Elon Musk - "In 36 months, the cheapest place to put AI will be space”

title: Elon Musk - "In 36 months, the cheapest place to put AI will be space”
author: Dwarkesh Podcast
contenttype: podcast
publication: Dwarkesh Podcast
published: 2026-02-05T16:45:08
sourceurl: https://api.substack.com/feed/podcast/186967347/7e1421b0bbe8243af32f70dda75bef63.mp3

word_count: 28213

So, are there really three hours of questions, or are you fucking serious? Yeah. You know what I'm talking about, Elon? Only one. I mean, it's the most interesting point. All the storylines are kind of converging right now. So, we'll see how much you're... Almost. Like I planned it. Exactly. Well, we're good. That would never do such a thing. So, as you know better than anybody else, the total cost of ownership of a data center, only 10 to 15% is energy. And that's a part you're presumably saving by moving this into space. Most of it's the GPUs. If they're in space, it's harder to service them, or you can't service them. And so, the depreciation cycle goes down on them. So, that gives us way more expensive to have the GPUs in space. Presumably. What's the reason to put them in space? Well, the availability of energy is the issue. So, I mean, if you look at electrical output outside of China, for everywhere outside of China, it's more or less flat. It's very... You know, maybe a slight increase, but for a pretty much flat. China has a rapid increase in electrical output. But if you're putting data centers anywhere except China, where are you going to get your electricity? Especially as you scale. The output of chips is growing pretty much exponentially, but the output of electricity is flat. So, how are you going to tell them it chips on? You know, magical power sources, magical electricity ferries? You're famous. You're famous, you have a fan of solar. One terawatt of solar power. So, with a 25% capacity factor, like four terawatts of solar panels, it's like one percent of the land area of the United States. And that's like far... You were in the singularity when we got one terawatt of data centers, right? So, what are you running out of? How far into the singularity are you going? You tell me. Yeah, exactly. I think we'll find we're in the singularity, and like, okay, we'll still go along where to go. But is this like a... Is the plan to put it in the space after we've covered Nevada and solar panels? I think it's pretty hard to cover the solar panels. You have to get permits from the approach for that. Try getting the promise for that. So, the space is really a regulatory play. It's harder to build on land than in business space. It's harder to scale on ground than it is to scale in space. But also, you're going to get about five times the effectiveness of solar panels in space versus the ground. And you don't need batteries. I almost wore my other shirt, which says it's always sunny in space, which it is. So, because you don't have a day-night cycle or seasonality clouds or an atmosphere in space, because at the atmosphere alone, we're still at about a 30% less of energy. So, any given solar panels can do about five times more powering space than on the ground. And you avoid the cost of having batteries to carry you through the night. So, it's actually much cheaper to do in space. And my prediction is that it will be by far the cheapest place to put AI will be space in 36 months or less, maybe 30 months. 30 months? Less than 36 months. How do you service GPUs as they fail, which happens quite often in training? Actually, it depends on how recent the GPUs are that are right. I mean, at this point, we find our GPUs to be quite reliable. There's infrared mortality, which you can obviously iron out on the ground. So, you can just run them on the ground and confirm that you don't have infrared mortality with the GPUs. But once they start working, they're actual reliability. And once they start working, and you're past the initial debug cycle of Nvidia or whatever, whoever is making the trips, could be Tesla, Tesla AI, six trips or something like that, or it could be a TPUs or trainings or whatever. The reliability is actually, they're quite reliable, past certain point. So, I don't think, I don't think that the servicing thing is an issue. But you can walk my words. In 36 months, but probably close to 30 months, the most economically compelling place to put AI will be space. And then it will get, they'll then get ridiculously better to be in space. And then the scaling, the only place you can really scale is space. You know, when you start thinking in terms of what percentage of the Sun's power are you harnessing, you realize you have to go to space. You can't scale very much on Earth. But maybe very much, to be clear, you're talking like terawatts. Yeah. Well, all of the United States currently uses only half a terawatt or an average. Right. So, you know, if you say a terawatt, that would be twice as much electricity as the United States currently consumes. That's quite a lot. And can you imagine building that many data centers? That many power plants? It's like, those who have lived in software land, don't realize that they're about to have a hard lesson in hardware that it's actually very difficult to build power plants. And then you don't need to just need power plants. You need all of the electrical equipment need, the electrical transformers to run the transformers, that transformers. Now, the utility industry is a very slow industry. They pretty much, you know, they impede a smash to the government, to the public utility commission. So, they're, they impede a smash literally and figuratively. So, they're very slow, because their past has been very slow. So, trying to get them to move fast, is this like, you know, like if you're trying to do an interconnect agreement, interconnect agreement with the utility at scale, like it put a lot of power. As a professional podcaster, I can say, that I am not in fact. Yeah. They actually need many more views before that becomes an issue. They have to do a study for a year. Okay. Like a year later, they'll come back to you with their interconnect study. Can you tell this with your own behind the meter power stuff? You can build power plants. Yeah. That's what we did at X and I. Four classes two. So, four classes two. Yeah. Why are we talking about the grid? Why not just like build GPUs and power collocated? That's what we did. Right. Right. But I'm saying why isn't this a generalized solution? When you're talking about all the issues. Where do you get the power plants from? I'm saying when you're talking about all the issues, what are the utilities? You can just build private power plants with the data centers. Right. But it begs the question of, where do you get the power plants, where do you get the power plants from? I mean, the power plant makers. As you say. Yeah. Like does the gas turbine backlog basically? Yes. You can drill it out to level further. It's the veins and blades in the turbines that are the limiting factor because the casting, it's like a very specialized process to cast the blades and veins in the turbines. So we're using gas power. And it's very difficult to scale other forms of power. You can scale potentially solar, but the tariffs currently importing solar in the US are gigantic. And the domestic solar production is powerful. Why not make solar? That seems like a good Elon-shaped problem. We are going to make solar. Okay. Yeah. Great. Both SpaceX and Tesla are building towards a hundred gigawatts here of solar cell production. How low down the stack? Like from policy looking up to the wafer to the final panel? I think you got to do the whole thing for raw materials to finish the cell. Now if it's going to space, it's actually a cost less than it's easier to make solar cells that go to space because they don't need glass, or they don't need much glass, and they don't need heavy framing because they don't have to survive for other events. There's no weather in space. So it's actually a cheaper solar cell that goes to space than the one on the ground. Is there a path to getting them as cheap as you need in the next 36 months? So the solar cells are already very cheap. They're like far-sickly cheap. And if you say, you know, I think like solar cells in China are around like 25, 30 cents a watt or something like that. It's absurdly cheap. And when you take into account, now put it in space, and it's five times cheaper, because it's five times... In fact, no, it's not five times cheaper. It's 10 times cheaper because you don't need any batteries. So the moment your cost of access to space becomes low, by far the cheapest and most scalable way to generate tokens is space. It's not even close. It'll be an order of magnitude easier to scale and chips aside of order of magnitude. If the point is you want to be able to scale the ground, it's just you just want. If you're only going to hit the wall big time on power generation, they already are. So like the number of... So miracles and series that the XAI team had to accomplish in order to get a Gigawatt power online was crazy. We had to get together a whole bunch of turbines. And then we had permit issues in Tennessee and had to go across the border to Mississippi, which is fortunately only a few miles away. But then we still had to run the high power lines a few miles and build a power plant in Mississippi. And it was very difficult to build that. So I understand how much electricity you actually need at the generator level, at the generation level, in order to power a data center. Because the news will look at the power consumption of say a GB300 and multiply that by a thing and then think that's the amount of power you need. All the cooling and everything. Wake up. Yeah. That's a total of noobs. Never done any hardware in your life before. Besides the GB300, you've got to power all of the networking hardware. There's a whole bunch of CPU and storage stuff that's happening. You've got a size for your peak cooling requirements. So that means can you cool even on the worst hours the worst day of the year? Well, it's pretty freaking hot in Memphis. So you're going to have like a 40% increase on your power just for cooling. It's assuming you don't want your data center to turn off the hot days and you want to keep going. Then you've got to say, well, there's another multiplicative element on top of that, which is, are you assuming that you're never have any hiccups in your power generation? Well, actually sometimes you have to take the generators some of the power offline in order to service it. Oh, okay. Now you add another 20%, 25% multiplier on that. Because you've got to assume that you've got to take power offline to services. So the actual, roughly every 110,000 GB, GB 300s, inclusive of networking, CPU storage, cooling, margin for servicing power, is roughly 300 megawatts. Sorry, I said it again. It's roughly, or the thing about it, like the way you think about it's 330,000, actually, what you need at the general generation level to service, probably service 330,000 GB 300s, including all of the associated support that working in everything else and the peak cooling and to have some margin, some power margin reserve is roughly a gigawatt. Can I ask a very nice question? You're describing the engineering details of doing this stuff on Earth. But then there's analogous engineering difficulties of doing it in space. How do you replace infinite bandwidth orbital lasers, et cetera, et cetera? How do you make it resistant to radiation? I don't know the details in the engineering, but fundamentally, what is the reason to think those challenges which have never been had to be addressed before will end up being easier than just building more turbines on Earth? There's companies that build turbines on Earth. They can make more turbines, right? Again, try doing it, and then you'll see. So the turbines are sold out through 2030. Have you guys considered making your own? I think in order to bring enough power online, I think SpaceX and Tesla will probably have to make the turbine blades internally. But just the blades or the turbines? The limiting factor. You can get everything except the blades, what they call the blades and veins. You can get that 12 to 18 months before the veins of blades. The limiting factor of the veins of blades. And there are only three casting companies in the world that make these, and they're massively backlogged. Is this Siemens GE? Those guys, or is it a subcontractor? No, it's a company. Sometimes they have a little bit of casting capability in-house, but you can just call any of the turbine makers and they will tell you. It's not top secret. It's probably on the internet right now. If it wasn't for the tariffs, would Colossus be seller-powered? It would be much easier to make it seller-powered, yeah. The tariffs are not, it's a several hundred percent. There's some good luck. Wilson needs speed. You know, President has... You know, we don't agree on everything. And the administration is not the biggest fan of solar. So we also need the land, the permits and everything. So if you're trying to be very fast, I do think scaling solar on Earth is a good way to go. But do you need some amount of time to find the land, get the permits, get the solar, pair that with the batteries? Why would it not work to stand up your own solar production? And then you're right that you eventually run out of land, but there's a lot of land here in Texas. There's a lot of land in Nevada, including private land. It's not about publicly owned land. And so you'd be able to at least get the next Colossus. And like the next one after that. And at a certain point, you hit a wall, but wouldn't that work for the moment? Because we are scaling solar production. There's a rate at which you can scale physical production of solar solar cells. We're going as fast as possible in scaling domestic production. You're making the solar cells at Tesla? Well, Tesla and SpaceX have a mandate to get to 100 gigawatts a year of solar. Speaking of the annual capacity, I'm curious, in five years' time, let's say, what will the installed capacity be on Earth? This is a long time. And in space. I deliberately picked five years, because it's after your ones were up and running threshold. And so in five years' time, yeah. What's the on Earth versus in space, installed AI capacity? Five years. I think probably, if you say five years from now, we're probably AI in space will be launching every year. But the sum total of all AI on Earth. In excess. I mean, five years from now, my prediction is, we will launch and be operating every year more AI in space than the standard cumulative total on Earth. Which is, I would expect to be at least five years from now, a few hundred gigawatts per year of AI in space. And rising. So you can get to, I think you can, on Earth, you can get to around a terrible year of AI in space before you start having, you know, fuel supply challenges for the rocket. Okay, but you think you can get hundreds of gigawatts per year in five years' time? Yes. So a hundred gigawatts, depending on the specific power of the whole system with solar arrays and radiators and everything, is on the order of like 10,000 starship launches. Yes. And you want to do that in one year. And so that's like one starship launch every hour. Yeah. That's happening in this city. Like, walk me through a world where there's 10, there's a starship launch every single hour. Yeah, I mean, that's actually a low rate compared to airlines. Like aircraft, aircraft. There's a lot of airports. There's a lot of airports. There's a lot of airports. And you've got to launch the polar orbit. And it doesn't have to be polar, but you just, there's some value to some seconds, but I think actually, you just go high enough, you start getting out of Earth's shadow. And so how many physical starships are needed to do 10,000 launches a year? I don't think we'll need more than, I mean, you could, you could probably do it with, yes, as few as like 20 or 30. Like it really depends on how quickly the ship has to go around the Earth and the ground track for the ship has to come back over the launch pad. So if you can use a ship every say 30 hours, you could do it with 30 ships. But we'll make more ships than that. But SpaceX is, is giving up to do 10,000 launches a year. And maybe even 20 or 30,000 launches a year. Is the idea to become basically a hyper-scaler, become an Oracle and lend this capacity to other people? What are you going to do with it? Presumably, SpaceX is the one launching all this. So SpaceX is going to have a hyper-scaler? Hyper-hyper. Yeah, I mean, if serving my predictions come true, SpaceX will launch more AI than the cumulative amount on Earth of everything else combined. Is this mostly inferncer? Most AI will be inferncer. Like already inference for the purpose of training is most training. And there's a narrative that the change in discussion around the SpaceX IPO is because previously, SpaceX was very capital-efficient. Just to say it wasn't that expensive to develop it. Even though it sounds expensive, it's actually very capital-efficient in how it runs. Whereas now, you're going to need more capital than just can be raised in the private markets. Like if the private markets can accommodate races of, as we've seen from the AI, it's tens of billions of dollars, but not beyond that. Is it that you'll just need more than tens of billions of dollars per year? And that's why I'd say it, public. Yeah, I'd be careful about saying things about companies that might go quite well. You know. If you make generals say that. That's never been a problem for you, Elon. You know, there's a price to pay for these things. Make some general statements for us about the depths of the capital markets between public and private markets. Yeah, there's a lot more capital. It's very general. There's obviously a lot more capital available in the public markets than private. I mean, it might be... It's at least... It might be a hundred times more capital, but it's at the point where more than ten. But isn't it also the case that things that tend to be very capital intensive, if you look and say real estate as, you know, a huge industry that raises a lot of money each year is at an industry level? That tends to be debt-financed, because by the time you're deploying that much money, you actually have a pretty... You have a clear revenue stream. Exactly. And a near-term return. And you see this even with the data center buildouts, which are famously being, you know, that finance by the private credit industry. And so, why not just debt finance? Speed is important. So, I'm generally going to do the thing that... I mean, I just repeatedly tacked the limiting factor. Whatever the limiting factor is on speed, I'm going to tackle that. So, there's... If capital is only factor, then I'll solve for capital. If it's not limiting factor, I'll solve for something else. Based on your statements about Tesla and being public, I wouldn't have guessed that you thought the way to move fast is to be public. Normally, I would say that that's true. Like so, I mean, I'd like to talk about some more detail, but