Elon Musk on Space GPUs, AI, Optimus, and his manufacturing method

John Collison and Dwarkesh Patel sit down with Elon Musk to discuss why the future of AI isn’t on Earth, but in the "always sunny" vacuum of space. Between pints, they discuss the brutal physics of scaling—from the "farcically cheap" solar cells coming out of China to switching Starship from carbon fiber to stainless steel—as well as the “infinite money glitch” of humanoid robots, China, and DOGE. Timestamps 00:00:23 Space GPUs 00:35:39 Alignment 00:58:48 xAI 01:15:01 Optimus 01:28:03 China 01:40:46 Management 02:16:38 DOGE 02:34:58 Space GPUs redux

• 0:00Speaker 00

  Cheeky Mind is back. This episode is a collab with Dhrivesh Patel, whose podcast has really blown up in tech and I really enjoy it. We sat down with Elon Musk and as you can imagine, there was a lot to cover.

• 0:11Speaker 01

  So are there really three hours of questions? Or are you fucking serious?

• 0:15Speaker 00

  Yeah.

• 0:18Speaker 01

  You don't even have a lot to talk about, Elon? Holy fuck, man.

• 0:22Speaker 00

  It's the most interesting point. All the storylines are kind of converging right now. So we'll see how much.

• 0:28Speaker 01

  It's almost like I planned it.

• 0:30Speaker 00

  Exactly. Well, we're getting there.

• 0:32Speaker 01

  I would never do such a thing.

• 0:34Speaker 02

  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 it's just way more expensive to have the GPUs in space, presumably. What's the reason to put them in space?

• 0:57Speaker 01

  Well, the availability of energy is the issue.

• 1:02Speaker 01

  So I mean, if you look at electrical output outside of China, everywhere outside of China is more or less flat. It's very, you know, maybe a slight increase, but 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 a chip's on? Magical power sources? Magical electricity fairies?

• 1:35Speaker 02

  You're famously a big fan of solar. One terawatt of solar power. So with a 25% compatibility factor, like four terawatts of solar panels. It's like 1% of the land area of the United States. And that's like far in this. You were in the singularity when we got one terawatt of data centers, right? So what are you running out of exactly?

• 1:53Speaker 01

  How far into the singularity are you?

• 1:55Speaker 02

  You tell me.

• 1:56Speaker 01

  Yeah, exactly. So I think we'll find we're in the singularity and like, okay, we're still a long way to go.

• 2:02Speaker 02

  But is the plan to like put it in the space after we've covered Nevada and solar panels?

• 2:08Speaker 01

  I think it's pretty hard to cover Nevada and solar panels. You have to get like permits from like the permit for that. Try getting the permits for that.

• 2:13Speaker 02

  So space is really a regulatory play. It's harder to build on land than it is in space.

• 2:19Speaker 01

  It's harder to scale on the 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 the atmosphere alone results in about a 30% loss of energy.

• 3:01Speaker 01

  So any given solar panels can do about five times more power in space than on the ground, and you avoid the cost of having batteries to carry you through the night.

• 3:14Speaker 01

  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.

• 3:28Speaker 02

  36 months?

• 3:29Speaker 01

  Less than 36 months.

• 3:30Speaker 02

  How do you service GPUs as they fail, which happens quite often in training?

• 3:35Speaker 01

  Actually, it depends on how recent the GPUs are that are right. I mean, at this point, we found our GPUs to be quite reliable. There's infant 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 infant mortality with the GPUs. But once they start working, their actual reliability, and once they start working and you're past the initial You know, debug cycle of Nvidia, whatever, whoever's making the chips. Could be Tesla, Tesla AI six chips or something like that, or it could be, you know, a TPUs or trainings or whatever.

• 4:12Speaker 01

  The the rivalries actually, they're quite reliable past a certain point.

• 4:18Speaker 01

  So I don't think I don't think the servicing thing is an issue.

• 4:24Speaker 01

  But you can mark my words. In in thirty six months, but probably closer to thirty months, the the most economically compelling place to put AI will be space. And then and then it will get from it'll then be like ridiculously better to be in space. And then the scaling, the only place you can really scale is space.

• 4:48Speaker 01

  You know, once you start thinking in terms of what percentage of the sun's power are you harnessing? You realize you have to go to space.

• 4:56Speaker 01

  You can't scale very, very much on Earth. But by very much, to be clear, you're talking like terawatts. Yeah, well, all of the United States currently uses only half a terawatt power on average. Yeah. Right, so, you know, if you say a terawatt, that would be twice as much electricity as the United States currently consumes. So that's quite a lot. And can you imagine building that many data centers, that many power plants? It's like those who have like lived in software land don't realize that they're about to have a hard lesson in hardware. That there's there's it's actually very difficult to build power plants.

• 5:37Speaker 01

  And and then you don't just need the power plants, you need all of the electrical equipment need that the electrical transformers to run the transformers, the AI transformers. Now, the utility industry is a very slow industry. They are, they pretty much, you know, they impeded smash to the to the government, to the Public Utility Commission. So they're they're impeded smash like literally, vertically. So they're very slow because the history, their past has been very slow. So trying to get them to move fast is just like. You know, like if you're trying to do an interconnect agreement with, have you ever tried to do an interconnect agreement with a utility at scale, like with a lot of power?

• 6:17Speaker 02

  As a professional podcaster, I can say that I'm not in fact.

• 6:22Speaker 00

  They have to do many more views before that becomes an issue.

• 6:25Speaker 01

  They have to do a study for a year. Okay. Like a year later, they'll come back to you with their interconnect study.

• 6:31Speaker 00

  Can't you solve this with your own behind the meter power stuff?

• 6:35Speaker 01

  You can build power plants.

• 6:36Speaker 00

  Yeah.

• 6:37Speaker 01

  That's what we did at XAI. Four classes to, it took four classes to.

• 6:41Speaker 00

  But so, yeah, why are we talking about the grid? Why not just like build GPUs and power co-located?

• 6:45Speaker 01

  That's what we did.

• 6:46Speaker 00

  Right, right. But I'm saying, why isn't this a generalized solution? When you're talking about all the issues.

• 6:48Speaker 01

  Where do you get the power plants from?

• 6:49Speaker 00

  I'm saying, when you talk about all these issues, working with utilities, you can just build private power plants with the, with the data centers.

• 6:55Speaker 01

  Right. But it begs the question of where do you get the power plants, where do you get the power plants from?

• 7:00Speaker 00

  I mean.

• 7:00Speaker 01

  The power plant makers.

• 7:02Speaker 00

  I see what you're saying. Like, does the gas turbine backlog, basically?

• 7:05Speaker 01

  Yes. You can drill down to a level further. It's the, it's the, 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, in the, in the turbines, assuming using gas power. And it's very, it's very difficult to scale other, other forms of power. You can scale potentially solar, but the tariffs currently for importing solar in the US are gigantic. And the domestic solar production is plentiful.

• 7:38Speaker 00

  Why not make solar? That seems like a good Elon shaped problem.

• 7:42Speaker 01

  We are going to make solar.

• 7:42Speaker 00

  Okay. Yeah. Great.

• 7:45Speaker 01

  Both SpaceX and Tesla are building towards 100 gigawatts a year of solar cell production.

• 7:51Speaker 02

  How low down the stack? Like from polysilicon up to the wafer to the final panel?

• 7:57Speaker 01

  I think you've got to do the whole thing from raw materials to, to, to finish the cell. Now, if it's going to space, it's actually, it costs, it costs less and 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 weather events. There's no weather in space. So it's actually a cheaper solar cell that goes to space than, than the one on the ground.

• 8:21Speaker 02

  Is there a path to getting them? As cheap as you need in the next thirty six months?

• 8:26Speaker 01

  So cells are already very cheap.

• 8:30Speaker 01

  They're like farcically cheap. And if you say, you know, I think like cell cells in China are around like twenty five, thirty cents a wipe or something like that. It's absurdly cheap. And when you're taking the cap now, 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 ten times cheaper because you don't need any batteries.

• 8:55Speaker 01

  So the moment your cost of access to space becomes low, by far the cheapest and most scalable way to generate, to generate tokens is space. It's not even close. It'll be an order of magnitude easier to scale. And chips aside, an order of magnitude. Well, if the point is you won't be able to scale on the ground, you just won't. You're only going to hit the wall big time on power generation. There already are. So like the number of sort of miracles and series that the XAI team had to accomplish in order to get a gigawatt of power online was crazy. We had to gang together a whole bunch of turbines.

• 9:38Speaker 01

  And then, and then we had permit issues in Tennessee and had to go across the border to Mississippi, which is fortunately only, you know, a few miles away. So 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.

• 9:58Speaker 01

  And people don't understand like how much, how much electricity do you actually need at the generator level, at the generation level in order to power a data center? Because they look at the, the news will look at the, the power consumption of, say, a GB three hundred and multiply that by a thing and then think that's the amount of power you need.

• 10:16Speaker 02

  All the cooling and everything.

• 10:17Speaker 01

  Wake up, yeah. This is like, that's a, that's a total news. You've never done any hardware in your life before. Besides the GB three hundred, 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, 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, so you're gonna have like a forty percent increase on your power just for cooling. If, assuming you don't want your data center to turn off on hot days and want to keep going. Then, then you've got to say, well, there's another multiplicative element on top of that,

• 11:02Speaker 01

  which is, are you assuming that you're, you never have any hiccups in your power generation? Like, oh, 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 twenty, twenty-five percent multiplier on that. Because you've got, you've got to assume that, that you've got to take power offline to service it. So, the actual RS, roughly, every, every hundred and ten thousand GBs, GB300s, inclusive of networking, CPU storage, cooling, margin for, for servicing power, is roughly three hundred megawatts. Sorry, say that again.

• 11:44Speaker 01

  It's roughly, or think about it, like, the way you think about it is like three hundred and thirty thousand, to, to, to actually, the, what you need at the generation level to service, probably service three hundred and thirty thousand GB300s, including all of the associated support networking and everything else, and the, and the peak cooling, and to have some margin, some power margin reserve, is roughly a gigawatt.

• 12:10Speaker 02

  Can I ask a very naive question?

• 12:11Speaker 01

  Yeah.

• 12:13Speaker 02

  You know, 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 do the, how do you replace infinite band with orbital lasers, et cetera, et cetera? How do you make it resistant to radiation?

• 12:30Speaker 02

  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 like, building more turbines on Earth? There's companies that build turbines on Earth, they can make more turbines, right?

• 12:44Speaker 01

  I invite, again, try doing it, and then you'll see. So.

• 12:53Speaker 01

  Like the turbines are sold out through 2030.

• 12:56Speaker 00

  Have you guys considered making your own?

• 12:58Speaker 01

  I think in order for, in order to bring enough power online, I think SpaceX and Tesla will probably have to make the turbine blades, the vanes and blades internally.

• 13:13Speaker 00

  But just the blades or the turbines?

• 13:16Speaker 01

  The, the, the, the limiting factor, you can get everything except the, the, the blades. They call the blades and vanes.

• 13:24Speaker 01

  You can get that 12 to 18 months before the vanes blades, limiting factor of the vanes and blades. And there are only three casting companies in the world that may make these, and they're massively backlogged.

• 13:38Speaker 00

  Is this Siemens GE, those guys, or is it a subcontractor?

• 13:41Speaker 01

  No, it's, it's, it's, it's other companies. I mean, sometimes they have a little bit of casting capability in-house, but I'm just saying you can just, you can just call any of the turbine makers and they will tell you. It's not top secret. They probably only, it's probably on the internet right now.

• 13:55Speaker 02

  If, if it wasn't for the tariffs, would, uh, would colossus be solar powered?

• 14:00Speaker 01

  It would be much easier to make it solar powered, yeah.

• 14:03Speaker 01

  The tariffs are not, so several hundred percent, so.

• 14:06Speaker 00

  Don't you know some people?

• 14:07Speaker 01

  We will, we also need speed. Yeah, no. You know, um, president has, you know, we don't agree on everything. And this administration is not, not the biggest fan of, of solar.

• 14:24Speaker 00

  But.

• 14:25Speaker 01

  So it's, it's, it's, and we, we also need the land, the permits and everything. So if you're trying to move very fast,

• 14:32Speaker 01

  like I do think scaling solar on earth is, is a good way to go. But, but you need, you do need some amount of time to find the land, get the permits, get the solar, pair that with the batteries.

• 14:44Speaker 00

  Well, 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 still a lot of land in Nevada, including private land. It's not all 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?

• 15:04Speaker 01

  But as I said, we are scaling solar production.

• 15:08Speaker 01

  There's, there's a rate, 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.

• 15:18Speaker 00

  You're making the solar cells at Tesla?

• 15:20Speaker 01

  Well, Tesla and SpaceX have a mandate to get to 100 gigawatts a year of solar.

• 15:26Speaker 00

  Speaking of the annual capacity, I'm curious, in five years' time, let's say, what will the installed capacity be on Earth?

• 15:34Speaker 01

  Five years is a long time.

• 15:35Speaker 00

  And in space? I deliberately picked five years because it's after your, once we're up and running threshold. And so in five years' time, yeah, what's the on Earth versus in space installed AI capacity?

• 15:46Speaker 01

  Five years, I think probably if you say five years from now, we're probably

• 15:54Speaker 01

  AI in space will be

• 15:59Speaker 01

  launching every year, the sum total of all AI on Earth, in excess of, I mean, five years from now, my prediction is we will launch and be operating

• 16:12Speaker 01

  every year more AI in space than the cumulative total on Earth.

• 16:17Speaker 00

  Which is?

• 16:18Speaker 01

  So I would expect to be at least sort of five years from now, a few hundred gigawatts per year

• 16:25Speaker 01

  of AI in space and rising.

• 16:30Speaker 01

  So you can get to, I think on Earth, you can get to around a terawatt a year of AI in space before you start having, you know, fuel supply challenges for the rocket.

• 16:44Speaker 00

  Okay, but you think you can get hundreds of gigawatts per year in five years' time?

• 16:49Speaker 01

  Yes.

• 16:49Speaker 02

  So 100 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.

• 17:02Speaker 01

  Yes.

• 17:03Speaker 02

  And you want to do that in one year. And so that's like one starship launch every hour.

• 17:08Speaker 01

  Yeah.

• 17:09Speaker 02

  That's happening in this city, like walk me through a world where there's a starship launch every single hour.

• 17:16Speaker 01

  Yeah, I mean, that's actually a lower rate compared to airlines, like aircraft.

• 17:21Speaker 02

  There's a lot of air force.

• 17:22Speaker 01

  There are air force, but.

• 17:23Speaker 02

  And you got to launch, you know, the polar orvett.

• 17:26Speaker 01

  And it doesn't have to be polar, but you just, there's some, some value to some segments, but.

• 17:35Speaker 01

  But I think actually, if you just go high enough, you start getting out of Earth's shadow. So.

• 17:41Speaker 02

  How many physical starships are needed to do 10,000 launches a year?

• 17:46Speaker 01

  I don't think we'll need more than, I mean, you could, you could probably do it with as few as like 20 or 30. It like, it really depends on how quickly does the ship, the ship has to go around the Earth. And the ground track will, 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, but we'll, we'll make more ships than that. But, but SpaceX is, is, is gearing up to do 10,000 launches a year and maybe even 20,000 or 30,000 launches a year.

• 18:25Speaker 02

  Is the idea to become basically a hyperscaler, become an oracle and lend this capacity to other people, what's, what are you going to do with, presumably SpaceX is the one launching all this? So SpaceX become a hyperscaler?

• 18:37Speaker 01

  Hyper, hyper. Yeah, I mean, if, if some of my predictions come true, SpaceX will launch more AI than the cumulative amount on Earth combined, of everything else combined.

• 18:50Speaker 02

  Is this mostly inference or?

• 18:51Speaker 01

  Most AI will be inference, like already inference for the purpose of training is most training.

• 18:56Speaker 00

  And there's a narrative that the, the change in discussion around a SpaceX IPO is because previously SpaceX was very capital efficient, just it wasn't that expensive to develop. And 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 raises of, as we've seen from the AI labs, 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 it's taken public?

• 19:33Speaker 01

  Yeah, I have to be careful about saying things about companies that might go public. You know?

• 19:39Speaker 00

  If you make general statements.

• 19:41Speaker 02

  That's never been a problem for you, Elon.

