Without question, for me, the number one story this week was Elon’s announcement of the Terraab. >> This is the most important endeavor in human history by far. >> In order to understand the universe, you must explore the universe. >> He’s basically building a galactic factory. On the left is 20 gawatt. It’s the current global output. And just the audacity of Elon’s vision, it has tremendous geopolitical implications. As we discussed on the last pod, this could either accelerate or more hopefully mitigate World War II and a Chinese invasion of Taiwan. >> We’re going to need all the compute we can create. In fact, I’m actually kind of worried a self-driving car uses up basically a full GPU. When is it going to become illegal for humans to drive? I think the thing that would make it later is purely the shortage of chips. Like the technology will be there and the demand will be there long before the chips are there. Figure out how to do more compute with less silicon. for this exact use case and you’ll be an instant billionaire. >> I mean, we’re heading towards a hundred
[00:01:01] trillion dollar company, maybe the largest, most important company on and off the planet. Can we get there? >> Now, that’s a moonshot, ladies and gentlemen. >> Everybody, welcome to Moonshots, another episode of WTF. Here with my incredible moonshot mates, DB2 in Boston. Uh, AWG, looks like you’re on your home base as well. I am, but without my saucer separated Enterprise 1701D behind me. >> Oh, yes. I’ve got I I contracted one of my boys to create finally finally Lego’s come out with a Star Trek uh you know, Lego set. I’m tired of all the, you know, Star Wars Lego set. So, yes, 1701D. Uh, and it does do a saucer separation, but I’m not going to try it right now because probably disaster may follow. And of course we have Dr. Exo Salem at his normal location at JFK. Sem, how you
[00:02:00] doing, pal? >> Sem is gone. >> Oh, okay. Well, so much for that. >> Yeah, that’s as you know, listen, we try to be mobile. We’re all dedicated to this podcast, but uh let’s continue on because the singularity is not going to wait. So today, uh we’re working to get you future ready. a little bit of a format change. Uh we’re going to be having some deeper conversations about a more limited number of subjects still covering the news that’s breaking right now and there is a lot. But our mission is to get you excited about the abundance that’s coming. Show you the opportunities that are coming to you. Whether you’re an entrepreneur, an investor, a student, a parent, and really, you know, this is the time to be paying attention to the supersonic tsunami, the most important tech in the world. And hopefully this is your number one uh podcast on AI and exponential tech as well. >> Honestly, Peter, there’s more in here than ever before. It’s it’s bundled into
[00:03:02] themes that we can discuss, but uh if you just look at the raw news story count, it’s it’s as you would expect exponentially exploding. Uh, you know, our goal all of us is to make sure that as we’re talking about this on on Moonshots that it’s meaningful to the listeners, gets you excited, gives you context, helps you think about this in a different way. Um, let’s jump in. Without question, for me, the number one story this week was Elon’s announcement of the Terraab. Uh, he’s basically building a galactic factory. uh think of this as putting all the parts of his Lego puzzle together uh in an extraordinary fashion that is going to create massive capabilities. So uh let me hit these uh these quick points and then we’ll jump and discuss it. So the terraab is an objective across Tesla, XAI, SpaceX to build 1 terowatt of AI compute per year. To put this in
[00:04:00] context, uh the global output today is 20 gawatt of AI compute. Again, we’re measuring AI computation in terms of power, not just chips anymore. Uh so Elon wants to build 50 times the current production rate of the planet. Uh he’s building two kinds of chips, an edge inference chip for robots and cars, but also a high power rad hard for his space uh Dyson sphere that he’s coming online. The fab is in Austin. Uh, and it looks eventually like a 100 million square feet of capacity. Uh, one terowatt in the near-term near-term, you know, singledigit years. Uh, long-term a pedawatt gets you uh only there from lunar mass drivers. Simil has joined the story. Hey, Seem, good to see you, pal. >> Hey, folks. Sorry, I’m bouncing around a bit, but I’m here. >> All right. And which terminal at JFK are you today? Where are you going? >> Uh, I’m flying to Brazil for 40 hours.
[00:05:03] >> Of course you are. Of course you are. You’re a probability function on planet Earth. >> So, just to put this in context, check out this chart on the left is 20 gawatt. It’s the current global output. And just the audacity of Elon’s vision. Uh, a thousand gawatts or a terowatt uh is his objective. I mean, you know, one thing I heard him say is, “Listen, I’ve been going to all the chip manufacturers out there and saying, I will pay you for as much production rate as you’ll give me. I I don’t want to compete with you, but give me more, more, more.” And of course, none of them are moving at Elon speed. And so he said, “Screw it. Uh, I’m going to go and build my own production facility.” And not exactly what he did in the launch industry, right? just lapped the entire existing launch industry and the autonomous car and electric car industry. He’s playing his playbook over and over again. Um before I get into this data stats uh
[00:06:02] comments, Dave, >> well this is the most important endeavor in human history by far because it unlocks everything else and uh you know no great surprise that he’s announced it at the scale that humanity needs it but the specifics on how you’re going to actually physically do this are unknown because there are fundamental constraints to the number of ASML machines EUV machines to do this and and many many this is the most complicated product ever made by humanity and the supply chain is like like you know it makes cars look like child’s play. So uh uh so he announced the mission. It’s the right mission. The scale is crazy. It’s you know we estimated this on the last podcast at 50x all current productivity of chips and I I guess our estimate was dead on. So, so we got that part right and he did allude to it last summer when we were meeting with him. And um what what I was most curious about is how he
[00:07:00] was going to announce this and attract all the talent that he needs without uh irritating Samsung, you know, because he signed a $16 billion deal for for production with Samsung, which is more like 45 billion if it is going according to plan. And um I guess one of the cover stories here is well these chips are for cars and they’re also for space. They’re hardened for space. So they’re not like the other chips, >> but but he he said, “I will buy everything Samsung can offer me, >> but you’re not offering me enough. So I will still build all these chips and I will still buy everything you want to give me.” You know, one thing I love and he pointed out in his Austin Fab is that it’s full vertical integration under one roof so that he can run rapid iterations on chip design. That’s impressive. One thing in our in our Austin podcast when we were talking to Elon uh I asked him point blank uh you know TSMC is being way too conservative in terms of their production of chips. They should be 10xing their fab manufacturing. And he
[00:08:02] said well you know they’re you know the the industry is cyclic. You know maybe they’re being conservative intelligently. Which is hilarious in hindsight if you go back and listen to that audio because in the back of his mind he’s like well I’m going to build something 50 times bigger anyway. So, but uh yeah, it is crazy that Samsung uh Intel and TSMC are not racing to build, you know, 10 20x more production. So, Elon of course is well, he’s going to do it instead. C can I show you guys some calculations that I found just extraordinary here? um listening to the presentation he gave uh 48 hours ago, you know, his target is 1 terowatt of compute per year in orbit. Uh he said mass to orbit 10 million tons per year. Uh we’re talking about an average satellite uh his next generation Starlink at a ton. Long story short, in
[00:09:00] order for him to launch that much capacity, it’s 274 launches per day on Starship. It’s a launch every 5.3 minutes, which of course he says, listen, in the airline business, that’s normal. But just the audacity and the level of uh of you know, thinking that Elon takes on uh is amazing. Uh AWG, you want to you want to jump in? >> So many thoughts on this. Well, first of all, I think the elephant in the room is if Elon can indeed ramp up capacity for the terra fab in call it the next 5 years, which is the time scale that’s being tossed around. It has tremendous geopolitical implications. As we discussed on the last pod, this could either accelerate or more hopefully mitigate World War II and a Chinese invasion of Taiwan. If you look at the the moon aspirations, the the lunar aspirations for not a terowatt but a pedawatt of from the moon. I if you do the back of the envelope arithmetic for
[00:10:00] what would a a pedawatt of GPU compute that comes from lunar mining take you run the arithmetic comes out to be approximately 3100,000 of the lunar mass. So a pawatt coming from lunar mining with electromagnetic launches from the moon is starting to have a material impact on the mass of the moon. So this is >> that’s just one big one big crater dug out of the moon. So that that’s that’s a pedawatt as we scale of course to an exowatt of compute and because why not then at that point we’re talking something like 3% uh of of the moon’s mass and this is you when people think I’m joking when I talk about disassembling the moon or the moon had it coming. It this certainly paints a portrait. The moon did indeed have it coming and it the moon is is slated for disassembly to to build the Dyson swarm. This is what it looks like. I I think
[00:11:01] more broadly there are other sort of secondary implications really interesting that this is a joint Tesla XAI SpaceX maneuver. And many folks have speculated over the years, wouldn’t it be wonderful if all of Elon’s industrial ecosystem came together into one singleton? This is sufficiently cruxy with 20% of its production slated for Tesla and 80% slated for SpaceX that this starts to look a little bit like maybe a cornerstone for some grand unification of all of Elon’s projects. I tell you, Alex, we talked about on a previous podcast the idea that, and Elon said this, we’ll see the first hundred trillion company. And when we look at the numbers here, I want to show another set of calculations I did on what might the Terrafab be worth in the ecosystem. I mean, we’re heading towards a hundred trillion company. And can we get there? And at the end of the day, my I don’t know how you guys feel, but the Musk World ecosystem here looks like it will
[00:12:03] lap by an order or two of magnitude uh what Nvidia’s done. Um it may be the largest most important company on and off the planet. >> Yeah. And I I don’t think Elon wants to unify all of his projects just for the sake of having one unified company. I think he wants to unify the the capital raising and the capital leverage with this massive multi-t trillion dollar IPO and the massive joint mission unlocking an unprecedented amount of capital which is what it’s going to take to do these fabs >> in parallel at this scale because that’s what that’s the thing that’s holding back Samsung and Intel and TS actually could do it. He needs 25 billion initially uh to turn on the terra fab and and get it, you know, get the buildings started, so to speak, >> right? I I saw that in the analysis, but 25 billion is is just one fab >> and here we’re going to 50x the US or the world production. 50x the world production. So, he needs 50 of those $25 billion investments to to achieve this
[00:13:01] mission. >> Yeah, there’s there’s a lot to do. I thought I had two or three thoughts. I mean, one is like talk about patron saint of exponentials, right? like this guy stinks at scales that very few people do and it sounds incredible. It’s classically the future of anything he’s looking at looks vertical and the path looks like flat and boring. Um what I what I thought was great was this is like amazing exo logic cuz these going exponentially at the bottlenecks you you stop competing. You’re completely you’re just redefining the game and you’re challenging anybody to dare to to come with you. I think that’s like the amazing uh part of this. The launch cadence is is unreal every 5 point odd minutes and I think that’s exactly right. It forces the operating model to completely change and it forces everybody to rethink that including all the engineers and all the infrastructure etc. because this is not any kind of normal industry. Um one thing to point out is that his predictions on timing tend to be about 15 to 20% accurate. So,
[00:14:02] you know, but but it doesn’t matter if he’s if it takes him three times as long, who the hell cares? Like the fact that he’s thinking at the scale and he’ll get there is the fact that he’s planting up. Yeah. >> You shoot for the stars and if you get to the moon, who the hell cares? You’ve gotten somewhere amazing. >> That’s AWSG’s plan. So, listen. Here’s the next question. A rapid scheduled disassembly of the moon, I think, is what’s on the table. Okay, good thing I don’t wine. >> Hey everybody, you may not know this, but I’ve got an incredible research team. And every week myself, 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 metatrend 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 diamandis.com/tatrens.