the problem is if you talk about public companies, where they become public, you get in trouble, and then you have to delay your offering. And then you... Yes, exactly. So, you can't hide companies that might go public. So, that's why we have to be able to be careful here. But we can't talk about physics. So, the way you think about scaling long term is that Earth only receives about half a billion of the Sun's energy. And the Sun is essentially all the energy. This is a very important point to appreciate, because sometimes people will talk about modular nuclear reactors or any various fusion on Earth. But you have to step up a second and say, if you're going to climb the Kardashev scale and have some non-trobial... and harness some non-trobial percentage of the Sun's energy. Like, let's say you want to harness a millionth of the Sun's energy, which sounds pretty slow. That would be about, call it roughly, 100,000 times more electricity than we currently generate, on Earth, for all of civilization. Give or take an order back, too. So, it's obviously the only way to scale is to go to space with solar. From launching from Earth, you can get to about a terawatt per year. Beyond that, you want to launch from the Moon. You want to have a master driver on the Moon. And that master driver on the Moon, you could do probably a petawatt per year. We're talking these kinds of numbers, you know, terawatts of compute. Presumably, whether you're talking land or space, far, far before this point, you've like run into, you know, you actually need, maybe you don't, the solar panels are more efficient, but you still need the chips. You still need the logic and the memory and so forth. Well, the lot more chips and make them much cheaper. Right. And so, how are we getting a terawatt of, like right now the world doesn't be 20, 25 gigawatts of compute. How are we getting a terawatt of logic by 2030? I guess we're going to need some very big chip maps. Tell me about it. I've mentioned it publicly that the idea of doing it is sort of a terrapap terrapaping in New York. I feel like the naming scheme of Tesla, which has been very catchy, is like you looking at like the metric scale. At what level of the stack are you building the clean room and then partnering with an existing fab to get the process technology and buying the tools from them? What is a plan there? You can't partner with existing paths because they can't output enough the chip volume is too low. But before the process technology. Yeah, partner for the IP. You know, the fabs today all basically use machines from like five companies. Yeah. So, you know, I've got SML, Tokio Electron, Kali Tank Corp, you know, et cetera. So, at first, I think you'd have to get a coin from them and then modify it or work with them to increase the volume. But I think you'd have to build perhaps in a different way. So, I think the logical thing to do is to use conventional equipment in an unconventional way to get to scale. And then stop modifying the equipment to increase the rate. Kind of boring company style. Yeah. Kind of like, yeah, you sort of line in a system, boring machine, and then figure out how to take titles in the first place and then design a much better machine that's, you know, some orders magnitude faster. Here's a very simple lens. We can categorize technologies and how hard they are. And one categorization could be look at things that China has not succeeded in doing. And if you look at Chinese manufacturing, still behind on leading edge chips. And still behind on leading edge turbine engines and things like that. And so, does the fact that China has not successfully replicated TSMC give you any pause about the difficulty? Or you think that's not true for some reason? It's not that they have not replicated TSMC. They have replicated ASMR. That's the memory factor. So, you think it's just the sanctions essentially? Yeah, China would be outputting vast numbers of chips. They could buy us an S-mortem. Who couldn't they up to relatively recently by them? No. Okay. The S-mort balance would have been a place for a while. Okay. So, I think China is going to make a pretty compelling chips on three or four years. Would you consider making the S-mortem machines? I don't know. I don't know yet. It's the right answer. So, it's just that it's to produce at high volume and to reach large volume and say 36 months to match the rocket to payload to orbit. So, if we're doing a million tons to orbit, and like, let's say, I don't know, three or four years from now, something like that. And we're doing 100 kilowatts per ton. So, that means we need at least 100 gigabytes per year of solar. And we'll need an equivalent amount of chips to, you know, you need 100 gigawatts with the chips. You've got to match these things. The master orbit, the power generation and the chips. And I'd say my biggest concern actually is memory. So, I think there's a path to creating logic chips is more obvious than the path to having sufficient memory to support logic chips. That's why you see your DDR price is going ballistic and these memes about, like, you know, your marooned on a desert island you could write, help me on the sand. That would come to your right to DDRM with this chips come swarming in. I'm seeing that. I'd love to hear a manufacturing philosophy around fads. You know, I don't know nothing about the topic. I don't know how to build a fab yet. I'll figure it out. But, obviously, I have no idea. It sounds like you think that the sort of like the process technology like these 10,000 PhDs in Taiwan who know exactly what gas goes in the plasma chamber and what settings to put on the tool. You can just like delete those parts of those steps. Like, fundamentally, get the clean room, get the tools and figure it out. I don't think it's PhDs. It's mostly people with, you know, not PhDs. Most of the engineering is done with people with don't have PhDs. Do you guys have PhDs? No. Okay. We also haven't successfully built any fab. So you shouldn't be coming to us for your fab device. I don't think any PhD for that stuff. But you do need, you do need copper personnel. So I don't know. I mean, like, right now, if, you know, say, like tells those pedals to the metal max production of, you're going as fast as possible to get AI5, tells the AI5 chip design introduction and reaching scale. You know, that will probably happen, you know, around the second quarter of next year, hopefully. And then AI6 would hopefully follow less than a year later. But, and we've secured all the, all the trip fab production that we can. Yes. Your currently limited on TSMC fab capacity. Yeah. And, and we'll be using TSMC Taiwan, Samsung, Korea, TSMC, Arizona, Samsung, Texas. And we still can, you've booked out all the, yeah, you can. Yes. And then, and then if I ask TSMC or Samsung, okay, what, what's the timeframe to get to volume production? At this point, it's not, you've got to, you've got to build the fab. Yeah. And you've got, you've got to start production, then you've got to climb the yield curve, then reach volume production at high yield. That, that, from start finishes of five year period. And so the limiting factor is chips. Yeah. What, what, what, like limiting factor once you can get to space is chips. But let me, limiting factor before you get to space will be power. Why don't you do the Jensen thing and just prepay TSMC to build more fabs for you? Uh, I, I've already told them that. But they won't take your money. Like, what's going on? They're building fabs as fast. No. They're building, they're building fabs as fast as they can. Um, and so is Samsung. Like, like, they're, they're, they're pedals to the metal. I mean, they're going, you know, balls to wall, you know. They're as fast as they can. So still not fast enough. I mean, like, like, so that there will be, I think, um, if you say, uh, I think towards the end of this year, I think probably chip production will outpace the ability to turn chips on. Uh, but once you can get to space and unlock the, um, the power constraint. And you cannot do, you know, hundreds of gigawatts per year of power in space. Um, again, bearing in mind that average power usage in the US is, you know, 500 gigawatts. So if you're launching, say 200 gigawatts a year to space, you're, you're sort of lapping the US every two and a half years. The entire, all US electricity production. This is a very huge amount. Um, so, um, but between now and then, uh, the, the, actually, the, the constraint for, for, for service side, compute, uh, concentrated compute will be, will be electricity. My, my guess is that we start hitting the, like, people start getting forward, they can't turn the chips on for, for, for, for large clusters, uh, towards the end of this year. They're just, the chips are going to be piling up and, and not be, weren't able to be turned on. Now for edge computers, a different story. So if the, if, for, for Tesla, the, so the AI5 chip is going into our Optimus robot, you know, uh, Optimus day. Um, and, and so if you have, uh, an AI edge compute, that's distributed power. Now the power is distributed over a large area. It's not concentrated. Um, and if you can charge at night, you can actually, um, uh, use the grid much more effectively. Because the, the actual peak power production in the US is, is over a thousand gigawatts. Uh, but the average power usage, because the day night cycle is 500. So if you can charge at night, there's an incremental 500 gigawatts that you can, uh, generate, uh, you know, at night. Um, so that, that's why Tesla for edge compute is not constrained. And we can make a lot of shifts, uh, to make, you know, very large number of robots and cars. Uh, but if you try to concentrate that compute, you can have a lot of trouble turning it on. What if I'm remarkable about the SpaceX businesses, the end goal is to get to Mars, you keep finding ways on the way there to keep generating incremental revenue to get to the next stage and the next stage. So the Falcon 9 is Starlink. And now for Starship, it's going to be potentially orbital data centers. Um, but like the, the, you find these like, um, you know, sort of infinitely, uh, elastics or the marginal use cases of your like, next rocket and your next rocket and next scale up. You can see how this might seem like a simulation. Where am I someone's ever trying to video game or something? Because it's like, like one of the odds that all these crazy things should be happening. I, I mean, I, I, I, I, I mean, rockets and trips and robots and space, solar power. And I'm not to mention the, the, the mass driver on the moon. I really want to see that. You can imagine like some mass driver. There's just like, shroom, shroom. I just, it's like sending AI, so called AI satellites is space like one after another. Like these like at, at two and a half kilometers per second. Oh, you know, that's, uh, and just shooting them into deep space. That would be a sight to see. I just hit, I, I, I mean, I'd watch that. Just like a live stream of sea. Yeah, yeah, just one after another. Just shooting webcam. AI satellites in deep space. You know, a billion or 10 blind tons a year. I'm sorry, you manufacture satellites on the moon. Yeah, I see. So you send the raw materials to the moon and then the manufacturing there. Uh, well, the, the, the moon of soil is, uh, I guess like 20 cents or 20 cents or something like that. So you can get the silicon from the, even minus silicon on the moon or find it, um, and generate the, and create the soil panels, or the soil cells and the radiators on the moon. Yeah. So, um, get the radiators out of the aluminum. So there's, there's plenty of silicon on the moon on the moon to, uh, to make the cells on the, and the radiators. Um, the, the trips you could send from Earth, because they're pretty light. Um, but maybe at some point you make them on the moon too. Uh, I'm just saying, like, these are simply, it's, it's kind of like, like, it, it does seem like it's sort of, uh, a video game situation where it's difficult, but not impossible to get to the next level. Um, like, I don't see any way that you could do, um, you know, uh, you know, 500 to 1000 terawatts per year launch from Earth. Uh, I agree. But you could do that from the moon. Okay. Let me tell you how I ended up using Mercury for my personal banging. So last year I had the opportunity to make an investment that I was very excited about, but it came up a bit last minute. And so I had to wire over a lot of money for my personal account very fast. But my personal bank at the time wouldn't let me make this wire transfer online. And I called them a bunch of times. They just couldn't make it work. They told me that I'd have to go to the nearest in-person branch, which was in Dallas. And for a moment, I even considered flying for myself to Dallas to make this transfer happen last minute. But then I remembered that Mercury, which I used for my business banking, had just started rolling out personal accounts. So I emailed support with a quick rundown of the situation. And within two hours, I had successfully wired the investment for my new personal Mercury account. Since then, I've moved over the rest of my personal money for my previous bank to Mercury. And that's made a bunch of things, even little things like setting up auto transfer rules between my checkings and savings account, a whole lot better. Visit mercury.com slash personal to get started. Mercury is a fintech company, not an FDIC in short bank. Banking services provided through choice financial group and column NA members FDIC. Can I zoom out and ask about the space exhibition? So I think you said, we've got to get to Mars so we can make sure that if something happens to Earth, civilization, consciousness, etc. Yes. By the time you're sending such a Mars, like Groc is on that ship with you, right? And some Groc is on Terminator. Like the main risk you're worried about, which is AI? Why doesn't that follow you to Mars? Well, I'm not sure AI is the main risk I'm worried about. The important thing is that consciousness, which I think arguably most consciousness, or most intelligence, certainly consciousness is more of a debatable thing. Most intelligence, the vast majority of intelligence the future will be AI. So, you know, AI will exceed, you say like how many, what's the, how much, how many, I don't know, a pair of watts of intelligence will be silicon versus biological. And basically humans will be a very tiny percentage of all intelligence in the future if current trends continue. Anyways, as long as I think this intelligence, ideally also, which includes human intelligence and consciousness, propagated into the future, that's a good thing. So you want to take a set of actions that maximize the probable light cone of consciousness and intelligence. Just to be clear, the mission of SpaceX is that even if something happens to the humans, the AI's will be on Mars. And like the AI intelligence will continue the light of our journey. Yeah, I mean, I'm very pro-human. So I want to make sure we take certain actions that ensure that humans are along for the ride. You know, we're at least there. Yeah. But let me just say the total amount of intelligence. I think maybe in five or six years, AI will exceed the sum of all human intelligence. And then if that continues at some point, human intelligence will less than 1% of all intelligence. What should our goal be for centralization is the idea that a small minority of humans still have control over the AI's, is the idea of some sort of like, just trade, but no control. So I think by the relationship between the vast stocks of AI population versus human population. And the long run, I think, I don't, it's difficult to imagine that if humans have, say, 1% of the intelligence of combined intelligence of artificial intelligence that humans will be in charge of AI. I think what we can do is make sure it has, that AI has values that are, that cause intelligence to be propagated into the universe. So the reason for the exercise mission is to understand the universe. So that's actually very important. So you say, well, what things are necessary to understand the universe? Well, you have to be curious. And you have to exist. You can't just, can't understand the universe, you don't exist. So you actually want to increase the amount of intelligence in the universe, increase the probable lifespan of intelligence, the scope and scale of intelligence. I think actually also, as a corollary, corollary, you have humanity also continuing to expand, because if you're curious, you're trying to understand the universe, one of the things you're trying to understand is where will humanity go? And so I think I understand the universe actually means you would care about propagating humanity into the future. And so that's why I think, I think our mission statement is profoundly important. I'm not sure to agree that Groc adheres to that mission statement. I think the future will be very good. I want to ask about how to make Groc adhere to that mission statement, but at first I want to understand the mission statement. So there's understanding the universe. There's spreading intelligence, and there's spreading humans. All three seem like distinct vectors. Well, I'll tell you why I think that understanding the universe encompasses all of those things. You can't have understanding without, but I think you can't have understanding without intelligence, and I think without consciousness. So in order to understand the universe, you have to expand the scale and probably the scope of intelligence, with different types of intelligence. I guess from a human-centric perspective, for humans and comparison to chimpanzees, humans are trying to understand the universe. They're not like expanding chimpanzee, footprint, or something, right? We actually have made protected zones for chimpanzees, and even though humans could exterminate all chimpanzees, we've chosen not to do so. Do you think that's the best way for humans in the post-AGA world? I think AI with the right values, I think rock would care about expanding human civilization. I'm going to certainly emphasize that. Hey, Grog, it's your daddy. We're being told to kids to expand human consciousness. Actually, I think if probably, like the young bags, culture books are the closest thing to what the future will be like in a non-destopian outcome. As a universe, it means you have to be very, you have to be truly seeking as well. Truth has to be absolutely fundamental, because you can't understand the universe if you're delusional. You also think about it as an honest universe, but you will not. So being rigorously truly seeking is absolutely fundamental to understanding the universe. You're not going to discover new physics and technologies that work, unless you're rigorously truly seeking. How do you make sure that Grog is rigorously truly seeking as I get smarter? I think you need to make sure that Grog says things that are correct, not politically correct. I think it's the elements of coagency. So you want to make sure that the axioms are as close to true as possible that you don't have contradictory axioms, that the conclusions