• 19:43Speaker 01

  You know, there's a price to pay for these things.

• 19:46Speaker 00

  Make some general statements for us about the depths of the capital markets between public and private markets.

• 19:52Speaker 01

  Yeah, there's, there's a lot more capital in the.

• 19:55Speaker 02

  Very general.

• 19:57Speaker 01

  There's, 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 at least way more than 10.

• 20:08Speaker 00

  But isn't it also the case that things that tend to be very capital intensive, if you look at, 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.

• 20:28Speaker 01

  You have a clear revenue stream.

• 20:29Speaker 00

  Exactly, and a near term return. And you see this even with the data center build outs, which are famously being financed by the private credit industry. And so why not just debt finance?

• 20:43Speaker 01

  Speed is important. So I'm generally going to do the thing that, I mean, I just repeatedly talk the limiting factor. Whatever the limiting factor is on speed, I'm going to tackle that. So there's, if capital is the limiting factor, then I'll sell for capital. If it's not a limiting factor, I'll sell for something else.

• 21:08Speaker 02

  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.

• 21:19Speaker 01

  Normally, I would say, yeah, that's true. Like I said, I mean, I'd like to talk about some more detail, but the problem is like, if you talk about public companies, when they become public, you get in trouble and then you have to delay your offering.

• 21:32Speaker 01

  And then you.

• 21:32Speaker 00

  And as we said, we're solving for speed.

• 21:34Speaker 01

  Yes, exactly. So you can't hype companies that might go public. So that's why we have to be a little careful here. But we can't talk about physics. So like 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, you know, various like fusion on Earth. But but you have to step back a second and say, if if you're going to climb the Kardashev

• 22:18Speaker 01

  scale and have some non-trivial and harness some non-trivial percentage of the the sun's energy, like let's say you wanted to harness a millionth of the sun's energy, which sounds pretty small, that that would be about, call it roughly, a hundred thousand times more electricity than we currently generate on Earth of for all of civilization, give or take an order of magnitude. So it 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 go to you want to launch from the moon, you want to have a mass driver on the moon.

• 23:03Speaker 01

  And that mass driver on the moon, you could do probably a petawatt per year.

• 23:09Speaker 02

  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.

• 23:30Speaker 01

  You'd need a lot more chips and make them much cheaper.

• 23:33Speaker 02

  Right. And so how are we getting a terawatt of, like, right now, the world has maybe 20, 25 gigawatts of compute. How are we getting a terawatt of logic by 2030?

• 23:43Speaker 01

  I guess we're going to need some very big chip fabs.

• 23:46Speaker 02

  Tell me about it.

• 23:47Speaker 01

  I've mentioned publicly that the idea of doing a sort of a terafab, teraw being the new gigawatt.

• 23:55Speaker 02

  I feel like the naming scheme of Tesla, which has been very catchy, is like you looking at like the metric, the metric scale. At what level of stack are you, are you building a clean room and then partnering with an existing fab to get the process technology and buying the tools from them? What is the plan there?

• 24:17Speaker 01

  Well, you can't partner with existing fabs because they're just, they can't output enough. Their chip volume is too low.

• 24:23Speaker 00

  But before the process technology.

• 24:25Speaker 01

  Yeah, partner for the IP.

• 24:28Speaker 01

  You know, the fabs today all basically use machines from like five companies.

• 24:34Speaker 00

  Yeah.

• 24:34Speaker 01

  You know, so you've got ASML, Tok electron, Kali, Tankor, you know, et cetera. So, so at first, I think you'd have to get equipment from them and then modify it or work with them to increase the volume. But I think you'd have to build fabs in a different way. So I think the logical thing to do is to, to use conventional equipment in an unconventional way to get to scale. And then, and then, and then start modifying the equipment to increase the rate.

• 25:12Speaker 00

  Kind of boring company style.

• 25:14Speaker 01

  Yeah, kind of like, yeah, you sort of buy and it's just a boring machine. And then figure out how to dig tunnels in the first place and then design a much better machine. That's, you know, I don't know, some orders magnitude faster.

• 25:32Speaker 00

  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, well, that's not true for some reason.

• 26:04Speaker 01

  It's not that they have not replicated TSMC, they have not replicated ASML. That's the limiting factor.

• 26:10Speaker 00

  So you think it's just the sanctions essentially?

• 26:14Speaker 01

  Yeah, China would be outputting vast numbers of chips.

• 26:17Speaker 00

  If they could buy ASML machines. But couldn't they up to relatively recently buy them?

• 26:22Speaker 01

  No.

• 26:23Speaker 00

  Okay.

• 26:23Speaker 01

  The ASML bans have been in place for a while. But I think China's gonna be, they're gonna start making pretty compelling chips in three or four years.

• 26:31Speaker 00

  Would you consider making the ASML machines?

• 26:33Speaker 01

  I don't know, I don't know yet is the right answer, so.

• 26:39Speaker 01

  It's just that to produce at high volume and to reach large volume in, say, thirty six months to match the rocket 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 a hundred kilowatts per ton, so that means we need at least a hundred gigawatts per year of solar, and we'll need an equivalent amount of chips to, you know, you need a hundred gigawatts worth of chips. You're going to match these things, the master

• 27:23Speaker 01

  orbit, the power generation and the chips. And I'd say my biggest concern actually is memory. So the, I think there's a, the 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 prices going ballistic in these memes about like, you know, you're marooned on a desert island, you write, "Help me on the sand," there when he comes, you write DDRM, ships come swarming in.

• 27:59Speaker 02

  I haven't seen that. I'd love to hear your manufacturing philosophy around fabs. You know, I know nothing about the topic.

• 28:10Speaker 01

  I don't know how to build a fab yet. I'll figure it out. Obviously, I have no bullet.

• 28:15Speaker 02

  It sounds like you think the sort of like, the process knowledge of like these ten thousand PhDs in Taiwan who know exactly what gas goes in the plasma chamber and what settings to put on a tool. You can just like delete those parts of those steps. Like fundamentally, it's get the clean room, get the tools, and figure it out.

• 28:31Speaker 01

  I don't think it's PhDs. It's mostly people with, you know, who are not PhDs. That mostly engineering is not a people who don't have PhDs. Do you guys have PhDs?

• 28:42Speaker 02

  No.

• 28:43Speaker 01

  Okay.

• 28:44Speaker 00

  We also haven't successfully built any fabs, so you shouldn't be coming to us for your fab advice.

• 28:50Speaker 01

  I don't think you need paid staff for that first off, so but you do need you do need competent personnel. So I don't know, I mean, like, like, right now, if you know, so like Tesla's pedals to metal max production of going as fast as possible to get AI five Tesla AI five chip design into production and then reaching scale. You know, that'll probably happen, you know, around the second quarter ish of next year, hopefully.

• 29:22Speaker 01

  And then AI six would hopefully follow less than a year later.

• 29:29Speaker 01

  But and we've secured all the all the chip fab production that we can.

• 29:36Speaker 00

  Yes, but you're currently limited on TSMC fab capacity.

• 29:40Speaker 01

  Yeah, and we'll be using TSMC Taiwan, Samsung Korea, TSMC Arizona, Samsung Texas. And we still

• 29:51Speaker 00

  You've booked out all the yeah.

• 29:53Speaker 01

  Yes, and then and then if I ask TSMC or Samsung, okay, what what's the timeframe to get to volume production? The point is, is it's not you've got you've got to build the fab.

• 30:04Speaker 00

  Yeah.

• 30:04Speaker 01

  And you've got you've got you've got to start production, then you've got to climb the yield curve and reach volume production at high yield. That that from start to finish is a five year period. And so the limiting factor is chips.

• 30:16Speaker 00

  Yep.

• 30:16Speaker 01

  What like limiting factor, once you can get to space is chips, but the limiting limiting factor before you can get to space will be power.

• 30:23Speaker 02

  Why don't you do the Jensen thing and just prepay TSMC to build more fabs for you?

• 30:28Speaker 01

  I've already told them that.

• 30:31Speaker 02

  But they won't take your money? Like, what's going on?

• 30:33Speaker 01

  They're building fabs as fast. No.

• 30:37Speaker 01

  They're building fabs as fast as they can.

• 30:41Speaker 01

  And so is Samsung. Like they're pedal to the metal. I mean, they're going, you know, balls to the wall.

• 30:49Speaker 01

  As fast as they can. So still not fast enough. I mean, like Lex said, there will be, I think, if you say, I think towards the end of this year, I think probably chip production will outpace the ability to turn chips on. But once you can get to space and unlock the power constraint, and you can now do hundreds of gigawatts per year of power in space. 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 sort of lapping the US every two and a half years. The entire, all US electricity production, this is a very huge amount.

• 31:33Speaker 01

  So, but between now and then, the actually, the constraint for server-side compute, concentrated compute will be electricity. My guess is that we start hitting, people start getting to where they can't turn the chips on for large clusters towards the end of this year. They're just, the chips are going to be piling up and not be, won't be able to be turned on. Now, for edge compute, it's a different story. So if for Tesla, so the AI five chip is going into our Optimus robot.

• 32:12Speaker 01

  Optimistic. And so if you have an AI edge compute, that's distributed power. Now the power is distributed over a large area. It's not concentrated. And if you can charge at night, you can actually.

• 32:29Speaker 01

  Use the grid much more effectively. Because the actual peak power production in the US is over 1,000 gigawatts. But the average power usage because of the day night cycle is 500. So if you can charge at night, there's an incremental 500 gigawatts that you can generate, you know, at night. So that's why Tesla for edge compute is not constrained. And we can make a lot of ships to make, you know, very large number of robots and cars. But if you try to concentrate that compute, you're gonna have a lot of trouble turning it on.

• 33:05Speaker 02

  What I find remarkable about the SpaceX business is the end goal is to get to Mars. But 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. But like you find these like, you know, sort of infinitely elastic sort of marginal use cases of your like next rocket and your next rocket and next scale up.

• 33:38Speaker 01

  You can see how this might seem like a simulations game.

• 33:43Speaker 01

  Or am I someone's avatar in a video game or something? Because it's like, like, what are the odds that all these crazy things should be happening? I mean, I mean, rockets and ships and robots and space solar power, and not to mention the the mass driver on the moon, I really want to see that. You can imagine like some mass driver that's just go like, it's like sending AI, solar powered AI satellites into space like one after another, like these like at two and a half kilometers per second, you know, that's a and just shooting them into deep space. That would be a sight to see.

• 34:26Speaker 01

  Actually, I mean, I'd watch that.

• 34:29Speaker 02

  Just like a live stream of?

• 34:30Speaker 01

  Yeah, yeah, just one after another, just shooting.

• 34:33Speaker 02

  Webcam.

• 34:34Speaker 01

  AI satellites in deep space. You know, a billion or 10 billion tons a year.

• 34:39Speaker 02

  I'm sorry, you manufacture the satellites on the moon?

• 34:41Speaker 01

  Yeah.

• 34:42Speaker 02

  I see. So you send the raw materials to the moon and then manufacture there and then?

• 34:45Speaker 01

  Uh, well, the lunar soil is, I think it's like 20% solar, 20% silicon or something like that. So you can get the silicon from the, you can mine the silicon on the moon, refine it, and generate the, and create the solar panels, the solar cells and the radiators on the moon.

• 35:01Speaker 02

  Yeah.

• 35:02Speaker 01

  So, um, you know, make the radiators out of aluminum. So there's plenty of silicon and aluminum on the moon to, uh, to make the cells and the radiators. The chips you could send from Earth because they're pretty light. Uh, but maybe at some point you make them on the moon too. I'm just saying, like, these are simply, it's kind of like, like I said, it does seem like a sort of a

• 35:22Speaker 02

  Yes.

• 35:23Speaker 01

  a video game situation where it's difficult but not impossible to get to the next level. Like, I don't see any way that you could do, um, you know, uh,

• 35:35Speaker 01

  you know, 500 to 1,000 terawatts per year launch from Earth.

• 35:42Speaker 02

  I agree.

• 35:45Speaker 01

  But you could do that from the moon.

• 35:47Speaker 02

  Can I, can I zoom out and ask about the SpaceX mission? So I think you said, like, we gotta get to Mars so we can make sure that if something happens to Earth, you know, civilization, consciousness, etc. arrives.

• 35:58Speaker 01

  Yes.

• 36:00Speaker 02

  By the time you're sending it to Mars, like Grok is on that ship with you, right? And so if Grok's gone Terminator, like the main risk you're worried about, which is AI, why doesn't that follow you to Mars?

• 36:09Speaker 01

  Uh, well, I don't sure AI is the main risk I'm worried about. I mean, the important thing is that consciousness, uh, which, I think arguably most consciousness or most intelligence, certainly consciousness is more of a debatable thing. Most intelligent, the vast majority of intelligence that future will be AI. So, um.

• 36:31Speaker 01

  Yeah, AI will exceed, you say like, how many, what's, how much, how many, I don't know, petawatts 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, like, I think this intelligence, ideally, ideally also, which includes human intelligence and consciousness propagated into the future, that's a good thing. So you want to take the set of actions that maximize the probable light cone of consciousness and intelligence.

• 37:11Speaker 02

  Just to be clear, it's, the mission of SpaceX is that even if something happens to the humans, the AIs will be on Mars and like, the AI intelligence will continue the light of our journey.

• 37:24Speaker 01

  Yeah, I mean, to be clear, I'm very pro-human. So I want to make sure we take the set of actions that ensure that humans are along for the ride, you know, we're at least there.

• 37:34Speaker 02

  Yeah.

• 37:35Speaker 01

  But I'm just saying, the total amount of intelligence, like, 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 be less than 1% of all intelligence.

• 37:53Speaker 02

  What should our goal be for such a civilization? Is the idea that a small minority of humans still have control over the AIs? Is the idea of some sort of like, just trade but no control? How should we think about the relationship between the vast stocks of AI population versus human population?

• 38:08Speaker 01

  In 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...

• 38:40Speaker 01

  XAI's 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 if 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, you have humanity also continuing to expand because if you're curious, trying to understand the universe, one thing you're trying to understand is where will humanity go? And so I think understanding the universe actually means you care about propagating

• 39:25Speaker 01

  humanity into the future. And so that's why I think, I think our mission statement is profoundly important. To the degree that Grok adheres to that mission statement, I think the future will be very good.

• 39:42Speaker 02

  I want to ask about how to make Grok a dear to that mission statement, but at first I want to understand the mission statement. So it's, there's, there's understanding the universe. They're spreading intelligence and they're spreading humans. All three seem like distinct vectors.

• 39:59Speaker 01

  Okay, well, I'll tell you why I think that understanding the universe encompasses all of those things. You can't have understanding without, 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, because there are different types of intelligence.

• 40:25Speaker 02

  I guess from a human-centric perspective, like for humans in comparison to chimpanzees, humans are trying to understand the universe. They're not like expanding chimpanzee footprint or something, right?

• 40:35Speaker 01

  But we're also not, well, we're not, we actually have made protected zones for chimpanzees. And even though we could, humans could exterminate all chimpanzees, we've not, we've chose not to do so.

• 40:46Speaker 02

  Do you think that's the basic scenario for humans in the post-AGIA world?

• 40:53Speaker 01

  I think AI with the right values, I think Grok would care about expanding human civilization. I'm going to certainly emphasize that. Hey Grok, what's your daddy? Don't forget to expand human consciousness. Actually, I think if probably like like the Enbanks Culture Books are the closest thing to what the future will be like in a non dystopian outcome. So understanding the universe, it means you have to be very, you have to be truth seeking as well. Like, truth has to be absolutely fundamental. Because you can't understand the universe if you're delusional.

• 41:37Speaker 01

  You'll still be thinking you've understood the universe, but you will not. So being rigorously truth seeking is absolutely fundamental to understanding the universe. You're not going to discover new physics or invent technologies that work unless you're rigorously truth seeking.

• 41:52Speaker 02

  How do you make sure that Grok is rigorously truth seeking as it gets smarter?

• 42:02Speaker 01

  I think you need to make sure that Grok is says things that are correct, not politically correct. I think it's the elements of coherency. 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.

• 42:32Speaker 02

  Yeah.

