> Right. So, so the the premise of the foundation novels is that a mathematician named Harry Seldon, this is grand uh galactic scale sci-fi. Harry Selden uh great mathematician invents a theory that he calls psycho history that's able to predict at grand scale human civilization so and he's able to predict the collapse of the galactic empire and a whole bunch of other interesting things so future sim is a benchmark just a minor correction I think future sim itself as a benchmark is not from open AAI it's from a group of independent researchers but it does benchmark a bunch of models including models from open AI and it's it's such a clever ever architecture. It It's benchmarking the ability for a variety of models to predict events beyond their knowledge cutoff date and without access to to the web. So they can't look up say more recent information. And the fact that the the state-of-the-art right now is already at 25% accuracy. This is the
[00:25:02] worst psycho history models if if I may borrow from Azimov will ever be. And I I agree with the contention that if you extrapolate this out, we'll get Monte Carlo research of policy decisions. We'll be able to predict to some extent predictable events in the future, at least as a function of perhaps human actions. And Peter, you and I spoke about this a little bit in solve everything in some of our forecasts for what would happen the latter half of the decade from 2026 to 2035 about planetary scale solutions. I I do think with the ability to predict planetary scale outcomes come the ability to predict planetary scale interventions. Not unlike how I would argue disease is probably going to get solved. This is this will be the the planetary analog of what a virtual cell can do for curing all disease in the sense that if you have a perfect digital twin of the system that you're trying to fix, you can exhaustively test all possible
[00:26:01] interventions to get from the bad state to a good state. greatest tools we could have for a positive outcome for humanity. Selene, what do you think about this? >> Well, this is uh going to be incredibly powerful for the boardroom because you go from quarterly kind of updates to real-time sensing. We've actually built this in the architecture following your paper into the organizational singularity architecture and I'll talk about that a bit later. There's another implication of this that we're kind of glossing over which is uh you know right now if you look at what New York does there are many many many hedge funds that specialize in different areas. So semiconductors and retail and whatever >> and those are all supported by prime brokerages. So these much bigger banks that do all the trading and accounting >> and those are prime brokerages. That entire industry could turn into just one or two AI models. Um, and so the concentration of wealth effect from this would be insane. But if the AI is just fundamentally better at picking markets, it's not going to sit there and just do
[00:27:00] one market. >> It's going to expand quickly across all markets. And so you're going to, if this plays out the way it's starting to, you're going to see a collapse into just a couple of mega funds that have massive AI budgets. >> The financial singularity. >> Yeah. Did Did I just hear Dave and Peter? Did I hear you argue in favor of indexing versus individual stock picking? >> Oh god, no. That's not an index. The index finds blind. This is so much better than an index. This Well, actually, it's an active index. I I'll call it a a half agreement if you call it an active index. Okay. Well, you know, the financial markets are still, you know, Dave, you and I have been going back and forth on our on our texting back and forth just, you know, the predictions that Leopold made on energy infrastructure and uh even reducing his level of interest in chips are still playing out. You know, we've seen this. The demand for tokens is outstripping supply. And what's keeping that limited is energy and
[00:28:01] infrastructure. Hey everybody, you may not know this, but I've got an incredible research team. And every week myself and my research team study the meta trends that are impacting the world. Topics like computation, sensors, networks, AI, robotics, 3D printing, synthetic biology. And these meta trend reports I put out once a week enable you to see the future 10 years ahead of anybody else. If you'd like to get access to the Metatrends newsletter every week, go to dmandis.com/metatrends. That's dmandis.com/metatrends. All right. Uh let's stay with OpenAI. So OpenAI launches personal finance in Chat GPT. Uh this is a finance mode that is able to access 12,000 financial institutions. Uh letting uh Chat GPT pro users ask personalized questions about their spending, their debt, their taxes, long-term planning. This is OpenAI eating another vertical. uh they did it to search, they're doing it to coding,
[00:29:00] and now they're coming after 12 billion dollar personal finance app market. You know, one could say maybe Mint will be dead. Nerd Wallet, you know, should be nervous. 200 million people already use AI for financial questions. Dave, what do you make of this? And Selene, let's go to you next after Dave. >> Well, this is part of an overall trend where the foundation model companies are starting to roll out legal. Now they're rolling out finance. So rolling out APIs that enable uh vertical, you know, dis disintermediation of all these vertical um companies, lawyers, financial accountants, whatever. Uh I think it's creating an ecosystem of new startups that are early adopters of these APIs that can then, you know, disrupt the markets. And I think those companies are going to do incredibly well while the foundation model companies do incredibly well. But what a lot of people are overlooking, you know, if you're in New York and you look at this massive financial institution with 20,000 financial adviserss, you can't envision it getting disrupted. There's just so much mass and concrete and meetings and files and so much regulatory barrier.
[00:30:02] But over here, this parallel economy is growing, which is the AI economy working within itself. And everyone in New York is like, "Yeah, but all the money's over here in the banks. It's not over there in that new economy." Well, after these next IPOs, that money will be in the new economy. It goes through the public markets, through SpaceX and through anthropic and through open AI back into the agentto agent economy, which is an entirely parallel banking and finance system >> independent of the original >> everything else. >> Yeah. Well, if Elon's right, it'll be 10 times bigger than everything you see in New York in about 10 to 20 years and growing on a much faster curve. And it doesn't care about any of your legacy baggage. is going to grow on its own. And so I think a lot of people are coming around to this view that, you know, we can leave the concrete world alone. We don't need to scare everyone and disrupt it because we're building a parallel AI world here anyway and it's going to be bigger anyway. And so I think these it's it's an interesting story within the story. Um love See, I'd
[00:31:01] love to get your take on this. >> Yeah. So this is essentially the solve everything thesis playing with large, right? You move from traditional models to an inner loop of intelligence and everything else wrapped around that. That's exactly the architecture we have in this organizational singularity stuff we're done. The bank should be terrified because the interface to money is shifting away from them to the AI, right? And they're going to lose control in a very dramatic way. The uh you know financial device will get layered around this, not around an individual. Um, and I think the other broader point is that you made Dave is that we're we're moving. We don't have to disrupt the legacy. You build this completely new architecture and just let that become the new gravity center. Go straight to the Buckminster Fuller uh old quote that he said you can't fix an existing system. You have to set up a new system at the edge and let that become the new gravity center. We're going to see that happen in in legal, in healthcare, and
[00:32:00] insurance and education. It's just going to happen over and over again. I think the thing that filled in just just just filled in this month and is so newsworthy is the conduit of the money from the old to the new is now really clear like or you know where Alex is an adviser is a really good example or is a security where you can deploy your money into AI and you can get it right on Robin Hood or buy it on the exchange and that conduit then moves the money from the old economy into the new economy and once it's in the new economy it doesn't care about the legacy banking system and the same with these IPOs that are coming up. Trillions of dollars that are moving. >> It's like it's like the Bitcoin ETFs. Once you've got it over there, you don't care. >> Yeah. Breaking news, by the way, on this and I'll hand it to you next, Alex, but breaking news is that OpenAI uh is uh sort of letting it be known they are preparing to file for their IPO as early as this week, as this Friday. You know, they just won the case against Elon. Uh they're feeling their oats. And we
[00:33:01] talked about this a few pods ago. It's a race to get access to the available capital. SpaceX is going to suck a lot of the oxygen out of the room. Uh, you know, their competition, Anthropic, is preparing for an IPO. I mean, it's interesting. Remember, we had had conversations from the CFO of Open AI, Sarah, saying they were going to file in 2027 because they weren't ready. But here they are talking about filing, you know, ahead of anthropic to get access to that cash flow. God knows they need the capital to build out their compute. Alex, over to you. >> Well, as I closed my newsletter today, the day of recording, Kajito, ergo IPO, I think, therefore I IPO >> is is the thinking of the moment. When I look at the announcement of OpenAI launching personal finance, I ask myself, where's the monetization? Open AI having, as I've mentioned numerous times on the pod, having largely pivoted away from consumer to enterprise and needing to justify very high value