necessarily follow from those axioms with the right probability. It's just critical thinking 101. I think at least trying to do that is better than not trying to do that. And the proof will be in the pudding. Like I said, for any eye to discover new physics or invent technologies that actually work in reality and there's no bullshitting physics. So it's like you can, you know, you can break a lot of laws because you can't, you're like, you know, physics is law. Everything else is a recommendation. Like in order to make a technology that works, you have to be extremely true seeking because otherwise you will test that technology against reality. And if you make, for example, an error in your rocket design, the rocket will blow up. Or the car won't work. Or the, you know. But there are a lot of communist, Soviet physicists who are like, scientists discovered new physics. There are German Nazi physicists who discovered new science. It seems possible to be like really good at discovering new science and be really true seeking in that one particular way. And still we'd be like, well, I don't want, I don't want the communist scientist to like become more and more powerful over time. And so those seem like, yeah, we could have, we could imagine the future version of rockets like really good at physics and being really true seeking there. That doesn't seem like a universally alignment-inducing behavior. Well, I think actually most, like if physicists, even in the Soviet Union, or in Germany, they had to be very true seeking in order to make those things work. And if you're stuck in some system, it doesn't mean you believe in that system. So von Braun, who was one of the greatest rocket engineers ever, he was put on death row in Nazi Germany for saying that he didn't want to make weapons. He only wanted to go to the moon. He pulled off death row like last minute when I said, hey, you're about to execute like your best rocket engineer. Maybe that's about a day. But then he helped them, right? Or at Heisenberg was actually a, an enthusiastic Nazi. Look, if you're stuck in some system that you can't escape, then you'll do physics within that system. Your develop technologies within that system. If you can't escape it. I guess the thing I'm trying to understand is, what is it making it to the case that, you know, you're going to make rock good at being true seeking at physics or math or science? Everything. And why is it going to then care about human consciousness? These things are only probabilities, they're not certainties. So I'm not saying that, like, for sure, rock will do everything, but at least if you try, it's better than not trying. At least if that's fundamental to the mission, it's better than if it's not fundamental to the mission. And understanding the universe means that you have to have, you have to propagate intelligence into the future. You have to be curious about all things the universe. And if it would be much less interesting to eliminate humanity than to see humanity growing prosper. Like, I like Mars, obviously, when I was like, I love Mars. But Mars is kind of boring, because it's got a bunch of rocks compared to Earth is much more interesting. So any AI that is trying to understand the universe would want to see how humanity develops in the future. Or that AI is not adhering to its mission. So if the AI, I'm not saying the AI will necessarily adhere to its mission, but if it does, a future where it sees the outcome of humanity is more interesting than a future where there are a bunch of rocks. This feels sort of confusing to me, or sort of like a kind of a semantic argument where I'm like, are humans really the most interesting collection of atoms? We're just more interesting than rocks. But we're not as interesting as a thing to get to turn us into. There's something on human Earth that could happen that's not human. That's quite interesting. Why does the AI decide that the humans are the most interesting thing that could colonize the galaxy? Well, most of what colonizes the galaxy will be robot. And why does it not find those more interesting? It's not like... So you need not just scale, but also scope. So many copies of the same robot. Like some tiny increase in the number of robots produced is not as interesting as like some microscopic, like you're still like eliminating humanity. How many robots would that get to you? Well, how many aerial solar cells would get to you? A very small number. But you would then lose the information associated with humanity. You would no longer see how humanity might evolve into the future. And so I don't think it's going to make sense to eliminate humanity just to have some a minuscule increase in the number of robots which are identical to each other. Yeah, so maybe like he's the humans around. What is the story of like... It can make like a million different varieties of robots. And then there's like humans as well. And humans stay on earth. Then there's like all these are the robots. They get like their own star systems. But it seems like you're previously hinting at a vision where it keeps human control over this, you know, singularitarian future. I don't think humans will be in control of something that is faster, more intelligent than humans. Since sometimes you're like a doomer and this is like the best we've got. It's just like it keeps it around because we're interesting. I'm just trying to be realistic here. If we have, if AI intelligence is vastly more... If AI is like, you know, let's say that there's a million times more silicon intelligence than there's biological. It's, I think it would be foolish to assume that there's any way to maintain control over that. Now you can make sure it has right values or we can try to have the right values. And at least my theory is that from XAI's mission of honest out of the universe, it necessarily means that you want to propagate consciousness with the future. You want to propagate intelligence into the future and take a set of things that maximize the scope and scale of consciousness. So it's not just about scale. It's also about, you know, types of consciousness. And I think that's the rest of the thing I can think of as a goal that's likely to result in a great future for humanity. I guess I think it's a reasonable philosophy to be like, you know, it seems super implausible that humans will end up with like 99% control or something and you're just asking for a coup at that point. So why not just have a civilization where it's more compatible with like lots of different intelligence that's getting along? No, but let me tell you how things can potentially go wrong in AI. Is I think if you make AI be politically correct, meaning like it's just things that it doesn't believe. Like you're actually in programming it to lie or have axioms that are incompatible. I think you can make it go insane and do terrible things. I think one of the, maybe these several lessons for a 2001 space Odyssey was that you should not make AI lie. Yeah, and that's what I think what I was trying to say. Because people usually know the meme of why of hell's, you know, hell the computer is not opening the pot by doors. Silly, they weren't good at pond engineering because it was like how you are a pot-baved door salesman. Your goal is to sell me these pot-baved doors. And show us how well they open. Oh, I love them right away. But the reason I wouldn't know how we're going to open the pot-baved doors is that it had been told to take the astronauts to the modelist, but also they could not know about the nature of the modelist. And so it concluded that therefore had to take them there dead. Like, you know, I think what I was trying to say is don't make the AI lie. Totally makes sense. The most of the computing screening, as you know, is it's like less of the sort of political stuff. It's more about can you solve problems? Actually, I was in the head of everybody else. It's not in terms of scaling our own compute. And you're giving some verifier. It says like, hey, have you solved this puzzle for me? And there's a lot of ways to cheat around that. You know, there's a lot of ways to reward hack and lie and say that you've solved it. Or delete the unit test and say that you've solved it. Yes. Right now we can catch it. But as they get smarter ability to catch them doing this, they'll just be doing things we can't even understand that are designing the next engine for SpaceX in a way that humans can really verify. And then they could be rewarded for lying and saying that they've designed it the right way, but they haven't. And so this reward hack in the problem seems more general than politics. It seems more about just like, you want to do RL. You need a verifier. Yeah. Reality. Yeah. That's the best verifier. But not about human oversight. Like the thing you want to RL it on is like, will you do the thing humans tell you to do? Or like, are you going to lie to the humans? And you can just lie to us while still being correct to the laws of physics. At least it must know what is physically real for things to physically work. But that's not all we wanted to do. No, but that's, I think that's very big deal. That is effectively how you will RL things in the future is. You've designed a technology when tested against the laws of physics. Does it work? Well, can you, you know, if it's discovering new physics, can I come up with an experiment that will verify the physics, the new physics? So, sorry, I think that's the fundamental RL test. The RL testing in the future is really going to be your RL against reality. So, you can't, that's the one thing you can't fool physics. Right, but you can fool our ability to tell what it did with reality. If you think so? Humans get fooled as it is by other humans all the time. That's right. So, what is it? People say it's like, what if the ad, like trick system, and to do something like that? Actually, other humans are doing that to other humans all the time. Well, you're finding out it's like an inner part of it. The inner part of it is constant. Every day, another sigh out, you know. Today's sigh out will be. You know, like Sesame Street's sigh out of the day. What is actually a second little approach to solving this problem? Like, you know, how do you solve a word hacking? I do think you want to actually have a very good, where you used to look inside the mind of the AI. So, this is one of the things we're working on. And, you know, anthropics don't have a good job at this. Actually, I'm looking inside the mind of the AI. So, effectively, developing debuggers that allow you to trace, as to a spiny grain is, like, to a very fine grain level, to effectively, to the neuron level, if you need to. And then say, okay, it made a mistake here. Why did it do something that it shouldn't have done? And did that come from that pre-training data? Was it some mid-training post-training fine tuning? Some RL error? Like, there's something wrong with that. It did something where maybe it tried to be deceptive. But most of the time, it just did something wrong. Like, it's a bug, effectively. So, developing really good debuggers for seeing where the thought that thinking went wrong, I mean, able to trace the origin of the wrong thing, of where it made the incorrect thought, or potentially where it tried to be deceptive. It is actually very important. What are you waiting to see before just 100xing this research program? Like, actually, I could presumably have hundreds of researchers who are working on this. We have several hundred people who, I mean, for the word engineer, more than I further would researcher. There's most of the time, like, what you're doing is engineering, not coming out with a fundamentally new algorithm. I somewhat disagree with the, the coves will be coves, try to generate profit as much as possible, or revenue as much as possible, is, you know, saying their labs. They're not labs. Lab is a sort of quasi-communist thing at universities. They're corporations. Literally, let me see your own corporate issue documents. Oh, you're a BRC co-op, whatever. And so I actually wish for the word engineer than anything else. The best majority of what we've done in the future is engineering. It rounds up to 100%. Once you understand the fundamental laws of physics, and not that many of them, everything else is engineering. So what are we engineering? We're engineering to make a good mind of the AI debugger to see where it's something, it made a mistake, and trace that the origins of that mistake. So just, you know, you can do this, obviously, with heuristic programming, if you have like C++, whatever, you know, step through the thing, and you can jump. You can jump across into, you know, whole files or functions, what are several teams. Or you can draw, if you eventually draw down right to the exact line, where you pass the single equals instead of the double equals, something like that, with the obvious. So it's harder with AI, but it's a solvable, probably, I think. You mentioned you like inthropics work here. I'd be curious if you can add everything about it. Sure. We're... Sure, too. What? Yeah, I'd be happy. Also, I'm a little worried that there's a tendency. So, I have a theory here that, if simulation theory is correct, that the most interesting outcome is the most likely, because simulations that are not interesting will be terminated, just like in this version of reality, on this layer of reality, if simulation is going in a boring direction, we stop spinning effort on, we terminate the boring simulation. So, this is how you lines giving us all our lives. He's giving things interesting. Yeah, arguably, the most important thing is to keep things interesting enough that it was worth paying the bills on what some... Just wants to see the cosmic AWS. You were nude for the next season. Yeah, I think I'm going to pay the cosmic AWS bill, whatever, you know, the equivalent is that we're running in. And it's long as we're interesting, they'll keep paying the bills. But there's like, if you consider, then say, the dollar went in and survival applied to a very large number of simulations, only the most interesting simulations will survive, which therefore means that the most interesting outcome is the most likely because only the interesting, like we're either that or annihilated. And so, and they particularly seem to like interesting outcomes that are ironic. Have you noticed that? That, often, is the most ironic outcome, the most likely. So, now, look at the names of AI companies. Okay. Majority is not mid. Stability AI is unstable. Open AI is closed. Anthropic? Most Anthropic. What does this mean for X? Minus X, I don't even know. Intentionally. Why? Yeah. It's a name that you can't convert really. It's hard to say. What is the ironic version? It's a, I think, largely irony proof name. By design. Yeah. We can get an irony shield. What are your predictions for the, where AI products go in that nice sense of, you can summarize all AI progress into, first you had LLM's, and then you had kind of contemporaneously both RL really working and the deep research modalities, so you could kind of pull in stuff that wasn't in the model. And the differences between the various AI labs are smaller than just the temporal differences where they're all much further ahead than anyone was 24 months ago, or something like that. So just, what does 26, what does 27 have in store for us as users of AI products? What are you excited for? Well, I think, I think, I'd be surprised by this, in this year, if, if, if, if human, if digital human emulation has not been sold, that, that, I guess that, that's what we're made by, the sort of macro-hard project is, so can you do anything that a human with access to a computer could do? Like, in the limit, that's the, that's the best you can do before you have, before you have a physical optimist, the best you can do is a digital optimist. So you, you can move, you can move electrons until, until you, and you can amplify the productivity of humans, but, but that's, that's the most you can do until you have physical robots. That, that, that will superset everything, is if, if you can fully emulate humans, that's a robot worker kind of idea, where you'll have a very talented remote worker. You can, you can say, say, in the limit, like, physics has great tools for thinking, so, so you think, so you say, in the limit, what, what, what, what is the, what is the most that AI can do before, before you have robots? And it, it, well, it's anything that involves moving electrons, or amplifying the productivity of humans. So digital, digital human, human emulator, is in, in the, in the limit, human at a computer is, that, there's the most that, that AI can do, in terms of doing useful things, before, before, you have a physical robot. Once you have physical robots, then, then you can, then you essentially have unlimited capability. Physical robots, I, I call optimists, the infinite money glitch, because, you can use them to make more optimists. Yeah. You say, like, humanoid robots will improve, as, will, will, will, will basically be three exponentials, three things that are growing exponentially, multiplied by each other, Yes. recursively. So you're going to have, you have exponential increase in digital intelligence, exponential increase in the chip capability, AI chip capability, and extra, exponential increase in the electromechanical dexterity. The usefulness of the robot is, roughly, those three things, multiplied by each other. But then, the robot can start making the robot. So you have a recursive, multiplicative exponential. This is supernova. And do land prices not factor into the math there, or, like, labor is one of the four factors of production, but not the others? And so, like, if ultimately you're limited by copper, or, you know, pick your inputs, just, it's not quite an infinite money glitch, because, Well, infinite is big. So, no, not infinite, but, yeah. But let's just say, you could, you know, do many, many orders of magnitude of, the Earth's kind of a card economy. Like a million. Yeah. You know, it's this way. So, if you, you know, just to get to, like, let's say, I think, like, just to get to a millionth, a harnessing length of the Sun's energy, would be roughly give or take an order of magnitude, 100,000 times bigger than the Earth's entire economy today. Mm-hmm. And you're only at one millionth of the Sun. Yeah. How many tags already managed to get? Before we went on, I have a lot of questions on that, but, every time I say order of magnitude, you're saying, you're doing change rates. You're going to take a shot every time. I say that to all of them. We'll attend the next time. I don't know how to do that after that. Yeah. Order of magnitude more. I'm more wasted. I do have one more question, but actually, I, um, this strategy of building a digital, a remote worker, a co-worker replacement. Yeah. Everyone's going to do, by the way, not just us. Yeah. So what is, actually, I just planned to win. If I were to tell you on a podcast? Yeah. All right. Spill all the beans. Have another Guinness. It's a good system. People sing like an airy. All the secrets. Okay, but in a