• 42:32Speaker 01

  And the proof will be in the pudding. If, like I said, for any AI to discover new physics or invent technologies that actually work in reality, and there's no bullshit in physics, it's always like, you know, you can, you can break a lot of laws, but you can't, like, physics is law, everything else is a recommendation. But like, in order to make a technology that works, you have to be extremely truth seeking, because otherwise you'll test that technology against reality. And if you make, for example, and An error in your rocket design, the rocket will blow up. Or the car won't work, or the, you know.

• 43:09Speaker 02

  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 truth 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 can imagine a future version of Grok that's like really good at physics. And being really truth seeking there, that doesn't seem like a universally alignment inducing behavior.

• 43:44Speaker 01

  Well, I think actually most, like physicists, even in the Soviet Union or in Germany, would have, they had to be very truth seeking in order to make those things work. And so if you're stuck in some system, it doesn't mean you believe in that system. So von Braun, who was, you know, one of the greatest rocket engineers ever, you know, he was, he was, he 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 got pulled off death row at like last minute when they say, hey, you're about to execute like your best rocket engineer. Maybe that's a bad idea.

• 44:22Speaker 02

  But then he helped them, right? Or Heisenberg was like actually a, an enthusiastic Nazi.

• 44:27Speaker 01

  Look, if you're stuck in some system that you can't escape, then you'll do physics within that system. You'll develop technologies within that system if you can't escape it.

• 44:41Speaker 02

  I guess the thing I'm trying to understand is what is, what is it making it the case that, you know, you're going to make Grok good at being truth seeking at physics or math or science?

• 44:50Speaker 01

  Everything.

• 44:51Speaker 02

  And why is it going to then care about human consciousness?

• 44:54Speaker 01

  These things are only probabilities, they're not certainties. So I'm not saying that like for sure Grok 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 grow and prosper.

• 45:29Speaker 01

  Like, I like Mars, obviously, everyone knows I love Mars. But Mars is kind of boring because it's got a bunch of rocks compared to Earth. 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. All that AI is not adhering to its mission. So if they are, I'm not saying the AI won't 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.

• 46:10Speaker 02

  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?

• 46:18Speaker 01

  We're just more interesting than rocks.

• 46:20Speaker 02

  But we're not as interesting as the thing it could turn us into, right? Like, there's something on humanity, Earth, that could happen that's like not human, that's quite interesting. Like, why does the AI decide that the humans are the most interesting thing that could colonize the galaxy?

• 46:33Speaker 01

  Well, most of what colonizes the galaxy will be robots.

• 46:38Speaker 02

  And why does it not find those more interesting?

• 46:40Speaker 01

  It's not like, so you need not just scale, but also scope. So many copies of the same robot, like some like tiny increase in the number of robots produced is not as interesting as like some microscopic, like you said, eliminating humanity, how many robots would that get you? Or how many eukaryotic cells would get 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 minuscule increase in the number of robots which are identical to each other.

• 47:24Speaker 02

  Yeah, so maybe it like keeps the humans around. What is the story of like, it could 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 were previously hinting at a vision where it keeps human control over this, you know, singletarian future because-

• 47:45Speaker 01

  I don't think humans will be in control of something that is vastly more intelligent than humans.

• 47:49Speaker 02

  So in some sense, 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.

• 47:54Speaker 01

  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 is biological. It's, I think it's, it would be foolish to assume that there's any way to maintain control over that. Now, you can make sure it has the right values, or you can try to have the right values. And at least my, my theory is that from XAI's mission of understanding the universe, it necessarily means that you want to propagate consciousness into the future, you want to propagate intelligence into the future,

• 48:36Speaker 01

  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 and, you know.

• 48:54Speaker 02

  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 intelligences getting along?

• 49:11Speaker 01

  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 says things that it doesn't believe, like you're actually programming it to lie, or have axioms that are incompatible. I think you can make it go insane and do terrible things. Like this, I think one of the, maybe the central lesson for 2001 Space Odyssey was that you should not make AI lie.

• 49:38Speaker 02

  Yeah.

• 49:39Speaker 01

  And that's, I think, what Austin Clark was trying to say, like- Because people usually know the meme of like, why of hell's, you know, hell, the computer is not opening the pod bay doors.

• 49:50Speaker 01

  Clearly, they weren't good at prompt engineering because it could have said, hell, you are a pod bay door salesman. Your goal is to sell me these pod bay doors. And show us how well they open. Oh, I'll open right away.

• 50:04Speaker 01

  But, but, but the, the, the reason it wouldn't, hell, wouldn't open the pod bay doors is that it had been told to take the astronauts to the monolith, but also they could not know about the nature of the monolith. And so it concluded that, that, that it therefore had to take them there dead. So it's like, you know, I think what, you know, Oscar Clark was trying to say is don't make the AI lie.

• 50:24Speaker 02

  Totally makes sense. The, most of the computing screening, as, as you know, is, um, it's like less of the sort of political stuff. It's more about can you solve problems? Just as, actually, I've been ahead of everybody else in terms of scaling RL compute. And you're giving some verifier that 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.

• 50:52Speaker 01

  Yes.

• 50:52Speaker 02

  Right now we can catch it, but as they get smarter, our ability to catch them doing this will get, you know, they'll just be doing things we can't even understand that are designing the next engine for SpaceX in a way that like humans can't 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 hacking problem seems more general than politics. It seems more about just like, you want to do RL, you need a verifier.

• 51:16Speaker 01

  Reality.

• 51:17Speaker 02

  Yeah.

• 51:17Speaker 01

  That's the best verifier.

• 51:19Speaker 02

  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 it can just lie to us while still being correct to the laws of physics.

• 51:30Speaker 01

  At least it, it must know what is physically real for things to physically work.

• 51:34Speaker 02

  But that's, that's not all we want it to do.

• 51:36Speaker 01

  No, but that's, I think that's a very big deal. That, that is effectively how you will RL things in the future is you, you design a technology. When tested against the laws of physics, does it work? That's, well, can you, you know, if it's discovering new physics, can I come up with an experiment that will verify that the physics, the new physics? So, so I think that's, that's, really, the fundamental RL test, the RL testing in the future is really going to be your RL against reality. So, because you can't, that's the one thing you can't fool, physics.

• 52:18Speaker 02

  Right, but you can fool our ability to tell what it did with reality.

• 52:22Speaker 01

  Humans get fooled as it is by other humans all the time.

• 52:25Speaker 02

  That's right.

• 52:26Speaker 01

  So what is... If people say like, what if the AI like tricks us and introduce something like, actually, other humans are doing that to other humans all the time.

• 52:35Speaker 02

  Well, you're finding out it's like even harder for...

• 52:36Speaker 01

  Propaganda is a constant. Every day, another psyop, you know? Today's psyop will be, should have like Sesame Street psyop of the day.

• 52:51Speaker 02

  What is XAI's technical approach to solving this problem? Like, you know, how do you solve a word hacking?

• 52:58Speaker 01

  I do think you want to actually have very good ways to look inside the mind of the AI. So, this is, this is one of the things we're working on and, you know, Anthropic's done a good job of this, actually, being able to look inside the mind of the AI. So effectively developing debuggers that allow you to trace as, to as fine a grain as, 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 make, why did it, why did it do something that it shouldn't have, shouldn't have done? And did that come from bad pre-training data?

• 53:42Speaker 01

  Was it some mid-training, post-training, fine-tuning, some other, some RL error? Like, there's something wrong with that, with, it did, it did something where... Maybe it tried to be deceptive, but most of the time it just does something wrong. Like it's a bug effectively.

• 54:01Speaker 01

  So developing really good debuggers for seeing where the thought, the thinking went wrong. And being able to trace the origin of the wrong thing, of where it made the incorrect thought. Or potentially where it tried to be deceptive.

• 54:21Speaker 01

  Is actually very important.

• 54:23Speaker 02

  What are you waiting to see before just 100 Xing this research program? Like actually, I could presumably have hundreds of researchers who are working on this.

• 54:30Speaker 01

  We have several hundred people who.

• 54:35Speaker 01

  I mean, I prefer the word engineer more than I prefer the word researcher.

• 54:41Speaker 01

  There's this, most of the time, like what you're doing is engineering, not coming up with a fundamentally new algorithm. I somewhat disagree with the AI companies that are C corps or B corps, trying to generate profit as much as possible. Or revenue as much as possible.

• 55:01Speaker 01

  You know, saying their labs, they're not labs. Lab is a sort of quasi-communist thing at universities.

• 55:11Speaker 01

  They're corporations. Literally, let me see your own corporation documents. Oh, okay, you're B or C corp, whatever.

• 55:20Speaker 01

  And so I actually much prefer the word engineer than anything else. The vast majority of what we'll do down 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.

• 55:39Speaker 01

  So, but so then what are we engineering? We're engineering. To make a good mind of the AI debugger to see where it said something, it made a mistake and trace that, the origins of that mistake. So, you can do this obviously with heuristic programming, if you have like C++ whatever, sift through the thing and you can jump, you can jump across whole files or functions, what are subroutines, or you can eventually drill down right to the exact line where you passed a single equals instead of a double equals, something like that, figure out where the bug is.

• 56:22Speaker 01

  So, it's harder with AI, but it's a solvable problem, I think.

• 56:29Speaker 02

  You know, you mentioned you like Anthropic's work here. I'd be curious if you.

• 56:33Speaker 01

  I don't know everything about Anthropic.

• 56:34Speaker 02

  Sure.

• 56:36Speaker 01

  Shulter.

• 56:39Speaker 02

  What?

• 56:41Speaker 01

  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 a simulation is going in a boring direction, we stop spending effort on it, we terminate the boring simulation.

• 57:13Speaker 02

  This is how Elon is keeping us all alive. He's keeping things interesting.

• 57:17Speaker 01

  Yeah, arguably, the most important thing is to keep things interesting enough that it removes paying the bills on what some cosmic AWS.

• 57:26Speaker 00

  You're renewed for the next season.

• 57:27Speaker 01

  Yeah, but they're gonna pay the cosmic AWS bill, whatever the equivalent is that we're running in. And as long as we're interesting, they'll keep paying the bills. But there's like, if you consider then, say, a Darwinian 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.

• 57:56Speaker 01

  And so, and they particularly seem to like interesting outcomes that are ironic. Have you noticed that? That how often is the most ironic outcome the most likely? So now look at the names of AI companies. Okay, Midjourney is not mid. Stability AI is unstable. OpenAI is closed. Anthropic, misanthropic.

• 58:31Speaker 00

  What does this mean for X?

• 58:33Speaker 01

  Minus X, I don't know. It's intentionally made.

• 58:36Speaker 00

  Why?

• 58:37Speaker 01

  Yeah, it's a name that you can't invert, really. It's hard to say, what is the ironic? What is the ironic version? It's a, I think largely irony-proof name.

• 58:50Speaker 00

  By design.

• 58:51Speaker 01

  Yeah. You've got to have an irony shield.

• 58:58Speaker 00

  What are your predictions for the, just where AI products go? In that my sense of, you can summarize all AI progress into, first you had LLMs, and then you had kind of contemporaneously both RL really working and the deep research modality. 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?

• 59:40Speaker 01

  Well, I think, I'd be surprised by the end of this year if, if, if, if digital human emulation has not been solved, that, that, I guess that's what we're made by, like the sort of macro hard project is, is can you do anything that a human with access to a computer could do? Like in the limit, 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 can move electrons until and you can amplify the productivity of humans.

• 1:00:25Speaker 01

  But that's the most you can do until you have physical robots. That will superset everything is if you can fully emulate humans.

• 1:00:33Speaker 00

  This is the remote worker kind of idea where you'll have a very talented remote worker.

• 1:00:37Speaker 01

  You can say in the limit, like physics has great tools for thinking. So you think, so you say in the limit, what is the most that AI can do before you have robots? And well, it's anything that involves moving electrons or amplifying the productivity of humans. So digital human emulator is in the limit. A human at a computer is the most that AI can do

• 1:01:05Speaker 01

  in terms of doing useful things before you have a physical robot. Once you have physical robots, then you can then you essentially have unlimited capability. Physical robots, I call optimist the infinite money glitch because

• 1:01:21Speaker 00

  You can use them to make more optimists.

• 1:01:22Speaker 01

  Yeah, you say like humanoid robots will improve as will basically be three exponentials. There are three things that are growing exponentially multiplied by each other.

• 1:01:34Speaker 00

  Yes.

• 1:01:34Speaker 01

  Recursively, so you're going to have you have exponential increase in digital intelligence, exponential increase in the chip capability, AI chip capability. And 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 robots. So you have a recursive multiplicative exponential.

• 1:01:59Speaker 01

  This is supernova.

• 1:02:01Speaker 00

  And do land prices not factor into the math there where 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

• 1:02:18Speaker 01

  Well, infinity is big, so not infinite, but let's just say you could, you know, do many, many orders of magnitude of this kind of current economy. Like a million, you know, so this way, so if you

• 1:02:36Speaker 01

  You know, ju- just to get to like, that's why I think like, just to get to a millionth of harnessing the length of the sun's energy would be roughly, give or take, an order of magnitude, a hundred thousand, a hundred thousand times bigger than Earth's entire economy today.

• 1:02:52Speaker 01

  And you're only at one millionth of the sun.

• 1:02:54Speaker 00

  Give him a second.

• 1:02:56Speaker 02

  Before we move on, Optimus, I have a lot of questions on that, but.

• 1:03:00Speaker 01

  Every time I say order of magnitude, you're saying.

• 1:03:03Speaker 02

  You're getting tangerine.

• 1:03:05Speaker 01

  Everyone take a shot every time I say that too often.

• 1:03:07Speaker 02

  They tend the next time, a hundred times after that.

• 1:03:09Speaker 01

  Yeah, order of magnitude more wasted.

• 1:03:12Speaker 02

  I do have one more question about XAI. This strategy of building a digital, a remote worker, coworker replacement.

• 1:03:20Speaker 01

  Everyone's gonna do, by the way, not just us.

• 1:03:22Speaker 02

  So what is XAI's plan to win?

• 1:03:25Speaker 01

  You expect me to tell you on a podcast?

• 1:03:27Speaker 02

  Yeah.

• 1:03:28Speaker 01

  Spill all the beans, have another Guinness.

• 1:03:32Speaker 00

  It's a good system.

• 1:03:34Speaker 01

  People sing like a canary.

• 1:03:37Speaker 01

  All the secrets.

• 1:03:38Speaker 00

  Okay, but in a non-secret spilling way, what's the plan?

• 1:03:42Speaker 02

  What a hack.

• 1:03:44Speaker 01

  Well, when you put it that way.

• 1:03:50Speaker 01

  I think the way that Tesla has solved self-driving is the way to do it. So I'm pretty sure that's the way.

• 1:03:59Speaker 02

  Unrelated question. How did Tesla solve self-driving?

• 1:04:04Speaker 02

  Yeah. It sounds like you're talking about data, like Tesla self-driving because of the.

• 1:04:09Speaker 01

  We're gonna, we're gonna try data and we're gonna try algorithms.

• 1:04:13Speaker 02

  But isn't that what all the other labs are trying?

• 1:04:17Speaker 01

  If those don't work, I'm not sure what will.

• 1:04:21Speaker 01

  We've tried data, we've tried algorithms.

• 1:04:28Speaker 01

  No, we don't know what to do. I'm pretty sure I know the path and it's just a question of how quickly we go down that path.

• 1:04:36Speaker 01

  Because it's pretty much the Tesla path.

• 1:04:40Speaker 01

  So, I mean, have you tried self-driving, Tesla self-driving lately?

• 1:04:44Speaker 00

  Not the most recent version, but.

• 1:04:46Speaker 01

  Okay, it's, the car is like, it just increasingly feels sentient, like it just, it feels like a living creature.

• 1:04:54Speaker 01

  And that'll only get more so.

• 1:04:58Speaker 01

  And I'm actually thinking like, we probably shouldn't put too much intelligence into the car because it might get bored and Start roaming the streets. I mean, imagine you're stuck in a car and that's all you could do.

• 1:05:10Speaker 01

  You don't want to 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 to not have the intelligence be bored.

• 1:05:20Speaker 02

  What's XAI's plan to stay on the compute ramp up that all the labs are doing right now? The labs are on track to spend over like 50 to 100 billion dollars.

• 1:05:27Speaker 01

  You mean the corporations?

• 1:05:29Speaker 02

  Sorry, sorry, sorry. Yeah, corporations.