[00:34:01] productivity per token. I ask myself, where's the value per token in this? Is OpenAI really seeking to become a financial institution? Doubt it. The value, I suspect, this is a bit of Kremlin, is advertising. OpenAI is following the Google playbook. Why did Google go into developing all of these financial verticals? Because some of the the financial queries, and Dave probably knows better than any of us, the financial queries can be enormously lucrative from an advertising perspective. So, if OpenAI hopes to monetize personal finance conversations, I think they're probably just going to do it by running OpenAI ads for consumers relating to personal finance. And the the more that they can tailor particular information to the particular circumstances of a retail investor with all of these integrations that will enable them to target far better ads in conversations. >> Today Dario announced no ads on anthropic just flat out we're not doing
[00:35:02] advertising >> which are very convenient post talk given that they're focused on enterprising. >> Yeah. They don't have the consumer anyway. So >> it's very easy to be an angel in >> great ethics. >> Yeah. >> Yeah. There you go. By the way, this one more thing about this was this feels also like a bit of a copycat. After Anthropic launched all these plugins for legal, etc. Here comes OpenAI doing the same thing for personal finance. >> But critically, Anthropic's suites of skills are targeted at businesses, whereas OpenAI is targeting this at consumers. Open AI wants to charge enterprises. So, yes, I I'm sticking with my ad theory. >> Yeah, that's right. Google, too. It's Open AI versus Google watching this ad evolution. >> I think you're right. Yes, >> I think you're right, buddy. Okay. Uh, I'm going to turn this over to Alex. Here's the story. Open eye model disproves a central conjecture in discrete geometry. So, an open eye model today or this week disproved a long-standing conjecture from Paul Erdos, one of the most prolific
[00:36:00] mathematicians in history. Alex, tell us about it. Uh, actually, let me run this video and then give us your blowby-blow. What does this mean? What does this mean to the average listener? >> I think what's significant about this moment is that it's the first really clear example of AI solving not just an unsolved mass problem but a really well-known unsolved mass problem. >> This is the first mathematical breakthrough due to an AI. it's it's been described as the the most well-known problem in combinatorial geometry. Uh so for for a whole sub field of mathematics, it's like maybe the best known problem there is. So I remember seeing an initial version of the model output. I sort of didn't really believe it. It took quite a while sort of reading over trying to figure out this problem is about points in a plane. It's a completely elementary geometric problem, but the solution involves really deep tools from
[00:37:00] algebraic number theory. And it was believed that the construction was uh was basically best possible. But what our model did was show that this construction could actually be improved by by quite a bit. >> Alex uh your explanation please. >> So first just a few seconds about what the problem is. So this is a problem posed by the famous Hungarian mathematician Paul Erdish about 80 years ago. And the the problem was basically asking the question if you have a plane a two-dimensional plane and you can put n points in the plane what is the maximum number of pairs of points that can be separated by the so-called unit distance basically by a fixed distance. It's a very simple to pose problem very hard to answer. So Erdish's original conjecture which has held essentially until now was that it was effectively impossible to do much better than in terms of the number of pairs that could be separated by this fixed unit
[00:38:00] distance. It was uh he conjectured uh impossible to do materially better than some number that's proportional to the number of points themselves. So basically some uh some number of pairs that's linear in the number of points. And now for the first time, OpenAI has revealed that an internal model that hasn't been publicly released has disproven that conjecture and found weekly superlinear scaling. Why should anyone care? This is just as as I like to say, math is cooked. This is going to be the new exhibit A that I cite for how cooked math is. This is one of the most important problems a as the video mentioned in combinatoric geometry that stood for the past 80 years and now AI has just solved it and notably if you unpack uh the all all of the accompanying uh documentation and commentary there's quite a bit of interesting commentary this wasn't say something like the fourcolor problem
[00:39:01] that one might imagine uh given that it involves combinatorics four color problem uh being if you have a a twodimen dimensional map. Um what's the uh what's the minimum number of colors that you can tile each or color each country in to make sure that no two adjacent countries are the same color. Uh there are problems in math in combinatorics like like the four coloring problem that uh that tend to be exhaustively solved by AI and then mathematicians and others point to those exhaustive brute force solutions and say gosh like maybe AI is more exhaustive. It's better at brute force but it lacks human brilliance. It lacks leaps of of creative insight. This is not a problem like that. This is a problem that has the top mathematicians in the world who specialize in in this particular area looking at its reasoning traces and concluding that not only was it faster
[00:40:01] in some sense, not only was it able to brute force lots of different theoretical approaches, but it was also smarter and it was smarter though in in an interesting way. uh one of the the commentaries and definitely I would encourage everyone to go to the OpenAI website and read a number of professional mathematician commentaries on the the reasoning chain of thought that it used to to solve really to disprove this conjecture. There were some interesting comments to the effect that from looking at the reasoning chain they could see that it was pursuing all sorts of call them exotic possibilities that humans would be too exhausted to pursue. So it was arriving at creativity by sort of both being faster but also being able to brute force all sorts of outlandish possibilities. And in the end, one of those possibilities and I I think the the language from the the chain of thought that ultimately led to the solution was began with something like optimistically
[00:41:01] if I pursued this something might happen and that turned out to be the solution. So if we remember like the the infamous move 37 from Alph Go's match with Lisa Doll and how being able to brute force but with clever learned policy search the reasoning tree of a go game. We're starting to see that play out in math. And by the way, that's going to play out everywhere else as well. It's going to play out in physics. It's going to play out in every science, engineering. Hashtags solve everything. Peter, we're seeing it. >> The starting gun. And you know, while people may not relate to math, uh they sure are going to relate to physics and chemistry and biology, material sciences. These are going to give birth to trillion dollar outcomes. Um, well, >> I think, you know, we in the past we've had very hard humanities last exam questions on the pod and and made the point that you would have to think hard for three or four hours to even understand the question and then of course then Alex says, I think the answer is four and it turns out to be right. But putting that aside, um, this
[00:42:02] one, you know, if you rewind the video and listen to the first couple sentences of what Alex said, that's the that's the whole problem. You know, you you can if you like crossword puzzles or you like sudoku, you're going to love this one. You know, listen to it again. I'd really encourage the team to splice in the images because what you know, for the last 80 years, humanity thought the best solution was this really simple grid. It's just points in a square. And if you look at the final, you know, AI solution, which is not proven to be the best solution, it's just better than the square. It's beautiful and it's elegant and it's not intuitive at all and it gives you a lot of new insight into wow AI is going to be really good at things like magnetic bottles and protein folding and um and and chip lay it looks a lot like a chip design problem. We're laying out the wires in an optimal way. The final answer doesn't look intuitive to you at all as a human, but it's better. And so it's a this is like a
[00:43:00] much bigger moment in history than just solving a math problem. It's it's so noteworthy. I thought, Alex, I thought you described it so beautifully and eloquently. Hopefully, history will >> I think you did a great job, too. I I think that the solutions, the optimal solutions to things aren't necessarily as human legible as the human solutions. And I I I agree with the sentiment, Dave. I I think this is heralding an era when AI solves very hard in some sense optimization problems and the solutions look positively exotic, inhuman, maybe even biological. And it's never any slower than it is today. See, you want to close us out? >> Uh, just that it reminded me when I first saw this of the move 37 analogy. And I think that applies. So, super exciting. >> Yeah. All right, let's move to China. So, a Chinese AI group pulls ahead of US rivals in video generation. China is winning the AI video race, not because they have better models, interestingly enough, but because they have better data. So, bite dance seance 2.0 know and
[00:44:00] cowshaw's cling now ranked number one and number two on independent video model leaderboards beating every American competitor simple reason Tik Tok uh has generated billions of hours of video data that no US company can match so uh interesting let's take a quick look at a video clip here uh from these models So, uh I won't play the whole video, but the idea here, uh and they kind of look the same, but you know, they, you know, according to the data, they're beating the pants. off us. Uh, Alex, what do you make of this? >> The rules of copyright seem to operate