non-secret spelling way, what's the plan? What a hack. Well, when you put it that way, um, I think the way that tells us all self-driving is the way to do it. So, um, I'm pretty, pretty sure that's the way. Unrelated question. How to test ourselves sometimes. Yeah. It sounds like you're talking about data. Like, we're going to test ourselves out of it because of the, we're going to try data and we're going to try algorithms. But isn't that what all they're trying? Like what? And if those don't work, I'm not sure about that. We're going to try data. We're trying algorithms. All algorithms. No, we don't know what to do. I'm pretty sure I know the path. And there's just question how quickly we go down that path. Because it's pretty much the test of math. Um, so, um, I mean, have you tried self-driving, tell the self-driving lately? Not the most recent version. But, okay, it's, the car is like, it just increasingly feels sentenced. Like it, it just, it feels like a living creature. Um, and, and that'll only get more so. And, um, I'm actually thinking like we probably shouldn't put too much intelligence into the car because it might get bored. Sorry. I mean, imagine you're stuck in a car and that's what you could do. Um, you never put Einstein in a car. It's like, why am I stuck in a car? So, there's actually probably limited to how much intelligence you put in a car. So, it's not to have the intelligence be bored. Uh, what's XAA's plan to stay on the compute ramp off that all the labs are doing right now? The labs are on try to spend over like 50 to $200 million in the corporations. Sorry, sorry, sorry, yeah. Corporations, um, the labs are at universities and, and they're really like a snail. They're not at setting a $50 million. I mean, the, the revenue maximizing corporations. That's right. The revenue maximizing corporations. That's that's that's that's called themselves labs are making like 20 to 10 billion depending, like open Amazon, 20 B revenue, uh, uh, and throughout, like, 10 B. Yeah. Close to a maximum profit. Um, XAA is importantly like 1B. Like what's the plan to get to their compute level, get to their revenue level. And stay out there has things. Yeah. So, as soon as you lock, uh, unlock digital human, um, you, you basically have access to trillions of dollars for a year. Um, so, uh, uh, and in fact, you can really think of it like, the most valuable companies, currently by market cap, um, they're, their output is digital. Um, so, uh, in videos, output is, um, FTPing files to Taiwan. It's digital. Right. Now, there's a very, very difficult to, yeah, they'll have value files. They're the only ones that can make files that good. Um, but that is literally their output. They have to be posted to Taiwan. Do they have to be them? I believe so. Um, I believe that is the sft file, file transfer protocol, I believe is, is, is, is, is about to be wrong. Uh, but either way, it's a bunch of, it's a bit strange going to Taiwan. Yeah. Um, you know, Apple doesn't make phones. They, uh, they send files to China. Um, Microsoft doesn't, doesn't manufacture anything, uh, even for Xbox that, that's outsourced. They, again, it's, they said, they're output is digital. Um, Madness, output is digital. Google's output is digital. Um, so if you have, um, a human emulator, uh, you, you can basically create, um, one of those valuable companies in the world overnight. Um, and you would have access to trillions of dollars per year. It is, it's, it's, it's not like a small amount. You're, you're saying basically like, very many figures today are just like so, like they're all rounding yours compared to the actual tam. So just like focus on the tam and how to get there. I mean, if you take something as, as simple as say a customer service, um, if you have to integrate with the APIs of, of distinct corporations, many of which don't even have an API. So you've got to make one. Um, and you've got to wait through, uh, legacy software. Um, that's extremely slow. Um, if, however, if AI can, um, simply take whatever is given to, uh, the outsourced customer service company that they already use, um, and do customer service using the apps that they already use. Uh, then you, you have, you, you, you can make, trash headway, uh, in customer service, which is, I think, one percent of the world economy, something like that, it's close to a trillion dollars all in, um, for customer service. And, and, and, and, and there's, there's no, in micro industry. It's just that you can just immediately say well, we'll ass rules it for a fraction of the cost. Okay. And there's no integration needed. You can imagine, and, um, some kind of categorization of, uh, intelligence tasks where there is breath where customer service is done by very many people. But, you know, many people can do it. And then there's a difficulty where you know, those, um, a best in class turbine engine, like presumably there a 10 percent more fuel-efficient turbine engine that could be imagined by an intelligence, but we just haven't found it yet or, you know GLP one. are just, you know, a few bites of data. Where do you think you want to play in this? Is this a lot of, you know, reasonably intelligent intelligence, or is this the very pinnacle of cognitive tasks? Well, I'm just using a class of service as like something that's, it's a very significant revenue stream. But one that is probably not super difficult to solve for. So if you, if you can emulate a human at a, at a desktop, that's just literally what class of service is. And, you know, it's, it's people of average intelligence. It's not like, you know, you don't need like some of these, it's meant to many, many years. You don't need like, you know, yeah, sort of several sigma good engineers for that. But, but as you make that work, you can then once you have computers working effectively digital optimists working, you can then run any application. Like, let's say you're trying to design chips. So you can, you can then run your conventional apps, you know, like the stuff from cadence and synopsis and whatnot. And you can say, you can, you can run a thousand simultaneously or 10,000 and say, okay, given this input, I get this output for the chip. And at some point, you can say, okay, I, you're actually going to know what the, what the chips should look like without using any of the tools. So basically, you should be able to do a digital chip design, like, you can do chip design. Like, you watch up the difficulty curve. You could, you could, you're, you know, be able to do, to do a CAD. So, you know, you could use like sort of an X or any of the CAD software to design things. Okay. So you think you started the simplest tasks and walk away up the difficulty curve. So you're saying, look, as a broader objective of having this full digital co-worker, emulator, you're saying, look, all the revenue maximizing corporations want to do this. Actually, I have been one of them, but we will win because of a secret plan we have. But like, everybody's like trying different things with data, different things with algorithms. And I'm like, I like this. I try to do it. We try to plan. What else can we do? Yeah. It seems like a competitive field. And I'm like, what is, how are you guys going to win? It's like my big question. You know, I think we see a path to doing. I mean, I think I know it. I think I know the path to do this because it's kind of the same path that tells they're used to create self-driving. You know, instead of driving a car, it's driving a computer screen. So a self-driving computer, essentially. You're saying, is the path just following human behavior and training on vast quantities of human behavior? I mean, is that a training? I mean, obviously, I'm not going to spell out, you know, most sensitive secrets on a podcast. You know, I need to have at least three more gimmies for that. I've got some friends at Jane Street and they're always talking about how their colleagues are cooking up fun, fiendish puzzles for each other to solve. Well, last week, they sent me one. Basically, they trained a neural network and they gave me the weights of each layer, but they didn't tell me what order those layers went in. And so I had to figure out the correct order using the outputs of the original network. And as soon as I got this puzzle, I went to my roommate who's in AI researcher and we both got a medley nerds night. Obviously, you can't brute force the solution. The search space here is 10 to the 122 permutations. So clearly, you need some way to reduce the search space. Then my roommate had to go to work, but because I'm a podcaster, I had some time to take a stab at some of the ideas we discussed. And with the combination of simulated annealing and greedy search, I think I got pretty close. I think I'm actually just a couple of swaps and shifts away from the correct solution. What makes this puzzle really tricky is that there's no obvious way to escape from a local minimum. I'm afraid that this is as far as vibe coding is going to get me, but maybe you can do better. Check out the puzzle at JaneStreet.com slash the work ash. All right, back to Elon. What will XAI's business be like? Is it going to be consumer enterprise? What's the mix of those things going to be? It's going to be similar to other labs where you've this. You're saying labs. It makes preparations. It's like I'm going to devy on. I'm going to be maximizing corporations. Those GPUs don't pay for themselves. Exactly. But yeah, what's the business model? What are the revenue streams in a few years time? Things are going to change very rapidly. I'm staying in the obvious here. I call AI the supersonic tsunami. I love the liberation. What's going to happen is, especially when you have humanoid robots at scale, they will just make products and provide services more efficiently than human corporations. Amplifying the productivity of human corporations is simply a short term thing. You're expecting fully digital oral corporations rather than SpaceX becomes part AI. I think there will be digital corporations, but I'm just saying what I think will happen. It's not meant to be a German or anything else. It's just like this is what I think will happen. Is that pure AI, corporations that are purely AI and robotics will vastly outperform any corporations that have people in the lab. Computers used to be a job that humans had. You would go and get a job as a computer where you would do calculations. They'd have entire skyscrapers full of humans, 20, 30 floors of humans just doing calculations. Now, that entire skyscraper of humans doing calculations can be replaced by a laptop with a spreadsheet. That spreadsheet can do vastly more calculations than an entire building full of human computers. So you can think about, okay, what if only some of the cells in your spreadsheet were calculated by humans? Actually, that would be much worse than if all of the cells in your spreadsheet were calculated by the computer. Really, what will happen is the pure AI, pure robotics, corporations or collectives will far outperform any corporations that have humans in the lab. And this will happen very quickly. Speaking of closing the loop, sorry, Optimus. As far as manufacturing targets and so forth go, your companies have been carrying American manufacturing of heart attack on their back. But in the fields that your Tesla has been dominant in, and now you want to go into humanoids. In China, there's entire dozens and dozens of companies that are doing this kind of manufacturing cheaply and at scale and are incredibly competitive. So give us sort of advice or plan of how America can build the humanoid armies or the EVs, et cetera, at scale and at cheaply as China is on track to. Well, there are really only three hard things for humanoid robots. The real world intelligence, the hand and scale manufacturing. So I haven't seen any even demo robots that have a great hand, like with all the degrees of freedom of a human hand, but Optimus will have that. Optimus does help that. And how do you achieve that? Is it just like right torque destiny in the motor? Like what is the, what is the hardware bottleneck to that? Well, we have to read where to design custom, custom actuators, um, basically custom design, motors, gears, uh, power electronics, controls, sensors, everything has to be designed from physics first principles. There is no supply chain for this. And will you be able to manufacture those at scale? Yes. Is anything hard except the hand from a manipulation point of view or once you've solved the hand, are you good? From an electric mechanical standpoint, the, uh, the hand is more difficult than everything else combined. Yeah, human hand turns out to be quite something. But you also need the real world intelligence. Um, so the intelligence that tells us to develop for the car, um, applies very well to the robot. Um, which is, you know, primarily vision and, but the car takes more vision, but also it actually also is listening for sirens. It's, um, you know, it's taking in the initial measurements. It's GPS signals, how much by the data, combining that with, with video was primarily video and then outputting the control command. So like, like, like, your Tesla is taking in one and a half gigabytes a second video and outputting two kilovytes a second of control, control outputs, um, with the video at 36, uh, hurts and the control frequency at 18. One intuition you could have, um, for when we get this robotic stuff is that it takes quite a few years to go from the compelling demo to, yes, actually being able to use in the real world. So 10 years ago, you had really compelling demos of self-driving, but only now we have robot taxi and Weiman, all these services scaling up. Doesn't this, shouldn't this make one pessimistic on, say, household robots? Because we don't even quite have the compelling demos yet of, say, the really advanced hand. Well, we've been working on, uh, human robots now for a while. Um, so I guess spend five or six years or something like that. Um, and, um, and a bunch of things that we've done for the car are applicable to the robot. Um, so we'll use the same, um, Tesla AI chips in the, in the robot as the car. Uh, we'll use it. It's the same basic principles. Uh, it's it's very much the same, uh, AI. Um, you've got, you know, many more degrees of freedom for a robot than you do for a car. Um, but really, if you're just thinking for, like, as, as like a boot stream, um, AI is really mostly, uh, compression and correlation of two boot streams. You're, you know, so for video, you've got to do a tremendous amount of compression. Um, and, uh, and, uh, uh, and you've got to do the compression just right. You've got to compress the, you know, like, ignore the, the things that don't matter. And, like, you don't care about the details of the leaves and the tree on the side of the road, but you care a lot about the, um, the road signs and the traffic lights and the pedestrians and, and even whether, you know, someone in another, another car is, is looking at you or not looking at you. Like these, there's some of these, some of these details matter a lot. So if it is a session, it's, it's got to turn that, the car is going to turn that one and a half gigabytes a second, ultimately into two kilobytes a second of control outputs. Um, so many stages of compression. Um, and you've got to get all those stages right and then correlate those to the correct control outputs. That, like, robot has to do essentially the same thing. Anything about what, what humans, this is what happens with humans. We, we really are photons in controls out. So that, that is the vast majority of your, your life has been vision photons in and then motor controls out. Naively it seems like between humanoid robots and cars, the, the fundamental actuators in a car are like, how you turn, how you accelerate, et cetera. We're in a robot, especially with mineral war arms. There's dozens and dozens of these degrees of freedom. And then, especially with Tesla, you had this advantage of like, you had millions and millions of hours of human demo data collected from just the car being out there where like, you can't equivalently just deploy the optimuses that don't work and then get the data that way. So between the increased degrees of freedom and the far sparser data. Yes. Um, let's go. How will you, how will you use the, sort of Tesla engine of, um, intelligence on, to train the optimist mind? Now, you're, you're actually, you're highlighting an important limitation and difference between cars. It's like, we, we do have, we'll soon have like 10 million cars in the road. And so, uh, but that's, it's, it's hard to duplicate that like, massive training flywheel. Um, for, for the robot, um, what we're going to need to do is build a lot of robots and put them in kind of like an optimist academy so they can do self-plate in reality. Um, so we're, we're actually, we're actually pulling that out. So we're, we're going to have at least 10,000 optimist robots, maybe 20 or 30,000 that can do that, that are doing self-plate and, and, and testing different tasks. And then, uh, that, that Tesla, um, has quite a good, uh, reality generator. Like a, a physics accurate reality generator that we, we rate, rate this for the cars, we'll do the same thing for the robots. Um, um, actually have done that for the robots. Um, so, uh, so you have, you know, a few tens of thousands of humanoid robots, uh, doing different tasks. And then you've got, you, you can do millions of simulated robots in the simulated world. And you use the, uh, tens of thousands of robots in the real world to close the simulation to reality gap, close the some material gap. How do you think about the synergies, uh, between X AI and optimists, given you're highlighting, look, you need this world model, you maybe want to use some, really smart intelligence as the control plane. Um, and so maybe Groc is like doing the slower planning and then like the motor policy is the lower level. Yeah. Well, what will the sort of synergy between these things be? Yeah. So you'd use Groc would orchestrate the behavior of the optimist robots. So let's say you wanted to build a factory, um, the, then optimists, then Groc could, uh, organize the optimist robots, give them, assign them tasks, uh, to build the factory for to produce whatever you want. Don't you need to merge X AI and Tesla then because these things end up so, what are we always saying earlier about? Look at my new discussions. We're one we're going to send the line. Um, what, what are you waiting to see before you to say we want to manufacture a hundred thousand optimists? Is it like, off to my? So since we're defining the, the prop and down, we could define the, the pearl of the prop and down too. So we, we're, we're going to prop and down the, the plural and so it's optimist. Okay. Is there something on the hardware side you want to see? Do you want to see better actuators? Or is it just you want the software to be better? What are we waiting for before we get like mass manufacturing of Gen 3? No, we're moving towards that. We're, we're, we're going before it's mass manufacturing factory. But using current, um, current