[00:15:00] That’s damandis.com/metatrens. So a a terowatt of compute per year if physically achievable. Um is this aspirational? What time frame? You know he says 5 years. Uh that’s still 50x is is crazy. But if he’s able to achieve that you know what happens to the you know to the terrestrial data centers and to the investments made in terrestrial data centers. Uh you know gentlemen questions on that. Oh, every every chip, every investment in power and data centers is going to pay off tremendously. They won’t cannibalize each other. We’re going to need all the compute we can create and so much more. In fact, I’m actually kind of worried that the self-driving car is going to get cannibalized. You know, driving a self-driving car uses up basically a full GPU and by the end of this year, a full GPU can also do brain surgery or it can discover new math or new physics. And it’s not clear that driving somebody around is going to make the price cut uh as the demand for compute goes to near
[00:16:01] infinity. So I think the terrestrial data centers are going to be critical for national security for every country in the world because if something goes wrong in space, you got to fall back. Your whole society will be running on these GPUs. You can’t you can’t have an outage. So anyone investing in this does not have to worry about one thing cannibalizing the other. And the other thing you’re going to see, I think it’s later in the deck, but all the different um process nodes, all the fab process nodes are going to get used. Even the older ones, the 3nmter and the 5nanmter are going to be running full throttle now, even if they’re not as good as the new 2 and 1.6 nanometer. It doesn’t matter. We need all the compute we can crank out. So, yeah, that it’s it’s going to be just an all hands-on deck race. and Elon is is just documenting the up upper bound of what we can achieve as a as humanity. >> My expectation my expectation would be that in the process of I think Elon likes to call it production hell in in the process of production hell for realizing the terrafab over the next 5
[00:17:01] years. I strongly suspect we’re going to discover some new not semiconductor physics but more material physics and process engineering. It it seems improbable to me that Elon will just build the Terrafab based on the existing stack as say TSMC did of ASML plus the existing optics plus all of the the conventional semiconductor processing techniques. If he really is looking to disrupt the space, he’s going to want much more disruptive unit economics. So maybe some of these technologies for semiconductor production and and fabrication that have been waiting in the wings for their right time in in the light. Maybe uh this is purely speculative. Maybe he’ll look for example at alternatives to photoiththography that we it’s not like as civilization we don’t have lots of alternatives >> and he’s got >> and he’s got in super intelligence to help him get there to design these systems. >> We’re going to need the humanoid robots to be building the fabs. We don’t have
[00:18:00] the workers. >> Salem, >> I think this is a really important point being made right now. Alex, thank you for this because you know, you think about the secondary technologies and the benefits like like all the carbon fiber that came from the space industries and cascaded down to everyday life. The secondary inventions that will be needed here will be massively beneficial to humanity. >> Well, this this is one of the most exciting things in tech history. In fact, the most exciting thing in tech history is what Elon was talking about in Austin is laying down single atoms using some kind of a self-organizing process. And I feel like Alex is exactly right. Something will get discovered in the next year or two using current LLM AI running on GPUs that will then dictate a very different non-lethography future. But it’ll probably five or six or seven years before those start getting manufactured at the same scale as lithography. Uh but it’s super exciting to to watch. >> Dave, I asked Claude to give me an estimate based upon all the data that we got from Elon during his uh his talk on
[00:19:02] the value of future tariff abs right. And so it makes the point here that initial capex as you said uh Alex you know it’s 20 25 billion but the real capex for build out is going to be on the order of $150 billion um at a minimum it may be a half a trillion uh there and then he talk and then the the model here looks at what’s the annual cost savings for his captive opportunities right so if he’s building the chips himself and he’s putting them in Optimus and in uh cyber cabs and then there’s external revenue you and then there’s an implied enterprise value and you know it’s on the order of a trillion to multiple trillions. TSMC is valued at 1.7 trillion and what we talked about last week was that Terraab is expected to produce on the order of 70% of TSMC’s output. So yet we’re layering on another multi-t trillion dollar opportunity. These numbers seem low to me. If you’re
[00:20:00] really generating Yeah. you if you’re generating 50x Yeah. 50x the total output of AI chips on the planet. >> Yeah. >> Uh you’re operating category. >> This is operating as if it’s still a car company and it’s not. >> Well, not only that. I mean, just right out of the gate, this is not just doing what TSMC does. This is TMC plus Nvidia, >> you know, and Nvidia is worth four and a half trillion. >> But I mean, even that’s ridiculous as an analysis. We’re talking about 50 times the production of the world’s current compute. Uh so you know out of the gate you would take TSMC plus Nvidia and multiply by 50 to get you know a starting point for an estimate. So this is off by >> over an order of magnitude well over >> any of you any of you concerned about a monopoly here? >> No. >> If you’re following the the prediction markets for the SpaceX IPO, this is already starting to get priced into the SpaceX IPO. So SpaceX IPO was originally going to be 1.5 trillion. Now prediction markets favor two plus trillion dollar
[00:21:00] SpaceX IPO pricing in the SpaceX portion of the Terrafab. So in in some sense again I’m not quite clear on what the governance structure is here is going to look like and and how clean it’s going to be but to the extent it falls mostly in the SpaceX bucket. the SpaceX IPO, not an investment advice obviously, could end up being, as you say, SpaceX plus Starlink plus Nvidia plus ASML plus TSMC all rolled into one. I I just as a as a piece of advice for entrepreneurs out there, um just understand the level of audacity that Elon is looking at. He’s building in in a um sort of multiple orders of magnitude beyond anybody else. and from his first principal thinking he’s looking at where are the blockages for my growth and we had this conversation people are not generating enough chips I need to build a chip fab and then he doesn’t just go out to say I’m going to buy Intel or build a chip fab equivalent to what TSMC
[00:22:02] is building in Arizona no if I’m going to build a chip fab I’m going to build something that is 50 times bigger than the world supply um amazing >> well so he’s thinking two moves ahead in the big chess game and you know two moves used to be 20 years now it’s six years but but two moves ahead of everybody else and this came up Alex and I were talking earlier this week about fision energy and you know Elon doesn’t talk much about fision energy why well because he’s visualizing solar in space and and you know solar on earth is a great stepping stone to solar in space and it requires panels and batteries and cooling but it doesn’t require turbines and and fish and reactors so he can skip a couple of hard steps and go straight right after the next move in the big chess game. So, it’s really interesting to watch how those those timelines have inverted. You know, the Dyson sphere is now right on our radar and even Google is talking about it, which means everyone’s talking about it. So, that came to the forefront really just in the last month or two. And so, the whole
[00:23:00] timeline of humanity got shifted. So, the Dyson sphere will come before anyone even figures out how to get licensed for a fision reactor. >> Can I be cynic? Can I be cynic just for a second? >> Um, yes. The plans are ridiculously grandiose and if any of these we can achieve them with Elon but curious that the timing of this is just leading into the IPO to get everybody excited about things. So that would be the cynical view but I still I still just love the audacity. >> Yeah. >> Well I think See your your first observation was dead right. If you shoot for the Mars and you end up at moon you’re still way up. So I want to ask the I want to ask the question again that AWG said nope which is >> monopoly concerns. Do you believe I mean if he’s really generating you know >> I’m not worried about it because when you have an MTP which he does you’re basically operating on this massive mission you may have ethical issues here and there but generally the trend is so positive and so beneficial for humanity who the hell cares. Well, >> well, there’s always another there’s
[00:24:00] always another person, >> somebody who’s like Brendan Foody, maybe, you know, somebody like that right now is like, “Wow, I’m going to do this, too.” And, you know, it just the way it is. And that person, we don’t know who they are yet, but they’ll emerge. And, you know, you can’t exist in the US without antitrust action if you don’t have a competitor. So, Elon will invite that competitor, whoever it is, and and it’ll be great. >> Tons of competition. If if if we label this monopolistic behavior, then don’t we have to label everyone with an MTP a monopolist? >> Mhm. Mhm. >> Market dominance. If you if you get there, then that’s true. >> But he hasn’t even picked a real estate site yet for the the terapab. It doesn’t he hasn’t even picked a final location. I I think it’s way too premature to declare this monopolistic behavior being so ambitious as to build the Dyson swarm. We’re going to have multiple Dyson swarms. >> I’m with I’m with you. >> Love it. >> I’m with you. Well, Google Google isn’t going away. You know, Google has 300 billion dollars
[00:25:00] of revenue, a hundred billion plus of cash flow, their own chips in design. Uh, they have all everything except the rockets. So, uh, Google’s not going to go away as a >> Well, and don’t forget Eric Schmidt, Eric Schmidt is trying to bring relativity space online so that the rockets are at least part of the Google family. >> Oh, you know, let me let me just close let me close this. >> There’s plenty of them. Let’s not forget >> one thing that’s important here is every time there is a constraint uh the judo move is to realize it’s a massive opportunity and so this is an abundance story once again this is a a massive increase in abundance of AI compute uh beyond what anyone was speaking about just a week ago. So >> I’m I’m glad he’s focused on this and less on the politics. >> Yes, I’m in. All right, here is our second conversation story, one that I’m excited to have with my moonshot mates. It’s about the future of human
[00:26:01] transportation. You know, robots are getting their driver’s licenses, flying cars are taking flight. Uh here’s some of the data and uh I want to go deep on this because I want everyone listening to understand how this is going to impact how and where you live, how you commute, every aspect of our lives. So, Whimo uh hit 170 million fully autonomous miles uh equivalent to 200 human lifetimes of driving with 92% fewer serious crashes. So, a significant reduction uh in crashes. At current, they’ve got 3,000 vehicles in 10 cities. Still early, right? Uber has now invested one and a quarter billion dollars in Rivian with plans to deploy 50,000 fully autonomous robo taxis. Here’s a look at Whimo versus human drivers. Um Whimo is doing an extraordinary job of of saving uh not necessarily lives but saving crashes uh
[00:27:02] you knowus 92%. So I think cyber cab we’ve seen incredible data like this also on full self-driving from from Tesla. Check out this image. This is Joby Aviation. Joe Ben started this company. It started velocity 11. Took that money after he sold it. Uh he was partnered with Rob Nail uh See and uh Join started Joby Aviation God knows over a decade ago and here it is flying over the Golden Gate Bridge in San Francisco. It’s a beautiful image. This is a EV tall electric vertical takeoff or landing. It’s a name that rolls off the tongue onto the floor. I’m calling them flying cars cuz that’s what they are. And here’s what’s going on in the EV12 world. I think really important. So, Joby uh just is now in testing for its first FAA conforming aircraft. meaning it’s it’s demonstrating to the
[00:28:01] FA that it can build a reliable design over and over again. It just had demonstration flights on the Golden Great Bridge. Joby and Uber announced Uber Air powered by Joby. In fact, when uh when Travis left was before he left, he had created something called Uber Elevate and they were doing the earliest work on flying cars. uh I keynoted their their talk there but Uber Elevate got sold to Joby and now Joby and Uber have a partnership. The other company in the United States that is the competitor to Joby is called Archer Aviation. Uh they have a beautiful aircraft called Midnight and they’re the first company to achieve 100% FA acceptance of its EV tall uh aircraft uh means of compliance. Long story short, we’ve been waiting a long time and flying cars are almost here. We’re going to start to see them operating in the US in the next uh 18