[00:45:01] somewhat differently in China. And if I if you have access to to more video from whatever source, however legal or illegal it is, you can train better models. I think when we were first disco discussing Sea Dance 2.0 on the pod, I made a a similar comment. It utterly remarkable to me some of the videos that seem to be popping out of Seance 2. Not because it's an seemingly an algorithmic innovation, but because for whatever reason, legal or otherwise, these Chinese frontier labs have access to to more data. And uh there are videos floating around the internet. I I think most astonishingly, there's a video floating around of a guy inserting himself into key moments in the Harry Potter cinematic universe. I I linked to it from my newsletter. key moments uh stabbing or otherwise violently intervening with unpopular characters. I I think probably we're going to see quite a bit of this. It it makes the
[00:46:00] boundary between fanfiction, which historically was limited to text or maybe images, and video just completely dissolves that divide. And I I think the the copyright lawyers will probably have a field day if anything like this is tried in the West. But for now, it's the Chinese models that seem to have all of the both Western and Chinese video training data to do it. >> Alex, is this the place that you see China leading American models the uh most at this time? That's I mean it's sort of a stereotype cliche at this point that China was always going to have more data because they could pull more data from civilians and maybe also pull more pirated video data and also maybe they're in a somewhat better position from an energy generation. Still a weaker position algorithmically and still in a weaker position from a chip perspective. So do they pull ahead in video generation? Right now it seems they're pulling ahead in consumer video
[00:47:01] generation even relative to say Gemini Omni which we discussed in the last pod for now. But maybe there will be some enormous algorithmic innovation that enables the west to again take the lead in a few months. I don't know. >> You know we we mentioned in the last pod that Google is really the only American lab still pursuing multimodal and we've seen all the open source openweight models out of China doing multimodal. >> That's right. Uh um interesting Dave, do you want to any comments here? >> Yeah, well I I think that um you know if you study how video generation actually works, it's using the same transformer algorithm that's caused all these other breakthroughs. So under the covers, it's the same 2017 massive breakthrough driving uh video generation as well. But what the Chinese have done here is, you know, you take video content and you compress it into a latent space and then you decompress it into into video and image creation. And within that latent space is where all
[00:48:01] the innovation happens. But other domains like chemistry, like biology, like you know all of physics, uh they also have latent spaces. And so if you see the Chinese get ahead in video generation and sustain the lead, that's a good leading indicator for every startup trying to say, I don't need to compete with anthropic and open AI because I'm better at chemical reactions. I'm better at robotics. I'm better at whatever. And if you can maintain a lead at better AI within any of those latent spaces, that's a really good sign for all the startups because they can then actually maintain that lead within, you know, like See within EXO. I can actually have the best company management latent space uh knowledge. And so it's it's a really interesting cool leading indicator. I'm kind of kind of cheering for them to keep, you know, keep their lead, >> you know, which is pretty daunting versus Google because Google has all the YouTube content. China has all the Tik Tok and all the short form content. But, you know, it doesn't seem like China has a massive data advantage. They're just
[00:49:00] working on this problem harder. >> Yeah. I am curious how these models in terms of the speed of generation, you know, we've all talked about the notion that in the future I'm going to be generating video on the fly as it's needed. Uh, you know, sort of Netflix on demand. Uh, Alex, what's the de, you know, when do you imagine we're going to see that level of video generation? >> A few months ago. Like world models in including Gene, we already already do that. That that's what you're asking for. interactive video generation and real time world models already do that. I'll tell you, we you remember I don't know if you remember, Peter, but we saw liquid AI a year ago, generate images as quickly as you could speak, like instantaneously coming out of liquid AI. If you try to do that now today, you wait like a minute or more. And the experience is nowhere near as much fun as the real time like creating as fast as you can think. >> But we're so short on compute. And
[00:50:00] that's one of the reasons liquid AI is is doing well is because it's so much more efficient. Um but you know that that h holiday experience we're trying to create is you know it is entirely possible to build the holiday today but nobody has the the compute available uh to to deliver it. >> Yeah, any thoughts? >> Uh just that the I'll echo what we talked about already the amount of data that China has. I mean, Tik Tok and Dian are huge essentially training loops disguised as entertainment platforms, right? And so they'll I think they'll stay ahead for a bit longer, but uh I think the models will catch up. >> This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprisecale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The
[00:51:00] Blitzy platform provides a plan, then generates and pre-ompiles code for each task. 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. Enterprises are achieving a 5x engineering velocity increase when incorporating Blitzy as their preIDE development tool, 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 is an interesting one. I want to focus on this a little bit. Uh here's the deal. Here's the idea. The friend of pod Eric Schmidt was giving a uh commencement address at University of Arizona. Uh as he mentions AI, he gets booed. Um and it's pretty brutal to watch. We'll show the video in a second.
[00:52:00] Uh Gloria Cfield, who's the VP at Tavistock, she's a friend, had the same treatment. Uh the generation today that's entering the workforce is angry and scared about AI disrupting their career. Let's take a look. >> Know what many of you are feeling about that. I can hear you. There is a fear. We do not know. >> Wow. >> We do not know the precise contours of what this transformation will. >> The rise of artificial intelligence is the next industrial revolution. All What happened? >> Okay, I struck a chord. >> Selene, let's go to you first on this one. >> Well, I mean, look, the students are sensing that um you know, companies and
[00:53:01] institutions are adopting AI and we've not redesigned the social contract, right? The the the the backlash is not anti-technology, it's anti- extraction. And we need a kind of a new narrative around agency, not replacement here. And and there's a huge legitimacy gap between AI elites and young people today. It's like a kind of ridicul. It's incredible to see. >> Yeah. Um Dave, >> well, this is clearly priority one for X-Prise now. I mean, you've got >> you've got a hundred billion dollars of charitable money suddenly unleashed at OpenAI. They don't want this at all. So I mean we but right you know historically no one expected this to happen this quickly. So they put very little effort. This is their wakeup call. You know look at look at these videos like Eric Schmidt is a hero of I mean what the one of the greatest if not the greatest business executive of all time but when the job market is basically zero for college graduates coming out what do you expect? you know,
[00:54:01] and and the sentences there weren't exactly inflammatory, right? AI is the next industrial revolution. That's not exactly a controversial like imagine if they'd said something controversial with the re like a a revolution in real time. But that's that's the reality outside of the the couple places in America where AI is happening. >> The rest of the country is that and and it's really important that people be aware of that and then, you know, take that hundred billion dollars of charitable money and get to work on this. You know, we just launched this week uh the uh build with Gemini X-P prize and again just going to take a second to talk about it. Uh we are challenging teams around the world. Google put up $3 million. It's a good start, right? Uh to encourage teams to become entrepreneurs, uh individuals become entrepreneurs. Pick a problem that impacts a h 100,000 people and build in public in 3 months something that generates and scales revenues. That's a real problem that generates real revenues. Uh, you know, we have
[00:55:02] after 24 hours over 2,000 who have registered. I hope we'll get to tens of thousands that register here. There is your chance to actually go and use these tools, learn to use these tools. Uh, Dan Martell, who's one of our donors, uh, you know, thank you to Dan for contributing to this X-P prize as well, and Dick Mkin as well, you know, and Dan saying he's getting a huge amount of interest from, uh, the people he's incredible educator age, you know, 13 to 25, uh, who are going to be going after this X-P prize. And just a shout out to the Gemini team at Google for their support on this. You know, rather than booing AI, use it to take control of your own future. Don't get a job, build a job, employ other people. That's your option right now. >> Can I say something more about this, >> please? >> What they really should be booing is the universities that have sold them a credentiing system that is radically out of date. Amen. >> Right. >> Amen. >> Um they should because universities need to stop defending that old credentiing
[00:56:01] system and become launch pads for agency entrepreneurship and we've talked about this ad nauseium on the pod. Um that's who they should be booing. Uh they've been sold a bum deal and they're in huge debt and no prospects of getting out. >> They spend $200,000 for a degree that's worthless by the time they graduate. >> Yeah. If you're graduating, don't don't compound the error by getting into a job training program at at an investment bank. >> But but just think about this for a second. >> If you go back to our age or our even your parents age, there's a very simple question we could ask when we all graduated whenever how many decades ago, which is how much of your university education did you actually use in the workplace? And the answer was nearly zero. And that was 30 years ago for today. So, this has been a problem that's been around for a very long time and it's not been solved and they're getting pissed off about it and it's understandable that they're pissed off about it. >> Yeah. >> I I'll just add I used virtually all of my education for for what I do. Just