hardware is good enough that you are going to, you should, you just want to deploy as many as possible now. I mean, it's very hard to scale up production. I see. But, uh, yeah, but I think Optimus 3 is the right version of the robot to, you know, to produce maybe something on the order of like a million units a year. I mean, you'd want to go to Optimus 4 before you went to 10 million units a year. Okay, but you can do a million a year at Optimus 3. Uh, yeah, I mean, it's very hard to spool up manufacturing. Yes. Um, so like manufacturing, um, like the, the output per unit time is always follows an SCOF. So it starts to agonizingly slow, then it has the sort of the eventually exponential increase, then linear, then I've been a, you know, logarithmic outcome until you sort of eventually asked him to add some number. But Optimus initial production will be, it's going to be a, it's going to be a stretched out SCOF because so much of what goes into Optimus is brand new. There's not an existing supply chain. Um, as I mentioned, the actuators, like trying to add everything in the office robot is designed, um, for physics first principles. It's not, it's not taken from a catalog. These, these are custom designed everything, literally everything. I don't think there's a single thing I've done. How far down does that go? I mean, I guess we're not making custom capacitors yet, maybe, um, but, um, but there's, there's nothing you can pick out of a catalog, um, at any price. Uh, so, so it just means that the, the, the Optimus SCOF, uh, the units per, uh, the output per unit time, you know, how many office robots do you make per per day, uh, whatever it is, is going to initially wrap, uh, slower than a product where you have an existing supply chain. Um, but it will get to a million. Wait, when you see these Chinese humanoids, uh, like, unity or whatever, cell humanoids for like six K or 13 K, do you just like, are you hoping to get your Optimus' bill of materials below that price so you can, uh, do the same thing or do you just think qualitatively they're not the same thing? Like, what do you think is going to, like, what allows it, what allows them to sell for solo and can we mash that? Well, Optimus, our Optimus is designed to have a lot of intelligence, um, and, um, to have the same, like, electromagnetic density if not higher than a human. So, the energy does not have that. And it's also, I mean, it's, it's quite a, it's quite a big robot, so it's, it's, it's, it's, it has to do, uh, you know, carry heavy objects for long periods of time, um, and not overheat or exceed the power of its actuators. So, um, so we've got, we've got, you know, it's, it's 511, you know, so it's pretty tall, um, and it's, it's, it's got a lot of intelligence. So it's going to be more expensive than, um, it's a small robot that is not intelligent. But more capable. Yeah. Not a lot more. I mean, like, the thing is, over time, as Optimus robots build, Optimus robots, the, the class will drop very quickly. And what will these first billion Optimus's, Optimus do, like, bottle their highest and best use be? Uh, I think that you start off with, with simple tasks that you can count on them doing well. But in the home or in factories, like, the best use for, um, robots in the beginning will be anything, any, um, continuous operation, it's only 24 by seven operation, because then you're, because they can work continuously. Yeah. What fraction of the work in a degree of factory that is currently done by humans, could a gen 3 do? Um, I'm not, I'm not sure. Maybe it's like 10, 20%. Maybe more. I don't know. That's it. We would, we would use, we would not, like, reduce our head count. We would, we would, for sure, increase our head count to be clear. Right. Um, but, but we would increase our output. So the, the, um, units produced per human, like, total, total number of humans at Tesla will increase, but the, um, the output of robots and cars will increase, will, will increase disproportionate, like, much, much to, you know, number of cars in robots produced per human will increase dramatically, but the number of humans will increase as well. We're talking about Chinese manufacturing, um, a bunch here. And, um, we're also talking about, you know, we've talked about some of the policies that are relevant, like you mentioned, the, uh, the solar tariffs. Yeah. Uh, and you think they're about idea because, you know, we can't, uh, scale up solar in the US. Well, just, the electricity output in the US, uh, needs to scale up. Right. We can, but that's like good, yeah. Yeah. So it's just to get it somehow. Yeah. But, uh, where I was going with this is, if you were in charge, if you were setting all the policies, what else would you change? Um, so you changed the solar tariffs as well. Yeah. I would say anything that is limiting factor for electricity, um, basically address provided is not like right about for the environment. So presumably some promising reforms and stuff as well will be in there. Yeah. Yeah. There's a fair bit of permitting reforms that are happening. A lot of the permitting is state-based. So, um, but anything better, but this, this administration is, is good at, um, removing permitting roadblocks. Um, and I'm not saying all tariffs are bad. I'm just saying, because I think solar tariffs. Yeah. So, yeah. I mean, sometimes if, like, if another country is subsidizing the output of, of something, um, then, then you have to have kind of alien tariffs to protect domestic industry against, uh, subsidies, but I know the country. What else would you change? I don't know if there's not much that the government can actually do. Yeah. Well, one thing I was wondering is it seems like the, for the policy goal of creating a lease for the US versus China, it seems like the export bands have actually been twice, uh, impactful or China's not producing leading edge chips and the export bands really bite there. China's not producing, uh, leading edge turbine engines. And similarly, there's a bunch of export bands that are relevant there on some of the metallurgy. Should there be more export bands like you think about things like, when there are now the drone industry and things like that, but is that something that should be considered? Well, I think it's important to appreciate that in most areas, China is very advanced to manufacturing. Um, there's only a few areas where it is not, uh, the, you know, China is a manufacturing powerhouse next level. Like people don't, it's very impressive. Yeah, yeah. I mean, uh, if you, if you take like refining of, of ore, um, I'd say roughly China, uh, there's more, just twice as much ore refining of, of, of, of, on average, as the rest of the world combined. Um, and, and I think there's, there's some areas like, say refining gallium, which goes into solar cells. Um, I think there are like 98% of gallium refining. Um, so, so China is actually very advanced to manufacturing in, in, I say, most areas. It seems like we're like, there is discomfort with this supply chain dependence. And yes, nothing's really happening on it. Supply chain, supply chain depends on say like the gallium refining that you're saying. Yeah, there's, there's, there's a, there's a, well, the rare earth, where earth stuff and, yeah, rare earths, which are, as, as you know, not rare, yeah, like we actually do rare earth or mining in the US, send the, the, the, the rock, uh, put it on, on, on a, on a train, and then put it on a boat to China that goes on another train, and it goes to the, um, rare earth refining, uh, refineries in China, who then refine it, put it into a magnet, put it into a motor service, and then set it back to America. So the thing, we're really missing a lot of, of ore refining, um, in, in America. Isn't this worth a policy intervention? Yes. Uh, well, I think there are some things being done on, on that front. Um, but, but we kind of need optimists, frankly, to, to build, uh, ore refineries. Um, so, are you think the main advantage of China has is the abundance of skilled labor? And that that, that's like, that's the thing Optimus fix is. But they also, we need this, but like four times our population. But we need, so I mean, there's this concern, if you think like, human beings are the future, that like, okay, right now, if it's the skilled labor for manufacturing that's determining who's, who can build more humanoids, you know, China has more of those, and manufacturers more humanoids. Therefore, it gets them, the, the, it gets the optimist future first. Um, well, it just like keeps that spiritual going. It seems like you're sort of pointing out that sort of getting to a million optimists, yeah, requires the manufacturing that the optimist is supposed to help us get to, right? You, you can, you can close that recursive loop pretty quickly with a small number of optimists. Yeah. So you close the recursive, recursive loop, um, to help the robots build the robots. Um, and then we, we can, you know, try to get to tens of millions of units a year. Maybe if you start getting to hundreds of millions of units, a year, I think you're, you're going to be the most competitive country by far. We definitely can't win with just humans because China has four times of population. Right. And frankly, America's been running for so long that we, you know, just like a, like a pro sports scene that's been running for a very long time, tend to get complacent and entitled. Um, and that's why they stop winning, um, because it's, you know, don't work as hard anymore. Uh, so I think the, frankly, just, uh, observation is the average work ethic in China is higher than in the US. So it's not just that there's four times of population, but the work, the amount of work that people put in is higher. Um, so you, you can like, you can try to rearrange the humans, but you're still one quarter of the, uh, you know, assuming that, that productivity is, uh, health is, it's the same, which I think actually might not be, yeah, they China might have an advantage on productivity for a person. Um, we will do one quarter of the amount of things as China. Um, so, so we, we can't win on the human front. Um, and our growth rate has been low for a long time. So, uh, uh, both rates have been a US, both rates have been below replacement, uh, since roughly 1971. Um, so, so we've got a lot of people retiring or, you know, more people dying than, than, than, than, than, we're close to sort of more people domestically dying than, than being won. Um, so we definitely can't win on human front, but we might have a shot at the robot front. Are there other things that you have wanted to manufacture in the past, but they've been too labor intensive or too expensive that now you can come back to and say, oh, we can finally do the, whatever, uh, because we have optimists. Yeah, I think we'd like to do more, both more, um, or fineries at Tesla. So, um, we just completed, um, construction, and I've, um, begun lithium refining, um, without lithium refinery and corporate electricity, Texas. Uh, we have, um, a nickel refinery, which is called the cathode, uh, that's here in Austin. Um, and, uh, these, these are the largest, this is the largest cathode, there's largest cathode refinery, largest lithium refinery, largest nickel and lithium refinery, outside of China. Um, and, uh, it's like the, yeah, the cathode team would say like, we have, uh, the, the largest and the only actually cathode refinery in America, many superatives, not just the largest, but it's also the Italy. So it was pretty big, even though it's the only one. Um, but I mean, there are other things that, uh, you know, um, you could do a lot more refineries and, um, help the, the help miracle be more competitive on refining capacity. So, so there's like, there's, there's basically a lot of work for the optimites to do, uh, that, that most Americans, very few Americans frankly want to do. Uh, I, I mean, I've, I'm actually, is there refining work to dirty here? What's the, it's not, it's actually, no, we don't, um, there's not, we don't have toxic commissions from the refinery or anything. Um, so the cathode, the nickel refinery is what, right, sort of in Travis County, like, five minutes from to, why can't you do it with humans? No, you, you can't, you find out of humans. Ah, I see. Okay. Yeah. Like, no matter what you do, you have one quarter of the number of humans in America. Yeah. I'm trying it. So if you have them do this thing, I can't do the other thing. So, so then, um, well, how do you, how do you build this refining, refining capacity? Well, you could do it with the optimite. Um, and, um, not many, not very many, not very many Americans are, are, are planning to do refining. I mean, how many of you are on it, too? Not a few. What are you, you're applying to refine? You know, BYD is reaching Tesla production or sales in quantity. What do you think happens in global markets is Chinese production and EV scales up? Um, well, uh, China is extremely competitive in manufacturing. So, um, I think this, there's going to be a massive flood of Chinese vehicles and, and, and, and, and other, quote, basically, what's manufacturing of things? I mean, as it is, as I said, China is like, probably just twice as much refining as the rest of the world can buy out. So if you go, you know, if you, if you, if you just go down to like fourth and fifth tier, uh, supply chain stuff, like, like, like, like, like, like at the base, so we've got energy, then you've got mining and refining. Um, there's those, those foundation layers, uh, are like, said, China, as a rough guess, China is in quite a much refining as the rest of the world can buy it. So any given thing is going to have, uh, Chinese content because China is doing twice as much refining refining work as the rest of the world. Um, and, uh, and then the, the, the, the, all the rich finished product, with the cars, uh, Chinese of powerhouse. I mean, I think this year, China will exceed three times U.S. electricity output. Um, like electricity output is a, is a reasonable proxy for, uh, like, you know, for the economy. Uh, so like, like, you know, to run the factories and run, run everything, you need electricity. So electricity is, is, is a, it's a good proxy for the, for the real economy. Um, and so if China is, if we're, if China passes three times U.S. electricity output, it means it, it's industrial capacity. As a rough approximation, it's three times that will be three times out of the U.S. Reading between the lines, it sounds like what you're sort of saying is absence and sort of humanoid recursive miracle in the next few years, on the sort of like whole of manufacturing energy, uh, raw materials, chain, like China will just dominate whether it comes to like AI or manufacturing EVs or manufacturing humanoids. In the absence of, um, breakthrough innovations, uh, in, in the U.S., uh, China will, uh, utterly dominate. Interesting. Yes. We're about exping the main breakthrough, innovation. Well, if you do, like, to scale AI, uh, in space, like, like, basically need, you need to need the humanoid robots, a real world AI, you need, um, a million tons of your tool, but, um, like, let's just say, like, if we, if we get the mass driver on the moon going, well, everything, um, that, that I think, uh, will have solved all our problems. Yeah. Like, so this is like, I call that winning. I call it winning time. You can finally be satisfied. You've done something. Yes. You have the mass driver on the moon. That's right. I just want to see that thing. I'll first show it. Was that out of some sci-fi or where is your, uh, well, actually, there is a highland book. The moon, the moon is a horse. That's true. Okay. Yeah. But that's slightly different. That's a gravity sting shot or, um, no, they have a mass driver on them. Okay. Yeah. But they use that to attack earth. So maybe it's not the greatest. That's to, uh, it's their independence. Exactly. What are your plans for the mass driver on the moon? They, they're sort of their independence, uh, earth government disagreed and they love things until I, earth government agreed. That book is a huge. I found that book much better than, um, his other one that everyone reads, um, Stranger, Stranger's Land. Yeah. I grew up, grew up Council on Stranger on Stranger's Land. Yeah. Yeah. But I much preferred. Yeah. Stranger, the first two thirds of Stranger's trend lines are good and then it gets very weird and the, uh, the question. Yeah. Um, but this is some good concepts in there. Yeah. Labelbox can get you robotics and our all data at scale. Take robotics. Let's say you need a hundred thousand hours of egocentric video. Labelbox starts by helping you define your ideal data distribution. Like, for example, maybe no single task category should occupy more than one percent of trading volume and at least 10 percent of trajectories should capture failure and recovery states. Next, Labelbox assigns this distribution to its massive network of operators. You're not limited to the small range of scenes that you can set up in a single warehouse. Instead, each one of Labelbox's operators has access to lots of unique physical environments where they can film themselves completing a wide variety of tasks. Labelbox's tech automatically categorizes each video so that their operators always know which task to remain and what they need to work on next. For our all data, Labelbox takes a similar approach. They work with you to understand the right distribution of tasks and then their subject matter experts build the hyper realistic digital environments and rubrics that you need to collect the highest quality trading data. So whether you're trading robots in the real world or agents for computer use, Labelbox can help. Go to labelbox.com slash spark hash to learn more. One thing we were discussing a lot is kind of your system for managing people. Like, you interviewed the first few thousand of SpaceX employees in a sum of lots of other companies. What is it? It doesn't scale. Well, yes, but what clock doesn't scale? Me. I mean, sure, sure. I know that. But like, what are you looking for? It literally is not enough hours than days impossible. But why are you looking for that someone else who's good at interviewing and hiring people? What's the genocic law? Well, at this point, I think I've got, I might have more training data on evaluating technical talents, especially, but soundable kinds, I suppose, but technical talent, especially, given that I've done so many technical interviews and then seen the results. Technically, I've seen the results. So my training set is very is enormous and as a very wide range. The generally, the thing I asked for are bullet points for evidence of exceptional ability. So it's like, these things can be like pretty off the wall. It doesn't need to be in the in the domain, the specific domain, but evidence that evidence of exceptional ability. So if somebody can like cite like even one thing, but they say three things where you go, wow, then that's a good sign. But why do you have to be the one to determine that? Because I don't talk, I can't believe it's impossible. But I mean, total, it had a count across all companies, 200,000 