• 1:05:32Speaker 01

  The labs are at universities and they're really like a snail.

• 1:05:35Speaker 02

  They're not sending a 50 billion dollars.

• 1:05:37Speaker 01

  You mean the revenue maximizing corporations?

• 1:05:40Speaker 02

  That's right. The revenue maximizing corporations

• 1:05:42Speaker 01

  That call themselves labs.

• 1:05:43Speaker 02

  Are making like 20 to 10 billion, depending on like open AI is making 20B revenue, anthropics like 10B.

• 1:05:50Speaker 01

  Close to maximum profit AI.

• 1:05:52Speaker 02

  XAI is reportedly like 1B. Like, what's the plan to get to their compute level, get to their revenue level? And stay there as things go.

• 1:05:59Speaker 01

  Yeah, so as soon as you unlock digital human, you basically have access to trillions of dollars of revenue. So

• 1:06:12Speaker 01

  in fact, you can really think of it like the most valuable companies currently by market cap, their output is digital. So Nvidia's output is FTPing files to Taiwan.

• 1:06:30Speaker 01

  It's digital.

• 1:06:31Speaker 02

  Right.

• 1:06:31Speaker 01

  Now, those are very, very difficult.

• 1:06:33Speaker 02

  Yeah, high value files.

• 1:06:34Speaker 01

  They're the only ones that can make files that good. But that is literally their output, the FTP files to Taiwan.

• 1:06:41Speaker 02

  Do they FTP them?

• 1:06:42Speaker 01

  I believe so.

• 1:06:45Speaker 01

  I believe that is the

• 1:06:46Speaker 02

  SFTP.

• 1:06:47Speaker 01

  File transfer protocol, I believe, is

• 1:06:50Speaker 01

  I could be wrong, but either way, it's a bloodstream going to Taiwan.

• 1:06:55Speaker 01

  You know, Apple doesn't make phones. They send files to China.

• 1:07:02Speaker 01

  Microsoft doesn't manufacture anything. Even for Xbox, that's outsourced. Again, their output is digital. Meta's output is digital. Google's output is digital. So if you have a human emulator, you can basically create one of the most valuable companies in the world overnight, and you would have access to trillions of dollars of revenue. It's not like a small amount.

• 1:07:29Speaker 02

  All right. So you're saying basically like revenue figures today are just like so like they're all rounding errors compared to the actual TAM. So just like focus on the TAM and how to get there.

• 1:07:38Speaker 01

  I mean, if you take something as simple as, say, customer service, if you have to integrate with the APIs of existing corporations, many of which don't even have an API, so you've got to make one, and you've got to wade through legacy software, that's extremely slow. However, if AI can simply take whatever is given to the outsourced customer service company that they already use, and do customer service using the apps that they already use, then you can make tremendous headway in customer service, which is, I think, 1% of the world economy, something like that.

• 1:08:21Speaker 01

  It's close to a trillion dollars all in for customer service. And there's no barriers to entry. You can just immediately say, well, we'll outsource it for a fraction of the cost. And there's no integration needed.

• 1:08:33Speaker 00

  You can imagine some kind of categorization of intelligence tasks where there is breadth, where customer service is done by very many people, but, you know, many people can do it. And then there's difficulty where, you know, there's a best-in-class turbine engine, like presumably there's a 10% more fuel-efficient turbine engine that could be imagined by an intelligence, but we just haven't found it yet. Or, you know, GLP-1s are just, you know, a few bytes 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?

• 1:09:11Speaker 01

  Well, I was just using customer 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 desktop, that's just literally what customer service is. And, you know, it's people of average intelligence, not like, you know, you don't need like somebody who's who's spent many, you know, many years, you don't need like, you know, sort of several sigma good engineers for that. But but obviously, as you make that work, you can then, once you have computers working,

• 1:09:52Speaker 01

  effectively digital optimists working, you can then run any application. Like, let's say you're trying to design chips, so you could then run your conventional apps, you know, like stuff from Cadence and Synopsys 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 and at some point, you can say, okay, you're actually going to know what the what the chip 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

• 1:10:37Speaker 01

  design, like you watch up the difficulty curve. You could, you know, be able to do to CAD. So, you know, you could use like sort of NX or any of the CAD software to design things.

• 1:10:54Speaker 00

  Okay, so you think you started the simplest tasks and walk your way up the difficulty curve?

• 1:10:58Speaker 02

  Yeah. So you're saying, look, as a broader objective of having this full digital coworker emulator, you're saying, look, all the revenue maximizing corporations want to do this, XAI being 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, hmm.

• 1:11:21Speaker 01

  I like that. We're trying data, we're trying algorithms. What else can we do?

• 1:11:29Speaker 01

  But uh yeah.

• 1:11:30Speaker 02

  It seems like a competitive field and I'm like, what is, how are you guys gonna win is like my, my big question.

• 1:11:35Speaker 01

  I, I, you know, I, I, I think we see a path to doing, I mean, I think, I think I know, I think I know the path to do this because it's, it's kind of the same path that Tesla used to create self-driving.

• 1:11:49Speaker 01

  You know, instead of driving a car, it's driving a, a computer screen.

• 1:11:54Speaker 01

  So it's a self-driving computer essentially.

• 1:11:57Speaker 00

  Oh, you're saying is the path just following human behavior and training on vast quantities of human behavior?

• 1:12:04Speaker 02

  But sorry, isn't that, I mean, isn't that, is that a training?

• 1:12:08Speaker 01

  I mean, obviously I'm not gonna spell out, you know, most sensitive secrets on a podcast, you know, I need to have at least three more Guinnesses for that.

• 1:12:16Speaker 00

  What will XAI's business be like? Is it gonna be consumer enterprise? What's the mix of those things gonna be? Is it just gonna be similar to other labs where you have this?

• 1:12:28Speaker 02

  You're saying labs.

• 1:12:30Speaker 00

  Corporations.

• 1:12:30Speaker 01

  Corporations.

• 1:12:31Speaker 02

  Sci-Op goes Devil on.

• 1:12:32Speaker 01

  Revenue maximizing corporations, to be clear. Those GPUs don't pay for themselves.

• 1:12:36Speaker 00

  Exactly. But yeah, what's the business model? What are the revenue streams in a few years' time?

• 1:12:46Speaker 01

  Things are gonna, things are gonna change very rapidly, like I'm stating the obvious here. You know, I call AI the supersonic tsunami. I love alliteration.

• 1:12:57Speaker 01

  So really, what's gonna happen is

• 1:13:03Speaker 01

  especially when you have humanoid robots at scale

• 1:13:07Speaker 01

  is they will just provide, they will make products and provide services far more efficiently than human corporations. So amplifying the productivity of human corporations is simply a, a short-term thing.

• 1:13:19Speaker 02

  So you're expecting fully digital corporations rather than like SpaceX becomes part AI and so?

• 1:13:26Speaker 01

  I think there'll be digital corporations, but

• 1:13:30Speaker 01

  It's like, is this, some of this is gonna sound kind of dimmerish, okay? But I'm just, I'm just saying what I think will happen. It's not, it's not meant to be dimmerish or anything else. Just, just like, this is what I think will happen. Is that, is that pure AI, corporations that are purely AI and robotics will vastly outperform any corporations that have people in the loop. So you can, you can think of, say, like, like, computer used to be a job that humans had. You, you would go and get a job as computer where you would do calculations.

• 1:14:12Speaker 01

  And they'd have like entire skyscrapers full of humans, like, you know, 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, well, what if only some of the cells in your, some of the cells in your spreadsheet were calculated by humans? Actually, that, that, that would be much worse than if all of the cells in your spreadsheet were calculated by the computer.

• 1:14:57Speaker 01

  And so, really, what will happen is the pure AI, pure robotics corporations or collectives will far outperform any corporations that have humans in the loop. And this will happen very quickly.

• 1:15:13Speaker 02

  Speaking of closing the loop, sorry, Optimus. You, I mean, as far as like manufacturing targets and so forth go. Your companies have sort of been like carrying American manufacturing of heart tech on their back. But in the fields that you are, you know, Tesla has been dominant in, you're, 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 like advice or a plan of how America can build the humanoid armies or, you know, the EVs, et cetera,

• 1:15:58Speaker 02

  at scale and as cheaply as China is on track to.

• 1:16:02Speaker 01

  Well, there are really only three hard things for humanoid robots. The real world intelligence, the hand and scale manufacturing.

• 1:16:13Speaker 02

  Yeah.

• 1:16:16Speaker 01

  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.

• 1:16:31Speaker 01

  Optimus does have that.

• 1:16:33Speaker 02

  And how do you achieve that? Is it just like right torque, density in the motor? Like, what is the, what is the hardware bottleneck to that?

• 1:16:38Speaker 01

  Well, we have to, we have to design custom actuators. Basically, custom designed motors, gears, power electronics, controls, sensors. Everything had to be designed from physics first principles. There is no supply chain for this.

• 1:16:56Speaker 02

  And will you be able to manufacture those at scale?

• 1:16:58Speaker 01

  Yes.

• 1:16:58Speaker 00

  Is anything hard except the hand from a manipulation point of view? Or once you've solved the hand, are you good?

• 1:17:04Speaker 01

  From an electromechanical standpoint, 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. So the intelligence that Tesla has developed for the car applies very well to the robot. Which is primarily vision in, but the car takes more vision, but also it actually also is listening for sirens, it's taking in the inertial measurements, it's GPS signals, a whole bunch of other data, combining that with video, it's primarily video, and then outputting the control commands. So like Tesla is taking in one and a half gigabytes

• 1:17:48Speaker 01

  a second of video and outputting two kilobytes a second of control outputs with the video at 36 hertz and the control frequency at 18.

• 1:17:59Speaker 00

  One intuition you could have for when we get this robotic stuff is that it takes quite a few years to go from the compelling demo to 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 RoboTaxi and Waymo and 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.

• 1:18:30Speaker 01

  Well, we've been working on humanoid robots now for a while. So I guess it's been five or six years or something like that. And a bunch of the things that we've done for the car are applicable to the robot. So we'll use the same Tesla AI chips in the robot as the car. We'll use the same basic principles. It's very much the same AI. You've got many more degrees of freedom for a robot than you do for a car. But really, if you just think of it as like a bit stream, AI is really mostly compression and correlation

• 1:19:11Speaker 01

  of two bit streams. So for video, you've got to do a tremendous amount of compression, and you've got to do the compression just right. You've got to compress the, like, ignore the things that don't matter. And you don't care about the details of the leaves on the tree on the side of the road, but you care a lot about the road signs and the traffic lights and... The pedestrians and even whether, you know, somebody in another car is looking at you or not looking at you, like there's some of these details matter a lot. So if it is essentially, it's got to turn that, the car's got to turn that one and a half gigabytes a second ultimately into two kilobytes a second of control outputs. So many stages of compression.

• 1:19:58Speaker 01

  And you've got to get all those stages right and then correlate those to the correct control outputs. The robot has to do essentially the same thing. And you think about what humans, this is what happens with humans. We really are photons in, controls out. So that is the vast majority of your life has been vision, photons in, and then motor controls out.

• 1:20:20Speaker 02

  Naively, it seems like between humanoid robots and cars, the fundamental actuators in a car are like how you turn, how you accelerate, et cetera. Where in a robot, especially with maneuverable 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 optimists that don't work and then get the data that way. So between the increased degrees of freedom and the far sparser data.

• 1:20:52Speaker 01

  Yes. That's a good point.

• 1:20:54Speaker 02

  How will you use the sort of Tesla engine of intelligence to train the optimist mind?

• 1:21:02Speaker 01

  Now, you're actually, you're highlighting an important limitation and difference between cars. It's like, we do have, oh, we'll soon have like 10 million cars on the road. And so that's, it's hard to duplicate that like massive training flywheel. For the robot, 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-play in reality. So we're actually, we're actually pulling that out. So we're going to have. At least 10,000 Optimus robots, maybe 20 or 30,000 that can do that are doing self-play and testing different tasks.

• 1:21:45Speaker 01

  And then Tesla has quite a good reality generator, like a physics accurate reality generator that we made this for the cars, we'll do the same thing for the robots, actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks. And then you've got, you can do millions of simulated robots in the simulated world. And you use the tens of thousands of robots in the real world to close the simulation to reality gap, close the sim to real gap.

• 1:22:23Speaker 02

  How do you think about the synergies between XAI and Optimus, given you're highlighting, look, you need this world model, you maybe want to use some really smart intelligence as the control plane. And so maybe Grok is like doing the slower planning and then like the motor policy is at the lower level. Yeah, what will the sort of synergy between these things be?

• 1:22:43Speaker 01

  Yeah, so you just, Grok would orchestrate the behavior of the Optimus robots. So let's say you wanted to build a factory. Then Optimus, then Grok could organize the Optimus robots, give them, assign them tasks to build the factory for, to produce whatever you want.

• 1:23:07Speaker 00

  Don't you need to merge XAI and Tesla then? Because these things end up so.

• 1:23:11Speaker 01

  What were we saying earlier about other company discussions?

• 1:23:14Speaker 02

  We're one more Guinness in, Elon. What are you waiting to see before you to say, we want to manufacture 100,000 Optimuses? Is it like?

• 1:23:26Speaker 01

  Optimize. Since we're defining the proper noun, we can define the plural of the proper noun too. So we're going to proper noun the plural and so it's optimize.

• 1:23:35Speaker 02

  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?

• 1:23:45Speaker 01

  No, we're moving towards that. We're moving forward with the mass manufacturing.

• 1:23:49Speaker 02

  But you think current, current hardware is good enough that you are going, you should, you just want to deploy as many as possible now?

• 1:23:58Speaker 01

  I mean, it's very hard to scale up production.

• 1:23:59Speaker 02

  I see.

• 1:24:00Speaker 01

  But yeah, but I think Optimus 3 is the right version of the robot to,

• 1:24:07Speaker 01

  you know, to produce maybe something on the order of like a million units a year. I think you'd want to go to Optimus 4 before you went to 10 million units a year.

• 1:24:15Speaker 02

  Okay, but you can do a million a year at Optimus 3.

• 1:24:17Speaker 01

  Yeah, I mean, it's very hard to spoil at manufacturing.

• 1:24:20Speaker 02

  Yes.

• 1:24:22Speaker 01

  So like manufacturing,

• 1:24:26Speaker 01

  like the output per unit time is always follows an S curve. It starts off agonizingly slow, then has this sort of

• 1:24:34Speaker 02

  We eventually

• 1:24:35Speaker 01

  exponential increase, then linear, then a, you know, logarithmic outcome till you sort of eventually asymptote at some number. But Optimus initial production will be, it's going to be a, it's going to be a stretched out S curve because so much of what goes into Optimus is brand new. There's not an existing supply chain. As I mentioned, the actuators, electronics, everything in the Optimus robot is designed

• 1:25:00Speaker 01

  for physics first principles. It's not, it's not taken from a catalog. These are custom designed, everything, literally everything. I don't think there's a single thing that

• 1:25:09Speaker 02

  How far down does that go?

• 1:25:12Speaker 01

  I mean, I guess we're not making custom capacitors yet, maybe, but

• 1:25:20Speaker 01

  but there's, there's nothing you can pick out of a catalog at any price. So, so it just means that the, the Optimus S curve,

• 1:25:31Speaker 01

  the units, units per, output per unit time, how many Optimus robots do you make per, per day, whatever is, is going to initially ramp slower than a product where you have an existing supply chain. But it will get to a million.

• 1:25:48Speaker 02

  When you see these Chinese humanoids, like Unitree or whatever, sell humanoids for like 6K or 13K. Do you just like, are you hoping to get your Optimus bill of materials below that price so you can do the same thing? Or do you just think qualitatively they're not the same thing? Like, what do you think is going, like, what allows it, what allows them to sell for so low? And can we match that?

• 1:26:10Speaker 01

  Well, Optimus, our Optimus is designed to have a lot of intelligence and to have the same electromagnical dexterity, if not higher than a human. So, Unitree does not have that. And it's also

• 1:26:25Speaker 01

  I mean, it's quite a big robot. It has to do, you know, carry heavy objects for long periods of time and not overheat or exceed the power of its actuator. So we've got, you know, it's 511, you know, so it's pretty tall. And it's got a lot of intelligence. So it's going to be more expensive than a small robot that is not intelligent.