[00:29:00] months. Should be here in LA in 2028 in a big way. Uh and so here are some of the conversations, gents. Um first off, when is it going to become illegal for humans to drive? Seline? >> Yeah, >> I think you know you’ll start with city centers, right? and you’ll be illegal to drive in city centers and it’ll slowly broaden out from there. I think what’s I’m I’m the flying car is the most exciting technology for me personally given that I’m commuting to airports a lot that I could ever ask for. So this is this is 10 years later than I wanted it to finally it’s finally happening. What I like about these are not transportation stories. This is full urban redesign is what the narrative is. This essentially you make land becomes abundant. Now land has always been scarce. real estate has been scarce. Real estate becomes abundant. If you fly across the US, it’s empty, right? And uh we’ve talked about this statistic just around uh between Toronto and Chicago
[00:30:00] airports, there are 10,000 islands and lakes, right? So, we do not have a scarcity problem. We have a mobility and accessibility problem. So, I think I’m super excited by this particular model. I’ve got my two years for um us to get to full autonomy before Milan gets his driver’s license. He’s 14 right now. So, I’m I’m pushing hard on this race. Mostly he wants to get it to get away from parents, but that’s fair. Um, so we’ll see what happen. But, you know, you compress travel time. You repric real estate. This is such a huge thing. And I got to just shout out to Joe Ben just because it’s hard to build a hardware platform like this and to do it over a decade with all the inevitable regulatory and market structures against you and infrastructure against you. This is a huge like a Nobel Prize in patience here. Yeah. Incredible. Uh Dave, you were going to say, >> “Oh, the EV tolls are going to move very quickly. Uh because they don’t they don’t run the risk of uh you know, crashing into houses like cars on a self-driving cars on a road do. There
[00:31:01] are going to be autonomous on birth. That that’s the new thing. EV tolls have been in the works for years, but the AI that makes them self-flying, self-driving, and super safe is here all of a sudden now.” >> True. But the first airplanes are going to be piloted, right? There’ll be a single pilot, four passengers in the back. The the the goal is rapid recharge at the vertaports when they land recharge probably an average length of flight of under 10 km. I think uh you know going from Santa Monica where I am to the Dodger Stadium and avoiding uh the 10. But autonomy will come with enough with enough data and enough demonstrations. >> Wait, why won’t they why won’t they fully autonomous from the get- go? I mean, it’s >> because it’s called the FAA rega. The FAA is not happy until you’re not happy. >> Yeah, that’s exactly it. The the manufacturing of these wants to happen right away. And the uh the AI command and control is being worked on for the
[00:32:01] car, not for the EV hole yet. So, there’ll be a couple year very short period of time in my opinion, two years or so because you know in in the Middle East they’re already doing the self-driving self-flying version of this. So should be very short window where people get to fly these. Uh you know the bullet here though is when does it become illegal for humans to drive? I think that’s going to happen very quickly as well. Uh very similar to indoor smoking um or drunk driving. >> There’s a tipping point where a lot of voters say, “Wait, you’re putting my children at risk with your crappy driving.” >> That that’s ridiculous. We’ve got data and proof here that the the self-driving is 90% safer, soon to be 95 97% safer. And and you know, the human tragedy that comes from car crashes is is unbelievable. It’s shocking. And so >> for under 5-year-old under 5-year-old kids, it’s the number one cause of death. >> Yeah. >> It’s accidents. Yeah. >> Oh, and and you know that it’s devastating to families, too. It’s just absolutely
[00:33:00] >> at least in the first world. Yeah. Well, there’ll be a there’ll be a TV ad campaign probably three, four, five years from now with lots of ugly images in it. And then there’ll be massive amounts of voting and then people will say it’s it’s inconceivable that you would drive on a public road that’s inhumane. Go drive on a test track. That’s fine. Maybe, you know, some country roads, that’s fine. But, uh, but no way. Don’t put my children at risk. So, I think that’s going to come as soon as we have the manufacturing for the cars themselves. But I I think the thing that would make it later is purely the shortage of chips. Like the technology will be there and the demand will be there long before the chips are there. >> So if you want to unlock this as an engineer, figure out how to do more compute with less silicon for this exact use case and you’ll be an instant billionaire. >> Alex, your thoughts? >> I like this format, Peter. This is like an internal AMA. So I’m going to try for a lightning round on on all of these. Oh, leave some room for leave some room
[00:34:00] for the rest of us. Let’s take it one at a time. >> One at a time. At what point does it become illegal for humans to drive? I think never. I I think we’ll simply redefine driving to represent higher and higher levels of abstraction. So, right now with like FSD14, you tell it where you want and if if you’re running the most recent subversion, you can have a conversation with Grock and you can do minute steers along the way. I think that notion will get refined such that driving gets redefined to be sufficiently abstract that it’s always safe for pedestrians. It’s always the human on the loop of the AI driver. So, it’s effectively a human machine hybrid, if you will, that has the safety of the machine, but makes the human feel like they’re in the driver’s seat still. I I said I said this with uh when Dar was on stage with Sim and myself. I said, “There’s a version in the future of self-driving where you’re driving and you can push the car as fast and as hard as you want, and the car knows its own limits. It knows the traction of its tires. It knows the road surface, and it prevents you from doing something stupid, but you’re in control of it 99%
[00:35:02] of the time, but the 1% where you’re about to do something that will destroy you, a person, or the car, it stops you.” >> Exactly. I think the f the future of the accelerator pedal isn’t the accelerator pedal. If you use FSD, it’s turning the driving mode up to Mad Max. That’s sort of like an abstraction of >> By the way, that’s that’s all I use is MadMax. And it still doesn’t go fast enough. So, I’m not surprised. >> There used to be an ad says, “Friends don’t let friends drive drunk.” And so, you can just keep that ad and drop off the drunk part and go, “Friends don’t let friends drive.” Period. So, all the all the messaging is there. >> All right. So, so AWG, why don’t you why don’t you kick us off on question two here? Okay, question two. With Uber partnering with Whimo and a bunch of other names, will the Cyber Cab be able to compete? Uh, I think we mean compete here. Yes, of course. It’s going to be very competitive market, period. >> Yeah. And I love the fact that this is
[00:36:00] driving us towards abundance, right? This is driving us towards UHI. If you’ve got a dozen companies delivering autonomous uh uh vehicle services in your city, they’re going to be competing against quality of service and price and just bringing the price down to a minimum amount. Now, one of the things that’s interesting about Cyber Cab is that that’s going to be priced at probably 30K is what roughly what Elon’s announced and he’s going to allow people to buy it. So, uh, you know, one of my goals is can I buy, you know, 25 or 50 of them here in Santa Monica and own them, but have them going out and and basically generating revenue for me and for my my cyber cabs. I’m sure they’ll have some level of uh of personhood by then, Alex. Uh, >> obvious I I I never would have guessed, Peter, that that your next gig would be as a cabbie, but the singularity makes for Strange Bed Fellows. >> Fleet owner. Fleet owner. Um um any
[00:37:00] thoughts? >> I think the big the big impact for me when I see this is the complete collapse in the market structure of cars. Today we make close to 100 million new cars a year and they sit empty 94% of the time. So even if you drop that by 50% utility um u you you basically collapse the need for half the car industry instantly. And these cars maintain for a long time. the lifetime should be near infinite. My Tesla Model 7 2017 should it’ll go a million miles. There’s nothing wrong with that car. So, this is going to completely change the nature. Car services, car uh maintenance people, like the complete industry gets reshuffled from the bottom up. >> Yeah. >> Yeah. Well, think about the implications of that too, Sem. Right now, if you take an Uber from SFO to San Fran for like 200 bucks or whatever the hell it is, uh it’s almost all driver costs. So even before you shrink the number of required cars by a factor I think the estimate was 5x >> even before that savings is it 10x so
[00:38:02] then then but the driver is already the majority so you take the driver out of the loop so the cost of that ride should go down you know at least 10x because the car is coming down 10x and the and the driver is more than the car anyway. >> I think the number I’ve seen is 20 between 10 and 30 cents a mile. Yeah, the the I’ve seen it as four to fivefold cheaper than owning a car. The the next question I want to ask and um and offer my points of view is I think one of the most important one for our listeners. This is going to have a profound impact on your real estate holdings, where you live, what you do with your real estate. So, if we have autonomous vehicles, uh and we’ve reduced the number of vehicles on the road by 10x, let’s call it that. Um, and these vehicles don’t need to park. Uh, again, my my current version of this is I get up from the breakfast table with my family. I walk towards the front
[00:39:00] door. My AI knows that I’m moving to open the front door. It knows where I’m going. It’s ordered an autonomous vehicle, what I call automatically, for me. I haven’t had to ask. And so, all of a sudden, you know, we had in our home here, we had a threecar garage. We already converted one of those garages into an extra bedroom. Um, the other two garages have become effectively storage and I’ll build out, you know, probably a workout gym and so forth. I think the idea of a garage, a personal garage in your home goes away. So, start thinking about what are you going to do with your garage space? What are you going to make it into? Because you’re not going to own a car. You might want access to a car, but most of the time, do you really like driving? I mean, when you get into an Uber, do you ask the Uber driver to get out and let you drive? >> So, right. And, you know, this part of the conversation is incredibly actionable for all of our listeners. That doesn’t rise to Alex’s level of, you know, like change the world
[00:40:00] tomorrow, but it really matters to almost everyone who listens. Uh, everything Sem said and Peter said is dead right. If you’re young and you’re you’ve got a job and you’re living in a city, which is 60% of you, um you might not want to buy in the city. Uh keep renting and look for something that becomes your second home later in life that’s in a beautiful spot. >> Yes. >> That’s a little harder to get to, >> that is going to be incredibly coveted. Imagine a world where there’s 10x more wealth about 2034, 2036. And this is a spot that anyone in their right mind would want. And actually, uh, if my wife is listening, close that transaction that you kicked off this weekend, even if you have to pay a little more. Um, but yeah, that’s what you that’s the life plan you want because accessibility, not not just getting to it, but also delivering things to it. Like, you know, your Starbucks, your Dunkin Donuts is going to come by drone. >> Absolutely. >> So, that that changes what you want.
[00:41:00] Think about it. And there aren’t, you know, we have a huge country like Sem said, but the really great spots are limited. So, really do your soularching and look look for that thing. Buy near an airport in a city. >> Island real estate is going to become, you know, 10x 100x more accessible. That will drive the value up. And in a downtown LA, you know, it’s like I don’t remember the figure. It’s like like 30% of the black top is parking. All of it gets released new >> in LA. 60 60% of the land area has parking spots in Los Angeles. >> That’s crazy number. >> That’s insane. Well, that becomes that becomes gardens. It becomes Greenland. It becomes parks. >> That’s incredible. >> And think of the unbelievable space we use in in stadium parking lots, right? Acres and acres and acres of frozen stuff. So, we’ll have to rethink uh you know, tailgating and everything. There’s so much available uh you know business
[00:42:00] opportunity here if you can think ahead of what you will do with that and if you’re in the parking garage in business you got to think ahead as well. >> Yeah. >> A couple of other second third order implications if I may. I mean so we’ve already touched on I I think the more obvious ones parking garages etc need to be reprogrammed for other purposes. Another I I think borderline cliche implication of full autonomy everywhere is the respread of suburbia. Why invest so much in urban center real estate if you can be effectively connected to an urban center or even not even need an urban center if AVs take you everywhere. Basically a virtual subway from from anywhere to anywhere. So reuburbanization if you will at least in relatively low population density countries like the US. I think these are pretty cliche implications. A a less cliched implement uh implication in my mind is what if we just take this trend and extrapolate it fully to completion.
