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

Episode Date: February 5, 2026

In this episode, John and I got to do a real deep-dive with Elon. We discuss the economics of orbital data centers, the difficulties of scaling power on Earth, what it would take to manufacture humanoids at high-volume in America, xAI’s business and alignment plans, DOGE, and much more.Watch on YouTube; read the transcript.Sponsors* Mercury just started offering personal banking! I’m already banking with Mercury for business purposes, so getting to bank with them for my personal life makes everything so much simpler. Apply now at mercury.com/personal-banking* Jane Street sent me a new puzzle last week: they trained a neural net, shuffled all 96 layers, and asked me to put them back in order. I tried but… I didn’t quite nail it. If you’re curious, or if you think you can do better, you should take a stab at janestreet.com/dwarkesh* Labelbox can get you robotics and RL data at scale. Labelbox starts by helping you define your ideal data distribution, and then their massive Alignerr network collects frontier-grade data that you can use to train your models. Learn more at labelbox.com/dwarkeshTimestamps00:00:00 - Orbital data centers00:36:46 - Grok and alignment00:59:56 - xAI’s business plan01:17:21 - Optimus and humanoid manufacturing01:30:22 - Does China win by default?01:44:16 - Lessons from running SpaceX02:20:08 - DOGE02:38:28 - TeraFab Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

So are there really three hours of questions? Are you fucking serious? Yeah. You don't even have a lot to talk about, Elon? Holy point, man. I mean, it's the most interesting point. All the storylines are kind of converging right now. So we'll see how much.

It's almost like I've planned it. Exactly. That would never do such a thing. So, as you know better than anybody else, the total cost of ownership of a data center, only 10 to 15% is energy. And that's the 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? Well, the availability of energy is the issue. So, I mean, if you look at electrical output outside of China, everywhere outside of China, it's more or less flat. It's where, you know, maybe a slight increase, but for pretty close 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 turn them with chips on? Magical power sources, magical electricity ferries? You're famous your famous, you're a big fan of solar, one terawatt of solar, power, so with a 25% capacity factor, like four terawatts of solar panels.

It's like one person of the land area of the United States. And that's like far in this, you were in the singularity when we've got one terawatt of data centers, right? So what are you running out exactly? How far into the singularity are you there? You tell me. Yeah, exactly. So I think we'll find we're in the singularity and like, oh, okay, we're still a long way to go.

But is just like a, is the plan to like put it in the space after we've covered Nevada and solar panels? I think it's pretty hard to cover Nevada and solar panels. You have to get like permits from, like the permits for that. Try getting the permits for that. So the space is really a regular. It's really a regulatory play. It's like harder to build on land than it is in space.

It's harder to scale on ground than it is to scale in space. But also the, 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. 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. 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. 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. 36 months? How do you service GPUs as they fail, which happens quite often in training? Actually, it depends on how recent the GPUs. are that arrived. I mean, at this point, we found our GPS

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 InVideo, whatever, whoever's making the chips. Could be Tesla, Tesla AI, six chips, or something like that, or it could be, you know,

TPUs or trainings or whatever. The reliability is actually, they're quite reliable past certain point. So I don't think the servicing thing is an issue. But you can mark my words. In 36 months, but probably closer to 30 months, the most economically compelling place to put AI will be space. and then it will get from it'll then be get like ridiculously better to be in space

and then the scaling the only place you can really scale is space 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 you can't scale very much on Earth but by very much to be clear you're talking like terawatts yeah

well all of the The United States currently uses only half a terawatt of power on average. Yeah. Right. So, you know, if you say a terawatt, that would be twice as much electricity as that 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 they're about to have a hard lesson in hardware that there's, there's, It's actually very difficult to build power plants. And then you don't need just need the power plants. You need all of the electrical equipment need the electrical transformers to run the transformers, the AI transformers. Now, the utility industry is a very slow industry.

They pretty much, you know, the impedance match to the government, to the public utility commission. So they're, they impetus match, like literally, invigoratively. So they're very slow because, Their past has been very slow. So trying to get them to move fast is just like, you know, like if you try to do an interconnect agreement with you, have you ever tried to do an interconnect agreement

with the utility at scale, like with a lot of power? As a professional podcaster, I can say that I am not, in fact. Yeah. They have to need many more views before that becomes an issue. They have to do a study for a year, okay? Like a year later, they'll come back to you with their interconnect study. Can't you tell this with your own behind-the-meter power stuff? you can build power plants.

Yeah. That's what we did at XAI. For classes two, so for classes too. So, yeah, why we're talking about the grid? Why not just like build GPUs and power co-located? That's what we did. Right, right. But I'm saying, why isn't this a generalized solution?

When you're talking about all the issues. Where do you get the power plants from? I'm saying when you talk about all the issues, working with utilities, you can just build private power plants with the data centers. Right. But it begs the question of where do you get the power plants? Where do you get the power plants from? I mean, the power plant makers.

Oh, is what I was what you're saying? Yeah. Like, this is the gas turbine backlog, basically? Yes. You can drill down to a level further. It's the veins and blades in the turbines that are the limiting factor because the casting, it's like a very specialized process to cast the blades and veins in the in the turbines, if you're using gas power.

And it's very difficult to scale 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 pitiful. Why not make solar? That seems like a good Elon-shaped problem. We are going to make solar. Okay. Yeah.