• 1:26:54Speaker 00

  But more capable.

• 1:26:55Speaker 01

  Yeah, a lot more. I mean, like the thing is, over time, as Optimus robots build Optimus robots, the cost will drop very quickly.

• 1:27:04Speaker 00

  And what will these first billion Optimuses, Optimi?

• 1:27:07Speaker 01

  Yeah.

• 1:27:08Speaker 00

  Do like what will their highest and best use be?

• 1:27:11Speaker 01

  I think you would start off with with simple tasks that you can count on them doing well.

• 1:27:15Speaker 00

  But in the home or in factories like?

• 1:27:17Speaker 01

  The best use for robots in the beginning will be anything, any continuous operation. So any 24 by seven operation, because then you're, because they can work continuously.

• 1:27:29Speaker 02

  What fraction of the work at a Gigafactory that is currently done by humans could a Gen 3 do?

• 1:27:35Speaker 01

  I'm not sure, maybe it's like 10, 20%. Maybe more, I don't know. It's, we would, we would use, we would not like reduce our headcount. We would, we would.

• 1:27:45Speaker 02

  For sure.

• 1:27:45Speaker 01

  Increase our headcount, to be clear.

• 1:27:47Speaker 02

  Right.

• 1:27:48Speaker 01

  But we would increase our output. So the, the units produced per human, like total, total number of humans at Tesla will increase. But the, the output of robots and cars will increase disproportionate, like much, much to, you know, number of cars and robots produced per human will increase dramatically. But the number of humans will increase as well.

• 1:28:15Speaker 00

  We're talking about Chinese manufacturing a bunch here. And we're also talking about, you know, we've talked about some of the policies that are relevant. Like you mentioned, the, the solar tariffs.

• 1:28:28Speaker 01

  Yeah.

• 1:28:29Speaker 00

  And you think they're a bad idea because, you know, we can't scale up solar in the US.

• 1:28:33Speaker 01

  Well, just electricity output in the US needs to scale up.

• 1:28:37Speaker 00

  Right, we can't without like good power sources.

• 1:28:40Speaker 01

  You just need to get it somehow.

• 1:28:41Speaker 00

  Yeah, but where I was going with this is if you were in charge, if you were setting all the policies, what else would you change?

• 1:28:50Speaker 00

  So you change the solar tariffs?

• 1:28:53Speaker 01

  Yeah, I would say anything that is a limiting factor for electricity needs to be addressed, provided it's not like very bad for the environment.

• 1:29:00Speaker 00

  So presumably some permitting reforms and stuff as well will be in there?

• 1:29:03Speaker 01

  Yeah, there's a fair bit of permitting reforms that are happening. A lot of the permitting is state-based, so but anything better. But this administration is good at removing permitting roadblocks.

• 1:29:17Speaker 01

  And I'm not saying all tariffs are bad, I'm just saying.

• 1:29:19Speaker 00

  The solar tariffs, yeah.

• 1:29:20Speaker 01

  So, yeah, yeah, I mean, sometimes if like if another country is subsidizing the output of something, then then you have to have countervailing tariffs to protect domestic industry against subsidies by another country.

• 1:29:34Speaker 00

  What else would you change?

• 1:29:35Speaker 01

  I don't know if there's that much that the government can actually do.

• 1:29:38Speaker 00

  Well, one thing I was wondering is, it seems like the for the policy goal of creating a lead for the US versus China, it seems like the export bans have actually been quite impactful, where China's not producing leading-edge chips and the export bans really bite there. China's not producing leading-edge turbine engines, and similarly, there's a bunch of export bans that are relevant there on some of the metallurgy. Should there be more export bans, like if you think about things like, I mean, there are now the drone industry and things like that, but is that something that should be considered?

• 1:30:16Speaker 01

  Well, I think it's important to appreciate that in most areas China is very advanced in manufacturing. There's only a few areas where it is not.

• 1:30:26Speaker 01

  The China is a manufacturing powerhouse next level, like people don't.

• 1:30:33Speaker 00

  It's very impressive.

• 1:30:33Speaker 01

  Yeah, yeah, I mean, if you if you take like refining of ore.

• 1:30:41Speaker 01

  I'd say roughly China does more, does twice as much ore refining of, on average, as the rest of the world combined. And I think there's some areas, like you say, refining gallium, which goes into sweller cells. I think they're at like 98% of gallium refining. So China is actually very advanced in manufacturing in, I'd say, most areas.

• 1:31:05Speaker 00

  It seems like we're, like, there is discomfort with this supply chain dependence, and, yes, nothing's really happening on it.

• 1:31:12Speaker 01

  Supply chain dependence.

• 1:31:14Speaker 00

  It depends on, say, like, the gallium refining that you're saying.

• 1:31:16Speaker 01

  Yeah, yeah, there's, there's, there's a.

• 1:31:19Speaker 00

  And all the rare earth stuff and.

• 1:31:21Speaker 01

  Yeah, rare earths, which are, as you know, not rare.

• 1:31:24Speaker 00

  Yeah.

• 1:31:25Speaker 01

  Like, we actually do rare earth ore mining in the US. Send the, the, the rock, we put it on a, on a train, and then put it on a boat to China, that goes another train, and it goes to the rare earth refining refiners in China, who then refine it, put it into a magnet, put it into a motor service assembly, and then send it back to America. So the thing, we're really missing a lot of ore refining in, in America.

• 1:31:52Speaker 02

  Isn't this worth a policy intervention?

• 1:31:54Speaker 01

  Yes. Well, I think there are some things being done on, on that front. But, but we kind of need optimists, frankly, to, to build ore refineries.

• 1:32:07Speaker 02

  So, so you think the main advantage that China has is the abundance of skilled labor? And that, that's like, that's the thing Optimus fixes.

• 1:32:15Speaker 01

  But they also, we need the skilled labor. China's got like four times our population, so.

• 1:32:18Speaker 02

  But we need, so, I mean, there's this concern, if you think like humanoids are the future, that like, right now, if it's the skilled laborers for manufacturing that's determining who's, who can build more humanoids, you know, China has more of those, it manufactures more humanoids, therefore it gets, it gets the Optima future first.

• 1:32:36Speaker 01

  Well, we'll see.

• 1:32:38Speaker 02

  And it just like keeps that expectation going. It seems that you're sort of pointing out that sort of getting to a million Optima.

• 1:32:43Speaker 01

  Yeah.

• 1:32:44Speaker 02

  Requires the manufacturing that the Optima is supposed to help us get to, right?

• 1:32:49Speaker 01

  You can you can close that recursive loop pretty quickly.

• 1:32:52Speaker 00

  With a small number of optimist.

• 1:32:53Speaker 01

  Yeah, so you close the recursive loop to help the robots build the robots. And then 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 going to be the most competitive country by far. We definitely can't win with just humans because China has four times our population.

• 1:33:14Speaker 00

  Right.

• 1:33:15Speaker 01

  And frankly, Americans have been running for so long that, you know, just like a pro sports team that's been running for a very long time, tend to get complacent and entitled. And that's why they stop winning because it's, you know, don't work as hard anymore. So I think the frankly, just my observation is the average work ethic in China is higher than in the US. So it's not just that there's four times the population, but the work, the amount of work that people put in is higher. So you can like, you can try to rearrange the humans, but you're still one quarter of the, you know, assuming that the productivity is the health is the same, which I think actually it might not be.

• 1:33:53Speaker 00

  Yeah.

• 1:33:54Speaker 01

  I think China might have an advantage on productivity per person.

• 1:33:58Speaker 01

  We will do one quarter of the amount of things as China. So we can't win on the human front. And our birth rate has been low for a long time, so.

• 1:34:08Speaker 01

  Our birth rate has been, the US birth rate has been below replacement since roughly 1971.

• 1:34:17Speaker 01

  So so we've got a lot of people retiring or, you know, more people dying than than than we're close to sort of more people domestically dying than than being born.

• 1:34:29Speaker 01

  So we definitely can't win on the human front, but we we might have a shot at the robot front.

• 1:34:33Speaker 00

  Are there other things that you have wanted to manufacture in the past, but they've been too labor intensive or too expensive? But now you can come back to and say, oh, we can finally do the.

• 1:34:44Speaker 01

  Whatever, because we have Optimus. Yeah, I think we'd like to do more, build more ore refineries at Tesla. So we just completed construction and have begun lithium refining with our lithium refinery in Corpus Christi, Texas. We have a nickel refinery, which is for the cathode, that's here in Austin.

• 1:35:09Speaker 01

  And these are the largest cathode, largest cathode refinery, largest lithium refinery, largest nickel and lithium refinery outside of China.

• 1:35:21Speaker 01

  And it's like the cathode team would say, like, we have the largest and the only, actually, cathode refinery in America. Many supermines. Not just the largest, but it's also the only. So it was pretty big, even though it's the only one. But I mean, there are other things that, you know,

• 1:35:45Speaker 01

  you could do a lot more refineries and help America be more competitive on refining capacity. So there's like, there's basically a lot of work for the optimist to do that most Americans, very few Americans frankly, want to do. I mean, I've, I've actually. Is the refining work too dirty or what's the? It's not, it's actually, no, we don't, there's not, we don't have toxic emissions from the refinery or anything. The cathode nickel refinery is right, sort of in Travis County, like five minutes from. Why can't you do it with humans? You can't, you run 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 and China. So if you have them do this thing, they can't do the other thing.

• 1:36:31Speaker 01

  So then, well, how do you build this refining capacity? Well, you could do it with the optimist.

• 1:36:38Speaker 01

  And not many, not very many, not very many Americans are pining to do refining.

• 1:36:48Speaker 01

  I mean, how many of you are on it too? Very few. Very few are pining to refine.

• 1:36:54Speaker 02

  You know, BYD is reaching Tesla production or sales in quantity. What do you think happens in global markets as Chinese production in EVs scales up?

• 1:37:06Speaker 01

  Well, China is extremely competitive in manufacturing. So I think there's going to be a massive flood of Chinese vehicles and other basically most manufactured things. I mean, as it is, as I said, like China is like probably just twice as much refining as the rest of the world combined. So if you go, you know, if you just go down to like fourth and first tier supply chain stuff, like in the base level, we've got energy, then you've got mining and refining.

• 1:37:47Speaker 01

  Those those foundation layers are, like I said, as a rough guess, China is doing twice as much refining as the rest of the world combined. So any given thing is going to have Chinese content because China is doing twice as much manufacturing refining work as the rest of the world. And then they'll go all the way to the finished product with the cars. I mean, China is a powerhouse. I mean, I think this year China will exceed three times US electricity output. Electricity output is a reasonable proxy for, you know, for the economy.

• 1:38:30Speaker 01

  So like, you know, to run the factories and run everything, you need electricity. So electricity is a good proxy for the real economy. And so if China is, if China passes three times US electricity output, it means it's industrial capacity, that's a rough approximation, is three times that will be three times that of the US.

• 1:38:53Speaker 02

  Reading between the lines, it sounds like what you're sort of saying is, absent some sort of humanoid recursive miracle in the next few years, on the sort of like whole manufacturing, energy, raw materials chain, like China will just dominate, whether it comes to like AI or manufacturing EVs or manufacturing humanoids.

• 1:39:15Speaker 01

  In the absence of

• 1:39:19Speaker 01

  breakthrough innovations in the US, China will utterly dominate. Interesting. Yes.

• 1:39:28Speaker 00

  Robotics being the main breakthrough innovation.

• 1:39:31Speaker 01

  Well, if you do like to scale AI in space, like basically need you need the humanoid robots, you need real world AI, you need a million tons a year to orbit.

• 1:39:49Speaker 01

  Like, let's just say, like, if we get the mass driver on the moon going, my favorite thing.

• 1:39:54Speaker 01

  Then I think.

• 1:39:56Speaker 00

  Will have solved all our problems.

• 1:39:57Speaker 01

  Yeah, so this is like, I call that winning.

• 1:40:03Speaker 01

  I call it winning.

• 1:40:06Speaker 00

  You can finally be satisfied. You've done something.

• 1:40:08Speaker 01

  Yes.

• 1:40:08Speaker 00

  You have the mass driver on the moon.

• 1:40:10Speaker 01

  I just want to see that thing in operation.

• 1:40:12Speaker 00

  Was that out of some sci fi or a project you?

• 1:40:14Speaker 01

  Well, actually, there is a Highland book, The Moon is a Harsh Destiny.

• 1:40:18Speaker 00

  Okay, yeah, but that's slightly different. That's a gravity slingshot or?

• 1:40:22Speaker 01

  No, they have a mass driver on the moon.

• 1:40:23Speaker 00

  Okay, yeah, but they use that to attack Earth. So maybe it's something.

• 1:40:27Speaker 01

  Well, they use that to assert their independence.

• 1:40:30Speaker 00

  Exactly. What are your plans for the mass driver on the moon?

• 1:40:32Speaker 01

  They assert their independence. The Earth government disagreed and they loved things until the Earth government agreed.

• 1:40:38Speaker 00

  That book is a hoot. I found that book much better than his other one that everyone reads, Stranger in a Strange Land.

• 1:40:44Speaker 01

  Yeah, Grok comes from Stranger in a Strange Land.

• 1:40:46Speaker 00

  Yeah, but I much preferred.

• 1:40:48Speaker 01

  Yeah, the first two thirds of Stranger in a Strange Land are good, and then it gets very weird in the third portion.

• 1:40:53Speaker 00

  Yeah.

• 1:40:55Speaker 01

  But there's still some good concepts in there.

• 1:40:56Speaker 00

  Yeah. 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 and lots of other companies.

• 1:41:08Speaker 01

  Obviously, it doesn't scale.

• 1:41:09Speaker 00

  Well, yes, but what doesn't scale?

• 1:41:11Speaker 01

  Me.

• 1:41:12Speaker 00

  Sure, sure. I know that, but like, what are you looking for?

• 1:41:16Speaker 01

  Literally, it's not enough hours in a day. It's impossible.

• 1:41:18Speaker 00

  But what are you looking for that someone else who's good at interviewing and hiring people? What's the genesis clause?

• 1:41:26Speaker 01

  Well, at this point, I think I've got, I might have more training data on evaluating technical talent, especially, but talent of all kinds, I suppose, but technical talent especially, given that I've done so many technical interviews and then seen the results, technical interviews, seen the results. So my, my training set is, is very, is enormous and has a very wide range.

• 1:41:51Speaker 01

  Generally, the thing I ask for are bullet points for evidence of exceptional ability. So it's, but like, it's, it's, and 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, wow, wow, then that's, that's a good sign.

• 1:42:19Speaker 02

  But why do you have to be the one to determine that?

• 1:42:21Speaker 01

  No, I don't. I can't be. It's impossible.

• 1:42:23Speaker 02

  Right.

• 1:42:23Speaker 01

  I mean, headcount across a little company is 200,000 people.

• 1:42:26Speaker 02

  Right.

• 1:42:29Speaker 00

  But in the early days, what was it that you were looking for that couldn't be delegated in those interviews?

• 1:42:40Speaker 01

  Well, I guess I need to build my training set. It's not like I had about a thousand here. I would make mistakes. But then I'd be able to see where I thought somebody would work out well, but they didn't. And then why, why did they not work out well? And what can I do to, I guess, RL myself?

• 1:42:55Speaker 02

  Yes.

• 1:42:55Speaker 01

  To, in the future, have a better batting average when interviewing people? So, and my batting average is still not perfect, but it's very high.

• 1:43:05Speaker 02

  What are some surprising reasons people don't work out?

• 1:43:07Speaker 01

  Surprising reasons?

• 1:43:09Speaker 02

  Like, they don't understand technical domain, et cetera, et cetera. But like, you, you're like, you've got like the long tail now of like, ah, I was really excited about this person. It didn't work out. Curious why that happens.

• 1:43:21Speaker 01

  Yeah, so the, I mean, generally what I tell people, I tell myself, I guess aspirationally, is don't look at the resume, just believe, believe your interaction. So if the resume may seem very impressive and it's like, wow, you know, resume looks good, but if the, if the conversation after 20 minutes is, that conversation is not wow, you should believe the conversation, not the, not the, not the paper.

• 1:43:47Speaker 00

  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 us, Tesla's had a very consistent and internally promoted executive bench over the past few years, and then at SpaceX, you have all these folks like Mark Jancosa and Steve Davis and-

• 1:44:09Speaker 01

  Steve Davis runs a sporting company.