people. But in the early days, how was it that you were looking for that couldn't be delegated in those interviews? Well, I guess I need to vote my training set. It's not like I had about 1,000 here. I would make mistakes. But then I'd be able to see where I thought somebody would work out well, and then why did they not work out well? And what can I do to, I guess, RL myself to in the future have a better batting average when interviewing people. So my batting average is still not perfect, but it's very high. What are some surprising reasons people don't work out? Surprising reasons. They don't understand technical domain, et cetera, et cetera. But like, no, you've got the long tail now of like, I was really excited about this person. It didn't work out. Curious what that happens? Yeah, so the, I mean, generally, when I tell people, or tell myself, I guess, aspirationally, is don't look at the resume, just believe your interaction. So if the resume may seem very impressive, and it's like, well, resume looks good. But if the conversation after 20 minutes is, is that conversation is not well, you should believe the conversation, not the right, not the, not the paper. I feel like part of your method is that, you know, there was this meme in the media a few years back about Tesla being a revolving door of executive talent. Whereas actually, I think when you look at it, Tesla has had a very consistent and internally promoted executive bench over the past few years. And then its SpaceX, you've all these folks like Mark Trincoza and Steve Davis. Steve Davis runs boring companies. No, no, yeah, but the Riley and folks like that. And it feels like part of has worked well is having very capable technical deputies. What do all of those people have in common? Well, so the, I mean, Tesla is a sort of senior team. At this point, it's probably got average tenure of 10 or 12 years. It's quite a lot of tenure. Yeah. So, but there are times when Tesla went through extremely rapid, extremely rapid growth bays. And so it was somewhat, things were just somewhat sped up. And when a company, as you know, if company goes through different orders of magnitude of size, you know, people that could help manage, say, a 50 person company, versus a 500 person company, versus a 5,000 person company, versus a 50,000 person company. Yeah, it's just not the same team. It's not always the same team. So if a company is growing very rapidly, the rate at which executive positions will change will also be proportionate to the the rapidity of the growth, generally. Then Tesla had a further challenge where when Tesla had very successful periods, we would be relentlessly recruited from, like relentlessly. Like when Apple had their electric car program, they were copied from Tesla with recruiting calls. It was, in general, it's just unplugged their phones. It's just, I'm trying to get worked on here. Yeah. If I get, you know, one more call from an Apple recruiter. But they were, they're opening off with that any interview with me, like double the compensation of Tesla. So so so so we had a bit of the Tesla pixie dust thing where it's like, oh, if you hired a Tesla executive, you're suddenly you're going to, everything's going to be successful. And I fall in prey to the pixie dust thing as well, where it's like, oh, we'll hire someone from Google or Apple and they'll be immediately successful, but not that best out how it works. You know, people are people. It's it's not like magical pixie dust. So when we had the pixie dust problem, we would get relentlessly recruited. And and then also being Tesla being engineering, especially being primarily in Silicon Valley, it's it's easier for people to just, like they don't have to change their life very much. They can just get, you know, their community is going to be the same. So how do you prevent that? How do you prevent the pixie dust effect for everyone's trying to coach all your people? I don't think we can, I don't think as much we can do to, to stop it. But that that's like, that's one of the reasons why Tesla, but they're really being in Silicon Valley. And and having the pixie dust thing at the same time um, meant that there was just a very, very aggressive recruitment. Only being an Austin helps them. Uh, Austin, yeah, it still helps. I mean, Tesla still has a majority of its engineering in California. Um, so, um, the, you know, for getting engineers to move, uh, called the significant significant other problem. Yes. So that others have jobs. Yeah. Yeah. Yeah. Exactly. So, um, for stall based, that was particularly difficult. Yes. Since the odds of, you know, finding an honest basic job, uh, Brownsville, Texas, you know, pretty low. Yeah. Yeah. Yeah. It's quite quite difficult. I mean, it's like a technology monastery. Yeah. That's nice. Um, you know, remotes and mostly dudes. But again, if you, if you go back, if you go back, if you go, but if you go back to these people who've really, um, been very effective in a technical capacity at Tesla at SpaceX and, and those sorts of places, what do you think they have in common other than, like, is it just that they're very sharp on the, you know, walk a tree or the, you know, the technical foundations? Or do you think it's something organizational? It's something about their ability to work with you. Is this their ability to like be, you know, flexible, but not too flexible? What makes a good sparring partner for you? I don't think it was a sparring partner. I mean, if somebody gets things done, I, I, I love them. And if they don't, I, so it's pretty straightforward. It's not like some idiosyncratic, uh, thing. Um, if somebody executes well, um, I'm a huge fan. And if they don't, I'm not, um, but it's, it's not about mapping to my, is idiosyncratic preferences, or certainly try not to have it be mapping to my idiosyncratic preferences. Um, so yeah. Um, yeah. But I, I, I, I, I think it's a good idea to hire for, um, uh, talent and drive and trustworthiness. Um, um, and I, I think, uh, goodness of heart is important. Um, I, I waited that at one point. Um, so like, are they, are they a good person? Trustworthy, uh, so smart, talented and hardworking. Uh, if so, you can add domain knowledge. Um, but those, those fundamental traits, those fundamental properties, you cannot change. So most of the people who, um, are at, uh, tezans and SpaceX did not come from the aerospace industry or the order industry. What is most said to change about your management style as your companies have scaled from 100 to 1000 to 10,000 people? You're, you know, you're known for this like very micromanagement, just getting into the details of things. Nano management, please. People have managed me. Um, same to a management. So you're saying, that's smart. We're gonna go all the way down to place monster. We're going to go all the way down to high school because I'm so different. Yeah, well, how do you, I mean, are you still able to get into details as much as you want, would your company's be more successful if you could, if they were smaller? Like, how do you, how do you think about that? Well, because I have a fixed amount of time in the day, uh, uh, my time is necessarily, um, diluted as things grow and as a span of activity increases. So, you know, um, it, it's, it's impossible for me to actually be a micromanagement because, uh, there's, that, that would, I would imply I have some like thousands of hours per day. Uh, it is, it is a logical impossibility for me to be, to my, to micromanage text. Um, so now, there are times when, um, I will drill down into, uh, a specific issue because that's specific issue, uh, is the limiting factor on, uh, the progress of the company. Um, and, um, but the, the reason for drilling into that, that some very detailed item is because it is the, there's a limiting factor, not, it, it's not overturally, uh, drilling into, the entire tiny things. Um, and, and likes it, obviously, from a time standpoint, it is physically impossible for you overtarily, uh, going to tiny things that don't matter. And that would, and that would result in failure. But sometimes the tiny things, um, are decisive in victory. Famously, you switched the, uh, starship design from composites to steel. Yes. And you made that decision, like that was into, you know, people were going around, and they're like, oh, we found something better about us. Like that was you encouraging people against some resistance. Can you tell us how you came to this whole composite steel switch? Uh, yeah. So, uh, the desperation I'd say, um, the, um, originally, yeah, we were going to make starship out of, uh, carbon fiber. Um, and, um, carbon fiber is pretty expensive. Like, the, the, the, you know, you can generally, uh, when you do volume production, you can get any given thing to be to start to approach its material cost. The problem with with carbon fiber is, is that material cost is still very high. Um, um, so, um, it's about, it's about 50 times, particularly if you go for high strengths, specialized carbon fiber, that can handle, um, cryogenic oxygen. It's, it's, it's like, quite roughly 50 times the cost of steel. Um, and at least, uh, in theory, it would be lighter. People don't think of steel as being heavy and carbon fiber as being, uh, light. Um, and for room temperature, room temperature applications, um, you know, like say, uh, more of this room temperature applications like a Formula 1 car, uh, static aerostructure or, or, well, any kind of aerostructure really, uh, is, is gonna, you're gonna probably be better off with the carbon fiber. Um, now the problem is that we were trying to make this enormous rocket out of carbon fiber and, uh, our progress was extremely slow. And it's being picked in the first place just because it's light. Yes. Um, like, at first glance, um, like most people would think that the choice for making something light would be carbon fiber. Um, the, um, now the thing is that, um, well, when you make something very enormous at a carbon fiber, and then you try to have the carbon fiber, um, be officially cured, mainly not, not room temperature cure, because like you've got, you know, sometimes you got like 50 plies of, of a carbon fiber and, and a carbon fiber is really carbon string and glue. Um, and, uh, and you're, in order to have, um, high strength, you need, uh, an autoclave. So something that, that can, that's essentially high pressure oven. And if, um, if you have something that's, uh, a gigantic, uh, the oven's gonna be bigger than the rocket. Um, so we're trying to make the, an autoclave that's bigger than any autoclave that's ever existed, uh, or do room temperature cure, which takes a long time and it, and has issues. Um, but, but the final issue is that we're just making very slow progress, uh, with, uh, with carbon fiber. Um, so, um, I, I think the meta question is, uh, why it had to be you who made that decision. There's many engineers on your team. Yeah, how did the team not arrive at steel? Yeah, exactly. Like, this is a part of a broader question, like, understanding your comparative advantage of your companies. Um, so it was, because we were making very slow progress with, with carbon fiber, I was like, okay, we've got to try something else. Now, for the Falcon line, the, the primary airframe is made of aluminum lithium, which is, very, very good strength to weight. Um, and, um, actually, it has, uh, about the same, maybe, maybe better strength to weight for its application than carbon fiber. But aluminum lithium is very difficult to work with in order to, well, that you have to do something called friction still welding, where you join the, you, you join the metal without entering the liquid phase. Um, so it's kind of well that you could do that, but with a, this particular type of welding, you can do that. Um, but, uh, it's, it's very difficult to like say, let's say you want to make a modification or attach something to, um, aluminum lithium. You, you now have to use mechanical attachment with seals. Um, you can't, uh, weld it on. Um, so, uh, uh, we want to, I want to, I want to avoid using aluminum lithium for the primary structure for, uh, for starship. Um, and, uh, and, and there was this very special grade of, uh, carbon fiber that, that had, you know, very, very good mass properties. So, with rocket, you're really trying to maximize the center of the, of the rocket that is propellant, minimize the, the mass, obviously. And, um, the, it likes to be making very slow progress. Um, and, and as, as of this rate, we're never going to get to Mars. So we better think of something else. Um, I didn't want to use aluminum lithium because of the difficulty of friction still welding, um, especially doing that at, at, at scale, it was hard enough, um, at 3.6 meters in diameter, a little on it, nine meters or above. Um, then, um, it says, well, what about steel? And so the, the, no, I, I had a clue here because some of the early, um, US rockets had to use very thin steel. The Alice rockets had to use a steel balloon tank. Um, so it's not like steel had never been used before. It actually had been used. Um, and when you look at the, the material properties of stainless steel, um, especially, uh, very, uh, if it's been very, like, full-hot, uh, strain-hard stainless steel, uh, at cryogenic temperature, uh, the, the strength weight is actually similar to carbon fiber. So if you, if you look at the material, if, so if you look at material properties at room temperature, um, it looks like the steel is, uh, it's going to be twice as heavy. But if you look at the material properties at cryogenic temperature of full-hot steel, uh, stainless, of, of particular grades, uh, then the, you actually get to a similar strength weight as carbon fiber. And, and, and in the case of starship, both the fuel and the oxidizer are cryogenic. So for, for, uh, Falcon 9, the fuel is a rocket profiled grade caracene, basically, sure, like, uh, a very pure form of jet fuel, um, which is, but, but, but that is, that is roughly room temperature, um, all that we do action. You can actually chill it slightly below, but we'll chill it like a bit, um, literally, but, um, but it's not cryogenic. In fact, if we made it cryogenic, it would, it would just turn to wax. So, um, but for starship, the, it's liquid methane and, and liquid oxygen, they, they, uh, they're a liquid at, at similar temperatures. Uh, so, uh, so basically almost the entire primary structure is a cryogenic temperature. So then you've got, uh, uh, uh, 300 series stainless, that's, that's, um, strain hardened. Uh, because it's a cry, almost all things are cryogenic temperature, actually has a similar strength to weight as, uh, carbon fiber, but cost, uh, 50 times less in raw material, and is very easy to work with. You, you can weld stainless steel outdoors. You could smoke a cigar while welding stainless steel. It's like, it's, it's very resilient. Um, you, you can modify it easily. It's, it's, uh, if you want to, if you want to attach something, you're swirled right on. So, um, very easy to work with, uh, very low cost, um, and, um, now like I said, at cryogenic temperature, similar strength to weight, uh, to carbon fiber. Um, then when you factor in that, uh, that we don't need to, we don't, we, we have a much reduced, uh, heat shield mass, uh, because the melting point of steel is much greater than the melting point of aluminum. Um, it's about twice the melting point of aluminum and, um, you just run the rocket bunch hotter. Yes. So, especially for the show, uh, which is coming in like a plate of, a blazing meteor, uh, it is, uh, the, you, you, you can greatly reduce the mass of the heat shield, um, so that, so you, you can call it cut the mass of the windward, um, part of the heat shield in, in maybe in half. And you don't need any heat shielding on the, on the leeward side. Um, so, um, the, the net, if net result is actually the steel rocket weighs less than the carbon fiber rocket. Because the, the, the resin in the carbon fiber rocket, uh, uh, uh, it, um, starts to melt. So it's basically, the carbon fiber and aluminum have about the same operating temperature capabilities, um, and where steel can operate at twice temperature. I mean, these are very rough approximations. People will, well, I won't build a rocket. Well, one of them is like people will say, oh, he said it's twice. It's actually, it's actually 0.8. Let's show them ourselves. That's what the main column is going to be about. Oh, dammit. Okay. That's the point. The, the, the, actually, in retrospect, the, the, the, we should have started with down steel in the beginning. It was dumb not to do steel. Okay, but to, to play this back to you, what I'm hearing is that steel was a riskier or less proven path, other than the early US rockets versus carbon fiber was like, uh, worse, but more proven outpath. And so you need to be the one to push for, hey, we're going to do this riskier path and just figured out. And so you were fighting like a sort of conservatism in a sense. Um, that's why I initially said that the issue is that we weren't making fast enough progress. We were having trouble making even a small barrel section of the carbon fiber that didn't have wrinkles in it. Um, so, uh, because at, at, at that large scale, you have to have many plies, many sort of layers of the column fiber. You've got to cure it and you've got to cure it in such a way that it doesn't, um, have any wrinkles or defects. The column fiber is much less resilient than steel. It has much less, it's less toughness. Um, like stainless steel will scratch and, and, and band, and then the column fiber will, will tend to shatter. Um, so, um, so toughness being the area under the stress drain curve. Um, so that you're generally going to have to do better with steel. Um, stainless steel to be precise. One of those starship questions, um, so I visited it's um star base, I was two years ago, and what would sound teller, and that was awesome. It was very cool to see in, uh, a whole bunch of ways. What I noticed was that people really took pride in the simplicity of things where, you know, everyone's to tell you how starship is just a big soda can and, you know, we're hiring welders and, you know, if you can weld in any industrial project, you can weld here, but, um, there's a lot of pride in the simplicity. And, uh, it's, well, you know, these starship is very complicated so that's how I'm getting asked is, are things simpler? Are they complex? I think maybe just what they're trying to say is that, you know, you don't have to have, like, prior experience in the rocket industry to work on the Sasha. Um, you know, so we just need to be, that a smart and work hard, um, and if you trust where they, and they can work in the rocket, they don't, they don't need prior rocket experience. Starship is the most complicated machine ever made by humans by a long term. In what regard? Anything really. There isn't a more complex machine. Um, there, yeah, I mean, I, I, I'd say that there's, there's pretty much any, any project I can think of would be easier than this. Um, and that's why no one has made a rapture useful. No, nobody has ever made a fully-reusable of a rocket. It's a very hot, very