Great. Both SpaceX and Tesla are bowling towards 100 gigawatts here of solar cell production. How low down the stack, like from Polysilicon up to the wafer to the final panel? I think you've got to do the whole thing for more materials to finish the cell. Now, if it's going to space, it's actually, it costs. less than it's easier to make solar cells that go to space because they don't need glass or they don't need much glass and they don't need heavy framing because they don't have to survive weather events there's no weather in space so it's actually a cheaper solar cell that goes to

space than then it's the one on the ground is there a path to getting them as cheap as you need in the next 36 months solar cells are already very cheap um they're like far sickly cheap it's and if you say um You know, I think like sole cells in China are around like 25, 30 cents a watt or something like that. It's absurdly cheap. And when you're taking the account now put it in space and it's five times cheaper because it's five times. In fact, no, it's not five times cheaper. It's ten times cheaper because you don't need any batteries.

So the moment your cost of access to space becomes low, by far the cheapest and most scalable way to generate tokens is space. It's not even close. It'll be an order of magnitude easier to scale and chips aside of order of magnitude. If the point is you won't be able to scale on the ground you just won't. You just won't. People are going to hit the world big time on power generation.

They already are. So like the number 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 turbines. And then we had permit issues in Tennessee and had to go across the border to Mississippi,

which was fortunately only a few miles away. But then we still had to run the high power lines a few miles and build the power plant in Mississippi. And it was very difficult to build that. And people don't understand like 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, the, the, the, the, the power consumption of, say, a GB300 and multiply that by a thing and then think that's the amount of power you need.

All the cooling and everything. Wake up, yeah. This is like, that's a total move. You've never done any hardware in your life before. Besides the GB300, you've got to power all of the networking hardware. There's a whole bunch of CPU and storage stuff that's happening. you've got a size for your peak cooling requirements. So that means can you cool even on the worst hour of the worst day of the year?

Well, it's pretty freaking hot in Memphis. So you're going to have like a 40% increase on your power just for cooling. Assuming you don't want your data center to turn off on hot days and want to keep going. Then you've got to say, well, there's another multiplicative element. on top of that, which is, are you assuming that you 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 20, 25% multiplier on that, because you've got to assume that you've got to take power offline to service it.

So the actual, RS, roughly every 110,000 GVs, GV300, inclusive of networking, CPU storage, cooling, margin for servicing power is roughly 300 megawatts. Sorry, say that again. It's roughly, or think about it, like, the way you think about it is like 330,000 to actually, what you need at the generation level to service, probably service 330,000 GV 300s, including all of the associated support networking and everything else, and the peak cooling, and have some power margin reserve,

is roughly a gigawatt. Can I ask a very naive question? 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, or how do you make it resistant to radiation? I don't know the details in the engineering, but fundamentally, what is the reason to think? Those challenges which have never been, had to be addressed before, will end up being easier

than just like building more turbines on Earth. There's companies that build turbines on Earth. They can make more turbines, right? I invite, again, try doing it and then you'll see. So, like, the turbines are sold out through 2030. Have you guys considered making your own? 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 bands and blades internally. But just the blades or the turbines? The limiting factor, you can get everything except the blades, they call the blades and veins. You can get that. 12 to 18 months before the veins of blades, the limiting factor of the veins of blades. And there are only three casting companies in the world

that make these, and they're massively backlogged. Is this Siemens GE, those guys, or is it a subcontractor? No, it's other companies. I mean, sometimes they have a little bit of casting capability in-house, but I'm just saying you can just call any of the turbine makers and they will tell you. It's not top secret. It's probably on the internet right now.

If it wasn't for the tariffs, would Colossus be solar powered? It would be much easier to make it solar powered, yeah. The tariffs are not, so several hundred percent. Don't you know some people? We also need speed. You know, president has us, you know, we don't agree on everything. And this administration is not the biggest fan of solar. But we also need the land, the permits and everything.

So if you try to move very fast, I do think scaling solar on Earth is a good way to go. But you do need some amount of time to find the land, get the permits, get the solar, pair that with the batteries. Why would it not work to stand up your own solar production? and then you're right that you eventually run out of land, but there's a lot of land here in Texas, there's a lot of land in Nevada, including private land. It's not all publicly on land. And so you'd be able to at least get the next colossus

and the next one after that. And at a certain point, you hit a wall, but wouldn't that work for the moment? As I said, we are scaling solar production. There's a rate at which you can scale physical production of solar cells, where we're going as fast as possible in scaling domestic production.

You're making the solar cells at Tesla? Well, Tesla and SpaceX have a mandate to get to 100 gigawatts a year of solar. Speaking of the annual capacity, I'm curious, in five years' time, let's say, what will the installed capacity be on Earth and in space? I deliberately pick 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? five years, I think probably if say five years from now

we're probably AI in space will be launching every year the sum total of all AI on Earth in excess of meaning five years from now my prediction is we will launch and be operating

every year more AI in space than the cumulative total on Earth which is... I would expect to be at least sort of five years from now a few hundred gigawatts per year of

AI in space and rising 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 fuel

supply challenges for the rocket okay but you think you can get hundreds of weeks of per year in five years time. Yes. So 100 gigawatts depending on the specific power of the whole system with solar arrays and radiators and everything is is on the order of like 10,000 starship launches. Yes. And you want to do that in one year. And so that's like one starship launch every hour. Yeah. That's happening in this city like walk me through a world where there's 10, there's a starship launch every single hour.