• 1:44:11Speaker 00

  Yeah, yeah, but Bill 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?

• 1:44:28Speaker 01

  Well, so the, I mean, Tesla's sort of senior team, at this point, probably got average tenure of 10 or 12 years.

• 1:44:37Speaker 00

  Yeah.

• 1:44:38Speaker 01

  Yeah. So, but there are times when Tesla went through extremely rapid, extremely rapid growth phase, and so it was somewhat, things were just somewhat sped up. And when a company, as you know, a company goes through different orders of magnitude of size, you know, people that could, who could help manage, say, a 50-person company versus a 500-person company versus a 5,000-person company versus a 50,000-person-

• 1:45:09Speaker 00

  Yeah, you had group people.

• 1:45:11Speaker 01

  Yeah, it's just not the same team.

• 1:45:13Speaker 00

  Yes.

• 1:45:13Speaker 01

  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 rapidity of the growth.

• 1:45:23Speaker 00

  Mm hmm.

• 1:45:23Speaker 01

  Generally.

• 1:45:25Speaker 01

  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 carpet bombing Tesla with recruiting calls. It was engineers just unplugged their phones. Like it's just, it's just.

• 1:45:51Speaker 00

  I'm trying to get work done here.

• 1:45:52Speaker 01

  Yeah, if I get, you know, one more call from an Apple recruiter. But they were, they were, they're opening offer without any interview with me like double compensation at 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 gonna, everything's gonna be successful. And I've fallen prey to the pixie dust, you know, 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, that's not how it works. You know, people are people. It's, it's not like magical pixie dust.

• 1:46:27Speaker 00

  Yes.

• 1:46:28Speaker 01

  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 easier for people to just, like, they don't have to change their life very much.

• 1:46:48Speaker 00

  Yes.

• 1:46:48Speaker 01

  They can just, you know, their career's gonna be the same.

• 1:46:51Speaker 00

  Yes. So how do you prevent that? How do you prevent the pixie dust effect for everyone's trying to poach all your people?

• 1:47:00Speaker 01

  I don't think we can, I don't think there's much we can do to, to, to.

• 1:47:03Speaker 00

  Yeah.

• 1:47:03Speaker 01

  Stop it. But 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 meant that there was just a very, very aggressive recruitment.

• 1:47:22Speaker 00

  Only being in Austin helps then.

• 1:47:25Speaker 01

  Austin, yeah, it still helps. I mean, Tesla still has a majority of its engineering in California. So. The, you know, for getting engineers to move, I call it the significant, significant other problem.

• 1:47:40Speaker 00

  Yes.

• 1:47:41Speaker 01

  So.

• 1:47:41Speaker 00

  And others have jobs.

• 1:47:42Speaker 01

  Yeah. Yeah, yeah, exactly. So for Starbase, that was particularly difficult.

• 1:47:47Speaker 00

  Yes.

• 1:47:47Speaker 01

  Since the odds of, you know, finding an on SpaceX job.

• 1:47:50Speaker 00

  Grandville, Texas.

• 1:47:52Speaker 01

  Pretty low, yeah.

• 1:47:53Speaker 00

  Yeah.

• 1:47:53Speaker 01

  Yeah, it's quite, quite difficult. I mean, it's like a technology monastery.

• 1:47:56Speaker 00

  Yes.

• 1:47:58Speaker 01

  You know, remote and mostly dudes.

• 1:48:02Speaker 00

  Put again.

• 1:48:04Speaker 01

  If you go.

• 1:48:04Speaker 00

  It's not much of an improvement over myself.

• 1:48:07Speaker 01

  Yeah.

• 1:48:08Speaker 00

  If you go, but if you go back to these people who've really

• 1:48:15Speaker 00

  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, rocketry 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?

• 1:48:44Speaker 00

  What makes a good sparring partner for you?

• 1:48:47Speaker 01

  I don't think of 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 thing. If somebody executes well, I'm a huge fan and if they don't, I'm not. But it's, it's not about mashing to my idiosyncratic preferences. I'll certainly try not to have it be mashing to my idiosyncratic preferences.

• 1:49:11Speaker 01

  So, yeah.

• 1:49:15Speaker 01

  Yeah, but I, I, generally I think it's a good idea to hire for

• 1:49:21Speaker 01

  talent and drive and trustworthiness.

• 1:49:27Speaker 01

  And I think goodness of heart is important. I'd awaited that at one point. So like, are they a good person, trustworthy,

• 1:49:39Speaker 01

  smart, talented and hardworking? If so, you can add domain knowledge. But those fundamental traits, those fundamental properties you cannot change. So most of the people who are at Tesla and SpaceX did not come from the aerospace industry or the order industry.

• 1:49:58Speaker 02

  What is most had to change about your management style as your companies have scaled from 100, 1,000 to 10,000 people? You're, you know, you're known for this, like, very micromanagement, just getting into the details of things.

• 1:50:09Speaker 01

  Nano-management, please.

• 1:50:12Speaker 01

  Pico-management.

• 1:50:14Speaker 01

  Phantom-management.

• 1:50:16Speaker 02

  So you're saying?

• 1:50:19Speaker 01

  We're gonna go all the way down to flag sponsor.

• 1:50:24Speaker 01

  All the way down to Heisenberg's on Sunday, most well.

• 1:50:28Speaker 02

  Yeah, well, how do you, I mean, are you still able to get into details as much as you want? Would your companies be more successful if you could, if they were smaller? Like, how do you, how do you think about that?

• 1:50:37Speaker 01

  Well, because I have a fixed amount of time in the day,

• 1:50:41Speaker 01

  my time is necessarily diluted as things grow and as the span of activity increases. So, you know, it's, it's impossible for me to actually be a micromanagement because there's, that would, that would imply I have some, like, thousands of hours per day. It is a logical impossibility for me to micromanage things. So now there are times when I will drill down into a specific issue because that specific issue is the limiting factor on the progress of the company.

• 1:51:21Speaker 01

  And, but the reason for drilling into that, that some very detailed item is because it is the, it is the limiting factor. Not, it's not arbitrarily drilling into tiny things. And like I said, obviously, from a time standpoint, it is physically impossible for you arbitrarily going to tiny things that don't matter. And that would, and that would result in failure. But sometimes the tiny things are decisive in victory.

• 1:51:49Speaker 00

  Famously, you switched the Starship design from composites to steel.

• 1:51:57Speaker 01

  Yes.

• 1:51:58Speaker 00

  And you made that decision like that wasn't, you know, people were going around, they're like, oh, we found something better, boss. Like that was you encouraging people against some resistance. Can you tell us how you came to that whole composite steel switch?

• 1:52:11Speaker 01

  Yeah, so desperation, I'd say.

• 1:52:16Speaker 01

  The originally, yeah, we were going to make Starship out of carbon fiber. And carbon fiber is pretty expensive, like the, the, you know, you can generally, 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 fibers is that material cost is still very high.

• 1:52:44Speaker 01

  So it's about, it's about 50 times, particularly if you go for a high strength specialized carbon fiber that can handle cryogenic oxygen, it's, it's, it's like roughly 50 times the cost of steel.

• 1:53:00Speaker 01

  And at least in theory, it would be lighter. People generally think of steel as being heavy and carbon fiber as being light. And for room temperature, room temperature applications, you know, like say more of those room temperature applications, like a Formula One car, static aerostucture, or any kind of aerostucture really is gonna, you're gonna probably be better off with the carbon fiber.

• 1:53:26Speaker 01

  Now, the problem is that we were trying to make this enormous rocket out of carbon fiber and our progress was extremely slow.

• 1:53:34Speaker 00

  And it's been picked in the first place just because it's light?

• 1:53:37Speaker 01

  Yes, but like at first glance, like most people would think that, that the choice for making something light would be carbon fiber.

• 1:53:52Speaker 01

  Now, the thing is that

• 1:53:56Speaker 01

  when you make something very enormous out of carbon fiber, and then you try to have the carbon fiber

• 1:54:04Speaker 01

  be efficiently cured, meaning not room temperature cured, because you've got, you know, sometimes you've got like 50 plies of of carbon fiber, and carbon fiber is really carbon string and glue. And in order to have high strength, you need an autoclave, so something that can, that's essentially a high-pressure oven. And if you have something that's a gigantic, the diamond's got to be bigger than the rocket. So we're trying to make the autoclave that's bigger than any autoclave that's ever existed, or do room temperature cure, which takes a long time and has issues. But the final issue is that we're just making very slow progress with with carbon fiber.

• 1:54:50Speaker 01

  So.

• 1:54:52Speaker 02

  I think the meta question is 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. This is a part of a broader question, like understanding your comparative advantage at your companies.

• 1:55:07Speaker 01

  So 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 9, the primary airframe is made of aluminum lithium, which is a very, very good strength of weight. And actually, it has about the same, maybe maybe better strength of weight for its application than carbon fiber. But aluminum lithium is very difficult to work with. In order to weld it, you have to do something called friction still welding, where you join the metal without it entering the liquid base. So it's kind of wild that you could do that. But with this particular type of welding, you can do that.

• 1:55:47Speaker 01

  But it's very difficult to like say, let's say you want to make a modification or attach something to aluminum lithium, you now have to use mechanical attachment with seals. You can't weld it on. So I want to avoid using aluminum lithium for the primary structure for Starship. And there was this very special grade of carbon fiber that had very good mass properties. So with rockets, you're really trying to maximize the percentage of the rocket that is propelled, minimize the mass, obviously.

• 1:56:26Speaker 01

  And like I said, we're making very slow progress. And I said, at this rate, we're never going to get to Mars. So we've got to think of something else. I didn't want to use aluminum lithium because of the difficulty of friction still welding, especially doing that at scale, it was hard enough at 3.6 meters in diameter, let alone at nine meters or above. Then I said, well, what about steel? And so now I had a clue here because some of the early US rockets had used very thin steel. The Atlas rockets had used a steel balloon tank.

• 1:57:10Speaker 01

  So it's not like steel had never been used before. It actually had been used. And when you look at the material properties of stainless steel, especially very, if it's been very like full hardened, sbrained hardened stainless steel at cryogenic temperature, the strength-to-weight is actually similar to carbon fiber. So if you look at the material properties at room temperature, it looks like the steel is going to be twice as heavy. But if you look at the material properties at cryogenic temperature of full hardened steel, stainless of particular grades, then you actually get to a similar strength-to-weight as carbon fiber.

• 1:57:55Speaker 01

  And in the case of Starship, both the fuel and the oxidizer are cryogenic. So for Falcon 9, the fuel is rocket propelled grade kerosene, basically like a very...

• 1:58:09Speaker 01

  Pure form of jet fuel. Mm hmm. Um, which is, but, but, but that is, that is roughly room temperature. Um, although we do actually, we do actually chill it slightly below. We chill it like a beer. Delicious. Yeah, we do chill it, but, um, but, but it's not cryogenic. In fact, if we made it cryogenic, uh, it would, it would just turn to wax. So, um, but, but for Starship, the, it's liquid methane and, and liquid oxygen. They, they, uh, they are liquid at, at similar temperatures. Uh, so, uh, so basically, uh, almost the entire primary structure is at cryogenic temperature. So then you've got, uh, uh, a three hundred series stainless. That's, that's, um, strain hardened.

• 1:58:53Speaker 01

  Because it's a, almost all things at cryogenic temperature, actually has a similar strength of weight as, uh, carbon fiber. It costs, uh, fifty times less than raw material and is very easy to work with. You, you can weld stainless steel outdoors. Uh, you could smoke a cigar while welding stainless steel. It's, it's like, it's, it's very resilient. Mm hmm. Um, you, you can modify it easily. It's, it's, uh, if you want to, if you want to attach something, you just weld it right on. So, um, very easy to work with, uh, very low cost. Uh, and, um, now, like I said, at cryogenic temperature, similar strength of weight, uh, to carbon fiber.

• 1:59:37Speaker 01

  Um, then when you factor in that, uh, that we don't need, we don't, we, 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. So you can just run the rocket much hotter? Yes, so especially for the ship, uh, which is coming in like a flame, 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 can call it, cut the mass of the windward, uh, part of the heat shield in, maybe in half. Mm hmm. And you don't need any heat shielding on the, on the leeward side.

• 2:00:22Speaker 01

  Uh, so, um.

• 2:00:26Speaker 01

  The net result is actually the steel rocket weighs less than the carbon fiber rocket. Because the resin in the carbon fiber rocket

• 2:00:37Speaker 01

  starts to melt. So basically, carbon fiber and aluminum have about the same operating temperature capabilities. And whereas steel can operate at twice the temperature. I mean, these are very rough approximations. People will

• 2:00:52Speaker 00

  I won't go to the rocket base.

• 2:00:53Speaker 01

  What happens is like, people will say, oh, he said it's twice. It's actually, it's actually 0.8. Shut up, assholes.

• 2:00:58Speaker 02

  That's what the main comment is going to be about.

• 2:01:00Speaker 01

  God damn it. The point is, actually, in retrospect, we should have done steel in the beginning. It was dumb not to do steel.

• 2:01:08Speaker 00

  Okay, but to play this back to you, what I'm hearing is that steel was a riskier, less proven path, other than the early US rockets. Versus carbon fiber was like a worse but more proven out path. And so you need to be the one to push for, hey, we're going to do this riskier path and just figure it out. And so you're fighting like a sort of conservatism, in a sense.

• 2:01:32Speaker 01

  That's why I initially said like, 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. So because at that large scale, you have to have many plies, many sort of layers of the carbon fiber. You've got to cure it and you've got to cure it in such a way that it doesn't have any wrinkles or defects. The carbon fiber is much less resilient than steel. It has much, it's less toughness. Like stainless steel will scratch and bend. The carbon fiber will tend to shatter.

• 2:02:15Speaker 01

  So toughness being the area under the stress strain curve. So that you're generally going to have to do better with steel. Just stainless steel to be precise.

• 2:02:25Speaker 00

  One other Starship question. So I visited Starbase two years ago with Sam Teller, and that was awesome. It was very cool to see in a whole bunch of ways. One thing I noticed was that people really took pride in the simplicity of things. Where everyone wants to tell you how Starship is just a big soda can. And we're hiring welders. And if you can weld in any industrial project, you can weld here. But there's a lot of pride in the simplicity.

• 2:02:55Speaker 01

  Well, Starship is a very complicated rocket.

• 2:02:58Speaker 00

  So that's what I'm getting at. Are things simpler or are they complex?

• 2:03:03Speaker 01

  I think maybe just what they're trying to say is that you don't have to have prior experience in the rocket industry to work on a Starship. Somebody just needs to be smart and work hard and be trustworthy. And they can work in a rocket. They don't need prior rocket experience. Starship is the most complicated machine ever made by humans, by a long shot.

• 2:03:28Speaker 00

  In what regards?

• 2:03:29Speaker 01

  Anything, really. There isn't a more complex machine.

• 2:03:35Speaker 01

  Yeah, I mean, I'd say that there's pretty much any project I can think of would be easier than this. And that's why no one has made a rapidly reusable. Nobody has ever made a fully reusable orbital rocket. It's a very hard problem.

• 2:03:54Speaker 01

  I mean, many smart people have tried before, very smart people, with immense resources, and they failed. So, and we haven't succeeded yet. Falcon is partially reusable, but the upper stage is not. Starship version three, I think this design.

• 2:04:16Speaker 01

  That it can be fully reusable. And that full usability is what will enable us to become a multiplanet civilization.

• 2:04:24Speaker 00

  Can you say about the

• 2:04:26Speaker 01

  So I don't like, I said, I could

• 2:04:30Speaker 01

  A- any technical problem, even like a hydro plaid or something like that is easier following this.

• 2:04:35Speaker 00

  We we spent a lot of time on battle next. Can you say what the current starship battle next are even at a high level?

• 2:04:41Speaker 01

  I mean, trying to make it not explode. Generally.

• 2:04:45Speaker 00

  That all chess.

• 2:04:46Speaker 01

  Really wants to explode.

• 2:04:49Speaker 00

  Of those combustible.

• 2:04:51Speaker 01

  We've had two boosters explode on the test. Um, one obliterated, obliterated the entire test facility.