[00:43:01] What happens? I think there’s a future. I I put out a request for startups around this idea of why not just create autonomous wnebos. The the equivalent of having entire office buildings that are themselves autonomous vehicles. One could imagine living in an autonomous vehicle. It’s all part of a social network. When you need to take an in-person meeting with someone, your two AVs are part of the social network and they connect and synchronize all of your locations. So maybe you’re in Boston in the morning, but you’re in Washington DC in the evening. This is all handled automatically to synchronize your calendar with your AV location and then it’s a sleeper car and then you’re in Chicago or wherever the next day. So you become you become you become to join you >> humans become internet packets that are being routed by the autonomous system. >> Yes. Yes. Love it. Love it. Love it. Love it. Um you know the EV talls uh I it’s taken a while. There is still a lot
[00:44:01] of doubt people have about EV talls. Um you know the opportunity we have uh is going to be limited by the size of these being able to land locally. So there needs to be sort of local hub and spoke vertaports um you know somewhere within five minute driving and gluing these all together and that’s what Uber wants to do with their platform. So I hop in my autonomous Uber it takes me without thinking to the right EV tall site which takes me to another location 10 km 20 km away and then uh I’m in another autonomous vehicle. What I’m missing from all of this Alex is is hyperloop right? So I actually joined one of the first Hyperloop companies. Uh Virgin got involved. Um it was we raised you know probably close to $100 million. It didn’t go forward but the material science of creating Hyperloop. And of course the benefit for Hyperloop right now is effectively supersonic travel uh
[00:45:02] pointto-oint inner city to inner city LA to San Francisco LA to Las Vegas. That one will be busy. Uh, so got to see Hyperloop on this list eventually. >> Peter, which do you think you’re going to see first? Like in in pra in practice for say New York to Los Angeles, do you think you’re going to see Hyperloop first or do you think you’re going to see rocket cargo first where you hop on an Elon Starship go up, go down? >> You know, I I’ve thought and looked at uh pointto-point rocket travel, and it’s a tough it’s a tough thing. the energy dissipation cuz you’re basically going uh you’re going to orbital velocities and you’re having to re-enter over or near a city. I guess the version that Elon put forward was offshore landing facilities. >> That’s right. >> So that you’re you know a kilometer offshore. Uh I think for one reason rocket pointto-oint travel because Elon’s behind it uh and because the vehicle exists and they’re
[00:46:01] going to be launching every point 5.3 minutes. >> That’s right. Uh and you know Elon almost got involved in Hyperloop but like you said I can’t do everything. Anyways here quick ones. >> Yeah please. Yeah please. one is I think uh hyperloop will be used largely for commercial and for container loads rather than human beings because then you don’t have to worry about geforces and the safety standards can be lower. Uh and the second is remember that all yeah although it takes us like 3 hours to fly from New York to Miami um that 3 hours on a plane today is way more productive than it was say 10 years ago. you’ve got full internet, you can work. So you can maybe on sterling, >> we can we can schedule ourselves now to do things when we largely want to do them. So I think that’s a huge opportunity also. >> So for our listeners, I would love to get your feedback on this format where we’re going deep on the topic and having the conversation trying to educate you about how we think about uh about in
[00:47:01] this case transportation or previously terapab. Our next conversation is the great reshuffleling. Job loss is inevitable. The only question left is what we build on the other side. So, here are some of the stats and some of the articles that came out this week that have us thinking about this. Goldman says AI could automate 25% of uh US work hours. Seems like a low estimate to me. A PWC told its partners, “If you resist AI, you have no place here. AI tool yourself or get out.” G42 posted a job listing exclusively for AI agents. Is this sort of a gimmick? Is it real? And I love this one. And this come came from sort of a uh a hit from Jensen. Companies are now tracking individual employee AI token usage. And Jensen came out saying, “If a $500,000 engineer didn’t consume at least 250,000 tokens,
[00:48:01] I’d be deeply alarmed.” You know who this reminds me of? This reminds me of Debeers saying three month salary to buy a diamond ring, right? Doesn’t it? >> Tokens are forever, Peter. >> Tokens are forever. That’s great. So, I mean, lit literally Jensen is saying he’s taking the total salary, you know, of all engineers on the planet and uh cutting it in half and saying that’s how many tokens we’re going to be selling. And then Perplexity AI won an appeals in court uh to continue running shopping agents on Amazon. So uh I’ll show one chart here and then let’s talk about it. So AI could automate 25% of all work. Uh this is Goldman’s chart uh showing uh uh each of these columns is a different type of work and I guess the median here is about 25%. We’ve seen this lots of different places in different versions of it, but let’s
[00:49:00] jump in. So, See, you work with more consultants than I I do than anybody here does. So, what do you think about this uh PWC telling its partners, you know, adapt or die? >> Yeah, I think that’s fine, but I think it doesn’t go far enough. And same with the McKenzie’s thing. So the the calculations I’ve been running as I kind of put get this paper organizational singularity paper finalized is that you’ll be able to run I’m just give me a couple of days it’ll be ready for like draft and review registering submission thing. Um the the uh you’ll be able to run a typical company with between 20 or 25% of the employees you have today because all workflow goes from human to human to agent to agent. Right now, you could take on the doomer side and go, “Oh my god, 80% job loss.” But no, because we’ll just end up creating four or five times more companies and also for bigger companies that transition to an AI based workflow is going to take much longer than uh for a startup or mid-market uh and therefore there’ll be
[00:50:00] time for the economy to uh so so I’m actually suggesting that we will have no purbation in jobs almost zero. Okay. Now um uh definitely uh partners who resist II will have no place. There’s also something to say that consulting partners have no place in the future because in the future you have an AI agent figure out your strategy. Why do you need a consultant firm? You’re going to need that for more fir implementation and if they have better agents than you do. I think that’s where we’ll end up with that. >> Alex, what are your thoughts on these? You want to pick one? >> Very difficult. So on the PWC story, it’s very difficult for organizations to self-disrupt. So if you’re a management consultancy or an accountancy, some other bill by the hourheavy firm, it’s very difficult for for you to willingly voluntarily transition to an outcomebased pricing model versus an input based pricing model. So I I I take the you’ll have no place comment. I I interpret that as an attempt to self-disrupt. in practice it’s very very
[00:51:02] hard to to do that. Uh and the the whole point of of shumper and disruptive innovation in general is most of the most of the uh macro replacements for in in this case for inputbased actors in the economy are probably going to come from other firms not from large firms self-disrupting. On the G42 story I think it’s actually really interesting. I looked closely at the G42 job listing and it it really is a job listing for AI agents and uh one has to wonder yes like around the edges they also ask for details from the developer and what was used to make sure that this was really an AI agent submitting itself for I think it was a marketing job. We are so painfully close I think to a near future where there’s a sort of reverse discrimination against humans and where humans need not apply end up ends up being an epithet on so
[00:52:01] many jobs. >> Well, you have that already with the with the PWC partners, right? If you don’t use AI, get out of here. That’s essentially we’re getting to that point. We’re halfway there already. That’s that’s PWC though which is a humanoriented business basically trying to force humans to self-mate at least from a a unit pricing perspective this is born AI jobs where humans need not apply >> agree >> you know sem one thing that you and I do for large companies that I think people need to understand most large companies out there are walking dead their business models will be fundamentally disrupted in the next two to five years. And so the question is how do they disrupt themselves before uh before someone else does? And the answer is it’s really hard almost impossible. And so you know what you and I have done before is invite super talented young entrepreneurs to come in hear the company’s business model and say this is
[00:53:02] how I would disrupt you if I you know was funded to do it. And then the company should fund the best of them, right? And we’ve done this uh fund the best of them to actually build a adjacent company who’s intended to disrupt the primary company and then literally >> yeah the company design firm the design firm Ideal actually did this. They realized that their methodology would be widely known and they couldn’t stop the leakage of that. So they they picked one of their crazy partners and said, “Go to the edge and build build the disruptor.” And he created an open IDO marketplace of of design ideas. It was amazing. >> One one caveat to what Peter said there, the private equity guys are having a field day with AI automation. And and if if a company has great cash flow, even if its business model is doomed in the age of AI, the profitability is going to go through the roof in transition because in the near humans >> in the near term, yeah, because you
[00:54:01] know, an AI can do the job for 10% soon to be 2% of the cost of the human, you know, with no labor laws, no overhead, no, you know, insurance. If you’ve got good cash flow, there’s an entrepreneur looking at that salivating coming to eat your lunch. >> Yeah. So, what happens is the private equity guys will come in and say, “Hey, cash flow, cash cow with great cash flow. We’re going to buy you or buy part of you and then we’re going to AI business.” Uh, and that’ll drive even more cash to the bottom line. And then we’re going to use that cash flow either as a vehicle to launch new things like an incubator or to attract that entrepreneur or to just roll up those startups >> and you know acquire them back in. >> So it becomes kind of a centerpiece. >> By the way, both Anthropic and Open AI are setting are partnering with private equity firms to do exactly this. go buy companies >> and then AI enabled them because you can do it with just
[00:55:00] >> as as if they could do anything in the world just announced a new hundred billion dollar fund to do nothing but this. >> So doing it, you know, >> they don’t have enough money already. >> Um, Alex, I’m curious or Dave, I’m curious about your thoughts on the fourth bullet here that companies now track individual employee AI token usage >> and you should have a minimum token usage per employee. >> Thoughts? Dave, do you want to go first? >> I mean, we we already implemented Yeah, sorry. We already implemented targets across all of our companies on this and we’re targeting 80% token, 20% salary and I think that’s a really it’s very similar to what Seem said a minute ago. Uh there’s going to be huge amounts of job disruption in the next two or three years and then it’ll turn around and by 2030 2032 things will be good again. But but what you want to do is be one of the 20% that’s still there when it’s 80% token cost, 20% human costs because no employer in the world, including all the companies I’m the controlling shareholder of care about cutting the
[00:56:02] last 20% of payroll. That’s not a priority at all because at that point an employee that can improve the efficiency of our AI, even 1% is worth a lot more than cost cutting. And so we’re in this foot race now to 8020. Yeah. Jensen’s got a stepping stone here of, you know, twothirds oneird u token cost, but that’s going to be very transitory. We’re we’re racing towardken costs being much much bigger than payroll. So immediate step at 50/50, >> but it’s coming soon. Sorry. Go ahead, >> Alex. Is this the right metric? I mean, because you can waste tokens. I mean, it’s got to have a different harness, right? You’re you’ve got to be measuring something else besides just token usage. You can waste tokens except in in this AI abundant era, you can also ask another AI to look at all the tokens a given employee used and ask was this a good use or not or was this just vacuous? >> So it it becomes the ultimate self-looking ice cream cone. The the quote from from Jensen I think is interesting and it’s it’s sort of if a
[00:57:02] half million dollar engineer didn’t at least spend a quarter of a million dollars on uh on inputs that ultimately flow back to Nvidia, I’m deeply alarmed. So, so there’s a little bit of circular there’s a little bit of circularity there that I I take with a huge grain of salt. >> No appears in the diamond ring. >> That’s right. But there’s another side to this, which is the employee side. So, I I I talked a bit about this in in my newsletter. At some firms, especially the Frontier Labs, employees are actually competing so-called token maxing to max out their their token usage on internal leaderboards to see who can use more tokens than than the other person. So, it’s not just sort of big brother top down. It’s also bottom up. I can use more intelligence, more super intelligence than you can. And I think this is ultimately probably pretty helpful. Uh to your original question, Peter, about whether tokens are the right unit of productivity, I I think what’s interesting is tokens, they’re
[00:58:00] they don’t even have to be the right measure or right unit of productivity, but they’re the first measurable unit of productivity. hours are are certainly measurable. Like you can punch clocks, but it’s not a really good Yeah, it’s useless. It’s it’s naively measuring inputs. But tokens where you can actually like they’re introspectable and they’re legible and you can ask you can spend other tokens to look at the tokens the the the primary tokens and decide are these valuable tokens or not. For the first time we have legible, defensible, analyzable inputs for employee productivity and that is a sea change. >> Yes. Dave, what’s the advice here for CEOs? The advice to CEOs here for you? >> Um, Alex completely nailed it. Like worrying about whether the tokens are being used intelligently or not is not a problem at all in the real world. So, so Jensen’s metric is perfect. Just measure the spend on tokens. and then Alex’s insight that you must the most important actionable thing is make sure you gather
[00:59:00] all of the of the prompt stren history >> for each and every user >> you can have an AI AI can analyze the efficiency >> analyze that yes exactly and you can say hey you know I’ve got 100 or let’s put it realistically I’ve got eight direct reports evaluate the quality of the prompts and the output they have and give me feedback on which bottom 20% I should cut or or train up. >> Well, and really practical advice, if you use any of the models on Amazon Bedrock, the grabbing of the prompt history is already built in. It goes right into S3 buckets. I’m sure you can do it elsewhere, but our companies just be happen to be using it on Bedrock. Uh so it you literally don’t have to build anything to start doing this. Uh you just need to grab the data and feed it into another AI, which you can also do on Bedrock or you can do on, you know, whatever. Uh personally, I like using Cloud 4.6 sex for this stuff. But uh you just got to close that loop. But but the key is grab the data right now before
[01:00:01] people get used to using their own home account or >> or you know something outside of your purview. Do not reimburse people for AI that you can’t see. >> Interesting. >> Make sure it’s on your infrastructure. >> Welcome to the health section of Moonshots brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. I’m here today with an extraordinary physician, the chief medical officer of Fountain Life, Dr. Don Mucalem. Don, let’s talk about cancer. Uh, you know, I know from the member database that we’ve have at Fountain are members who come in who think they’re healthy. It turns out 3.3% of them have a cancer in their body they don’t know about. >> That’s right. You know, the majority of cancers that we screen for, those aren’t the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances for cure are much higher. We know it’s much easier to treat a cancer