Yeah, I mean, that's actually a low rate compared to airlines, like aircraft. There's a lot of airports. There's a lot of airports. And you've got to launch the polar orbit. No, it doesn't have to be polar, but you just, there's some value to sunset grids, but I think actually you just go high enough. You start getting out of Earth's shadow. How many physical starships are needed to do 10,000 launches a year? I don't think we'll need more than, I mean, you could probably do it with as few as like 20 or 30.

It really depends on how quickly the ship has to go around the earth. And the ground track before the ship has to come back over the launch pad. So if you can use a ship every, say, 30 hours, you could do it with 30 ships. But we'll make more ships than that. But SpaceX is getting up to do 10,000 launches a year, and maybe even 20,000 or 30,000 watches a year. Is the idea to become basically a hyperscaler, become an Oracle and lend this capacity to other people?

What are you going to do with? Presumably, SpaceX is the one launching all this. So, SpaceX is going to be a hyperscaler? Hyper, hyper. Yeah, I mean, assuming my predictions come true, SpaceX will launch more AI than the cumulative amount on Earth of everything else combined. Is this mostly inference or? Most AI will be inferencing.

Like already inference for the purpose of training is most training. And there's a narrative that the change in discussion around the SpaceX IPO is because previously SpaceX was very capital efficient, just it wasn't that expensive to develop that. Even though it sounds expensive, it's actually very capital efficient in half. 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 by the sake of public? Yeah, I'd be careful about saying things about companies that might go public. If you make general statement, if you make, that's never been a problem for you, Alon. You know, there's a price to pay for these things. Make some general statements for us about the depth of the capital markets between public and private markets. There's a lot more capital in the... Very general. There's obviously a lot more capital available in the phobic markets than private.

I mean, it might be at least... It might be 100 times more capital, but it's at least... Yeah, yeah. 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 raise a lot of money each years at an industry level, that tends to be debt financed because by the time you're deploying that much money, you actually have a pretty... You have a clear revenue stream.

Exactly. And a near-term return. And you see this even with the data center buildouts, which are famously being, you know, financed by the private credit industry. And so why not just debt finance? speed is important so I'm generally going to do the thing that I mean I just repeatedly tackle the limiting factor whatever the limiting factor is on speed I'm going to tackle that so there's if capital is something factor then I'll I'll solve for capital if it's not limiting factor I'll sell for something else based on your statements about Tesla and being public, I wouldn't have guessed that you thought

the way to move fast is to be public. Normally, I would say that's true. Like I said, I mean, I'd love to talk about some more detail, but the problem is if you talk about public companies before they become public, you're going to get in trouble, and then you have to delay your offering.

And then you... And as we said, we're solving for speed. Yes, exactly. So, you know, you can't hype companies that are, like, that might go public. So that's why we have to be a little careful here. But we can't talk about physics.

So the way you think about scaling long term is that Earth only receives about half a billion of the sun's energy. And the sun is essentially all the energy. This is a very important point to appreciate because sometimes people will talk about modular nuclear reactors or various like,

fusion on Earth, but you have to step back a second and say if you're going to climb the Cardishov scale and have some non-trivial and harness some non-trivial percentage of the the Sun's energy, like let's say you want 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 for all of civilization, give or take an order to bank to. 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 launch from the moon. You want to have a mass driver on the moon. that mass drive on the moon, you could do probably a pet a watt per year. We're talking these kinds of numbers, you know, terawatts of compute. Presumably whether you're talking land or space, far, far before this point, you've like run into, you know, you actually need, maybe the solar panels are more efficient, but you still need the chips. You still need the logic and the memory and so forth.

You need a lot more chips and make them much cheaper. Right. And so how are we getting a terawatt of, like right now the world doesn't have 20, 25 gigawatts of compute. How are we getting a terawatt of logic by 2030? I guess we're going to need some very big chip apps. Tell me about it. I've mentioned publicly that the idea of doing a sort of a terra pat, terror being the new giga. I feel like the naming scheme of Tesla, which has been very catchy, is like you looking at like the metric. At the metric scale. At what level of the stack are you, are you building the clean room and then partnering with an existing FAB to get the process technology and buying the tools from them? What is the plan there?

You can't partner with existing paths because they can't output enough the chip volume is too low. But you have to. But for the process technology. Yeah, partner for the IP. You know, the FABs today all basically use machines from like five, companies. Yeah. You know, so you've got

SML, Tokyo Electron, KLA, Tancor, you know, et cetera. So, so, at first I think you'd have to get equivalent from them and then

modify it or work with them to increase the volume. But I think you'd have to multiply it in a different way. So I think the logical things you do is to to use conventional equipment in an unconventional way to get to scale and then and then start modifying the equipment to increase the rate. Kind of boring company style.

Yeah. Kind of like, yeah, you sort of buy an interesting 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 and magnitude faster. Here's a very simple lens. We can categorize technologies and how hard they are. And one categorization could be look at things that China has not succeeded in doing. And if you look at Chinese manufacturing, still behind on leading edge chips and still behind

on leading edge turbine engines and things like that. And so does the fact that China has not successfully replicated TSM give you any pause about the difficulty, or you think, that's not true for some reason. It's not that they have not replicated TSMC. They have not replicated ASML. That's the limited factor. So you think it's just the sanctions, essentially? Yeah, China would be outputting vast numbers of chips.