[00:57:01] saying. >> Yes. >> If if if Eric Schmidt and and Gloria Caulfield were getting booed at Harvard, MIT or Stanford or similar for mentioning AI, then I'd be concerned. If I remember correctly, these weren't Harvard, MIT, Stanford, or similar. >> No, but there's a majority of the graduating class of 2026. >> What that says to me is uh I is that virtually everyone in that class is being is is anchoring their expectations for stagnation. And that's not a great place to anchor expectations at all. But also I mean to Sim to your point about uh and Peter your point about how they should be booing the university or booing the college there's selection bias. You don't get to graduation if if that's your attitude in general. You probably skip college altogether. So I I think there there's a bit of selection bias here that we focus just on those
[00:58:02] who've narrowed their possibility space while not maybe leveraging AI to its fullest and then choose their graduation ceremony as a time to channel their enst regarding the disappearance of the lower rungs of a ladder a professional ladder that AI is automating away. It's probably too little too late and they should be I I would argue uh starting businesses >> inevitably I'll get I'll get flamed for saying not everyone but not everyone wants to graduate either. >> Speaking to the parents who are listening who've got kids in high school or in college today or speaking to recent graduates who have not gotten a job. um you know this is only going to you know get more dramatic and it's coming and you know you need to encourage your kid uh to learn entrepreneurship you know hop into uh the build with Gemini X-P prize go to
[00:59:00] geminisexprize.com and learn about it I'm going to do that for my kids who are who are turning 15 next month you know this summer you know you know if they should win will donate the money back because I'm biased But at the end of the day, use this as an excuse to play, right? The two most important mindsets you have are purpose and curiosity mindsets. You can learn anything you want. Yeah. Go ahead. Go >> I need to just go on a little rant here. You know, we've been talking about purpose for more than a decade. We coined MTP back in 2012. It's not decorative. In a world of AI capability, purpose is how you orient human beings. And somewhere around 2017, I was asked to give a keynote at a at a conference and my team said, you know, there's the logistics are really hard to work out for you to do this. I'm like, what's the conference? And they said, oh no, it's a conference of 700 deans of business schools. Who knew? But they all get together. I'm like, hell, I absolutely want to go do that. So, I get up on stage. I'm giving the opening keynote at
[01:00:00] this event. Um, announcer says, hey, we're happy to have Sim here. He's going to tell us the latest CCing and EXO, etc., etc. I'm standing side stage and what you see from the crowd is completely blank looks. They have no idea who I am or what the book's about or anything. He notices this, the announcer. And he goes, "Uh, how many of you read Exponential Organizations?" And out of 700 deans, two put their hands up. Right now, not that everybody in the world should read the book, but if you're a car designer and the Tesla comes out, you should jolly well know what the hell the thing is. And here you got 700 deans of business schools that had no idea that there was this other paradigm out there. And I think that's just like criminal negligence in a sense. Universities have been sitting on their hands for decades knowing this problem. But the immune system in academia is very strong. They're not able to get out of this. This is why we need completely new systems that ride around legacy. End of rent. >> Yeah. Uh agreed. A parallel story here comes out of Stanford. Um, so a Stanford
[01:01:00] survey found 49% of 849 computer science majors would rather cheat than fail. Students say AI tools are used in nearly every class for homework, coding, and essays. Stanford brought back proctored in-person exams for the first time ever to deal with this. The honor code that Stanford was famous for is effectively dead. Uh, this is happening at the world's top CS program. Imagine what's going on in the rest of the world. Alex, what do you make of it? This is Stanford. This is not just, you know, >> it shows to me that there there's a bit of an overhang for the skills that are being taught are being automated away by AI. So, it's not unnatural for uh for these students to at least be considering using AI for all of their homework, coding, essays. doubly so at schools like Stanford and like Princeton that have historically had honor codes. I I've never quite understood the logic
[01:02:00] of a so-called honor code. It seems to me a recipe for laziness on the part of faculty or otherwise proctors. They should just be supervising the exams. If you want to make sure that calculators or AIs aren't being used, earn them the the vast tuitions that are being paid to university and supervise. Uh I I think if anything this points the direction if if higher ed is going to remain at all recognizable at all and I'm not sure that it it will or even deserves to. I I think the direction of far more supervision, far more proctoring, uh maybe even some sort of wilderness camp at a university level where students are denied technologies and asked in the style of Verer VI and Rainbows and and Fast Times at Fairmont High. Student students have to interact with actually the real world uh and interact with the real world in a way that's impossible to cheat without succeeding like building their own actual businesses instead of writing business plans. Actually solving
[01:03:01] hard problems in computer science instead of solving formulaic tests that require humans be in the room to supervise. >> Use the direction this goes in. >> Use AI to go a 100 times bigger than you normally would. >> Exactly. Dave, you're you're teaching at MIT. What are your thoughts on this? >> Well, I teach a a class called Foundations of AI Ventures where you have to build a business plan. So, it's already on exactly the mission that Alex was outlining. So, you know, if your business plan gets funded, you get an A. So, it's not it's not in this it doesn't have this particular problem, but it seems obvious to me that you want to be teaching the students to use AI. And if you just look at their prompt stream, you can grade them. You don't have to have tests at all anyway. So, it's what Alex said that the schools are struggling to hold on to something that, you know, it goes back to when when graduation speakers would show up on horseback and you'd expect the crowd to hear wisdom they couldn't hear any other way from some great human being. You know,
[01:04:01] why why do we need graduation speakers today if we have podcasts? Well, we don't. Well, then why do we do it? Well, it's traditional. It goes back 150 years. Okay. Well, you know, the exams do too. Of course, it makes no sense. Uh, but you know, it's really hard to let go of tradition after tradition after tradition. But what do you think the singularity is going to be like? You know, >> this is >> I mean, is this is this actually cheating or is this what students should be doing? I mean, do you expect these students not to be using AI when they get to the real world? Why are you training them for something that isn't going to exist in the future? >> It's even worse. you're putting him in an impossible situation. Like, you know, forget the tests, but like even in the homework, like, >> no, we don't think you should use AI. Like, what does that mean? You you just put the kid in an impossible ethical situation. All you're doing is torture. Now, >> look, the the you're not testing durable human capability. You're testing for
[01:05:00] compliance and formatting. And that's just not the right test for this. uh you know the just to connect this to the previous slide, universities have a choice. They either become credentiing museums, right? Or they become AI native talent accelerators and and you're going to have to make that bifurcation and you're going to have to make it fast, which also yields, let's go to the positive side, this is the biggest entrepreneurial opportunity in the history of education right now because the next generation of education entrepreneurs and companies, they're not going to sell you courses. They're going to sell you capability acceleration >> for the rest of your life and not just for a four-year period of time. >> Yeah, it's going to be an ongoing partnership throughout life, through your entire life as your education, learning, coaching partner. >> All right, next story here. Meta has installed software on employee computers that track mouse movements, clicks, and screen activity. The stated reason, training AI agents to understand how humans use computers. The real implications, Meta is recording
[01:06:00] everything its employees do so AI can learn to replace them. Employees launched protests at multiple US offices and engineers internal post uh about it was viewed 20,000 times. This happened the same week that Meta cut 10% of its global workforce. We're seeing this uh in a lot of places. Um, and this ultimately is Elon's plan uh with uh with Megasoft, right? Um I mean Mega Megaart, you know, he wants to be able >> Macrohart. Thank you. >> Megaoft is a trademark. >> Megasoft. Okay. >> Megaart is a great name though. >> Macro hard. you know, he wants to be able to come in and replace all your employees with uh some percentage of a GPU um after uploading their capabilities. So, uh I mean, everybody figure it out. This is what Amazon is doing with its delivery workers, putting, you know, eyeglasses on them to track their movements so that future