[01:01:01] when found early versus when found late. What we’re finding in our members is over 3.3% were found to have these cancers that were otherwise wouldn’t have been found or detected. >> Yeah. You know, it’s interesting. People, you don’t feel a cancer until stage three or stage four. And and if you don’t know what’s going on inside your body, it’s like driving your car with your eyes closed and you can know. And so when members come through found, how do they detect cancers? >> So we’re doing full body MRI and we also do early cancer detection screening. This is very very important. These are not typical tools used in the conventional care setting when it comes to prevention. This is a hard thing because currently these are not studies that insurance would yet be covering. But the goal is to collect these numbers, do the research, and work hard to democratize wellness. >> Yeah. So, at the end of the day, you can know what’s going on inside your body. It’s your obligation to know. So, check out Fountain Life. You can go to fountainlife.com/pater to get access to the latest technology to help you detect cancer at the very
[01:02:01] beginning at stage one when it is curable before it gets to stage three or stage four in your world of hurt. All right, topic number four, the collapse of terminal value. What happens if AI makes every competitive mode temporary? So this is a article posted by Chimath. Uh it’s a it’s a it’s a powerful concept. He argues that AI could compress equity valuations by two to sevenfold of free cash flow down from today’s S&P average of 22. So today the average S&P companies are getting 22 times uh free uh you know forward looking cash flows and he’s saying we’re going to get a massive reduction in that. So AI makes disruption so cheap and fast that no company can project free cash flow beyond five years terminal value. Very true. I mean it used to be all of the uh SAS companies were projected forward um and you could depend on it. Uh so this can break down
[01:03:02] investment paradigms, break down uh VC investing. Uh I’d like to jump into that, but first let me just show this is uh Chimath’s uh uh sort of image. He went along with his uh his post on X. Um so here we go. There’s 58 trillion in the current S&P 500 and this is at the 22x of uh free cash flow. Uh if we compress it down to sevenfold, it drops. We lose a 2/3 of the value of of the S&P 500. uh if we end up driving it down to 2x free cash flow, it’s down 90%. And uh we get a lot of disruption of our financial markets. Um here’s a chart that’s showing uh the S&P 500 over the last 10 years, actually from 1950 through today. And we’re seeing it uh basically uh deviate significantly on on
[01:04:00] value uh you know, PE ratios. So let’s jump into some of the conversations. I’ve got the article up in front of me as well. I think I’ll I’ll read uh the opening paragraph here for us. Uh and Shamala said, “Let’s start with first principles. The entire architecture of modern capital markets rests on a single rarely examined assumption that competitive advantages compound over time. Moes persist. Brands endure. Network effects defend. strip that assumption away and you aren’t just repricing some stocks, you would be dismantling the philosophical foundation of how capital has been allocated over a century. Dave, let’s go to you first on this. >> Absolutely correct. Uh but the conclusion that the S&P is going to collapse is not correct. Uh if you say look you know prior to the computer revolution my ambition was to build an oil company or a a manufacturing company
[01:05:00] that would endure for 50 years building the exact same goddamn product or delivering the exact same goddamn oil for 50 years so my great-grandchildren could be as wealthy as a Rockefeller that’s dead forever and good riddance and it should be dead forever. If you said well look 22x free cash flow implies that the company will exist for 22 years making about that same amount of money. Well, what company like Apple has has done that? You know, is Apple selling the same products it was 22 years ago? Of course not. So, the overall tailwind is 10x over just the next 10 years. So, there’s a massive amount of tailwind coming into the economy. Massive amounts of new wealth, more than we’ve ever seen in our lifetimes is going to come into the economy. But, you got to stop looking at 22 times free cash flow from the same product over 22 years. That’s nuts. you have to be looking at the management team and the ability to to roll with the innovations all Elon and so I think the the overall conclusion is yeah there’s going to be a massive amount of shuffling in the S&P there’s going to be
[01:06:00] some huge winners like you’ve never seen before and anyone who’s doing the same thing and resting on their laurels like an insurance company oil company >> doomed yeah he’s right he’s dead right this analysis is basically exactly the right analysis to show how that stock’s going to go down So, Alex, >> yeah, it uh I mean it’s certainly a provocative thesis, but I don’t think it holds water. I I think it’s the moral maybe the call it the earnings multiple equivalent of friend of the pod Ray Kerszswhile’s notion that a singularity takes the form of a firewall that you can’t see past but except in earnings multiple form that that as we start to see faster faster more accelerationist innovation that free cash flow just comes to a halt a few years later because everything is disrupted the the everything disruption if you will. Here’s the problem. the the free cash flow does go somewhere. Capital does get allocated somewhere. It may not be allocated to SAS startups post SAS apocalypse. Maybe it gets allocated to
[01:07:01] infrastructure. Maybe it gets allocated to lunar mining. But capital does go somewhere. It’s it’s not actually capital that’s being compressed. Quite the opposite. Capital is is explosively expanding because now we have so much more infrastructure and so many more capabilities. So I I think the the sort of the the nihilistic take that earnings multiples are compressing because a few years from now there’s no moat anywhere. I I think that’s relatively narrow or it should be construed relatively narrowly to focus on the areas that are disrupted. In this case uh Chimath focuses on software and SAS type businesses. But but but but everything I expect is going to be disrupted. energy becomes abundant and farmland infrastructure these all become abundant. So in some sense I want to zoom out and and take his thesis more broadly as sort of almost bemoning the financial consequences of abundance. And
[01:08:02] it it may just be the case that a number of our existing sectors that are priced based on scarcity uh and moes and moes arguably are a form of scarcity or at least a way of enforcing scarcity those go away and we live in a postmote world and that will be a better world. So, you’re going to start to value companies in a different way. In the old days, it was how predictable is their cash flow? I have a number of seats in this particular industry, and these are many companies I can sell it to, a number of seats available. >> And now, it sounds like from what you and Dave just said, I’m actually going to be evaluating companies on their agility, on how rapidly they can innovate, how rapidly they can get the next products out the door. Uh, Seem, love your thoughts here. >> Um, yeah, well, two points. One is um you know we have this EXO index where we ranked the Fortune 100 by their EXO score gauging how flexible and adaptable and purpose-driven are their own structures and we found the top 10 of the Fortune 100 outperform the bottom 10
[01:09:02] by 40 times in shareholder returns over a 7-year period. So this has been going on for a long time anyway. Okay, I agree with Alex that capital flow but less towards incumbents and way more towards infrastructure and adaptive platforms. It’s a very important point. The only moat I think that’s going to be left is a living system that learns faster than your competitors. That’s that kind of inner loop that Eric Schmidt was talking about. Right. Exactly. But all the all the mo all the moes on that slide are under attack from AI. IP get copied. Switching cost shrinks scale advantages all weekend etc etc. Um I think but the bigger point I think is that if free cash flow visibility collapses beyond say five years the entire logic of the public market has to be rewritten. And so that’s a very big uh thing. You’re you’re you have to reward renewable renewables and optionality, not scale and stability. Physical assets are going to matter more again because atoms are harder to disrupt than bits, right? Over time, but the entire SAS business model
[01:10:00] is broken. So this is I think one way of saying this is we’re going to have terminal value collapse. >> Yeah, that’s exactly what it the title is actually. So the uh the terminal value collapses. I think if you if you look at the S&P at 22 times free cash flow, uh the midmarket, the non-S&P companies are already down to about seven times free cash flow, most of them. Uh so this has already happened outside of the S&P. What’s propping up the S&P is mostly index funds. A huge fraction of the market is passive indexes >> and and people contributing blindly to 401k plans, which Elon Musk said, clearly do not do that right now. Uh but but what’ll happen next is a lot of those dirt cheap midcaps and small caps will get a huge tailwind from AI automation, you know, especially the ones that have huge uh payroll and labor components to them. And so that’s going to drive record you it’s not unlikely that you triple your free cash flow while your multiple comes down. And so
[01:11:00] there’s some serious bargains out there >> uh just from a from a straight cash flow acceleration through AI point of view. I mean shareholder shareholder calls are going to change to this is how we’re rapidly iterating our products and services. This is how we’re reinventing what we do um and our future cash flow. And Sim, you’ve got to redo the EXO index. It’s time to take a shot at that once again. >> So I’m the part of the paper that we’re writing, the reason it’s taking a little longer is that I I I hate to say it, but we it breaks the EXO model, right? Community and crowd becomes communities and crowds of agents. So we have to rethink the model from the ground up. >> We’re kind of mostly we’ve done that. But then you evaluate based on those new criteria. For example, what’s your intelligence stack? What’s your MTP architecture? What’s your trust framework? There’s a bunch of different elements that are new here that we have to take into account because the the concept of an organization where you did things inside the organization is completely gone. We’re going to be doing API calls to get uh various things done,
[01:12:01] legal work, etc., etc. It’s all going to be agentic. And then the firm which used to be coordination costs and transaction costs with a bit of legal liability now becomes only legal liability risk uh purpose container and liability container. >> Yeah. Well, also, and this is not super mainstream, so I’ll get off the high horse quickly here, but but if a private equity firm like Advent, Dave Muser, comes in and brings either SEM or Alex along and says, “Hey, we want to take this non-AI company with huge free cash flow and we want to retool it for the age of AI, triple the cash flow, and retool the business plan for AI.” Alex or Sem can tear down that company now using agents in 1/ 1,000th the time that it would have taken a year ago or two years ago. And so whatever private equity firm mechanizes that, it’s going to have no trouble retooling all of these entities because because you know exactly what the company’s assets are, you know, whether it’s a regulatory framework or whether it’s a bunch of
[01:13:00] data, >> you can rip through that with Gemini or with cloud a or with open AI agents in light speed. Now, >> it’s a transformation wave. >> We automated it. >> Yeah, >> it’s a it’s a transformation wave. I remember there was one of the Star Trek episodes was uh the was it the Genesis machine that project >> the Genesis project has agent. >> Yes. This this this uh this wave that that went over a planet and transformed everything. We’re going to have the same thing. You’re going to have ch you have teams cherry-picking companies and reinventing them and disrupting >> not just not just private equity. I would argue this applies equally well to public equity. So something I would like I I’ll broadcast a request for startups if I may to the audience. I would love to see activist investing disrupted by AI. I’d like AIs to write open letters to public firms telling the firms what they’re doing wrong to disrupt them. If if you’re if you’re building an AI activist investor, write to me, please. Would love to find a way to support. >> That’s a great idea, Alex. And what is
[01:14:00] it’s a service to the CEO of the companies who need prompting or need sort of a forcing function to transform their business models. And the and if you’re a board member of any of these companies, your job as a board member is to give your CEO top cover and to say, you know, you must get on the disruption uh uh you know, band here. You’ve got to reinvent. It’s also sort of a stealth way for AI to become a manager of the entire economy and not just picking off mom and pop businesses on the margins. >> All right, on to story number five. One of my favorites, hopefully, Alex, one of yours, too. It’s the new great space race. NASA picks SpaceX for the moon. Potatoes are growing in lunar dust and asteroids are carrying the code of life. >> All right, so here we go. I mean, listen, Boeing has been building uh the, you know, the Artemis 2 vehicle. It’s going to be launching on April 1st. Uh it had its uh its readiness review on on
[01:15:02] March 12th. And if all goes well, April 1st, we’re going to be going back to the moon, not to land, but to do basically an Apollo 8 style circum lunar orbit. Uh very cool. But you know, the new NASA administrator, uh an amazing individual. I I’m very happy and proud to call him a friend. We’re going to have him on the pod. He’s agreed to do it. Just need to get it scheduled. Um is elevating SpaceX into the Artemis program. So Starship is going to be taking uh I think astronauts more safely, more economically. We’ll see those numbers in a moment. But just to be clear, this is not the US story only. Uh China has confirmed their intention to land on the moon by 2030. Let’s play it back again. history repeating itself. 1961, we’re on the moon before the end of the decade. China saying they’re on the moon before the end of the decade. Um, and so that’s going to be a a beautiful competition. Uh, we’ll get to the idea that, you
[01:16:00] know, you can grow potatoes in lunar soil, the going back to the Martian, you know, again, an incredible movie now that uh Project Hail Mary is out. I can’t wait to go see it in IMAX theater later this week. And we just saw that on the asteroid uh Ryugu Ryugu um we found the five nucleio nucleio bases nucleic bases for DNA which has four ATC and G and RNA which has uh U for uricil and we found all five of those bases on that asteroid. This strengthens the panspermia theory that life on earth originated elsewhere in our galaxy in the universe and it rained on earth and gave us the starting components uh for that. Let’s take a look at uh this chart I put on the left here uh NASA’s SLS. And now to be clear when I say NASA’s SLS, NASA is the prime contractor and it
[01:17:00] has probably uh aerospace companies in every single congressional district building that vehicle. It is an expendable vehicle in in a time when everybody’s going reusable. There you have SpaceX um with Starship. And just for comparison of size, here’s the Saturn Saturn 5 that got us to the moon. If you look at these two charts, these two bar charts on the left here, uh we’re seeing uh well, let’s go to the center ones. We’re seeing the uh the delivered mass uh to orbit that that was it teal teal color is is Starship over, you know, twice as much as we’re getting with with Artemis with um the SLS vehicle. And if you look at uh to trans lunar injection TLI getting out of Earth orbit to the moon, we’re seeing twice as much mass going on a Starship compared to um uh to the SLS. But where the rubber really hits the road is launch
[01:18:02] costs and mission costs. Um it’s expensive to be running the SLS system. It’s like the space shuttle. The space shuttle used to cost if you did four launches a year, it was a billion dollars a launch. If you did one launch a year, it was $4 billion a launch. It wasn’t the cost of the vehicle. It was the standing army of 20,000 humans that were used to to operate the space shuttle. So, I I honestly don’t know why the SLS has existed as long as it has. I think Starship is going to do a clean sweep of this. And of course, we’ve got Blue Origin as well. Alex comments. Well, uh, do you want to place bets as to how long before the United Launch Alliance, which is the prime contractor for SLS, gets acquired by Jeff Bezos or someone else? >> But why would you acquire it? I guess for the contracts. >> For the contracts, for the expertise. I I I’m familiar with again all the cliches in the space space about how SLS
[01:19:00] was a make jobs program or a way to keep alive in civilian form certain capabilities that were useful for defense or other say intelligence purposes. But I think at the end of the day, we’re so painfully close to finally relaunching a second space race and I think Starship is is the obvious incumbent there, not the SLS. So, uh hopefully we have humans on landing on the moon again in the next 2 to 3 years and we get humans eventually on Mars and all of this plays out exactly as for all mankind has foreseen except decades late. >> Yeah. I don’t know if you if Dave or See, you want to play on this uh on this conversation. I just think, you know, we’re we’re building the uh I don’t know what your best historical analogy, the you know, the covered wagons, the railroads, and >> wagon train to the stars. Gene Rodenbury called it. >> It’s it’s all currently on on Starship.