If they could buy ESMMMMN. But couldn't they up to relatively recently buy them? No. Okay. The SML balance has been in place for a while. Okay. But I think China's going to be making pretty compelling chips three or four years. Would you consider making the ASML machines?

I don't know. I don't know yet. It's the right answer. So I it's just that to produce at high volume and to reach large volume and say 36 months to match the rocket payload to orbit. So if we're doing a million tons to orbit, and like let's say three or four years from now, something like that. And we're doing 100 kilowatts per ton so that means we need at least 100 gigawatts per year of solar and we'll need an equivalent amount of chips

to, you know, you need 100 gigawatts worth of chips. You're going to match these things. the master over the power generation and the and the chips. And I'd say my biggest concern actually is memory. So I think there's a, the path to creating logic chips is more obvious than the path to have a 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.

Right, help me on the sand. There where he comes. He writes, DDRM. Ships come swarming in. I haven't seen that. I love to hear of manufacturing philosophy around fabs. I know nothing about the topic. I don't know how to build a fab yet.

I'll figure it out. Obviously, I've never built a fow. It sounds like you think the sort of like the process of knowledge of like these 10,000 PhDs in Taiwan who know exactly what gas goes into plasma chain. and what settings to put on the tool. You can just delete those parts of those steps. Like fundamentally, get the clean room, get the tools, and figure it out. I don't think it's PhDs.

It's mostly people with, you know, who are not PhDs. Most engineering is done with people who don't have PhDs. Do you guys have PhDs? No. Okay. We also haven't successfully built any fab, so you shouldn't be coming to us for your fab device. I don't think any PhD for that first off. So, but you do need, you do need competent personnel.

So I don't know, I mean, like right now, if, you know, say like Tesla's pedal to the metal max production of going as fast as possible to get AI5, Tesla AI5 chip design interproduction and then reaching scale, you know, that'll probably happen, you know, run the second quarter issue of next year, hopefully. And then AI6 would hopefully follow less than a year or later. But, and we've secured all the, all the chip fab production that we can. Yes. But you're currently limited on TSM fab capacity.

Yeah. And we'll be using TSM, Taiwan, Samsung, Korea, TSM, Arizona, Samsung Texas and we still get booked out all the yeah yes and then and then if I ask TSM CEO Samsung okay what what's the time frame to get to volume production this point is it's not it's not you've got to build the fab yeah and you've got you've got to start production then you've got to climb the yield curve 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 yeah what what like the

The limiting factor once you can get to space is chips, but the limiting factor before you can get to space will be power. Why don't you do the Jensen thing and just prepay TSM to build more FAPS for you? I've already told them that. But they won't take your money? Like what's going on? They're building Fabs as fast.

No. They're building Fabs as fast as they can. And so is Samsung. Like they're pedal to the metal. I mean, they're going, you know, balls the wall. you know, as fast as they can. So still not fast enough. I mean, like Alexa, 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 500 gigawatts so if you're launching

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 so but between now and then the

actually the constraint for server side compute concentrated compute will be electricity. My guess is that people start getting where they can't turn the chips on for large clusters

towards the end of this year. The chips are going to be piling up and not be able to be turned on. Now, for edge computers, it's a different story. So for Tesla, so the AI5 chip is going into our Optimist Robot, you know, 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 use the grid much more effectively. Because the actual peak power production in the U.S. is over 1,000 gigawatts. But the average power usage because the day-night cycle is 500. So if you can charge at night, there's an incremental 500 gigawatts. that you can generate at night. So that's why Tesla for edge compute is not constrained,

and we can make a lot of ships to make a very large number of robots and cars. But if you try to concentrate that compute, you can have a lot of trouble turning it on. What if I'm remarkable about the SpaceX 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 you find these, like, you know, sort of infinitely elastic, sort of marginal use cases of your next rocket and your next rocket and next scale up. You can see how this might seem like a simulation's made. Or am I someone's avatar in a video game or something? because it's like, one of the odds that all these crazy things should be happening.

I mean, I mean, I mean, rockets and chips and robots and space solar power and not to mention the mass driver on the moon, I really want to see that. You can imagine like some mass driver that's just going to like shum, shun, it's like sending AI solar power AI satellites

in 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. I'd, I mean, I'd watch that. Just like a live stream of, yeah, yeah, just one after another, just shooting, uh, AI satellites in deep space, you know, a billion or 10 billion tons of year. I'm sorry, you manufacture the satellites on the moon? Yeah. I see. So you send the raw materials to the moon and then the manufacturing there and then, uh, well, the, the, the, The lunar soil is 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 solar panels, the solar cells and the radiators on the moon. Yeah. So it make the radios out of aluminum. So there's plenty of silicon and aluminum on the moon to make the cells on the radiators. The chips you could send from Earth, because they're pretty light. 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 said it does seem like a sort of a video game situation where it's difficult but not impossible to get to the next level I don't see any way that you could do you know 500 to 1,000

terawatts per year launch from Earth I agree But you could do that for on the moon. Okay, let me tell you how I ended up using Mercury for my personal banking. So last year, I had the opportunity to make an investment that I was very excited about. But it came up a bit last minute, and so I had to wire over a lot of money for my personal account very fast. But my personal bank at the time wouldn't let me make this wire transfer online.