[01:07:01] robots can do deliveries at home. We're seeing this everywhere. Uh and it's just beginning. Um, you know, right now 44% of Gen Z workers are deliberately sabotaging the AI they're supposed to train. So, so that's the uh the backlash that you see there. Uh, I think this is the difference between an AI coach or an AI cop, right? Same data but very different outcome. It depends how you frame it. And I think they need to be very careful about how they frame it. And frankly, the big issue that Meta has is there's a pretty big understandable lack of trust around what they've done. They've repeatedly said, "We'll never sell user data." And then so privacy data. You can back out of these things. Then you found you can't back out of these things. WhatsApp was supposed to be encrypted. Now WhatsApp is not be is taking the encryption away. It's what Kyrie Corey Doctoro calls the whole inchitification stuff. It's it's seeing we're seeing that now internally to the organization not just externally in the products. >> I'll give a hot take on this one. Peter
[01:08:02] this it seems like such a strange decision given that the frontier labs of which meta right now I don't consider a frontier lab. I think we have at this point two and a half American frontier labs none of which is meta at the moment. The frontier labs are all purchasing enormous amounts of synthetic data for computer use assistance training across a wide variety of environments. It seems a little bit strange to me, maybe I'm missing something, for Meta to view its own internal computer use as a valuable pre-training or more likely post-training source. It it strains a little bit of credibility for for me at least to think that there's enough diversity of computer use just within meta for this to be worth all of the hostility it's inevitably incurring from employees on top of all of the workforce cuts. It makes me almost wonder if this is a way of basically encouraging a subset of employees to just quit out of
[01:09:00] meta anyway. If if if I were looking for better post-training data at this point for computer use, I'd be leaning heavily into synthetic data and I I wouldn't be antagonizing thousands of employees with mouse tracking. That that's my hot take on this. >> That's fascinating. Dave, what do you make of that? >> I completely agree. I was going to say a much more violent version of what Alex said. This that the use of this data is nonsensical as a tool internally. This is entirely about knowing what your employees are doing and using it to evaluate uh and sort performance. Um but I think people better get used to it because it's going to happen pretty much everywhere. Also, I I think uh if you look at all the data that Google has gathered for, you know, decades now. Um I think a point that Eric Schmidt made years ago very quietly was that if that if Google ever wanted to make more money, they see every quarterly earnings report. >> I I had that exact conversation with him. He said we could make a ton of money just once before before we're
[01:10:00] we're you know lawsuits start flying. They see >> exactly the higher level point >> they see all the searches for all the purchases ahead ahead of earnings reports. >> Oh and once Gmail took took over everyone sends their board reports out to board member. I'm on so many boards. They arrived by Gmail you know naked you know just as attachments and and Google's terms of service they write in them. Yeah. look at we human beings won't look at all your emails but our algorithms will uh and you know humans could too but they don't say they won't but they imply they won't but the algorithms look at them so yeah but I think the high level point there is that the data gathering is happening for sure and and you're crazy to misuse the data because of the backlash and this to me is what Alex said like why you just created a negative news report that you didn't need why why do that we know you're gathering the data But don't don't abuse that gathering in a way that creates some massive uproar like this because you know that's what's been going on in China for for decades.
[01:11:02] But it's also Google has all the data, Apple has all the data. They don't really look at it. Um so the data gathering is happening and Peter you've made that point over and over again. >> So don't going getting in an uproar and fighting it is crazy though. You're just labeling yourself as a difficult to hire person. It's not going to stop because of your like having a rant about it. So, I'm not saying, you know, just adopt it either, but I'm I'm I'm saying don't don't just rant and then go home and feel like you did something. You didn't achieve anything. Uh, tying a story here back to the conversation about job loss. Mark Cuban proposed federal token tax. Uh, this is his quote. We should tax token at a provider level less than 50 cents per million. uh as it will push big AI players to optimize tokenization. Will also reduce energy usage and generate 10 billion dollars a year, maybe growing 30x or 100x while also creating a source for paying down the federal debt. You know, uh we've talked
[01:12:01] about the idea that we're going to potentially tax uh you know AIs that replace employees or robots replace employees, but these numbers here that Mark puts forward are dimminimous, right? This is for 300 million Americans. This is like 33 bucks uh per year. Um I mean interesting idea, but I don't think this moves the needle anywhere. >> Certainly. I mean, so there there are so many perverse incentives when one introduces novel types of taxes or or floats taxes like this. The first obvious novel incentive is get rid of tokens entirely. There are many token free or tokenization free approaches to auto reggressive and non-auto reggressive machine learning at this point. We could swap to diffusion models entirely if we wanted. No, there are ways to do diffusion models with no tokens or we could skip the tokenizer step entirely and and stick with transformers. All all this like naively
[01:13:00] all this would do is just push for the uh the abolition of tokens alto together. I I think then the response is okay fine let's tax the flop let's let's tax floatingoint operations or something there more perverse incentives I I think it's very difficult to come up with an input resource that would be best taxed short of actual dollars uh or some function of dollars that would actually enable a pure play targeting of the AI labs without creating severe per perverse incentives like this >> I mean Alex you remember that's that Sam recently talked about. Well, we should have the general public own a part of national compute, right? This is the equivalent of the permanent fund in Alaska or what occurs in Saudi or the Emirates. you know, as all of the capital starts flowing into the Agentic ecosystem, into these frontier labs, um, and they start generating huge amounts of wealth, you know, how do how does
[01:14:01] part of that wealth get distributed, uh, to the average American? Um, I think we're going to start >> testing that. I I think we we saw it in the so I I'm I'm not sure if we have a slide for this, but he's testing that right now in the form uh just in the past 24 hours of offering $2 million of Open AI tokens in return for a safe to all YC companies like 2,000 companies. That I view that as like a preview of some uh universal basic compute type offering to everyone. Not quite obvious what the equity equivalent is, but something like that. Yeah, Dave. Oh, >> it's amazing how quickly we've decided that uh that LLM AI and the word token are a fixed thing that can be taxed like bananas or shipping containers. But anyone who actually works in the industry knows that you can you can actually tokenize with bite pair encoding, which is what you usually do. But you can use one bite encoding, you can use four bite encoding, you can use no encoding at all. Like suddenly what
[01:15:01] you tax disappears from the world like a minute later. Uh, so yeah, it's it's just funny that people feel like, "I've got an idea. Let's tax per token." Like it reminds me of a conversation in Riad where one of the royal family, you know, that we were meeting with was was um saying, "What do you think a good cost per token is?" Because we're big investors in a couple of companies and their cost per token is like a buck a million. I'm like, "With what context window?" And what that's a nonsensical question. And they they're like, "Well, we'll just answer it." I was like, "So, I gave an answer and they're like,"Great, because we're less than that. We cost less." Okay. Uh, yeah, this is going to be really tricky and amorphous and fastm moving. >> Look, it's interesting at one level because it trades AI usage as an economic activity, right? It goes to Alex's comment from another pod where we talked about what's the economic return per token and and that's really interesting because it's not it's not just a software feature. Right now,
[01:16:01] historically, governments was always taxed what society's dependent on, whether it was land or labor or trade or income. Um, if AI becomes a new engine of productivity, then tokens or some flavor, compute, energy become obvious tax targets. The challenge is that if you try and tax it, then there's nothing more liquid than compute usage today. It'll go to people that don't tax it, and you risk doing that. The other I think big danger is that you may put an inadvertent fence around meta and and open AAI and the ones that can navigate this easily and stop slow down innovation because startups will have a harder time around this. So um there's a whole bunch of different decisions here but I think the the the flaw in trying to tax tokens itself the intent is right but the mechanics may not be right. >> Yeah. I think you you've already like laid out the portrait for for maybe Neil Stevenson's next cyberpunk novel. It's got to be about like compute tax havens
[01:17:02] on Peter Teal funded like data centers, seasteads on the ocean to avoid all all the token taxes. >> We should just list all of the science fiction novels with a number and in each episode we just list out here are the 45 uh science fiction plots we talked about in this episode. >> Bingo. All right, here's a fun conversation. Uh, I'm a huge fan of Ben Lamb, George Church, and Colossal, and they just had an announcement that I wanted to share with everybody. Colossal Biosciences has hatched chicks with an artificial egg. Uh, let's take a listen to this video. >> Begins with the chicken egg. >> We didn't want to just reimagine the egg. We wanted to completely re-engineer it. Now showing the world that we can actually >> grow this whole bird now in an incubator outside of an eggshell. >> Complete game changer. >> Unlocks massive potential. >> I am looking at the world changing and I'm holding it in my hand. >> I think that this is really a big step for science. So obviously