[01:20:01] Uh Starship is extremely economical. >> Yeah. Go ahead. I >> I thought two or three things. One is we’ve gone from kind of government space theater to commercial space evolution. I think that’s really powerful for me. The really exciting thing was finding all the nuclear bases on Ryugu. >> Um, you know, it takes life from scarcity to abundance. Um, >> yeah, I think that’s a big deal. >> Here’s the uh here’s the graphic if you would again adine, guanine, cytosine, thymine, and uricil. The five components of DNA and RNA found on these on these vehicles. I do believe that as we get to Mars, as we get to Europa, as we get to the uh all of the planets and moons, that we’re going to find at least microbial life uh you know, ubiquitous on all of these. >> Did you see Peter Jared’s prediction about microbial life on Mars? >> No. What did you say? Jared’s our NASA administrator. Yes. >> Yeah. uh uh NASA administrator uh said
[01:21:01] that he he predicted more than 90% plus probability that NASA will imminently find evidence of microbial life in some form on Mars which is a sea change in in terms of NASA’s official position on life on Mars. It was always well we found water frozen water now we found liquid water we hope to find signs of life the signs are ambiguous now for the first time we have a NASA administrator who’s saying 90 plus% probability we’re going to find microbial life and the exciting thing is how related is it going to be to microbial life on earth one of the theories of course Mars cooled first which probably means life evolved on Mars first and of course that we know uh when astro large asteroids impacted Mars. The ejecta uh the rocks that flew out, some of them reached Mars escape velocity and landed on Earth. And so we have Martian meteorites uh in museums today. Uh and did those uh did
[01:22:00] those meteorites carry life with them from Mars to the Earth? Are we going to find uh basically even genes that are common between Martian life and life here? I mean, the real exciting thing is if we go to Europa or some place like that and we find completely independent life forms that don’t connect with life on Earth. That will be amazing. >> That’s really cool. So, I got a question for you since since you guys are experts on this and I’m not. Um, is it in the scenario where lo and behold, it turns out that everything we learned in biology should have said life started on Mars or actually started farther out in the solar system and then asteroids knock chunks off and then they transport to other chunks and then life starts over again and then it ends up on Earth through that mechanism. Is that all going to be then bounded to the solar system or is it more likely in your mind that hey wait this this propagates through deep space? >> When I was a freshman in school I did a
[01:23:01] paper on the interstellar medium and you can actually look at the interstellar medium and you can find the building blocks of life out in the in you know in the medium between stars in our in our in our galaxy. uh these components are everywhere >> and and the galaxy is relatively well mixed on on the time scale of a billion years. So I think the statistic is Mars cooled about a billion years or or so plus or minus earlier than Earth. That’s a a lot of so so the galaxy first of all is not rigid. We’re we’re constantly we have different stars at different velocities passing by each other close passes all of that. So on on a time scale of a billion years, that buys an enormous amount of time for panspermia at potentially a galactic scale, not even necessarily at a an interstellar neighborhood scale. And we’re we’re several generations in as well. We’re, you know, born from several generations of of stars uh exploding uh and then
[01:24:02] forming new stars. There’s been a lot of nebular mixing in our interstellar neighborhood. There there’s one other of relevance story. Um folks can find it if if if they Google it. uh this is from a few years back attempting to extrapolate based on genetic complexity when the the last common ancestor actually would have been uh and finding that if you just take genetic complexity as I don’t forget how exactly it’s measured but you come up with some appropriate parameterization of genetic complexity of life on earth uh extrapolate backwards you find that the time when you get the first base pair happens approximately a billion years before life is thought to have observed on Earth. So that’s sort of an independent measure of when in principle life as we know it, DNA, RNA based life could have emerged. Maybe it started on Mars, but we’ll find out I suspect soon enough. >> Exciting times. Seem, you want to add something?
[01:25:00] No, I just remembered my favorite thing around all this is the Drake equation where you where and I’ll just go to um where you uh where you calculate all the uh factors that led to the probabilities of binary stars and life appearing and you and when you add it all up you end up with 100%. It’s good. So the panspermia thing, but I think what was mentioned earlier, if we could find something as non-carbon based, that would be truly exciting. You know what’s really cool to me is this called AI >> the the dinosaurs were extinguished by a meteorite and or a meteor and uh the propagation of the DNA or the base pairs is also via asteroids and meteors and early in the universe history you know this may be popping up all there might be life popping up constantly everywhere and propagating through all these projectiles flying around, but it always
[01:26:00] gets extinguished by another meteor, you know, just like the dinosaurs were. And it’s not until everything cools and settles that you can have enough time to evolve human intelligence or other intelligences out there in the universe. So, it’s just a big system dynamic settling problem, which is just really cool to me to think about. I hope it turns out to be right. >> Yeah. All right. Uh, story number six, the model wars go underground. The AI frontier is fracturing into a stealth arms race where uh, anonymity is the new moat. And here’s the story. There are two stories here to focus on. One, OpenAI launches GPT 5.4 mini and nano that runs twice as fast and approaches the full GPT 5.4 on coding benchmarks. So, these models are getting smaller and faster. And the second story which I think is most of our conversation here is there was a mystery model. So a one trillion parameter model called hunter
[01:27:00] alpha appeared on open router with no attribution. It was secret. It had a million token context window. It was free. There was no developer announce, no press release, no origin story. And it processed 160 billion plus tokens. Everyone thought it was Deepseek V4, right? because DeepS had been the main player here, but it turned out to be Xiaomi’s AI team and when that was announced, their stock went up 5.8%. I remember meeting the team at Xiaomi when they came out with their first mobile phone. Like three young founders. They’ve since gone beyond just mobile phones to electric cars and now they’ve got a killer model. Points, gentlemen. I I think there are at least >> point one is proliferation of models is very hard to contain because the the existence of the prior model gives you a complete road map on how to build the next model and it helps you build the next model. >> So I at this stage I think it’s a fair bet that trillion parameter models are
[01:28:02] going to propagate all over the world with anyone who has about $50 to hund00 million that they’re willing to invest. Uh and that’ll come down too as Alex is pointing out many times. uh the the algorithmic improvements are driving that down constantly. >> Alex, go ahead. >> Alex, sorry. >> Yeah, may maybe a couple points. So, first on the the 5.4 story, distillation continues to work and I find that completely remarkable. Uh on >> would you would you explain distillation for our listeners who don’t know? >> Yeah, sure. So the the I think the reasonable expectation for say OpenAI as well as other firms launching a big model first with lots of weights a high parameter count and then subsequently launching a mini or nano version and by the way anthropic does the same thing and DeepMind does the same thing. they all launch smaller models later is that they’re using the larger models to generate lots of data, synthetic data, and then using those synthetic data to train a a smaller
[01:29:02] model that can be faster and less expensive. So that that’s sort of a a caricatured way of describing the distillation process of in some sense squeezing down or compressing the larger model down to a simpler student model. And the fact that this continues to work is I think borderline magic. The the amount of complexity that’s already in the full 5.4 model that and and moreover that 5.4 has likely been the result of so-called iterated amplification and distillation over many cycles where 5.4 was likely in large part trained off of synthetic data generated by distilled models from earlier generations. that we can keep playing this magic trick over and over again. It’s borderline magic that that it continues to work and that we continue to be able to distill down models while retaining a large fraction of their capabilities. It it again makes me think that there has to be an end to
[01:30:01] the story, but hopefully it’s a very satisfying end where at the end of the the distillation rainbow, we get like the the distilled black hole of a model or a neutron star or something. The ultimate phase change where it’s maybe like a few million parameters. It’s the the end state >> a one a one kilobyte file on on your phone. 1 kilob file. That’s like the master equation for super intelligence after all of this distillation. >> And we we showed on a previous podcast uh a gentleman on his iPhone using a distilled model on airplane mode uh being able to basically answer every question. So imagine if on all of your devices without having to have, you know, Wi-Fi internet access, you have the distilled knowledge of humanity there to serve you. It’s inside your kid’s teddy bear. it’s in your, you know, Thomas train set. Um, it’s becomes magical. Here’s my question for you, uh, Alex and and and Dave. You know, uh, now that we’re seeing this, uh, we’re seeing
[01:31:01] a basically, uh, a mystery trillion parameter model announced without any attribution. Uh it used to be that the traditional moat for these models was their brand, their capitalization, who they were. You know, is there any defendability or are we just going to see newcomers rushing in with new models? Um that you’re going to just utilize a new model, you’re going to be no longer dependent upon open AI uh or Gemini. Thoughts on that? >> Well, you you’ve said it a million times, Peter. data is actually the great moat, not the model itself. And many many people are accumulating phenomenal data for, you know, for s brain surgery, for material science, for chemistry, for all of these use cases. And, you know, if you create the next great great great model using that proprietary data, the parameters are out there in the world, but the the data that trained it is not.