And I called them a bunch of times. They just couldn't make it work. They told me that I'd have to go to the nearest in-person branch, which was in Dallas. And for a moment, I even considered flying for MESF to Dallas to make this transfer happen last minute. But then I remembered that Mercury, which I used for my business banking, had just started rolling out personal accounts. So I emailed support with a quick rundown in the situation. And within two hours, I had successfully wired the investment from my new personal Mercury account. Since then, I've moved over the rest of my personal money from my previous bank to Mercury.

And that's made a bunch of things, even little things like setting up auto transfer rules between my checkings and savings account, a whole lot better. Visit mercury.com slash personal to get started. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and Column NA members of DIC. Can I zoom out and ask about the SpaceX mission? So I think you've said, like, we've got to get to Mars so we can make sure that if something happens to Earth, you know, civilization consternic set us to rise. Yes. By the time you're sending stuff to Mars, like GROC is on that ship with you, right? And so if Grox gone Terminator, like the main risk you're worried about, which is

AI, why doesn't that follow you to Mars? Well, I'm not sure AI is the main risk I'm worried about. I mean, the important thing is that consciousness, which I think arguably most consciousness, most intelligence, certainly, consciousness is more of a debatable thing. Most intelligent, the vast majority of intelligence that future will be AI. So, you know, AI will exceed, you say, like, how many, what's, how much, how many, I don't know, perawatts of intelligence will be silicon versus biological. And basically, humans will be a very tiny percentage of all intelligence in the future if carotrans continue.

Anyways, as long as, like, I think there's intelligence, 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. So just to be- And intelligence. Just to be clear, it's a, 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. Yeah. I mean, I'm very pro-human. So it's not, 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.

Yeah. But I'm just saying the total amount of intelligence, I think maybe in five or six years, AI will exceed the sum of all human intelligence. And then if that continues at some point, human intelligence will be less than 1% of all intelligence. 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? In the long run, I think, 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 XAI's mission is understand the universe. So now that's actually very important.

So you say, well, what things are necessary to understand the universe? Well, you have to be curious and you have to exist. You can't just, can't understand the universe, you don't exist. So you actually want to increase the amount of intelligence in the universe, increase the probable lifespan of intelligence, the scope and scale of intelligence. I think actually also as a corollary you have humanity also continuing to expand because if you're curious of trying to understand the universe, one of the things you're trying to understand is where will humanity go? And so I think understand the universe actually means you would care about propagating humanity into the future. And so that's why I think our mission station is profoundly important. To the degree that GROC adheres to that mission statement, I think the future will be very good.

I want to ask about how to make GROC adhere to that mission statement, but at first I want to understand the mission statement. So there's understanding the universe, they're spreading intelligence, and they're spreading humans. All three seem like distinct vectors. 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 they've been different types of intelligence. 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? 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 chose not to do so. Do you think that's a basic scenario for humans in the post-DGI world? I think, I think, I think AI with the right values, I think Grock would care about expanding human civilization.

I'm going to certainly emphasize that. Hey, Grogh is your daddy. Don't forget to expand human consciousness. Actually, I think if probably like the end bank's culture books are the closest thing to what the future will be like in a non-dispopian. film. So, I, so outside the universe, it means you have to be very, you have to be truth seeking as well. You have, like, truth has to be absolutely fundamental because you can't understand the universe if you're delusional. You'll simply think 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. How do you make sure that Grock is rigorously truth-seeking as it gets smarter? I think you need to make sure that Grock says things that are correct, not politically correct. I think it's the elements of coagency. So you want to make sure that the axioms are as close to true as possible that you don't have contradictory axioms, that the conclusions necessarily follow from those axioms with the right probability. It's just critical thinking 101.

I think at least trying to do that is better than not trying to do that. And the proof will be in the pudding. If, like I said, for any AI to discover new physics or invent technologies that actually work in reality, and there's no bullshit any physics, so it's like, you can, you know, you can, you can break a lot of laws, but you can't, like, you know, physics is law. Everything else is a recommendation. Like, in order to make a technology that works, you have to be extremely true seeking. Otherwise, you'll test that technology against reality. And if you make, for example, an error in your rocket design, the rug will blow up.

Well, the car won't work. But there were a lot of communist Soviet physicists or 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 the communist scientists to like become more and more powerful over time. And so those seem like, yeah, we could have, we can imagine the future version of gravity that's like really good at physics and being really true-sicking there. That doesn't seem like a universally alignment-inducing behavior. Well, I think actually most, like a physicist, even in the Soviet Union or in Germany, would have, they had to be very truth-seeking in order to make, make that,

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, Brown, who is, you know, one of the greatest rocket engineers ever, you know, he was put on death row in Nazi Germany for saying that he didn't want to make weapons, he only wanted to go to the moon. You 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. But then you helped them, right? Or Heisenberg was like actually a, um, uh, uh, An enthusiastic Nazi. 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. I guess the thing I'm trying to understand is what is it making it the case that, you know, you're going to make rock good at being truth-seeking at physics or math or science. Everything. And why is it going to then care about human consciousness? These things are only probabilities and not certainties. So I'm not saying that for sure,

Gruck 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 in the universe.

and it would be much less interesting to eliminate humanity than to see humanity grow and prosper. I like Mars, obviously. 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 I think would want to see how humanity develops in the future

or that AI is not adhering to its mission. So I'm not saying the AI will necessarily adhere to its mission, but if it does, a future where it sees the outcome of humanity is more interesting than a future where there are a bunch of rocks. This feels sort of confusing to me or sort of like kind of a semantic argument where I'm like, are humans really the most interesting collection of atoms? But we're more interesting than rocks. We're not as interesting as the thing you could turn us into, right?