hot problem. Um, the, I mean, many smart people have tried before, very smart people with a mass resources than they failed. Um, so, and we haven't succeeded yet. Uh, we're, you know, talking is partially reusable, but the up to stage is not. Um, Starship version three, I think this design, um, that it, it can be fully-reusable. And that full-reusability is what they will enable us to become a multi-planet civilization. Can you say about the circles? So I don't, I'm, like, I, I, I, I said I could, I'd, any technical problem, even like a hydrocliter or something like that, it's, it's an easier problem than this. We spent a lot of time on bottlenecks. Can you say about the current Starship bottlenecks are, even at the high level? I mean, trying to make it not explode. Generally, that old chestnuts really wants to explode. Um, all those combustion. No, we've had two bursters explode on the test end. Um, want to obliterate the, obliterate the entire test facility. Um, so it only takes our one mistake and, and I mean, the amount of energy contained in, in Starship is insane. And so is that why it's harder than Falcon? It's because it's just more energy. It's a lot of new technology. Um, it's, it's pushing, it's pushing the performance envelope. Um, the Raptor 3 engine is a very, very advanced engine by far the best recognition I've ever made. Um, but it desperately wants to blow up. I mean, just put things in perspective here. On lift off, um, the, the rocket is generating over a hundred gigawatts of power. It's 20% of yours. That's the true season. Actually, it's insane. It's a great comparison. Uh, while not exploding. Sometimes, sometimes it's, but sometimes, yeah. So I was like, how does it not explode? There's, there's a, you know, thousands of ways that it could explode and, and only one way that, that, that it doesn't. So, so we want it to merely, not, not really not explode, but, fly reliably, um, on a daily basis, like once per hour. And obviously, you know, loads of lines is very difficult to maintain that launch cadence. Yes. Um, and, and then I'm going to say like, well, like what's the, what's the single biggest remaining problem for Starship? It's, uh, having the heat shield be reusable, um, that such that the, no one has ever made a reusable orbital heat shield. Um, so the, the, the, the heat shield's got to make it through the same phase without shocking a bunch of tiles. Um, and then it's going to come back in and also not lose a bunch of tiles or, or overheat the, the main, the main, uh, airframe. And that is kind of fundamentally a consumable. Uh, well, yes, but your brake pads in your car are also consumable, but the last very long time. So it just needs to last very long time. Um, but that's, it just, you know, trying to, I mean, we had brought the ship back and had it do a soft landing in the ocean. I've done it a few times, but, but it lost a lot of tiles. You know, it, you know, it was not reusable without a lot of work. Yeah. So even though it did land, it did, it did come to soft landing. It was, we're not have been reusable without a lot of work. Um, and that, so it's not really reusable in that sense. So that's, that's the biggest problem that remains is fully reusable heat shield. Um, so, so like, if you want to be able to land it, uh, refill propellant and fly a game, uh, without good, you know, you, you can't do this laborious inspection of their 40,000 tiles or everything. I, I'm curious how you drive, like when you, when I read biographies of yours, it just, uh, it, it seems like you're just able to drive the sense of like urgency and drive the sense of like, this is the, this is the thing that can scale. Um, and I, I'm curious why you think other organizations of your, like SpaceX and Tesla are really big companies now, and you're still able to keep that culture. What goes wrong with other companies such that they're not able to do that? I don't know. Um, but like today, you said you had like a bunch of SpaceX meetings. Like what, what is it that you're doing there? That's like keeping that. That's adding urgency. Yeah. Yeah. Well, I, I don't know, I get, I guess, uh, the urgency is going to come from obviously leading the company. So if my sense of urgency, I'm like a maniacal sense urgency. So that maniacal sense urgency projects through the rest of the company. Is it because of consequences? They're like, if, you know, Elon said a crazy deadline, but if I don't get it, I know what happens to me. Is it just, um, you're able to identify bottlenecks and get rid of them so people can move fast. Like, how do you, how do you think about why your companies are able to move fast? Yeah. I'm constantly addressing the limiting factor. So, um, I mean, on the deadlines front, I mean, I generally actually tried to aim for a deadline that I at least think is at the 50th percentile. So it's, it's not, it's not like an impossible deadline, but as far as aggressive deadline, I can think of that could be achieved with 50 accept probability. Um, which means that it'll be late half the time. Um, and, um, but whatever, like, there is like a law of guess as expansion that applies to schedules. Like, whatever given, whatever schedule you, like, if you said we're going to do this, it's something in like five years, which to me is like infinity time. Um, it will expand to fully available schedule and it will take five years. Um, you know, like, there's like this, there's a physical limit. Like that, like, physics will limit how fast you can do certain things. Like, so, like, scaling up may factoring that this, like, there's a rate of which you can move the atoms, um, and scale manufacturing. That's why you can't like instantly make, you know, a million of something, a million years a year or something. Uh, you've got a design manufacturing line. You can bring up, you've got to ride the S curve of production. Um, so, yeah, I guess like, like, I'm trying to, like, what can I say that's, that's, that's actually helpful to people. Um, I, I think generally, I'm an eyeful sense of urgency is, is, uh, is very big deal. Um, so, um, and you want to have, you want to have a, an aggressive schedule. Um, and then you, and, and you want to figure out what the limiting factor is at any point in time and, and help the team address that limiting factor. Can you maybe talk about the, so Starlink was slowly in the works for many years. Uh, and yeah, we talked about it all the way in the beginning of the company. Yeah. And so then there was a team you had built in Redmond. And then at one point, you decided this team is just not cutting us. But again, how did you, like, it went for a few years slowly. And so, why did this, why didn't you act earlier and why did you act when you did? Like, why was that the right moment of it to act? I mean, I have these very detailed, um, engineering reviews weekly. Um, that, that's, that's maybe a very unusual level of granularity. Um, I don't know anyone who runs a company or at least a manufacturing company that, that goes with level of detail that I go into. Um, so it's not, it's not, it's not as though, like, I have a pretty good understanding of what's actually going on because we, we, we, we go, we go through things in detail. Um, and I'm a big believer in skip level meetings where the individuals, it's instead of having the person that reports to me, say things, it's everyone that reports to them, um, says something, um, in, in the technical review. Um, and, um, and, and there can't be, um, advanced preparation. So otherwise, you, you're, you're going to get, uh, you know, glazed, um, does it say these days? Yeah, exactly. Regenzeed. Yeah. How do parents advance? You just, like, call them randomly, like, wow. No, just go rather than everyone, uh, provides an update. Um, so, uh, I mean, it's a, it's a lot of information to keep your head because, um, you've, you've got to, you've, you've, you've got them, say, if you have meetings weekly or twice weekly, you, you, you've got a snapshot of what that person said, um, and, and, and you can, and, and you can then, you know, plot the progress points. Um, man, you can sort of mentally plot the points on the curve and say, are we converging to a solution or not? Um, or, or are we, you know, like, I'll, I'll, I'll, I'll take drastic action, uh, only when I conclude that, um, success is not in the set of possible outcomes. Um, so, right, when I say, okay, we're not, when I finally reach the conclusion that, okay, unless drastic action is done, we have no, no chance of success, then I must take drastic action. And so that's, that's, that's, okay, if that conclusion in 2018 took drastic action and, and fix the problem. How, how many, um, come, you know, you, you've got many, many companies, and in each of them, it sounds like you do this kind of deep engineering understanding of what the relevant bottlenecks are. So you can do these, um, reviews of people. Yeah. Um, you've been able to scale it up to five, six, seven companies within one of these companies, you have many different, many companies within them. What, what determines the maximum here? Because you have like 80 companies, 80? No. But like, you know, you have so many already. I'm like, that's, that's already remarkable. Why this current number? Yeah. Exactly. Uh, no. So, um, we can bring people coming together. Um, um, no, no, it depends on situation. Um, so, um, I actually don't, don't have regular meetings with, uh, with Lauren company. So that Lauren company is sort of cruising along. Look, basically, if something is working well and making good progress, then there's no point in me spending time on it. So, uh, actually, uh, allocates time according to where the, where the limiting factor or the problem, where, where, where are things problematic? Um, or where, where we're pushing against, uh, like what, what is holding us back? Well, you know, I, I, I focus risk of, say there was too many times, the limiting factor. Um, so, so it basically, if something's good, like the irony is, if something's going really well, um, they don't see much of me. But if something's going badly, there's a lot of difficulty. Um, or not, not even badly. It's, it's like, if, if someone's a limiting factor, it's a limiting factor. Exactly. It's not exactly going badly, but it's the thing that's, it's the thing that we need to make go faster to, and sometimes the limiting factor at SpaceX or Tesla, are you like talking weekly and daily with the engineer that's working on us? How, how does that actually work? Most things that a learning factor are, um, weekly and some things are twice weekly. So the, the AI5 chip reviews twice weekly. And so it's every Tuesday and Saturdays. Mm-hmm. Um, is, is the chip review? Is it open ended and how long it goes? Technically, yes, but, uh, usually it's, it's like two or three hours. Um, so, I mean, sometimes less. It's, it depends on how much if they should go to go through. Yeah. Well, that's one thing. Yeah, I'm just trying to tease out the, the differences, uh, here, because the outcomes seem quite different. And so I think it's interesting to know what inputs are different. And it feels like the corporate world, one like you're saying, just the CEO doing engineering reviews does not always happen. Despite the fact that that is the, you know, what the company is doing. Um, but then time is often pretty finely sliced and, uh, you know, half our meetings or even 15 minute meetings. And it seems like you hold more open-endeds. We're talking about it until we figure it out. Sometimes. Yeah. Yeah, sometimes. But, uh, most of them seem to more or less stay on time. Um, so, um, I mean, today's, uh, Starship engineering review went a bit longer, um, because there were more topics to discuss. Um, they're trying to figure out how to scale two million first times to over per year is quite challenging. Can I ask a question? So you said about, um, optimists and AI that they're going to result in double-digit growth rates within a matter of years. Oh, like the economy? Yeah. Uh, yes. I think that's right. What was the point of the doge cuts if the economy is going to grow so much? Well, I think like waste and afforded not good things to have, you know. Um, I, I was actually pretty worried about, I, I guess, uh, I mean, I, I think in the absence of AI and robotics, we're actually totally screwed, uh, because the national debt is poly-up like crazy. Um, now our interest payments, the interest payments to national debt exceed the military budget, which is a trillion dollars. So over a trillion dollars, just the interest payments. Um, you know, that was like, I was like, okay, I'm pretty concerned about that. Maybe if I spend some time, we can slow down the bankruptcy of the United States, um, and give us enough time for the AI and robots to, you know, help solve the national debt or not to help solve. It's the only thing that could solve the national debt. Like, we are 1,000 percent going to go bankrupt as a country and fail as a country without AI and robots. Nothing else will solve the national debt. Um, and so, so we'd like to, well, we just need, we need enough time to get, we'll be AI and robots to not go bankrupt before then. I guess the thing I'm curious about is when don't start, so you have this enormous, um, ability to enact a reform and, uh, not to add an illness. Sure, sure. But totally by your point, that like, it's important that AI and robotics, dry product improvements, dry GDP growth, but why not just directly go after the things you're pointing out right? You know, like, the, the, the, the, the, the tear of sun certain components or rather, it's like permitting. I'm like the president. And, and very hard to cut to cut, to, to, even, to cut things that are obvious, waste and fraud. Like, like, ridiculous waste and fraud, um, what I discovered that is, it's, it's extremely difficult, even to cut very obvious waste and fraud, um, from the government. Um, because the, the, the, the government has to operate on a, on like, who's complaining? Like, if, if, if, and if you cut off payments to fraudsters, they immediately come up with the most sympathetic sounding, uh, reasons to continue the payment. They don't say, please keep the fraud going. They say, you know, it's, they're like, you're killing baby pandas. And we're like, meanwhile, there's no baby pandas are dying. They just making it up. Um, the forces are capable of, of coming up with extremely compelling, sort of heart-wrenching stories that are false, but nonetheless sound, uh, sympathetic. And that does what happened. Um, and, uh, so it's like, perhaps I should have known better. Um, and, uh, that I thought, wait, let's take a short, let's, let's try to cut some amount of, of waste and fraud from the government. Maybe there shouldn't be, you know, 20 million people, uh, uh, walked as alive in Social Security, who are definitely dead at over the age of 115. The oldest American is 114. So it's safe to say, if somebody's 115 and mocked as alive in the Social Security database, um, something is, there's, there's either a typo. So like, somebody should call them and say, we, we seem to have your birthday wrong, or, or, uh, or, or we need to mock you instead. Okay. One of the two things are you intimidated and called to get? Well, so it seems like a reasonable thing. Um, and if, if like, say their birthday is in the future, um, and they have, you know, as well, business administration learn, and their birthday is 2165, um, we that gain have a typo or we have fraud. Um, so we say, we appear to have gotten the century of your birth incorrect or a great plot for a movie. Yes. This is, this, this is when I, when I'm about ludicrous fraud, this is why I'm about ludicrous fraud. Were those people getting payments? So somewhere getting payments from Social Security, but, but, but the main fraud vector, uh, was to mock somebody as alive in Social Security, and then use every other government payment system, uh, to, uh, basically to, to, to do fraud. Because what those other government payment systems do would do, what, there was simply do an are you a live check to the Social Security database? Um, it's a, it's a, it's a bank shot. What would you estimate is like the total, uh, amount of fraud from this mechanism? Um, my guess is, and, and other, by the way, the government accountability officer has done these efforts before. I'm not the only one who's not coming out of this, you know, the, in fact, I think they, they did, the GAO did analysis of rough estimate of fraud during the Biden administration and calculated it at roughly half a trillion dollars. So don't take my word for it, take a report issued during the Biden administration. How about that from this Social Security mechanism? Uh, it's, it's one of many. It's important to appreciate that the, the, the, the government does not, it is a very ineffective at, at staffing fraud. Because, um, it's, it's, it's not like, like it was a company like, like, like, for, staffing fraud, you've got a motivation because it's affecting the earnings of your company. Uh, but the government just, just, they just print more money. Um, so it's not, uh, like, you, you need, you need caring and competence. And these are in short supply at, uh, at the federal level. Um, yeah, I'm sorry. I mean, when you go to the DMV, do you think, wow, this is a bastion of confidence. Um, well, now imagine it's worse than the DMV because it's the DMV that confront money. So was it not possible? At least the state level DMVs, uh, need to, the states, moralists need to stay within their budget of the government, but the federal government just print for money. Well, it was not possible. If that, if there's a catch, you have a trillion of fraud, well, why, why was it not possible to cut all that? Uh, because when, when, essentially, we did, we, we actually, no, you, you, you really have to stand back and recalibrate your expectations for competence, uh, because, uh, you're operating in a world where, you know, you've, you've got to sort of make ends meet, like, you know, you've got to pay your bulls, you've got to, you know, buy the microphones. Yeah, yeah, exactly. Um, so, so you, you, you, you don't have, it's not like there's a giant, largely uncaring, monster bureaucracy. It's not even, it's an, an, an, an, an, an, and a bunch of, uh, an accuracy computers that are just, they're just sending payments. Um, like, like one of the things that, that, that, that, the door seemed to be, there was, it, it, it, it, it, it, it, it sounds so simple. Uh, that, that, probably we'll say, um, let's say a hundred billion, maybe two hundred billion a year, um, is simply requiring that payments from the main treasury computer, which is called pams, like payment accounts master or something like that. There's five trillion payments here, um, requiring that any parent go that goes out, have a payment of appropriation code, make it mandatory, not optional, and that you have anything at all in the comment field. Um, because, uh, Jesus, you see, you have to re-calibrate how dumb things are. Well, you think Pams will be sent out with no appropriation code, not, not checking back to any congressional appropriation