[01:18:01] >> hold on to your chickens. >> This is the colossal artificial egg. >> As we developed the design, we began with a natural egg. So the colossal artificial egg is a world's first in terms of the design level and sophistication. >> The first major structure is a rigid outer shell. The shell provides the protection rigidity. >> Our permeable membrane allows oxygen to diffuse into the system through the membrane at ambient temperatures. >> It's got a really big window on the top that you can actually really look at and see and understand exactly what is happening to that embryo. >> Real time visibility into every stage of embryionic development. This is a real breakthrough. >> So this is ex uterero gestation. Uh this is one of three artificial womb programs that uh Colossus has going on. Remember Colossal is Colossal has going on. Colossal is the company that's bringing back extinct species. They're famously the first one is the woolly mammoth. They brought back the direwolf. They're bringing back the dodo bird. They have
[01:19:00] 15 different species in process. But it turns out like the MOA um uh is is got a huge uh egg requirement. Uh and just doing it uh is going to be challenging. Uh super curious. And just for the numbers, there's 1.9 trillion eggs per year. Uh that's generated by about 33 billion chickens. So interesting thoughts on this one, gentlemen. I I'll just note I I can't wait for the Dodo, which I think is one of the the targets for this. They've they've tried this now with a couple dozen birds. I think it's a far cry from what we saw in Jurassic Park, where the emphasis in in that Mr. DNA reel was more on the molecular biology of uh of resurrecting extinct species and not the organism level or egg level focus. I I think it's it's important and as I understand it, there
[01:20:01] have been some major challenges that prevent the naive Jurassic Park type approach from working in the case of uh of eggs for these extinct birds. Apparently, it's the case that late in gestation, they require large amounts of oxygen that are difficult to supply through artificial eggs and so at least have historically have been difficult to supply. So creating uh effectively uh an oxygen permeable artificial egg that's supportive of late gestation metabolism seems like an important step. And the the the giddy sci-fi fan in me wants to know how well will some of these techniques generalize to humans? Can we perhaps appropriately generalize these to exudo just humans? >> They're working on on mammals. Um and obviously uh you know aven uh and also uh uh you know bringing back uh I think of the 15 species in the pipeline 2/3
[01:21:04] are mammals right now. So yeah, the dodo bird which is native to Maitius or was native to Maitius uh on their money, on their stamps, on their flag is extinct and uh the country's very excited to bring it back drive tourism revenue. >> Well, at least two of the 15, the dodo and what was the deer? The the red or blue deer. >> Some Yeah, the blue buck I think or something like that. >> Blue buck. >> Blue buck. Yeah, those two were extinct because they were delicious and easy to hunt. So, Salem is is opening Dodto Fillett restaurants. Uh, getting ready and maybe a burger, a Blue Buck burger. Yeah. Anyway, I just wanted to, you know, this is the this is the science fiction future materializing before us. We're not going to have uh uh, you know, dinosaurs per se. Though, what you know, Coloss what Colossal is doing is not actually bringing back an exact species. It's
[01:22:01] bringing back a singular smiling being able to say, "We're going to make these 300 edits in the genome that will make this species look like this. We have a longer tusk, so it'll have woolly hair. It'll be more cold tolerant. It will have a longer snout." And so you can sort of design your species. I had Ben Lamb on stage in uh in Miami at FII and said, you know, can you uh can you create a dragon? And the answer is yes. probably not fire breathing, but we can add the wings and we can make it look like a dragon. >> Um, >> I think we'll get dinosaurs, Peter. I'm I'm pretty confident we'll get dinosaurs. Not sure whether >> fossil we'll get some version of a dinosaur. >> You know, I don't know if you remember, but my visioning uh proposal a few years ago was that genotype to phenotype mapping problem where you start with a picture of the final outcome and then you try and create the DNA that matches the picture. >> Exactly. That's what they do. >> Yeah. >> Yeah. They're building >> back. You know, you could blind test and see if you know one one theory is you
[01:23:00] created a completely different animal. It just looks like the original. The other is no. The only way you can create the original is by getting the DNA within what could have been, you know, the reproducing line of that original species. No other DNA is going to get you to that exact look, feel, behavior, shape, whatever. And you could back test that and get the answer to that. Well, we're going to have we're going to have Ben Lamb with us on stage at the Moonshot Gathering in September, so you guys can drill in on this. He'll be one of the Moonshot entrepreneurs we're going to be bringing with us. >> Be ironic if we could resurrect stochastic parrots with stochastic parrots. >> No one's going to get that. >> You don't think so? Um, someone >> the audience will appreciate it. >> There's a thread that's been unraveling for a while and it continues to do so, which is that biology is becoming programmable. Yes, >> that's just a huge huge hu huge thing. >> Well, he's working on not only biology for animals, but biology for plants. Being able to design a plant that is drought resistant, uh disease resistant, can grow twice as fast or twice as big.
[01:24:01] I mean, uh this is the intersection of AI and synthetic biology. Um yeah, very exciting times. 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 Malem, the chief medical officer of Fountain Life and a part of my medical team. Don, a pleasure. >> Great to be fair. 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
[01:25:01] 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 sleep is so important. You know 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/pater. Make sure you become the CEO of your own health. All right, now back to the episode. All right, three stories on
[01:26:00] data centers and energy. Uh, worth noting. Uh, let's take a look. So, a Gallup poll found that 70% of Americans oppose data center construction in their community. Nearly half strongly oppose them. Some residents said that they'd rather live near a nuclear plant than a data center. Uh, main concerns, rising electricity costs, water usage, environmental damage. Uh this is a not in my backyard problem uh that needs to be addressed. You know, we've just either stopped or delayed 7 gawatts of data centers, nearly half of the data centers being proposed. Uh it's a real challenge and one of the questions is you know who's who's funding the protests, who is uh you know giving these individuals the information. Um I don't know. So comments on this gent >> I mean this is this XP probably should pick this up immediately and say okay the information is nonsensical and this is proof positive that people will freak
[01:27:01] out about anything that they don't understand that's a change that's coming to their neighborhood. And so the solution is some combination of PR and education and making it a smooth thing and and it's just such a solvable problem if you put some budget and energy and thinking behind it. But you know these act like the idea that you would rather live next to a nuclear power plant instead of a data center which you would never even know it was there. It's not taking your water. I promise you it's not taking your water. And the electrical effect they already said like you've got to find your own power. You can't disrupt the local power. Like like there's nothing in those objections. But if you've been to a town hall meeting, you know there's a group of people they they will object to anything that's brought up any given day. That's what they do. Let's get the data here for the next story and then then talk about it. So, uh, Nevada Energy is redirecting 75% of Lake Taho's electricity supply to data centers by 2027. Um, you know, these are the
[01:28:02] numbers that are projected. Uh, and of course, we've talked about this. We're going to have Michael Katzios on the show. We'll be talking about policy uh in this area. Uh, you know, we have solutions here. We have the data centers being able to stand up their own supplies. I mean, I think that the hyperscalers need to be saying, "Listen, if we build a data center in your backyard, you get free electricity, right? That would be the solution to turn the tide here." >> Well, this this isn't what what's actually going on here is different from what the story implies. What's going on here is California in November is going to vote on whether to take away 5% of the money of 200 billionaires in California. uh just not their income, but we're just whatever money you've made in your life, we're going to take 5% of it as a one-time tax. People are leaving to go to Incline Village, Nevada, by the ton. Nevada doesn't have much of an economy,
[01:29:00] but they have a beautiful shore of Lake Tahoe, where all the Californians are are snatching up houses at an incredible clip to try and get out of the state. So Nevada is now saying we like data centers because we like the inflow of money and success this is going to bring to our otherwise desert state. I mean this makes Vegas look like a rounding error compared to what we could achieve with this. So you got this cultural divide between the California protect everything view and Nevada saying whoa what an opportunity view. So you know no one's going to drain Lake Tahoe. That's not that's not really what's going on. does state versus state completely different perspective on which way to go with compute and AI and data centers. >> Alex, what's your take here? >> I've commented in the past over the past two to three episodes about how the tokens want to flow to the highest dollar per token productivity ratio. I think we'll see in the early leadup to