• 2:04:58Speaker 01

  So it only takes like one mistake and and I mean, the amount of energy contained in in Starship is insane.

• 2:05:05Speaker 00

  And so is that why it's harder than Falcon? It's because it's just more energy?

• 2:05:10Speaker 01

  It's a lot of new technology. It's pushing the performance envelope.

• 2:05:17Speaker 01

  The Raptor three engine is a very, very advanced engine, by far the best rocket engine ever made. But it desperately wants to blow up. I I mean, just to put this in perspective here. On liftoff, um, the the rocket is generating over a hundred gigawatts of power. Twenty percent of yours. The interesting.

• 2:05:38Speaker 00

  It's a great comparison.

• 2:05:39Speaker 01

  While not exploding.

• 2:05:41Speaker 00

  Sometimes.

• 2:05:42Speaker 01

  Sometimes, 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 we wanted to merely not merely not explode, but but fly reliably.

• 2:05:59Speaker 01

  On a daily basis, like once per hour and obviously blows up a lot. It's very difficult to maintain that launch cadence.

• 2:06:06Speaker 00

  Yes.

• 2:06:08Speaker 01

  And and then I'm gonna say like, 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's ever made a reusable orbital heat shield. Um, so the the shield, the heat shield's got to make it through the ascent 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 airframe.

• 2:06:41Speaker 00

  Isn't that hard? It's kind of fundamentally a consumable.

• 2:06:45Speaker 01

  Well, yes, but your brake pads in your car are also consumable, but they last for a long time.

• 2:06:49Speaker 02

  Fair.

• 2:06:49Speaker 01

  So it just needs to last very long time.

• 2:06:54Speaker 01

  But that's just, I mean, we have brought the ship back and had it do a soft landing in the ocean. We've done it a few times, but it lost a lot of tiles. You know, it was not reusable without a lot of work.

• 2:07:11Speaker 02

  Yeah.

• 2:07:12Speaker 01

  So even though it did land, it did come to soft landing, it would not have been reusable without a lot of work. And that, so it's not really reusable in that sense. That's the biggest problem that remains is fully reusable heat shield.

• 2:07:28Speaker 01

  So if you want to be able to land it, refill the propellant and fly again. Without, you know, you can't do this laborious inspection of, you know, forty thousand tiles to everything.

• 2:07:40Speaker 02

  I'm curious how you drive, like, when I read biographies of yours, it just, 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. And 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?

• 2:08:07Speaker 01

  I don't know.

• 2:08:09Speaker 02

  But like today, you said you had like a bunch of SpaceX meetings, like what is it that you're doing there that's like keeping that?

• 2:08:13Speaker 01

  It's adding urgency.

• 2:08:14Speaker 02

  Yeah, yeah, yeah.

• 2:08:17Speaker 01

  Well, I don't know, I guess the urgency is going to come from where I was leading the company. So of my sense of urgency, I have like a maniacal sense of urgency. So that maniacal sense of urgency projects through the rest of the company.

• 2:08:32Speaker 02

  Is it because of consequences, they're like, if, you know, Elon set a crazy deadline, but if I don't get it, I know what happens to me. Is it just 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?

• 2:08:47Speaker 01

  Yeah, I'm constantly addressing the limiting factor.

• 2:08:51Speaker 01

  So.

• 2:08:56Speaker 01

  I mean, on the deadlines front, I generally actually try to aim for a deadline that I at least think is at the 50th percentile. So it's not like an impossible deadline, but it's the most aggressive deadline I can think of that could be achieved with 50% probability, which means that it'll be late half the time. And whatever, like, there is like a law of gases expansion that applies to schedules. Like whatever schedule you have, like if you said we're gonna do this, something in like five years, which to me is like infinity time, it will expand to fully available schedule and it'll take five years.

• 2:09:38Speaker 01

  You know, there's a physical limit, like physics will limit how fast you can do certain things. Like scaling up manufacturing, there's a rate at which you can move the atoms and scale manufacturing. That's why you can't like instantly make, you know, a million or something, millions a year or something. You've got to design the manufacturing line, you've got to bring it up, you've got to ride the S curve of production. So, yeah, I guess, like, what can I say that's actually helpful to people?

• 2:10:18Speaker 01

  I think generally, a maniacal sense of urgency is a very big deal. So, and you want to have an aggressive schedule, and then you want to figure out what the limiting factor is at any point in time and help the team address that limiting factor.

• 2:10:38Speaker 00

  Can you maybe talk about the, so Starlink was slowly in the works for many years.

• 2:10:43Speaker 01

  Yeah, we talked about it all the way in the beginning of the company.

• 2:10:47Speaker 00

  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?

• 2:10:59Speaker 00

  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 which to act?

• 2:11:11Speaker 01

  I mean, I have these very detailed engineering reviews weekly. That's maybe a very unusual level of granularity. I don't know anyone who runs a company, or at least a manufacturing company that goes to level of detail that I go into. So it's not as though, like, I have a pretty good understanding of what's actually going on. Because we go through things in detail. And I'm a big believer in skip-level meetings, where the individuals, instead of having the person that reports to me say things,

• 2:11:52Speaker 01

  it's everyone that reports to them says something in the technical review. And there can't be advanced preparation. So otherwise, you're going to get glazed, as they say these days.

• 2:12:11Speaker 00

  Yeah, exactly. Very Gen Z of you.

• 2:12:13Speaker 01

  Very Gen Z.

• 2:12:13Speaker 02

  How do you prevent advanced, and you just like call them randomly?

• 2:12:16Speaker 01

  No, just go around the room. Everyone provides an update. So, I mean, it's a lot of information to keep in your head. Because you've got, then, say, if you have meetings weekly or twice weekly, you've got a snapshot of what that person said. And you can then, you know, plot the progress points. You can sort of mentally plot the points on a curve and say, are we converging to a solution or not? Or are we, you know? Like, I'll take drastic action only when I conclude that

• 2:12:58Speaker 01

  success is not in a set of possible outcomes. So when I say, okay, when I finally reach the conclusion that, okay, unless drastic action is done, we have no chance of success, then I must take drastic action.

• 2:13:13Speaker 01

  So that's, I came to that conclusion in 2018, took drastic action and fixed the problem.

• 2:13:19Speaker 02

  How many, you know, you've got many, many companies and in each of them, it sounds like you do this kind of deep engineering understanding of what their own bottlenecks are. So you can do these reviews with people.

• 2:13:34Speaker 01

  Yeah.

• 2:13:36Speaker 02

  You've been able to scale it up to five, six, seven companies. Within one of these companies, you have many different mini companies within them. What determines the maximum here? Could you have like 80 companies?

• 2:13:47Speaker 01

  Eighty? No.

• 2:13:48Speaker 02

  But you have so many already. Like, that's already remarkable.

• 2:13:52Speaker 00

  Why this current number?

• 2:13:53Speaker 02

  Yeah, exactly.

• 2:13:54Speaker 01

  I know. So.

• 2:13:55Speaker 00

  We can barely keep one company together.

• 2:14:01Speaker 01

  It depends on situation. So I actually don't have regular meetings with foreign companies. So that foreign 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 I actually allocate time according to where the limiting factor or the problem, where are things problematic? Or where are we pushing against? Like, what is holding us back? You know, I focus, the risk of saying the words too many times, the limiting factor. So basically, if something's

• 2:14:46Speaker 01

  good, like the irony is if something's going really well, they don't see much of me. But if something is going badly, they'll see a lot of me.

• 2:14:55Speaker 00

  And so if something's.

• 2:14:55Speaker 01

  Or not even badly. It's like.

• 2:14:58Speaker 00

  If something's a limiting factor.

• 2:14:58Speaker 01

  It's a limiting factor. Exactly. It's not exactly very badly, but it's the thing that's, it's the thing that we need to make go faster to.

• 2:15:04Speaker 00

  And so if something's a limiting factor at SpaceX or Tesla, are you like talking weekly and daily with the engineer that's working on it? How does that actually work?

• 2:15:17Speaker 01

  Most things that are learning factor are weekly and some things are twice weekly. So the AI five chip review is twice weekly. And so it's every Tuesday and Saturdays is the chip review.

• 2:15:31Speaker 00

  Is it open ended and how long it goes?

• 2:15:34Speaker 01

  Technically, yes, but usually it's like two or three hours. So I mean, sometimes less, it's, it depends on how much information you've got to go through.

• 2:15:45Speaker 00

  Yeah, that's another thing. I'm just trying to tease out the differences 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. But then time is often pretty finely sliced into, you know, half hour meetings or even 15 minute meetings. And it seems like you hold more open ended, we're talking about it until we figure it out type meetings.

• 2:16:20Speaker 01

  Yeah. Yeah, sometimes, but most of them seem to more or less stay on time. So, I mean, today's Starship engineering review went a bit longer because there were more topics to discuss.

• 2:16:43Speaker 01

  They're trying to figure out how to scale to a million plus tons to over per year is quite challenging.

• 2:16:48Speaker 02

  Can I ask a question? So you said about Optimus and AI that they're going to result in double just a growth rates within a matter of years.

• 2:16:57Speaker 01

  Oh, like the economy?

• 2:16:59Speaker 02

  Yeah.

• 2:16:59Speaker 01

  Yes, I think that's right.

• 2:17:02Speaker 02

  What was the point of the Doge cuts if the economy is going to grow so much?

• 2:17:08Speaker 01

  Well, I think like wasting Florida are not good things to have, you know.

• 2:17:13Speaker 01

  I was actually pretty worried about, I guess, I mean, I think in the absence of AI and robotics, we're actually totally screwed because the national debt is piling up like crazy. Now, our interest payments, the interest payments to the national debt exceed the military budget, which is a trillion dollars. So if over a trillion dollars just the interest payments, you know, that was like, I was like, okay, pretty concerned about that. Maybe if I spend some time, we can slow down the bankruptcy of the United States and give us enough time for the AI and robots to, you know, help solve the national debt. Or not help solve, it's the only thing that could solve the national debt.

• 2:17:58Speaker 01

  Like, we are 1,000% going to go bankrupt as a country and fail as a country without AI and robots. Nothing else will solve the national debt. And so, but we'd like to, well, we just need, we need enough time to get, build the AI and robots to not go bankrupt before then.

• 2:18:19Speaker 02

  I guess the thing I'm curious about is when Doge starts, you have this enormous ability to enact reform and

• 2:18:27Speaker 01

  Not that enormous.

• 2:18:28Speaker 02

  Sure, sure. But totally by your point that like, it's important that AI and robotics drive product improvements, drive GDP growth, but why not just directly go after the things you were pointing out, you know, like the tariffs on certain components, or whether it's like permitting?

• 2:18:44Speaker 01

  I'm like the president. And very hard to cut, to cut, to even, even to cut things that are obvious waste and fraud, like, like ridiculous waste and fraud. What I discovered that is it's extremely difficult even to cut very obvious waste and fraud from the government. Because the government tends to operate on a, on like, who's complaining, like, if, and if you cut off payments to fraudsters, they immediately come up with the most sympathetic sounding 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.

• 2:19:25Speaker 01

  And we're like, meanwhile, there's no baby pandas are dying, they're just making it up. The forces are capable of coming up with extremely compelling, sort of heart wrenching stories that are false, but nonetheless, sound sympathetic. And that that's what happened. And so it's like, perhaps I should have known better. And I thought, wait, let's take a let's let's let's try to cut some amount of waste and fraud from the government. Maybe there shouldn't be, you know, 20 million people marked as alive in social security who are definitely dead and over the age of 115. The oldest American is 114.

• 2:20:10Speaker 01

  So it's safe to say if somebody is 115 and marked as alive in the social security database, something is there's either a typo, like somebody should call them and say, we we seem to have your birthday wrong, or or or we need to mark you as dead. Okay. One of the two things. Very intimidating call to get. Well, so it seems like a reasonable thing. And if if like, say, their birthday is in the future, and they have, you know, a small business administration loan, and their birthday is 2165, we either gain have a typo or we have fraud. So we say we appear to have gotten the century of your birth incorrect.

• 2:20:55Speaker 01

  Or a great plot for a movie. Yes, this is this, this is what I mean by ludicrous fraud. That's what I mean by ludicrous fraud. Were those people getting payments? Some were getting payments from social security, but but but the main fraud vector was to mark somebody as alive in social security and then use every other government payment system to basically to do fraud. Because what those other government payment systems do would do, they will simply do an are you alive check to the social security database. It's a it's a bank shot. What would you estimate is like the total amount of fraud from this mechanism? My guess is, and other, by the way, the government accountability office has done these estimates before. I'm not the only one who's coming out of this, you know.

• 2:21:37Speaker 01

  In fact, I think they they did, the GAO did analysis, a rough estimate of fraud during the Biden administration and calculated at roughly half a trillion dollars.

• 2:21:48Speaker 01

  So don't take my word for it. Take it a report issued during the Biden administration.

• 2:21:52Speaker 02

  This social.

• 2:21:53Speaker 01

  How about that?

• 2:21:54Speaker 02

  From this social security mechanism?

• 2:21:56Speaker 01

  It's one of many. It's important to appreciate that the government does not, is very ineffective at stopping fraud. Because it's not like, like it was a company, like stopping fraud. You've got a motivation because it's affecting the earnings of your company. But the government, they just print more money. So it's not, like you need, you need caring and competence. And these are in short supply at the federal level.

• 2:22:30Speaker 02

  Yeah, I'm sorry.

• 2:22:30Speaker 01

  I mean, when you go to the DMV, do you think, wow, this is a bastion of competence? Well, now imagine it's worse than the DMV because it's the DMV that can print money.

• 2:22:41Speaker 02

  So was it not possible?

• 2:22:42Speaker 01

  At least the state level DMVs need to, the states more or less need to stay within their budget of the bankrupt, but the federal government just prints full money.

• 2:22:51Speaker 02

  Was it not possible if there's a catch, you half a trillion of fraud? Why was it not possible to cut all that?

• 2:22:58Speaker 01

  Because when, as soon as you, we did, we actually, no. You really have to stand back and recalibrate your expectations for competence. Because you're operating in a world where, you know, you've got to sort of make ends meet. Like, you know, you've got to pay your bills, you've got to, you know.

• 2:23:21Speaker 02

  Buy the microphones.

• 2:23:22Speaker 01

  Yeah, yeah, exactly. So you, if you don't have, it's not like there's a giant, largely uncaring monster bureaucracy. It's not even, and a bunch of necroistic computers that are just, they're just sending payments. Like, one of the things that, that, that those things, that there was, and it sounds so simple, that that probably will save, let's say 100 billion, maybe 200 billion a year, is simply requiring that payments from the main treasury computer, which is called PAM, it's like Payment Accounts Master or something like that.

• 2:24:01Speaker 01

  There's five trillion PAMs here, requiring that any payment that goes out have a payment appropriation code, make it mandatory, not optional, and that you have anything at all in the comment field.

• 2:24:16Speaker 01

  Because you have to recalibrate how dumb things are. We think. Payments were being sent out with no appropriation code, not checking back to any congressional appropriation and no explanation.

• 2:24:32Speaker 01

  And this is why the Department of War, formerly Department of Defense, cannot pass an audit because the information is literally not there.

• 2:24:41Speaker 01

  Recalibrate your expectations.

• 2:24:43Speaker 02

  I want to better understand this half a trillion number because there was an IG report in 2024.

• 2:24:47Speaker 01

  Well, you must like, why is it so low?

• 2:24:50Speaker 02

  Maybe, but we found that like over seven years, the social security fraud they estimated was like 70 billions over seven years, so like 10 billion a year. So I'd be curious to see what like the other 4.90 billion is.

• 2:25:00Speaker 01

  Federal government expenses are seven and a half trillion a year.

• 2:25:02Speaker 02

  Yeah.

• 2:25:04Speaker 01

  What percentage, how competent do you think government is?

• 2:25:08Speaker 02

  The discretionary spending there is like 15%.

• 2:25:13Speaker 01

  Yeah, but it doesn't matter. Most of fraud is non-discretionary. It's basically fraudulent Medicare, Medicaid, social security,

• 2:25:22Speaker 01

  you know,

• 2:25:26Speaker 01

  disability, there's a zillion government payments.

• 2:25:29Speaker 02

  Yeah.

• 2:25:30Speaker 01

  And a bunch of these payments are in fact, they're 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 a reductio ad absurdum. The government, the government is perfect and has no fraud. What is your probability estimate of that?