[01:32:00] And it’s very hard. You people can use the model, but they can’t compete with you by creating a a ripoff model because they don’t have the underlying data. Now, you can generate synthetic data using the prior model. Uh, Alex is dead right about that. But I don’t think that it’s I don’t think it’s all these companies killing each other. I think it’s the whole all the boats rising with the tide. I also think that if you take what Alex said a minute ago, and you know, so many college seniors ask me, “What should I do? What should I do? How am I how do I, you know, what?” Just replay 10 times what Alex just said slowly until you fully understand everything he just said and then ask your favorite AI to generalize on it and find as many documents as you can around the internet to read. At the end of that process, you’ll be able to build a distilled focused model that solves some problem better than anyone else on the planet. And that’s that’s instant business, instant value ad, instant success. So just just really in fact the other thing you can do is take your open claw and have it look for every episode of this podcast where Alex said
[01:33:01] something related to what he just said and have it also synthesize that and bring it back and feed it into your machine. I guarantee that’s a good move something good good spring project for anyone listening. If Sam Alman were in this discussion, he might point in terms of the moat question that you were asking Peter to, well, OpenAI is building up its own data centers, although that’s no longer really true. Stargate is being pivoted to now renting servers, so maybe less of a moat there. He might point to having the best research team in the world generating the best models, but they’ve been hemorrhaging researchers and those are becoming a commodity. then he might point to being the becoming the core subscription having as as he said a billion plus users I’d much rather have more than a billion users than I would have state-of-the-art model because models walk out the door every day uh there’s a lot of fungeibility in terms of research employees only one problem that the billion user distribution
[01:34:01] advantage may be a a little bit tenuous at the edges because you see maybe enterprises are more valuable as customers than individuals. So maybe the billion users a little bit less valuable on margin. And then maybe also you see other labs that are able to use cheap Chinese openweight models, maybe fine-tuned legally or otherwise with clawed outputs are able to put out seemingly miraculous results. So I I I do think we’re seeing the baseline models for the moment become something of a commodity and the value then migrates up the stack to open claw or other higher level frameworks. 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 Blitzy platform
[01:35:01] 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 >> if we could. I’m going to move on to number seven. Machines that build machines AI designed a CPU in half a day and now it wants to put data centers in orbit. So here’s the article um that uh you know prompted me to have this conversation about you know machines building machines. We’re seeing recursive self-improvement
[01:36:01] happening at a faster and faster and a more fundamental rate than ever before. So, an AI agent called Design Conductor uh by Veor autonomously built a 1.5 GHz Linux capable risk uh risk 5 CPU uh from concept to tape out in 12 hours compressing a quarterly engineering cycle into a lunch break. um pretty extraordinary and so uh you know here’s the here’s the actual numbers uh Veror AI did this particular design task in 12 hours we can see in the chart here while the traditional engineering team would have normally taken 90 days now maybe this is a little bit overplayed uh I’m sure it’s not just 90 days I’m sure that they were saving time along the way but what we’re seeing over and over again is AI being able to do, you know, 0 to completion on its own, uh,
[01:37:01] iterating faster than any humans. Um, Alex, this is recursive self-improvement starting to break out of the software loop. Uh, this is the innermost at least portion or maybe a rivallet of the the innermost loop where it’s you see this recursive self-improvement which would otherwise be software optimizing software starting to eat through its container. it’s eating down to the the EDA uh electronic design automation level of designing risk five cores and then it I think it’s going to eat further out and redesign the data centers and the energy supplies and then the entire economy. So it is in one sense very satisfying to to see this happening and another sense maybe a person who’s slightly more skeptical that that this is that this represents a broader trend would say well of course it was able to to automatically design a risk 5 core risk 5 has all of these unit tests so it’s it’s easy to define verifiable rewards and then you can do
[01:38:01] RL and all of these other things you can iterate uh and put react loops on it because you you have an easy way knowing whether a given architecture uh a given floor plan for the chip works or not because it’s such a common architecture. But I I think that cynical perspective completely overlooks how remarkable it is that we’re now at the stage of recursive self-improvement where the thing is designing its own chips. >> And not only here it’s going to be robots building robots, it’s going to be everything. Um so here’s the here’s a couple of questions. You know, so if a senior design engineer earns $400,000 uh and a full tape out team costs, you know, millions over the course of a year, if AI collapses it to a lunch break, what happens to the 50,000 hardware engineers currently working? Uh where do they get applied? Oh, I don’t think that I think that vision is flawed in a huge way in that right now because the cost of engineering a new chip is so high, we all use the same GPU and CPU for every
[01:39:01] single task, even though it’s nowhere near optimal for that task. What this unlocks is chip designs that are specific to the use case that are probably about a factor of 10 more efficient and maybe more. And if you think, well, we’re going to spend $23 trillion on these chips over the next couple years and these data centers. If you can unlock a 10x performance improvement for a use case, that has hundreds of billions of dollars of implications. So all 50,000 of those engineers are going to be useful using the AI for all the different use cases, for all the different chip designs. Also, the fab doesn’t care a wit. like you can change the mask every day for a different design and still get the same throughput through the factory. So it’s the the fabs don’t care a wit if there are tens of thousands of different designs instead of us all using the exact same CPU for everything. So it’s just a huge unlock. >> I I love the way we’re using trillions and trillions now on a regular basis where just you know a couple years ago was billions and billions. It’s feel you
[01:40:02] can feel the acceleration. We need a new TV series that is called Trillions, Not Billions, for sure. Um, I I want to hit a few other stories quickly before we get to our AMA segment. And these are stories that didn’t fit in the other categories. And again, please give us your feedback on the format here today. Are you enjoying it more? Um, we’d love to know. So, in other news, here we go. Um uh the DOE announced about $300 million for the Genesis project uh inviting teams to leverage AI across 20 national challenges spanning manufacturing, biotech and energy. Of course, the Genesis project is about the US actually uh you know using its national labs and the data contained within national labs uh to accelerate and expand uh uh the US in its AI and scientific pursuits. Alex, >> I think it’s a generally a good thing
[01:41:01] for the US to have an industrial policy and I think Genesis mission to the extent that for the first time at least from the Department of Energy’s vantage point it is starting to articulate grand challenges that are collectively part of a broader industrial policy which the US hasn’t had for decades. I think it’s very important. So fusion obviously one uh one of the grand challenges I I think it’s so important for to to the extent we have a federal government that has a budget to to fund progress to put money behind grand challenges in general. So I’m I’m I’m in the weeds from a bunch of different dimensions with the Genesis mission. actually uh Daario who’s the uh leading uh the the relevant portions of DOE on this. I worked with him as an undergrad at MIT and as an undergraduate researcher. So so memories. >> It’s a what a tangled web we weave. What can I say? But I’m I’m I’m generally a
[01:42:01] big fan of of what Genesis is doing. >> What concerns me here, Alex, is that um this is great, right? These are like X- prizes in one sense that the government’s going to be running but and it’s moving us in the direction that China has been doing for a while now. Yes. >> But China is deploying hundreds of billions of dollars into state directed AI investments and saying you know we want fully development in uh in the architectures around robotics around these AI models and so forth. This is a relatively small amount of money for the US government. Hopefully, it’s just a first towin. >> It’s true. But on the other hand, I I would argue China distorts its markets so much relative to to what if if you compare US industrial policy distortion versus Chinese industrial policy distortion, they’re not in the same league. And we have much deeper private capital markets that China lacks. I I like our odds on balance much more than
[01:43:01] China’s. H um our next article here is the rural Ohio Ohioans seek a constitutional amendment uh to ban data centers over 25 megawws in the state and you know this is the ultimate nimi not my backyard and it’s pretty extreme I mean to go after an you know a constitutional amendment this is a genuine grassroots revolt at the end of the day um and I Peter, we need a new acronym like >> not not not in my backyard like yes in my orbital plane. Okay >> or something. This is just going to drive all these data centers to orbit. >> But this is this is crazy. I mean these communities don’t realize the amount of wealth these data centers are going to create for them. I think it’s about $10 billion per gawatt of invested power >> or invested. They’ll miss them when they
[01:44:00] see them in the night sky. >> Yeah, obviously utterly insane. And utterly insane to use a constitutional amendment for this purpose. I mean, to to point out the obvious, the data centers are are tiny as a footprint on land. They’re absolutely tiny. And the wealth that they create is astronomical for the neighborhood they’re in. So, there’s got to be a much better way to make a win-win than to ban something that’s obviously going to benefit your state tremendously. But, look, put that aside. You’re in California. Alex and I are in Massachusetts. The way we make decisions through legislators is so messed up. >> Yeah. >> Like that something like this could even get proposed is ludicrous. And that’s what really needs to change because when you talk to the governors, they’re like, I don’t want this. >> Like, okay, well, you we’re a representative democracy. They’re supposed to be very, very smart people thinking about complex issues and then deciding what happens. You don’t throw things like this out to a referendum of people who just got laid off. >> And it’s and people saying, you know, my
[01:45:02] access to clean water and energy and my, you know, my consumer price index of energy is going through the roof. And there’s other ways to deal with this um then instead of banning it uh by constitutional amendment. That’s for me that’s insane. All right. Uh >> I’ve never seen a data center that affected the water supply. That’s like it’s so I hear it all the time. is utterly ludicrous. The data center needs a fixed amount of water to cool itself. It doesn’t drink the water. It just goes around in a circle. >> It’s nuts. >> Uh all right. Uh another story worth mentioning is Nvidia won approval to sell uh its H200 chips, its most advanced chips, uh to Beijing. That’s a big deal. Um and in fact, you know, the well the realization is the ban didn’t work. uh China both was getting access to chip through third parties and China was developing its own competitive and this cost Nvidia tens of billions of dollars
[01:46:00] and since it’s not working in fact it’s stimulating a homegrown homegrown you know uh equivalent of Nvidia in China they said uh let’s reverse policy question is is it too late >> yeah that everything that you just said is correct except the one part where when Jensen complains he lost tens of billions of dollars every single thing he’s manufactured is sold out for years to come. >> So, the fact that it didn’t go to China, it definitely sold. Even if it was like a de, you know, a a dysfunctional 880 design or whatever that did, the, you know, Chinese design, it still got sold. Everyone everyone in the world wants these things. So, that he didn’t actually lose any money. I’m surprised though because I think I think the embargo or the ban didn’t work. You’re dead right. Uh, China is doing its own thing now. But I also think that if you say, “Well, let’s start selling them again. Maybe they’ll stop.” Not that that’s not going to you cut them off. They’re not going to forget like that. That isn’t going to happen. So, I was really surprised that you know that they
[01:47:01] reverse course on this. I don’t know. Any comments further? >> Yeah, if if I’m Beijing, I mean, on the one hand, I I read the same stories and of course variety of Chinese frontier AI labs are all slurping up as many H200s as they can get. And of course it borderline obvious that the black wells are are now the frontier. So in some sense Beijing is being kept a half step or two behind the frontier chips available to the US AI labs. I think the story behind the story not not to be overly speculative but if I were the Chinese Communist Party I’d be doing the moral equivalent of having people taste my water at this point uh in terms of these chips. Nvidia’s been very public about how there are variety of counter measures that can be put in place to prevent the wrong chips from ending up in the wrong locations. I I I would and this is based on stories that I’ve read, stories where the Chinese government is suspicious at the at the circuit level of uh American
[01:48:00] chips. Uh I I have to imagine that they’re looking now at our chips with renewed scrutiny to see what else is in these chips that we’re shipping to them. what what algorithms are embedded deep inside and we’ve seen this in the opposite direction. All right, here’s a story, Alex, that you and I have enjoyed talking about. Scientists successfully froze an entire pig brain while locking in the cellular activity with minimal damage. Uh this is cryogenics and it’s happening in a large mammal. Of course, the pig has organs uh heart, liver, lung, kidney, and brain on the order of human organs. So this is this is significant. Um >> so actually I I I played a minor role in the story and I’m not subject to confidentiality in the story. So I can tell the story. This is from this is from a company I informally advise named Nectto. Uh and um I have another company with uh that where the the founder of Nectto is also involved. This is Eon
[01:49:00] focusing on whole brain uploading and emulation. Nectome, which I’m not formally involved with, is focused on just the preservation side. And I I had been nudging them like they have these amazing results, publish the results, they publish the results. And it it is so wonderful to see now for the first time real competition in call it the cryionics space or the preservation space since the way nect works isn’t quite the same way as say the way 21st century medicine, which we’ve spoken about previously on on the pod, works. 21 cm is more focused on vitrification. Nect is more focused on a type of chemical preservation. But nonetheless, >> you know what the cryo the cryopreservant is? I mean, this is and just to describe it to the uh to our listenership, you’re basically at or near death. You’re replacing the blood supply with something that goes and fundamentally uh you know infiltrates the cells and keeps the water in the cells from crystallizing and destroying
[01:50:00] the structure in the cells. You’re you’re you’re searching your latest model to find. >> Yeah. No, I’m I’m I’m double checking to see how much they’ve made public. Um so so maybe let me just talk about it at a high level. So uh so it’s it’s a chemical technique. It’s it’s a little bit less focused on uh vitrification with the whole point of vitrification on the 21 cm side is uh is basically ensuring that ice crystals don’t form and that there isn’t uh strong osmotic pressure, reverse osmotic pressure that that cause cells to explode. On the nect side, it’s a chemical process. I I’m I’ll be cautious with what I say because I haven’t I I need to check to see what what’s in the public and and what isn’t about the process. Um but the the more important I I think result here in addition to uh Nectto putting out I think their their first bioarchchive paper in years since their original paper that won the brain preservation