[01:30:00] the Dyson swarm the the kilowatts flowing to the highest dollar per kilowatt value applications as well. And and for those in the audience there there will inevitably be someone who takes issue with the story and says no you're misconstring this has been in the the works since the late 2000s since 2009. It's not actually an AI data center story. It is actually an AI data center story. Uh that there it's a bit inside baseball, but um based on public reporting, the transition away of some of the funding from Envy Energy to the local electric utility for Lake Tahoe has been planned since 2009 to be switched off or switched away from Lake Tahoe, but has been perpetually extended. And this time around it looks like it's not going to be extended because there are data centers that could be far more productive consumers of that electricity supply than the residents of Lake Tahoe. So I I view this ultimately as a kilowatts until we
[01:31:01] build the Dyson swarm. The kilow or or radically expand our energy supply and we're probably I think going to do both at the same time. The kilowatts want to flow naturally to their highest productivity outcomes. Well, speaking about expanding energy, uh here's a story that I just want to show these charts. Again, it's part of our abundance story that we've talked about in the past that Texas surpasses California in utility scale solar. And and Elon, you know, friend of the pod has made this comment a number of times. We can get all the energy we need from solar. Uh here are the numbers on utility scale. We see in this chart here, Texas uh doubling the amount of solar in Texas, uh surpassing California over the last 5 years. Um we see Texas, you know, again, just about doubling the amount of storage and Texas, you know, going what does it look like almost 8x the amount of wind. Uh you can build
[01:32:01] renewables in your state. I mean, every part of the US has some version of renewables it can put in place. Uh, and I just don't see why we're not doing more of this. You know, China has done laps around us here. But good on for Texas. >> That's quite the indictment of the California state government. Doubly so given that Texas has all of these amazing oil and gas resources on top of it all. >> Good point. >> That's a permitting story. >> Yeah. >> Yeah. Well, and and one of the things that makes America strong is that we have 50 states that that actually can compete. And the more we more we allow them to compete, the better off we are in the long run. You know, California right now has incredible tailwinds. Like, I can't even tell you. But, you know, historically, the legislature will will say, "Oh, more tailwinds, great, more taxes." And just, you know, just keep absorbing a bigger and bigger fraction of the growth. But, I don't think any I don't see anything slowing down California's AI success. It's just is just off the charts. But Texas is doing a great job of taking control of
[01:33:02] the data center side of it and they're going to benefit tremendously from that. >> And the data centers can live wherever the energy flows. I think that's the point Alex you brilliantly made. You know, we're going to see a geothermal. There's lots of geothermal hotspots around the country. We can go and mine and we can build solar. I mean, God, every desert out there deserves solar farms. >> Right now, Texas is the closest thing the United States has to a special economic zone. Maybe th this new agreement in the Philippines that the White House has made, maybe that will become the American Shenzhen, but short of that, Texas is our special economic zone. So, I I'd say we should make the maximum amount that we can from this federalist system and use it. >> I mean, becoming the balance sheet of of AI, right? >> It's it is very clear. >> It's a key input. It's not the only input, but it is an input. >> Yeah. It's the limiting input for a lot of uh of the growth right now. All right, we're going to turn to our final story here. Uh is the organizational
[01:34:00] singularity with See. So See, you and I recorded I think an extraordinary hour-long conversation about the singularity. uh and I encourage people to go and watch it. But those of you who haven't or have or won't uh I want to give Seem a chance on this pod to present his thesis and then have the mates uh kick it back and forth. So See, over to you, pal. >> Yeah, so you know, we've had 80 years of thinking about the organization. Coast said that transaction costs and coordination costs are cheaper inside rather than outside. Then you had people thinking about okay how well do human human beings make decisions. Then we had um uh Clay Christensen come up with this innovators dilemma. Uh then we had lean startup thinking. Uh then we had platform business models. We had exo 1.0 that took uh the coian firm and stretched it outside the boundary using community and crowd and AI. But in the face of AI, all of that breaks, right?
[01:35:02] The best the co the the the cost of doing any transaction inside a company is is more expensive than doing it outside a company. And the metabolism of almost every company in the world is slower in the outside world. And so essentially Kos's law essentially breaks because of the externalities have broken. The famous tweet that I keep remembering is it's easier to build the product feature than having the meeting about building the product feature. Right? We just nailed it for me in one thing. And so in this world and yet 80% if you can go to the next slide Peter we're seeing this totally change. We need a totally new architecture for what an organization looks like and we've come up with a kind of three four-fold shape here. At the core of it you have your MTP which actually becomes a a protocol uh for how you think about this and then a an intelligence engine uh and then an organizational form around that intelligence engine. I think the next slide may have like a rocket ship picture of this. Uh well, let me go go
[01:36:02] to this. The the the heart of it is an intelligence stack. And as we after we built this, which is like a sensing layer, an orientation layer, we realized that this is essentially an UDA loop, right? And we've seen this in the military world where you can sense, organize, react, and with a feedback loop. And you need very strong signals around this. Um because in this new world even the exo model doesn't work. You need a totally different architecture. So if you put an intelligence stack at the core and instead of organizing around hierarchy you organize around intelligence you now have a a proper wrapper for what 21st century organizations should look like. The boundary of the firm changes. It used to be that we had a legal entity called the organization that served primarily for execution and coordination. But but if you can now do execution and coordination automatically or automatically with AI, then the firm becomes a purpose container, a fiduciary container, a legal container like a
[01:37:00] glorified SPV essentially a liability container, but not where execution gets done because AI agents are going to make be making uh uh API calls all over the place. And so you need this stack which learns constantly with a very strong governance loop around it. uh and I think we have it laid out in the next uh slide a little bit uh what that looks like. You need for every agent very trusted eval suites. You need searchable logs. You need a roll back capability. You need a human review cue. This is where as we're watching AI agents propagate. It turns out you need a huge amount of human oversight because they're they're kind of Martin Versowski talked about this. They're like junior employees. They go rogue pretty easily. You have to watch them very carefully. And so you need a very strong governance architecture around all of these. So you have a totally new architecture around this. Um and the problem today is that 80% plus of AI projects are failing. And the reason that they're failing is we're taking existing AI and trying to cram it
[01:38:01] into human centric workflow loops and all you're trying to do is automate the humanto human bottleneck. Of course it's going to fail. You need a totally different model. And so we've been at for a decade plus we've been going down the old trope of you have to do disruptive innovation at the edge of the organization. You can't do it in the core organization. If you can go to the next slide Peter. Um you you have to do it in a very Oh okay. So you have to do it in a very different way. The only way to do it is to rewrite your organization create an AI native digital twin at the edge and then move workflows over one by one. red team it for a while so that you're not threatening the mother ship. And once you have recursive self-improvement at the workflow level, you can start to slowly deprecate the old human beings become oversight, dashboard monitoring, exception handling, problem solving, etc. And we're going to need a huge amount of oversight. So I'll give a quick uh analogy here. Imagine you had a uh you're running a trucking company and a
[01:39:01] competitor announces refrigerated trucks as a new line of business. So first you have sensing agents that tech figure this out and go hey they bring back information that hey competitors launch refrigerated trucks. Now you have a layer of strategy agents because strategy cannot be a static process. It's got to be a living protocol saying okay how big a deal is this? How big is the market? Should we think about this or not and evaluate whether this is a strategic option. The next layer is kind of a an analytical layer that says, "Okay, if we were to do this, there's kind of two, three ways. We could buy our refrigerating trucking company. We could launch our own trucks. We could do this or that." Comes to a decision layer. Do you buy a startup? Do you launch your own trucks? Do you lease some trucks as a pilot? Maybe you say you want to draw launch. Then a set of execution agents goes and does that. What are the human beings doing? They're hitting approval at every point and slowly uh letting the AI run most of this and instead of a typical seauite taking weeks or months to make that