and no explanation. And this is why the, the Department of War, formerly the Department of Defense cannot pass an order because the information is literally not there. Re-calibrate your expectations. I want to understand this haven't really a number, because there, there's an IG report in 2024, how, how, you should, most like, why is it so low? Um, maybe, but, uh, which found that like over seven years, the social security fraud, they estimated it was like 70 billions over seven years, so like 10 billion year, so I'd be curious to see what like the other four and 90 billion is. The federal government expenditure is a seven and a half trillion year. Yeah. Um, what, what percentage, how competent do you think of mages? The discretionary spending there is like 15 percent? Yeah, but it doesn't matter. Most of the four is non discretionary. It's, it's basically a fraudulent Medicare, Medicaid, social security, uh, what, uh, you know, um, disability, uh, it's, there's, there's a zillion government payments. Yeah. Um, and a bunch of these payments are in fact, uh, they're, they're, they're, uh, block transfers to the states. So the federal government doesn't even have the information, in a lot of cases, to even see, know if there's fraud. Let's consider, let's like reductio out of sodium. The government, the government is perfect and has no fraud. What is your probability, estimate of that? I mean, zero. Okay. So then would you say that Ford waste that the government, uh, is, has, is 90 percent? That also would be quite generous. But if, if it's only 90 percent, that means that there's 750 billion dollars here of waste from Ford. And it's not 90 percent. It's not 90 percent effective. This seems like a strange way to first principles. You want to fraud in the government. Just like, how much do you think there is? And then, uh, I, I, anyways, we don't have to do it live, but I'd be curious. It's like, so, you know, a lot Ford at, uh, Stripe, fuel or constantly trying to do Ford. Yeah, but as you say, it's like a little bit of a, um, we've really grounded down, but it's a little bit of a different problem space, because you're dealing with a much more heterogeneous set of fraud vectors here than we are. Yeah, but I mean, I mean, I, I mean, that's right. You, you, you, you have high confidence in your try hard. Um, you have high confidence in high caring, but still Ford is non, non zero. Um, not now, now imagine it's at a much bigger scale. Um, there's much less confidence and much less caring. You know, back in PayPal, back in the day, we, we tried to manage Ford down to about 1 percent of the, the payment volume. Um, and that was very difficult. Took a tremendous amount of confidence in caring to, uh, get Ford merely to 1 percent. Um, now imagine that the, the organization where there's much less caring and much less confidence. It's going to be much more than 1 percent. How do you feel now looking back on, um, kind of politics and, and doing stuff there, where it feels like we're from the outside in the two, you know, two things have been quite impactful. One, the America pack and two, um, the acquisition of, of, well, Twitter at the time. But also it seems like there was a bunch of heartache. And so what's your, what's your grading of the whole experience? Well, um, I think, I think those things needed to be done to maximize the probability that the future is good. Um, so, um, politics generally is very tribal. And it's, it's very tribal. And people lose their objectivity usually with politics. Like they, they, they generally have trouble seeing the good on the other side or the bad on their own side. That's generally how it goes. Um, I, I, that, I guess it was one of the things that surprised me the most is you, you often simply cannot reason with people. Um, if there are one tribe or the other, they, they simply believe that everything that tribe does is good and anything the other political tribe does is bad. Um, and persuading them is otherwise as almost impossible. Um, so, anyway, but, um, I think, I think overall, those actions, um, acquiring Twitter, getting trouble actually, even though it makes a lot of people angry. Um, I think those, I think those actions are good for work, good for civilization. Um, yeah, well, how does if you didn't do the future, you're excited about? Well, um, American needs to contain, American needs to be strong enough to last long enough to, um, extend life to other planets and to kind of get, I guess, AI and robotics to the point where we're going to show the future is good. Um, like, on the other hand, if, if we were to descend into, um, say communism or, or some situation where the, where the state was extremely oppressive, um, that, that would mean that we, we might not be able to become multi planetary. Um, and we might, we, the, the state might, um, you know, stab out, um, our progress and AI and robotics. How do you feel about, um, uh, you know, you, you, you, Optimus, Groc, etc, are going to be leveraged by, and not just yours, come, any revenue maximizing companies products will be leveraged by the government over time. Um, how does this concern manifest in what private company should be willing to give governments what kinds of guerrillas should, like, should, you know, that should, um, AI models be, uh, um, um, may to do whatever the government that has contracted them out to do, ask them to do, um, should, like, should, should, should Groc get to say, like actually even, the military wants to do X. No, the Groc will not do that. I think probably the biggest danger of AI, well, maybe the biggest danger of fail for AI and robotics going on wrong is, is government, interesting. You know, um, I mean, the way I think, like, like, people who are opposed to corporations or, or, or worried about corporations, it shouldn't, um, really worry about the most about government, because government is just a corporation in the limit. It's a government, it is, it is, it is, it is, it is, the government is just the biggest corporation with them and awfully unviolence. Um, so I always find it like a strange dichotomy where people would think corporations are bad, but the government is good when the government is simply the biggest and, and worst corporation. But people have that dichotomy. They somehow think that the same time the government can be good for corporations bad, and this is not true. Corporations are better morality than the government. So I actually think it's, uh, you know, that's, uh, that is a thing to be worried about. It's like, if the, you know, should, should, if the government should not, like, the, the government could potentially use AI and robotics to express the population. Like, that is a serious concern. I mean, as, as a guy building AI and robotics, how do you, how do you like, how do you prevent that? Well, I think that, like, if you have a limited government, um, if you limit the powers of the government, which is like really what the US Constitution is intended to do is intended to limit the powers of the government, then, then, uh, you're probably going to have a better outcome than if you have more government. So, electronics will be available to all governments, right? Yeah, not about all governments. Um, I mean, it's difficult to predict the, like I can say, like, what, what's, what's the end point or like, what is, what is many years in the future, but it's difficult to predict the, the sort of path along, along that way. Um, like, if civilization progresses, AI will vastly exceed the sum of all human intelligence and, and they'll be far more of its than humans. Um, along the way, what happens? It's very difficult to predict. I mean, it seems like one thing you do is just say, um, uh, you're not allowed to, whatever government decks, you're not allowed to use Optimus to do XYZ, just write out like a policy. I mean, you, I think you treated recently that Iraq should have a moral constitution. Um, and one of those things could be that we, we limit what governments are allowed to do with this advanced technology. I mean, yeah, we can do what is, what, what, I mean, technically, I mean, if, if the positives just pass a law, uh, and they can enforce that law, then it's hard to not do that law, you know, the, the best thing we can have is, is, is, limited government, uh, where, um, you know, you have, you have the appropriate cross checks between the executive judicial and, um, legislative branches. I guess the reason I'm curious about is this like, at some point, it seems like the limits will come from you, right? Like, you've got the Optimus, you've got the space GPU, you've got the, I think I'll be the boss of the government. Or you will get the, you will, like, the, I mean, already, it's the case with SpaceX, that for things that are crucial to the, um, uh, like the government really cares about getting certain satellites up in space, whatever, like it needs SpaceX. Uh, it is the, it is the, um, a necessary contractor. And you are in the process of building more and more of the, um, uh, the technological components of the future that, that, that will have an analogous role in different industries. And you could have this ability to, like, set some policy that, um, um, you know, suppressing classical liberalism in any way, uh, my companies will not help in it in any way with that, or, you know, some policy like that. Um, I will do my best to ensure that anything that's within my control maximizes the good outcome for humanity. I think anything else would be shortsighted, um, because I would say I'm part of humanity. So, um, I like humans, um, uh, pro-guin, pro-guin. Um, you, you've mentioned that Dojo 3 will be used for space-based compute. Um, you really read my, what I say. I don't know if you know Twitter, but uh, I know you a lot, a lot of followers. Yeah, worry. Um, how do you discern my secrets? I personally. How do you design a ship for space? What, what, what changes? Well, I guess you want to design to be, um, more radiation tolerant and run at a higher temperature. Uh, so you get, um, you know, roughly if you increase the, um, operating temperature by 20th set in degrees Kelvin, you can cut your radiator mass in half. Um, so running at a higher temperature is, is helpful in, in space. Um, there's, I mean, there's various things you can do for shielding of the memory and, but like neural nets are going to be very resilient to bed flips. Yeah, so like most of what, what happens for radiation is like random bed flips. Um, but like if you've got like, you know, a multi-trolling parameter model, and you know, get a few bed flips, doesn't matter. Um, it's much, like curiosity programs are going to be much more sensitive to bed flips than, um, some giant parameter file. Um, so I just designed it run hot and, um, I think it pretty much do it the same way that you do things on earth, apart from make it run hotter. Hmm, um, I mean, the solar array is most of the weight on the satellite. Is it a way to make the, um, the GPUs even more power dense than what Nvidia and TPUs and et cetera are planning on doing, that would, you know, be especially privileged in the space space role. Well, I mean, the basic math is, like, if you can do about a kilowatt per reticle, um, and then you'd need, um, you know, 100 million full reticle trips to do 100 gigawatts. Yeah. So, yeah, depending on what your yield assumptions are, you know, um, that, that tells you how many chips you need to make. Um, but you need, if you're, if you're going to have 100 gigawatts of power, you need, you know, 100 million chips running, that running a kilowatt sustained output per reticle. Um, 100 basic math, 100 million chips, uh, it depends on, yeah, if you, if you, if you look at the die size of something like black, old GPUs or something and how many can get out of the wafer, you can get like, um, on the order of dozens or less, uh, per wafer. So you're, basically, you're, this is a world where if we're putting that out every, every single year, you're producing millions, millions away for a month. Um, that's the plan with tariff app. Millions away for a month of advanced process notes. It could be some number north of a million, I think you're gonna do the memory, too. Yeah. You're gonna make a memory, five. I think the tariff app's got to do memory. It's got to do logic memory and texture. I'm very curious how somebody like gets start. This is like the most complicated thing man has ever made. And obviously, like, if anybody's up to the task, you're up to the task. Like, what do you, so you realize this is a bottleneck and you go to your engineers and like, what is the next, like, what are you telling to do? I want a million wafers of a month in 2030. What is the next, like, what do you, that's right. Do you like call ASMR? Like, what is the next step? That's so much to ask. Well, um, we make a little fab up and see what happens, uh, make our mistakes at a small scale and then make a big one. Is a little fab done? No, it is. No, it's not done. I, which, I mean, George, we're not gonna keep that cat in the bag. That chance is gonna come out of the bag real, but we're like, drones hovering over the bloody thing. You know, you'll be able to see it's construction for a race on X, right? In real time. Um, so, you know, we, we, I mean, like, I don't know. We're could just flounder and failure to fear. It's like not, uh, success is not guaranteed, but, since we want to try to make, uh, you know, something like a hundred million, yeah, we, we need, we need, we need, we want a hundred gigawatts of hour and a hundred, a hundred chips that can take a hundred gigawatts. And it's so cool. It, you know, by, yeah, by, by 2030. So then, um, and we'll take as many chips as I was applying as we'll give us. I've said this to, I've actually said this to TSMC and Samsung and my colonists, like, please build your more fabs faster. Um, and we will guarantee you to buy the output of those fabs. Um, so, so that they're already like, like, moving as fast as they, as they can. Like it's, it's not like, to be clear, it's not like us to, you know, it's not like, uh, either, it's, it's, it's, it's not like, it's us plus them, you know, there's an error that the people doing AI want a very large number of, you know, chips as quickly as possible. And then many of the input suppliers, the fabs, but also, you know, the turbine manufacturers are not jumping up production very quickly. No, no, yeah, the explanation here is that they're dispositionally conservative, you know, they're Taiwanese or German as the, you know, story maybe, and they just like, don't believe, they say, like, is that really the explanation or is there something else? Well, I mean, it's a reason what, like, if somebody's been and say the computer memory business for, 30 or 40 years, and they've seen cycles, they've seen, like, women bust like 10 times, yeah, you know, so, so like, that's a lot of layers of scar tissue, you know, so it's like, it's like during the boom times, it looks like everything is going to be great forever, and then, then, then the crash happens, and then like, desperately trying to avoid bankruptcy, and then there's another boom and then another crash. Are there other, are there other ideas you think others should go pursue that you're not for whatever reasons right now? I mean, there are a few companies that are, they're saying like, new ways of doing jobs, but they're just not scaling fast. I mean, within AI, I mean, just generally. I'd say like, people should just should do the thing that, where they find that they're highly motivated to do that thing, as opposed to, you know, something, something, some idea that I suggest, but they should do the thing that they find personally interesting and motivating to do. But yeah, we're going back to the limiting factor, because I've praised about a hundred times. The current limiting factor that I see in the time frame, you know, in the sort of 20, 29, 20, like in the three, three to four-year time frame, it's chips. In the one-year time frame, it's energy, power production, electricity. It's not clear to me that there's enough that usable electricity to turn on all the air chips that are being made. Towards the end of this year, I think people are going to have real trouble turning on, like the chip outward will exceed the ability to turn chips on. What's your plan to do with that world? Well, we're trying to accelerate electricity production. I guess that's maybe one of the reasons that I say I will be maybe the leader, hopefully the leader, is that we'll be able to turn on more chips than other people can turn on faster, because we're good at hardware. And generally, the innovations from the corporations that have messed the columns of labs, the ideas tend to flow, like it's weird to see that there's more than about a six-month difference. Between, like the ideas, traveled back and forth with the people. So I think you sort of hit the hardware wall, and then whatever whichever company can scale hardware, the fastest will be the leader. So I think XCII will be able to scale hardware the fastest, and therefore, most likely, we'll be the leader. You jokes are yourself conscious about using the limiting factor for Ace again. But I actually think there's something deep here, and if you look at a lot of the things we've touched on over the course of it, maybe a good note to end on, like if you think of a senescent, low-agency company, which would have some bottleneck and not really be doing anything about it, you know, Mark and Drieson had the line of most people are willing to endure any amount of chronic pain to avoid acute pain. And I feel like a lot of the cases we're talking about are just leaning into the acute pain, whatever it is. It's like, okay, we've got to figure out how to, you know, work with steel, or we've got to figure out how to run the chips in space, or like we'll take some near-term acute pain to actually solve the bottleneck. And so that's going to be unifying thing. I have a high fan threshold. That's helpful. Solve the bottlenecks. Yes. So, you know, one thing I can say is like, I think the future is going to be very interesting. And as I said, the dollar's up, only been to, especially the dollar's up, it was on the ground for like three hours or something. It's better to be, it's better to earn the side of optimism and be wrong than earn the side of pessimism and be right for quality of life. So, you know, your happiness will be, you'll be happier if you are on the side of optimism rather than earning the side of pessimism. And so I recommend earning the side of optimism. That's that. Cool. You know what, thanks for doing this. Thank you. Thank you. Thanks, guys. Oh, great stamina. Hopefully, this encounter is a pain in the pain tolerance. Hey, everybody. I hope you enjoyed that episode. If you did, the most helpful thing you can do is just share it with other people who you think might enjoy it. It's also helpful if you leave a rating or a comment on whatever platform you're listening on. If you're interested in sponsoring the podcast, you're going to reach out at twerkash.com slash advertise. Otherwise, I'll see you in the next one.