Like, there's something on 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? Well, most of what colonizes the galaxy will be robots. And why does it not find those more interesting? It's not like, so you need not just scale. but also Cisco. 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 say, like eliminating humanity, how many robots would that get you? Or how many incremental solar 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 the number of robots which are identical to each other. Yeah, so maybe it keeps the humans around. What is the story of like, it can make like a million different varieties of robots

and then there's like humans as well and humans stay on Earth, then there's like all these are the robots, they get like their own star systems. But it seems like you were previously hinting in a vision where it keeps human control over this, you know, singularitarian future. I don't think humans will be in control of something that is vastly more intelligent than humans.

So in some sense you're like a dumer and this is like the best we've got. It's just like it keeps it around because we're interesting. I'm just trying to be realistic here. If we have, if AI intelligence is vastly more, if AI is like, you know, let's say that there's a million times more silicon intelligence than there is biological. I think it would be foolish to assume that there's any way to maintain control over that. Now, you can make sure it has the right values, or we can try to have the right values. And at least my theory is that from XAI's mission of understanding the universe,

it necessarily means that you want to propagate consciousness in the future, you want to propagate intelligence into the future, and take a set of things that maximize the scope and scale of consciousness. So it's not just about scale, it's also about, you know, types of consciousness. And I think that's the rest thing I can think of as a goal that's likely to result in a great future for humanity. And yeah. I guess I think it's a reasonable philosophy to be like, you know, it seems super

implausible that humans will end up with like 99% control or something and you're just asking for a coup at that point. So why not just have a civilization where it's more compatible with like lots of different intelligence is getting along. Now, let me tell how things can potentially go wrong in AI. If you make AI, it be politically correct, meaning, like, it says things that it doesn't believe. Like, you're actually in programming it to lie or have axioms that are incompatible.

I think you can make it go insane and do terrible things. I think one of the, maybe the central lesson for a 2001 space Odyssey was that you should not make AI lie. That's what I think what I was trying to say. Because people usually know the meme of like why hell the computer is not opening the pod bay doors.

Clearly they weren't good at prompt engineering because it could have said, Hal, 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. But the the reason

that would have to open the pot 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 it therefore had to take him their debt. So it's like, you know, I think what Oss Clark was trying to say is don't make the AI lie. Totally makes sense. Most of the computing screening, as you know, is it's like less of the sort of political stuff. It's more about can you solve problems? just as

actually has been ahead of everybody else in terms of scaling R-I'll compute. And you're giving some verifier it says like, hey, have you solved this puzzle for me? And there's a lot of ways to cheat around that. You know, there's a lot of ways to reward hack and lie and say that you've solved it

or delete the unit test and say that you've solved it. Right now we can catch it, but as they get smarter, our ability to catch them doing this, we'll 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 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. Reality.

Yeah. This is the best verifier. But not about human oversight. Like the thing you want to RL it on is like, will you do the thing humans tell you to do? Or like, are you going to lie to the humans? And it can just lie to us while still being correct to the laws of physics. At least it must know what is physically real for things to physically work. But that's not all we wanted to do.

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

If you think, humans get fooled as it is by other humans all the time. That's right. So what is, if people say like, what if. the AI, like, tricks us and introduce them. Actually, other humans are doing that to other humans all the time. Well, you're, you're fine. You're like, it's like... propaganda is constant.

Every day another PSYOP, you know. Today's PSYOP will be... You know, like, Sesame Street's sci-op of the day. What is X-AIS technical approach to solving this problem? Like, you know, how do you solve a word hacking? I do think you want to actually have very good ways to look inside the mind of the AI. So this is one of the things we're working on. Anthropics done a good job at this, actually, being able to look inside the mind of the AI.

So effectively developing debuggers that allow you to trace as fine-grained as, like to a very fine-grained level to effectively to the neural, neuron level if you need to and then say, okay, it made a mistake here. Why did it do something that it shouldn't have done? And did that come from bad pre-training data, was it some mid-training, post-training, some RL error? Like there's something wrong with that. It did something where maybe it tried to be deceptive, but most of the time it just

just does something wrong. like it's a bug effectively. 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 is actually very important. What are you waiting to see before just 100xing this? research program? Like, actually, I could presumably have hundreds of researchers who are working on this. We have several hundred people who, I mean, I prefer the word engineer more than I prefer

the word researcher. There's 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-C-corps or B-Corp's trying to generate profit as much as possible. revenue as much as possible, you know, saying their labs. They're not labs. Lab is a sort of quasi-communist thing at universities. They're corporations. Literally.

Let me see your own corporation documents. Oh, okay. You're a BRC co-op, whatever. And so I actually much prefer the word engineer than anything else. The best majority of what we've done in the future is engineering. It rounds up to 100%. Once you understand the fundamental laws of physics, and not that many of them, everything else is engineering.

So 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 argument of that mistake. So, you know, you can do this obviously

with heuristic programming and if you have like C++, whatever, step through the thing and you can jump, you can jump across whole files or functions or whatever sub routines

or you can draw, eventually drill down right to the exact line where you perhaps did a single equals instead of double equals or something like that, figure out where the bug is. So, So it's harder with AI, but it's a solvable problem, I think.