[01:40:01] choice, you're doing it in days, right? Our our when you when you have this kind of AI native architecture, our our current assessment is that you should have an organization that's between 100 times or more performant than the legacy. And so that gives you a huge incentive and we can see what was that company that got 73 times ARR in a in a year by by running AI native right we can see this happening we've seen this transition fully in call centers that went from human to chatbot assisted and then AI native or marketing content which was agency driven then uh AI assisted now most AI content is AIdriven so we've developed an entire methodology we call rewrite to help companies create this digital twin at the edge. What we're going to do is take batches of CEOs and run them through this process. So, if you're interested, uh, watch the go watch the longer video where Peter and I go through this in a bit of detail
[01:41:00] and then you we give you a process and and a so we're releasing this whole book as an AI because every two three days like the world changes, we have to update it uh like to version 15.643 643 will be released and and we're going to launch it as an AI take batches of uh CEOs through this. Uh we're also launching by the way an organizational singularity venture fund that goes along with this because companies may want funding to either build a company in this modality or uh for doing it this in this new way. So if you're interested and happy to discuss it further with the pod base here >> and where do they go to get more information >> go to organizationals singularity.com it'll point you back to a page uh companies can apply to go through this process I think we've got uh four of the initial 15 slots filled um the largest education group in Brazil is going to run a bunch of universities through this process interestingly that's a big kind of category because they need to adapt
[01:42:01] and change but theore core thesis is the way we've been building companies for 100 years now completely changes to be very uh centric. In the book we actually have written a whole chapter on how uh as you guys have done your whole um logic for um modeling the future right solve everything but what's the organizational design that allows you to take domain after domain and bring about domain collapse in that. So we've got a whole chapter saying how does this work for that? There's a chapter we're putting in on governments and nonprofits because there's an obvious most government processes are very prescriptive and lend themselves well to this type of AI native architecture. And so uh we've uh we've built written all this out. We're going to keep evolving it as as things change and as models change and as agent architectures change. We've compared this to all the writing. When we wrote the first EXO book, Peter, I think we were we were probably seven, eight years ahead of the market. The second one, it was maybe three years ahead. I think this one we're maybe a year ahead so it's much
[01:43:01] more timely than the other ones which were way further in the future. I think the simplest way to talk about this is is you have you don't have a choice and here's the question that every CEO and se Sweden board member needs to be asking can two guys with open claw replicate a major line of business a high margin line of business that you have in 60 to 90 days. If that's the case you have an existential threat right now. So you have to do something like this to move to this model. >> All right, let's take a couple questions from from the mates and uh yeah Dave or Alex >> uh I'll I'll bite on this one. So See, I I I'd love to push on this a bit and from the perspective of falsifiability. So you mentioned coast earlier. I've written uh a fair amount at this point in the context of some of my own investments regarding coasts are arguably the intercontinental railroad for example following coian economics
[01:44:01] pushed the firm size to be larger. Now you needed huge continent spanning companies to encompass intercontinental railroads. I've argued that AI pushes the firm size smaller because maybe you can uh as I've argued in past you could have one person conglomerates. Can you make a falsifiable prediction given all of this about the size of the firm? >> Um, it can go either way. We're writing I I've mentioned in other positive writing a whole economics technical paper that how would you measure the size of firms because the size is not that relevant. It's the amount of economic throughput you can put through the organization, right? And that will depend on domain. It'll depend on what kind of emote you have. So for example, regulated companies will have a will be able to defend themselves a lot more. If you have proprietary data, you'll be able to defend a lot more. The best defense will be if you have an inner intelligence loop per your thinking, Alex, right? Once you have that, that's very defensible and you're now have a
[01:45:00] flywheel that keeps building. So we don't know what the false viable model would be. Certainly for some entities like very customizable work. If you're building a a sports kit car business that's very manual and labor intensive etc. Uh that won't fit the model uh very much uh at least early on. Uh but we'll definitely get to that point over time. But uh we we think that this will apply overall as you replace human centric hierarchical coordination with an AI native loop and then that will just keep flywheeling away. >> Let me try maybe an adjacent question if I may. So uh you're laying out as I understand it a theory of the future of the firm or future of organizations. It sounds like it has both descriptive and normative, in other words, what should happen versus what will happen or vice versa components to it. H how should an organization that is intrigued by this?
[01:46:00] How should it judge whether this is at least an accurate description of the future of the firm? How do we quantify this to know whether this is actually like what what are your benchmarks? How do we know whether this is even accurate? So we have early signals from the market right you can see the rise of some of the AI native companies the CLA has done an amazing job of turning their customer service environment into a profitable uh center etc. Uh Cognizant or Cogniant or whatever that was you've got companies like Pulsia that are creating an entire scaffolding and launching companies without blinking. Um it's not so much the number of people is how much economic throughput you can put into it. But I think the the other way to answer this question is when we launched the EXO model, we measured all the Fortune 100 against it. And then 7 years later, we found the more the top 10 of the Fortune 100 that followed the EXO characteristics the most delivered 40 times the shareholder returns of the
[01:47:00] ones that delivered it the least that used the model the least. Right? Because why? Because as the external world becomes more volatile, your ability to adapt will drive market value. We totally should have set up an index fund. It would have been the highest uh performing index fund ever. Uh in the same way, given how accurate we were that time, we're basically going on that reputation and saying, look, we were way ahead of our time, but we nailed the architecture. We nailed MTP purpose-driven organizations. Now, we're seeing companies hire based on the alignment of a personal MTP with the organizational MPPP. otherwise why bother etc. As we drive this forward, I think the broad thesis of intelligence being at the heart of it is a very defensible one. We maybe have to adapt some of the attributes around it, but you need a trust architecture. You need very proper scaffolding around the agentic harness to track what they're doing, audit trails, etc. Um, and then we think that general direction is totally defensible. We'll have more and more examples over time. There's an
[01:48:01] insurance company that we saw that is using 2500 agents and that's doing the work of like 500 people, right? And so there we'll start to document these and do tear down. So we're launching a YouTube channel called the shift that's going to go through and go go through this in detail and interview people that are doing this and and bring out signal from noise as quickly as possible on this overall architecture. and Sealem and I do an hour in which I think it was one of the best conversations I've had with Sem on this topic, walking you through what it means, how to implement it, and honestly, it's a it's an hourlong conversation I'm going to be sending to the CEO of every company I'm involved with. So, See, uh, thank you so much. I want to thank everybody for listening. By the way, we're at 499,000 subscribers. Are you going to be the 500,000th subscriber? Hit subscribe now. Turn on notifications. We're now beginning to put out two podcasts a week on a pretty regular basis. Uh living
[01:49:00] with my moonshot mates here cuz I feel completely out of the loop if I'm not having these conversations with you guys. >> Me, too. >> It is awesome. >> It is fun. >> And I want to be in the innermost loop, right? >> Yes, it is. All right. Okay, I'm going to play this outro from Simon Gerity called Who We Are. Let's uh listen up. Some good energy here. >> Rather than inevitably leading to dystopia, AI could catalyze spiritual growth and global harmony. As machines replicate aspects of human cognition, we are compelled to reconsider what it means to be aware, to feel, and to exist. AI exposes biases, flaws, and inequalities in human institutions, revealing patterns that are often hidden. This reflection can be uncomfortable, but creates opportunities for collective selfcorrection. >> Very nice.
[01:50:02] >> All right, gentlemen. Uh, always a pleasure to be uh spending time with you. Um, anyway, I could not be more excited. Uh, it really is the best time ever to build. Everybody go and build, be an entrepreneur. Jump in with your LLM. Tell them who you are, what you love doing, your purpose in life if you know it, and ask, you know, what are some business ideas and how would I go for? You can program in English today, you know. Join us uh at the geminexprise.com website and register. All right. Uh, Tale, good luck in Brazil. Dave, enjoy the Bay Area. Uh, Alex, enjoy wherever in the virtual world you might happen to be. >> Just by meat body, Peter. >> Yes. See you guys soon. >> All right, folks. See you soon. >> Be well. Thanks, Peter. >> 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
[01:51:01] 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 damandis.com/metatrends. Thank you again for joining us today. It's a blast for us to put this together every week.