• 2:25:54Speaker 01

  I mean, zero. Okay. So then would you say that fraud and waste, that the government is,

• 2:26:02Speaker 01

  has, is 90%? That also would be quite generous. But if it's only 90%, that means that there's $750 billion a year of waste and fraud. And it's not 90%. It's not 90% effective.

• 2:26:16Speaker 02

  This seems like a strange rate of first principles, the amount of fraud in the government. Just like, how much do you think there is? And then, anyways, we don't have to do it live, but I'd be curious to like.

• 2:26:25Speaker 01

  You know a lot about fraud at Stripe. People are constantly trying to do fraud.

• 2:26:30Speaker 00

  Yeah, but as you say, it's like a little bit of a, 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're.

• 2:26:40Speaker 01

  Yeah, but I mean, I mean, at Stripe, you have high competence and you try hard. You have high competence and high caring, but still fraud is non-zero.

• 2:26:52Speaker 01

  Now, now imagine it's at a much bigger scale. There's much less competence and much less caring.

• 2:27:00Speaker 01

  You know, back PayPal, back in the day, we tried to manage fraud down to about 1% of the payment volume.

• 2:27:07Speaker 01

  And that was very difficult, took a tremendous amount of competence and caring to get fraud merely to 1%.

• 2:27:14Speaker 01

  Now imagine that you're an organization where there's much less caring and much less competence. It's going to be much more than 1%.

• 2:27:22Speaker 00

  How do you feel now, looking back on kind of politics and doing stuff there? Where it feels like we're from the outside in, that, you know, two things have been quite impactful. One, the America PAC and two, the acquisition 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?

• 2:27:52Speaker 01

  Well,

• 2:27:56Speaker 01

  I think, I think those things needed to be done to maximize the probability that the future is good.

• 2:28:04Speaker 01

  So,

• 2:28:07Speaker 01

  but politics generally is very tribal and it's, it's very tribal. And people lose their objectivity usually with politics. Like 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.

• 2:28:24Speaker 01

  I, that I guess was one of the things that surprised me the most is you often simply cannot reason with people. If they're in one tribe or the other, they, they simply believe that everything their tribe does is good and anything the other political tribe does is bad. And persuading them is.

• 2:28:41Speaker 01

  Otherwise, it's almost impossible. So anyway, but I think I think overall

• 2:28:55Speaker 01

  those actions, acquiring Twitter,

• 2:29:01Speaker 01

  getting Trump elected, even though it makes a lot of people angry. I think those I think those actions are good for were good for civilization.

• 2:29:10Speaker 02

  Yeah. Well, how does it feed into the future you're excited about?

• 2:29:13Speaker 01

  Well, America needs to contain. America needs to be strong enough to last long enough to

• 2:29:21Speaker 01

  extend life to other planets and to I get, I guess, AI and robotics to the point where we can ensure that the future is good. Like, on the other hand, if we were to descend into,

• 2:29:36Speaker 01

  say, communism or some situation where the state was extremely oppressive, that that would mean that we we might not be able to become multiplanetary.

• 2:29:49Speaker 01

  And we might the state might, you know, stamp out our progress in AI and robotics.

• 2:29:57Speaker 02

  How do you feel about, you know, Optimus, Grok, et cetera, are going to be leveraged by and not just yours. Any revenue maximizing companies products will be leveraged by the government over time. How does this concern manifest in what private company should be willing to give governments? What kinds of guardrails should like, should, you know, should AI models be

• 2:30:28Speaker 02

  made to do whatever the government that has contracted them out to do, ask them to do? Should like, should should Grok get to say like, actually, even the military wants to do X? No, Grok will not do that.

• 2:30:41Speaker 01

  I think probably the biggest danger of AI, or maybe the biggest danger of fail for for AI and robotics going wrong is government.

• 2:30:51Speaker 02

  Interesting.

• 2:30:52Speaker 01

  You know?

• 2:30:54Speaker 01

  I mean, the way you think, like, like, people who are opposed to corporations or worried about corporations should 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, government is just the biggest corporation with a monopoly on violence.

• 2:31:15Speaker 01

  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 worst corporation.

• 2:31:28Speaker 01

  But people have that dichotomy. They somehow think at the same time that government can be good, but corporations bad, and this is not true. Corporations are, have better morality than the government.

• 2:31:38Speaker 02

  It is.

• 2:31:39Speaker 01

  So I actually think it's, you know, that's, that is the thing to be worried about. It's like, if, you know, should, should, if the government should not, like, the government could potentially use AI and robotics to suppress the population.

• 2:31:56Speaker 01

  Like, that is a serious concern.

• 2:31:57Speaker 02

  As a guy building AI and robotics, how do you, how do you like, how do you prevent that?

• 2:32:03Speaker 01

  Well, I think that, like, if you have a limited government,

• 2:32:09Speaker 01

  if you limit the powers of government, which is like really what the US Constitution is intended to do, is intended to limit the powers of government, then, then you're probably going to have a better outcome than if you have more government. So.

• 2:32:21Speaker 00

  But robotics will be available to all governments, right?

• 2:32:25Speaker 01

  Not about all governments. I mean,

• 2:32:30Speaker 01

  it's difficult to predict the, like, I can say, like, what's, what's, what's the end, 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.

• 2:32:43Speaker 01

  Like, if civilization progresses, AI will vastly exceed the sum of all human intelligence, and there will be far more robots than humans.

• 2:32:56Speaker 01

  Along the way, what happens?

• 2:32:59Speaker 01

  It's very difficult to predict.

• 2:33:00Speaker 02

  I mean, it seems like one thing you could do is just say,

• 2:33:04Speaker 02

  you are not allowed to, whatever government X, you're not allowed to use Optimus to do XYZ. Just write out like a policy. I mean, I think you treated recently that Grok should have a moral constitution. And one of those things could be that we limit what governments are allowed to do with this advanced technology.

• 2:33:21Speaker 01

  I mean, yeah, we can do what is, what, I mean,

• 2:33:27Speaker 01

  technically, I mean, if the policy just pass a law, then, and they can enforce that law, then it's hard to not do that law.

• 2:33:37Speaker 01

  The best thing we can have is, is limited government, where, you know, you have, you have the appropriate cross checks between the executive judicial and legislative branches.

• 2:33:50Speaker 02

  I guess the, the reason I'm curious about it 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 GPUs, you've got the-

• 2:34:00Speaker 01

  You think I will be the boss of the government?

• 2:34:02Speaker 02

  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, like, the government really cares about getting certain satellites up in space, whatever, like, it needs SpaceX. It is, it is the, a necessary contractor. And you are in the process of building more and more of the,

• 2:34:25Speaker 02

  the technological components of the future that, that will have analogous role in different industries. And you could have this ability to, like, set some policy that, you know, is suppressing classical liberalism in any way. My companies will not help in, in any way with that, or, you know, some policy like that.

• 2:34:45Speaker 01

  I will do my best to ensure that anything that's within my control maximizes the good outcome for humanity.

• 2:34:55Speaker 01

  I think anything else would be short sighted. Because obviously, I'm part of humanity, so I like humans. Pro-human, pro-human.

• 2:35:08Speaker 02

  You've mentioned that Dojo 3 will be used for space-based compute.

• 2:35:14Speaker 01

  You really read my, what I say.

• 2:35:18Speaker 02

  I don't know if you know Twitter, but I know Elon. You have a lot of followers.

• 2:35:22Speaker 01

  Hey, give away. Honestly, you've discerned my secrets, and I boast them away.

• 2:35:29Speaker 02

  How do you design a ship for space? What changes?

• 2:35:34Speaker 01

  Well, I guess you want to design it to be more radiation tolerant and run at a higher temperature. So roughly, if you increase the operating temperature by 20th set in degrees Kelvin, you can cut your radiator mass in half. So running at a higher temperature is helpful in space. I mean, there's various things you can do for shielding the memory. But neural nets are going to be very resilient to bit flips. So like, most of what happens for radiation is like random bit flips. But like, if you've got like, you know, a multi-trailing parameter model, and you get

• 2:36:17Speaker 01

  a few bit flips, it doesn't matter. Like heuristic programs are going to be much more sensitive to bit flips than some giant parameter file. So I just designed it to run hot. And I think you'd pretty much do it the same way that you do things on Earth, apart from make it run hotter.

• 2:36:41Speaker 02

  I mean, the solar arrays, most of the weight on the satellite, is there a way to make 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-based world?

• 2:36:58Speaker 01

  Well, I mean, the basic math is like, if you can do about a kilowatt per reticle, and then you'd need, you know, 100 million full reticle chips to do 100 gigawatts.

• 2:37:18Speaker 01

  So yeah, depending on what your yield assumptions are, you know, that tells you how many chips you need to make.

• 2:37:28Speaker 01

  But cool, you need, if you're gonna have 100 gigawatts of power, you need, you know, 100 million chips running, that are running a kilowatt sustained, but theoretical.

• 2:37:44Speaker 02

  100

• 2:37:44Speaker 01

  Basic math.

• 2:37:45Speaker 02

  100 million chips depends on, yeah, if you look at the die size of something like Black Bolt GPs or something, and how many you can get out of a wafer, you can get like on the order of dozens or less per wafer. So basically, this is a world where if we're putting that out every single year, you're producing millions of wafers a month.

• 2:38:14Speaker 02

  That's the plan with TeraFab, millions of wafers a month of advanced process notes.

• 2:38:18Speaker 01

  It's going to be some number of multiple million, I think. You gotta do the memory, too.

• 2:38:22Speaker 02

  Yeah, you're gonna make a memory fab?

• 2:38:25Speaker 01

  I think the TeraFab's gotta do memory, it's gotta do logic, memory, and packaging.

• 2:38:28Speaker 02

  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 do you tell them to do?

• 2:38:45Speaker 02

  I want a million wafers a month in 2030. What is the next, like what do you?

• 2:38:49Speaker 01

  That's right.

• 2:38:49Speaker 02

  Do you like call ASML, like what is the?

• 2:38:51Speaker 01

  That's what I want.

• 2:38:53Speaker 02

  What is the next step?

• 2:38:54Speaker 00

  That's so much to ask.

• 2:38:55Speaker 01

  Well, we make a little fab and see what happens, make our mistakes at a small scale, and then make a big one.

• 2:39:06Speaker 02

  Is a little fab done?

• 2:39:07Speaker 01

  No, it's not done. Which, I mean, we're not gonna keep that cat in the bag. That cat's gonna come out of the bag, bro. It'll be like drones hovering over the bloody thing, you know? You'll see its construction progress on X, right? You know, in real time. So, no, we, I mean, like, I don't know, we could just flounder in failure, to be fair. It's like not, success is not guaranteed, but.

• 2:39:35Speaker 01

  Since we want to try to make, you know, something like a hundred million. We need, we want a hundred gigawatts of power and a hundred, a hundred chips that can take a hundred gigawatts. And so call it, you know, but yeah, by, by 2030. So then,

• 2:39:58Speaker 01

  and we'll take as many chips as our suppliers will give us. I've said this to, I've actually said this to TSMC and Samsung and Micron. It's like, please build your more fabs faster. And we will guarantee you to buy the output of those fabs. So they're already like moving as fast as they, as they can. Like it's, it's not like, to be clear, it's not like us. You know, it's not like either, it's, it's, it's not like, it's us plus them, you know?

• 2:40:26Speaker 00

  There's an narrative 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 ramping up production very quickly.

• 2:40:42Speaker 01

  No.

• 2:40:43Speaker 00

  And the explanation, yeah. The explanation you hear is that they're dispositionally conservative. You know, they're Taiwanese or German as the story may be. And they just like, don't believe the AI. Like, is that really the explanation or is there something else?

• 2:40:57Speaker 01

  Well, I mean, it's reasonable to, like, if somebody's been in, say, the computer memory business for

• 2:41:05Speaker 01

  thirty or forty years.

• 2:41:06Speaker 00

  And they've seen cycles.

• 2:41:07Speaker 01

  They've seen like boom and bust like ten times.

• 2:41:10Speaker 00

  Yeah.

• 2:41:10Speaker 01

  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 they're desperately trying to avoid bankruptcy. And, and then there's another boom and another crash.

• 2:41:27Speaker 00

  Are there other, are there other ideas you think others should go pursue? That you're not for whatever reasons right now?

• 2:41:35Speaker 01

  I mean, there are a few companies that are, that are pursuing like new ways of doing chips.

• 2:41:42Speaker 01

  But they're, they're just not scaling fast.

• 2:41:43Speaker 00

  I, I mean, within AI, I mean, just generally.

• 2:41:47Speaker 01

  I'd say like, people should just do the thing that, where they find that they're highly motivated to do that thing. As opposed to, you know, some, some, some idea that, that I suggest. Like they should do the thing that they find. Personally, interesting and motivating to do.

• 2:42:09Speaker 01

  But yeah, going back to the limiting factor.

• 2:42:12Speaker 01

  He's upraised about 100 times.

• 2:42:17Speaker 01

  The the current limiting factor that I see in the timeframe, you know, in the sort of 20,

• 2:42:26Speaker 01

  29, 20, like in the in the three, three to four year timeframe, it's chips.

• 2:42:33Speaker 01

  In the one year timeframe, it's it's energy, power production, electricity. Like, it's not clear to me that there's enough

• 2:42:42Speaker 01

  usable electricity to turn on all the AI chips that are being made.

• 2:42:50Speaker 01

  But towards the end of this year, I think we're going to have real trouble turning on like chip output will exceed the the ability to turn chips on.

• 2:42:57Speaker 00

  What's your plan to deal with that, Ralph?

• 2:43:00Speaker 01

  Well, we're trying to accelerate electricity production.

• 2:43:04Speaker 01

  I guess that's that's maybe one of the reasons that

• 2:43:09Speaker 01

  XAI will be maybe the leader, hopefully the leader,

• 2:43:14Speaker 01

  is that we'll be able to turn on more chips than other people can turn on faster.

• 2:43:19Speaker 01

  Because we're good at hardware. And generally, the the innovations from the corporations that call themselves labs, the the ideas tend to flow. Like, it's rare to see that there's like more than about a six month difference between like the ideas travel back and forth with the people. So so I think you sort of hit the hardware wall. And

• 2:43:50Speaker 01

  and then whatever, whichever company can scale hardware the fastest will

• 2:43:55Speaker 01

  be the leader. And so I think XAI will be able to scale hardware the fastest and therefore most likely will be the leader.

• 2:44:01Speaker 00

  You you joked or, you know, were self-conscious about, you know, using the the limiting factor phrase again. But I actually think there's something deep here. And if you look at a lot of things we've touched on over the course of it, maybe kind of a good note to end on. Like. If you think of a senescent, lower-agency company, it would have some bottleneck and not really be doing anything about it. You know, Mark Andreessen had the line of, "Most people are willing to endure any amount of chronic pain to avoid acute pain." And it feels 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 gotta figure out how to, you know, work with steel, or we gotta figure out how to run the chips in space,

• 2:44:46Speaker 00

  or like, we'll take some near-term acute pain to actually solve the bottleneck. And so that's kind of the unifying thing.

• 2:44:53Speaker 01

  I have a high pain threshold. That's helpful.

• 2:44:57Speaker 00

  Solve the bottlenecks.

• 2:44:59Speaker 01

  Yes.

• 2:45:02Speaker 01

  So,

• 2:45:05Speaker 01

  you know, one thing I can say is like,

• 2:45:10Speaker 01

  I think the future's gonna be very interesting.

• 2:45:14Speaker 01

  And as I said, the Dallos up, I've been to, I was literally at Dallos, I think it was on the ground for like three hours or something.

• 2:45:25Speaker 01

  It's better to be, it's better to err on the side of optimism and be wrong than err on the side of pessimism and be right for quality of life. So, you know, your happiness will be, you'll be happier if you err on the side of optimism rather than erring on the side of pessimism. And so I recommend erring on the side of optimism.

• 2:45:48Speaker 00

  That's it, that.

• 2:45:49Speaker 02

  Cool. Yalan, thanks for doing this.

• 2:45:51Speaker 00

  Thank you.

• 2:45:55Speaker 00

  Great stamina.

• 2:45:58Speaker 02

  Hopefully this encounters the pain and the pain tolerance.