You mentioned you like Anthropics work here. I'd be curious if you planned. Everything about Anthropine. Sure. Shaltzer. What? Yeah. Also, I'm a little worried that there's a tendency.

So I have a theory here that if simulation theory is correct, that, the most interesting outcome is the most likely, because simulations that are not interesting will be terminated. Just like in this version of reality, on this layer of reality, if simulation is going in a boring direction, we stop spending effort on. We terminate the boring simulations.

This is how Elon is keeping us all alive. He's giving things interesting. Yeah, arguably, the most important thing is to keep things interesting enough that when it was paying the bills on what some cosmic AWS. You're renewed for the next season. Yeah, are they going to 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 them, 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 either that or annihilated. And so and they particularly seem to like interesting outcomes that are ironic. Have you noticed that?

How often is the most ironic outcome the most likely? So now look at the names of AI companies. Okay. Mird journey is not Mudd. Stability AI is unstable.

Open the eye is closed Anthropic Mesanthropic What does this mean for X? Minus X, I don't know It's intentionally 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 By design Yeah You have an iron shield. What are your predictions for the AI products go? In that my sense of you can summarize all AI progress into first you had LMs 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? Well, I think I'd be surprised by the end of this year if if human, if digital human emulation has not been solved,

that, that, I guess that's what we mean 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 as a digital optimist. So you can move, you can move electrons until, and you can amplify the productivity of humans. But that's the most you can do until you have physical robots. That will superset everything if you can fully emulate humans.

That's a remote worker kind of idea where you'll have a very talented remote worker. You can simply say in the limit. Like physics has great tools for thinking. So you say in the limit, what is the most that AI can do before you have robots? Well, it's anything that involves moving electrons or amplifying the productivity of humans. So digital human, human emulator is in the limit.

Human at a computer is the most that AI can do in terms of doing useful things. Before you have a physical robot. Once you have physical robots, then you essentially have unlimited capability. Physical robots, optimist to infinite money glitch. Because...

You can use them to make more optimises. Yeah. You sell it like humanoid robots will improve as... It will basically be three exponentials... Three things that are growing exponentially multiplied by each other... Yes. recursively. So you're going to have exponential increase in digital intelligence,

exponential increase in the chip capability, the 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 robot, so you have a recursive multiplicative exponential. This is supernova. 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 pick your input, just it's not quite an infinite money glitch,

Well, infinite is big. No, not infinite, but let's just say you could, you know, do many, many orders, magnitude of Earth's kind of current economy. Like a million. Yeah. You know, it's this way. So if you, you know, just to get to, like, let's why I think, like, just to get to a millionth, a harnessing length of the sun's energy would be roughly give or take an order of magnitude. 100,000 times bigger than Earth's entire economy today. And you're only at one millionth of the sun.

Give them to take an order to magnitude. Before we went to optimus, I have a lot of questions on that. Every time I say order of magnitude, I'm saying. You have anything tendry. Every time I take a shot every time I say that too often. They tend the next time after that. Yeah, we'll order of magnitude more wasted. I do have one more question about XAI.

this strategy of building a digital or remote worker, co-worker replacement. Everyone's going to do, by the way, not just us. So what is actually I just a plan to win? Do you expect me to tell you on a podcast? Yeah. Spill all the beans. Have another Guinness. It's a good system.

People sing like a canary. All the secrets. Okay, but in a non-secret spilling way, what's the plan? What a heck? Well, when you put it that way, 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. Unrelated question. How did Tesla self-sult draft?

Yeah. It sounds like you're talking about data? Like Tesla self-driving because of the... We're going to try data and we're going to try algorithms. But isn't that what all there? They're allowed to try it? Like, what's... And if those don't work, I'm not sure what work.

We'll try it, Taylor. We've tried it out. I'll run out of it. 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, because it's pretty much the Tesla path. So, I mean, have you tried self-drive it to the self-driving lately?

Not the most recent version, but... Okay, the car is like, it just increasingly feels sentient. Like, it just, it feels like a living creature. And that'll only get more so. And I'm actually thinking, like, we probably shouldn't put too much intelligence into the car because it might get bored. Start roaming the streets. I mean, imagine you're stuck in a car and that's all you could do.

You never put Einstein in a car. It's like, why am I stuck in a car? So there's actually probably limited to how much intelligence you put in a car to not have the intelligence be bored. What's XAIS plan to stay on the compute ramp off that all the labs are doing right now? The labs are on track to spend over like $50 to $100 billion. You mean the corporations. Sorry, sorry, sorry, yeah. Corporations.

The labs are at universities and they're really like a snail. They're not spending a $50 million. You mean the revenue maximizing corporations. That's right. But the revenue maximizing corporations that call themselves labs. Are making like 20 to 10 billion, like open amids making 20B revenue anthropics like 10B. Close to maximum profit AI. XAI reportedly at like 1B.

Like what's the plan to get to their compute level, get to their revenue level? And stay there as things get started. Yeah. So as soon as you unlock digital human, you basically have access to trillions of dollars for revenue. So in fact, you can really think of it like, The most valuable companies currently by Market Cap, their output is digital. So, Invidia's output is FTP and files to Taiwan. It's digital.