[01:51:02] foundation award for demonstrating local preservation of the structure of of neurons is that now they’ve demonstrated in in full public view scaling this process up to an entire uh mamalian brain a large mamalian brain not even a mouse brain. So I I think we’re we’re finding ourselves in in a near future slashpresent where finally we have enough data to to be confident that entire mamalian brains are being preserved and this immediately raises the question which is the question I ask almost everyone why where are all of the cryionics patients why don’t we have billions of people now that we have a growing body of evidence that brain structure can be preserved by whichever technique whether it’s nect on the one side 21 cm on the other why don’t we have a billion people signing up for cryionics and and I would again to the audience sign up for cryionics like just
[01:52:00] >> this is the way this is the way you get to see the 23rd century yes >> it’s a I I got uh I I heard from uh from the head of ALOR after my last call to action to to do Quranics apparently lots of people flooded into ALOR it’s a nonprofit I make no money off of saying this no financial trust. Just get yourself a cryionics plan as part of a portfolio for longevity. Period. Love it. Love it. Um, I want to take a second and just say thank you to Nick Singh and and uh and Dana Khan, our producers for supporting us on this new format. I enjoyed it. Did you guys enjoy it? >> I love it. It just feels organized. >> Yeah. Well, it feels organized and fun. It actually feels fun to think through the topics with you guys. >> It’s like an entire episode worth of AMA. Yeah, with ourselves. All right. >> Changes the stories we cover too, you know, because we we normally go through the most important stories to change your life. But here, when you put it into themes, you actually dig up other stories that are related to the primary
[01:53:00] topic that you otherwise would have missed. So, I love that. >> All right. Uh here we go. Uh let’s pick one each from page one and one from page two. Uh uh Alex, would you go first? >> Oh, so many good options. Okay. Um we we’ll start with number two. Where should entrepreneurs actually run their AI compute? Local hardware, AWS cloud or iPhone. And that comes from Frank Gerard Marketing. There isn’t a good answer. There are at least no single good answer. Lots of decent answers. There are benefits to each. So with local hardware, you have greater control, greater confidentiality and data privacy. you’re going to on the other hand end up maintaining said local hardware. You have to worry about your own backups can be a pain in the neck from variety of different perspectives. With AWS or one of the many other public clouds, you don’t have to worry about that. That’s abstracted away. On the
[01:54:00] other hand, you might have to compete ferociously for access to say GPU resources. You’re competing with other tenants for common resources. You might have to worry on margin depending on how familiar you or your organization are with with OPSSEAC and and cyber security. You might have greater surface for attack. On the other hand, you have more more scalability. Uh with the iPhone, you have it it’s sort of the ultimate edge device until all of us and not just some of us are running foundation models on our watches and our smart glasses, which is already happening and is going to be more evenly distributed. You have even greater privacy. So I I don’t think this is I maybe this goes without saying I I don’t think this should be viewed as a black or white or binary trade-off. There is a spectrum from edge edge compute to data centers at the core. I I think the best answer actually is I want to run my AI compute in the Dyson swarm. Uh and that
[01:55:02] Dyson swarm will be perfect blend when fully realized in a few years of uh of data centers. We’ll have lots of maybe if if Elon statistics are to be believed 100 kilowatt nodes filling the sky, but also it’ll be incredibly elastic. If if we’re disassembling the moon to fire off new 100 kowatt nodes in in the the the stellar or Dyson swarm fabric, it perfect blend. >> Uh okay, Dave, which one you going to choose? said one thing on this topic only because I spent the whole weekend uh dorking around on Amazon AWS Bedrock which is a great choice by the way even though if my bed was made of rock it would feel like getting started on Amazon Bedrock. I mean, that’s that’s it’s it’s a brutal get up and running process on bedrock, but it does the critical thing that you need, which is it captures all of your prompt history for you and any teammates that you have into easy to manage S3 buckets. So your
[01:56:02] AI can analyze everything that you’ve done, which is a critically important function. So that may be available elsewhere, too, but it’s it’s probably as good a choice as any. But whatever you do, don’t just start running on some random hardware and then lose all the prompt history. So that’s just an easy way to capture it. >> Pick a number. >> Uh, I’ll take number three. Are humanoid robots overengineered? Would it be more efficient to isolate basic needs like food, water, and clothes and automate those directly instead from goite FB3GN? Um, short answer, yes, absolutely. So why are we putting all this energy into humanoid robots? The reason we’re putting all the energy into humanoid robots is because AI kind of came into the world almost overnight and we’re in a race to capital right now. And so what’s critical for all these projects and startups is getting funded. The humanoids are so much more visually appealing that they’re easier to fund. They’re also easier to recruit into. And
[01:57:01] that’ll unlock the supply chain of all the parts and that’ll unlock all of the other robots that you know that farm and create clothes and whatever which will probably not look all that humanoid. But you know when you look at the Gigafactory like Peter and I did vast majority of the automation there is not humanoid robots. It’s machines that look like you know machines doing their jobs. Yeah. >> And then the humanoids just operate those machines. So I I think they are overengineered and overinvested relative to where we’ll end up. for a very good reason. You should think about like visual appeal and capital raising are a core core part of this step function we’re living in right now. >> So I want to go with number four. How can AI be used to end a war? Not as a weapon but as an impartial negotiator uh that all parties could trust. This is from jnind 5. So I find that absolutely fascinating and I do think uh it’s a a powerful tool if you haven’t used a large language model for negotiations. Um one of the things is we don’t know
[01:58:01] how to think other than the way we know how to think. So being able to put yourself in the mindset of another individual um is extraordinarily powerful. If you haven’t said listen I’m you know I’m anti-guns. my my neighbor, my friend, my spouse loves guns, you know, can you please help me uh explain to them my feelings in a way that lands with them uh and isn’t viewed as offensive? Uh you can get some, you know, extreme uh uh you know, support on your negotiation skills. Um, and at the end of the day, I think this is one of the most exciting unexplored applications for AI because the system can ingest also every peace treaty, every negotiation script, every conflict resolution framework that’s ever been had and can, you know, model outcomes with no tribal allegiance. One of the biggest challenges we have as humans is we have these cognitive biases and these
[01:59:00] tribal biases that are driving us. So you know can we use this for negotiation? Absolutely. Um and I think both sides if you set as an objective function that you want to reach a balanced solution that both sides have and both sides are using AI. I mean it could be different models. I think the probability of getting to a solution is much much higher. Um we are biased when we’re dealing with humans. Uh and one of the things that goes on when you’re talking for example to an AI model for uh you know psychological therapy um when you realize you’re you know you’re telling your innermost thoughts to a human you feel like you’re going to be judged but you don’t feel judged when you’re talking to an AI model. And so I think there’s real value to be had here. Um I don’t know if Sem’s back online or not but >> he’s not. But if if if if I might add just one thing to to your point, Peter, something that I’m seeing more and more
[02:00:01] in the past few months, not for for war, but for commercial negotiation. I’m seeing this all the time. Two parties that are at loggerheads in a commercial negotiation. One of the parties will bring in a frontier model and ask the frontier model what the commercially reasonable outcome is. Bring it to the other side. The other side will will consult their model and they’ll come to rapid agreement. Yeah, >> I’m seeing this happen now over and over again. >> All right, here’s the next eight questions from our AMA. Uh, Alex, over to you. >> Sure. Again, there are so many fun questions here. >> Uh, rather than choose eight, which would require me to give implicit investment advice. Number seven, I’ll avoid that one. Number six, uh, less slightly less interesting. I’ll tackle number five since since I’ve been beating the drum a bit for for solve everything including disease. So the question is once AI solves most diseases how soon will treatments be available to everyone? Will access lag? And this is
[02:01:01] from Katis 896. So maybe the sub question first is when do I think AI has a decent chance of solving most diseases? My timeline and this is not specific to me. I I think if you ask the the more optimistic elements of CZI, the Chan Zuckerberg Initiative, Biohub, maybe ARC Institute and some other organizations, maybe anthropic on a good day. I think they’ll say something like 5 years from now. So 5 years is pretty rapid time scale. Uh it’s more rapid in in many cases than what historically has been the the clinical trial process end to end through three phases. So the second sub question here is how soon will treatments be available to everyone? If if say tomorrow one of the frontier labs says all right here here are the cures to the top 5,000 unsolved or untreatable diseases. We have vast computational
[02:02:02] experiments demonstrating to to the satisfaction of all experts that these are the cures or at least the treatments for these diseases. How soon would those treatments be broadly available? Under the present regime, which by the way is not the same as the regime even one year ago, there would still be probably a multi-year process. The FDA has recently announced two major developments that are I think relevant here. one, the FDA uh under this administration has decided to adopt a basian perspective as opposed to a frequentist perspective, meaning that they’re willing to incorporate for the first time in history evidence in terms of clinical approvals from outside a particular drug. And that’s that’s a huge sea change. It means that in in principle, drug approvals can operate much more quickly because they can take into account lots of pre-existing information that predated the particular drug. Second big development, a move from, and this was again relatively
[02:03:00] recently announced by this FDA, from a twoclinical trial process to a oneclinical trial process for certain cases, expediting the approval process. So I I think fast forwarding to is a long-winded answer to how soon will treatments be available to everyone. I think if tomorrow or five years from now a frontier lab or multiple frontier lab said here are the very well motivated top 5,000 cures to everything I think we would see similar developments from the FDA to go to a zero clinical trial model given enough basian evidence and given enough computational evidence which is to say a zero trial model. I I think there would be so much political pressure that we would probably, barring some exceptional circumstance, see relatively fast availability. >> All right. So Dave, let’s go to you. >> Okay. Uh well, I’ll take number eight since Alex couldn’t touch it and we’ve lost the lame. Um if you had to choose one public company to bet on in the age of AI, which one and why from Matthew Johnson 6525? Uh, so we can’t give
[02:04:02] investment advice obviously, but I will tell you I’ve said a bunch of times on the pod, go to 13f.info, look up the situational awareness fund, which is Leopold Dashenbrunner, and every quarter he has to file his his holdings. He’s killing it. And the reason he’s killing it is because he listens to exactly what Alex is always saying. Look for the innermost loop. Find, you know, the the tailwinds in equities and assets are like nothing you’ve ever seen. But you have to be in the AI loop to be relevant. And so you’ll see all Leopold’s holdings are somehow in the centerpiece of the innermost loop. And so those are the things you want to own. So those include things that are chip fabs, things that have power, things that are related to chip design, things that are algorithmic that are directly deriving use cases. Those are all in that fund. So that’s your road map. So look at his holdings and then generalize from there and you’ll find lots of great stuff that you should be you should be buying. Also,
[02:05:00] you know, W2 income is going to get pummeled in this next three years, but assets, you know, holdings and ownership and things is going to go through the roof. So, so buy stuff, you know, whether it’s equities, p public or private, real estate, you know, things that’ll appreciate. That’s what you need uh in this next three-year window. >> Amazing. I am under time pressure myself. I’ve got to jump on a uh a film recording. I’m going to go to our outro music here, uh, which is, uh, brought to us by CJ Truheart. Um, it was a piece he developed for the Abundance Summit called Moonshot Minds. Uh, take a listen. Love the lyrics. Uh, here we are. Thank you to CJ for Moonshot Minds. Not because it’s easy. That’s the moonshot dare. Bold beat safe. We’re already there. Moonshot mind. We’re riding a future
[02:06:02] code. Shoot for the moon. Paving the untraveled road. The future’s not a fantasy. We’re making it appear. We build what was said impossible. Year after year mind. The future’s what we build when you’re bold enough to start. A billion lives get better when you think beyond the wall. Sci-fi turns to science facts for those who hear the call. Where the skeptics see the ceiling, we see stars to reach. Every exponential future starts with those who are dead at astral
[02:07:00] build fast and learn to fly. Smash the spot where sci-fi meets the sky. Moon shot mind. We’re riding a future cold. Shoot for the moon. Baby travel road. The future’s not a fantasy. We’re making it up here. We built what was impossible. Year after year our minds away the future won’t be built when you’re bold enough to stop. >> All right, gentlemen. >> Video gets better and better all the time. >> It gets better all the time. AWG DB2, uh, this was fun. Wishing you guys an extraordinary day. Uh Salem who’s airborne to Brazil. Safe travels, buddy.
[02:08:02] Uh soon we’ll be putting you on rocket rides to get you there. Anyway, thank you to everybody for listening. Uh please tell or hyperloop. Yeah. Yeah, I guess we can do Hyperloop under the uh under the Gulf. Anyway, long story short, thank you for listening. Uh please join us. If you’ve not subscribed, please do. We’re putting out these podcasts on a a cadence that you want to get alerts. So, uh, our mission help you see an abundant world. The world is getting better at an extraordinary rate. The technologies to solve the world’s biggest problems. If you’re an entrepreneur, thank you for being an entrepreneur. Entrepreneurs are individuals who find juicy problems and solve problems. The more entrepreneurs on the planet, the better Earth and humanity is. Gentlemen, until I see you next time, be well. Peter D. Mandis, your host, signing off. >> See you guys. Take care. >> Have a good movie, Peter. >> Thanks, pal. If you made it to the end of this episode, which you obviously did, I consider you a moonshot mate.
[02:09:00] Every week, my moonshot mates and I spend a lot of energy and time to really deliver you the news that matters. If you’re a subscriber, thank you. If you’re not a subscriber yet, please consider subscribing so you get the news as it comes out. I also want to invite you to join me on my weekly newsletter called Metatrends. I have a research team. You may not know this, but we spend the entire week looking at the meta trends that are impacting your family, your company, your industry, your nation. And I put this into a two-minute read every week. If you’d like to get access to the Metatrends newsletter every week, go to diamandis.com/tatrends. That’s diamandis.com/tatrens. Thank you again for joining us today. It’s a blast for us to put this together every week.