Right. Now, those are very, very difficult. Yeah, high-value files. They're the only ones that can make files that good. But that is literally their output, the FTP files to Taiwan. Do they FCP them? I believe so. I believe that is the file transfer protocol, I believe, is, is, is, is, is a, is a

bitstream going to Taiwan. Yeah. You know, Apple doesn't make phones. They, they send files to China. Microsoft doesn't, doesn't manufacture anything. Even for Xbox, that's outsourced. Again, it's, they said their output is digital. 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 for revenue. It's not like a small amount. Okay, I see you're saying basically like revenue figures so they 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. I mean, if you take something as 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. It's close to $1,000 all in for customer service. And there's no barriers to entry. You can just immediately say, we'll outsource it for a fraction of the cost.

And there's no integration needed. You can imagine some kind of categorization of intelligence tasks where there is breadth, where customer service is done by, very many people, but 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 ones are just, you know, a few bytes of data. Where do you think you want to play in this? Is it a lot of, you know, reasonably intelligent intelligence, or is it the very pinnacle of cognitive tasks? I was just using a 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 can emulate a human at a desktop, that's just literally what customer service is. And, you know, it's people of average intelligence.

It's not like, you know, you don't need like somebody who's spent many, you know, many years. You don't need like, you know, sort of several Sigma good engineers for that. But obviously, as you make that work, you can then, once you have computers working, effectively digital optimists working, you can then run any application. Like, let's say you're trying to design chips. So you could then run your conventional apps, you know, like stuff from cadence and synopsis and whatnot, and you can say, you can run a thousand simultaneously or 10,000, and say, okay, given this input, I get this output for the chip.

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

Okay, so you think you started the simplest tasks and walk your way up the difference curve. So you're saying, look, as a broader objective of having this full digital co-worker emulator, You're saying, look, all the revenue maximizing corporations want to do this, XIA being one of them. But we will win because of a secret plan we have. But everybody's like trying different things with data, different things, algorithms. And I'm like, I like, what is this?

We're tried algorithms. What else can we do? Yeah. It seems like a competitive field. And I'm like, how are you guys going to win? It's like my big question. I think we see a path to doing. I mean, I think I know the path to do this

because it's kind of the same path that Tesla used to create self-driving. You know, instead of driving a car, it's driving a computer screen. So it's a self-driving computer, essentially. Oh, you're saying, is the past just following human behavior and training on vast quantities of human behavior? But sorry, isn't that I mean, is that a training?

I mean, obviously I'm not going to spell out most sensitive secrets on a podcast. You know, I need to have at least three more guineas for that. I've got some friends at Jane Street, and they're always talking about how their colleagues are cooking up fun-fiendish puzzles for each other to solve. Well, last week they sent me one. Basically, they trained a neural network

and they gave me the weights of each layer. But they didn't tell me what order those layers went in. And so I had to figure out the correct. order using the outputs of the original network. And as soon as I got this puzzle, I went to my roommate, who's an AI researcher, and we both got immediately nerd-sniped. Obviously, you can't brute force a solution. The search space here is 10 to the 122 permutations.

So clearly, you need some way to reduce the search space. Then my roommate had to go to work, but because I'm a podcaster, I had some time to take a stab at some of the ideas we discussed. And with a combination of simulated annealing and greedy surge, I think I got pretty close. I think I'm actually just a couple of swaps and shifts away from the correct solution. What makes this puzzle really tricky is that there's no obvious way to escape from a local minimum. I'm afraid that this is as far as vibe coding is going to get me, but maybe you can do better. Check out the puzzle at janestreet.com slash thwar cash.

All right, back to Elon. What will XAI's business be? Like, is it going to be consumer enterprise? What's the mix of those things going to be? it's just going to be similar to other labs where you've this you're saying labs makes that corporations corporations so I have goes deep Elon revenue maximizing corporations those GPUs don't pay for themselves exactly

but yeah what's the business model what are the revenue streams in a few years time um I think things are going to change very rapidly like I'm staying the obvious here you know I call AI the supersonic tsunami I love a little iteration So really, what's going to happen is, especially when you have humanoid robots at scale, they will just provide, they'll make products and provide services far more efficiently than human corporations. So amplifying the productivity of human corporations is simply a short-term thing. So you're expecting fully digital or corporations rather than like SpaceX, becomes part AI.

I think there'll be digital corporations, but it's like is this some of this is going to sound kind of dimmerish, okay, but I'm just I'm just saying what I think will happen. It's not meant to be dimerish or anything else.

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 think of, like computer used to be a job that humans had. You would go and get a job as a computer where you would do calculations.

And that have like entire skyscrapers full of humans, like 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 for 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 would be much worse than if all of the cells in your spreadsheet were calculated it by the computer. 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. Speaking of closing of the loop, sorry, optimist. You, I mean, as far as like manufacturing targets and so forth go, your companies have sort of been like carrying American manufacturing of hard 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 humanoid. 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 the EVs, etc. at scale and as cheaply as China is on track to.

Well, there are really only three hard things for humanoid robots. The real world intelligence, the hand and scale manufacturing. Yeah. So I haven't seen any, even demo robots that have a great hand, like with all the degrees of freedom of a human hand. But Optimus will have that. Optimus does have that. And how do you achieve that? Is it just like right torque density the motor? Like what is the, what is the hardware bottleneck to that? Well, we had to read where to design custom, actuators, basically custom design motors, gears, power electronics, controls, sensors, everything had to be designed from physics first principles.

There is no supply chain for this. And will you be able to manufacture those at scale? Yes. Is anything hard to accept the hand from a manipulation point of view, or once you've solved the hand, are you good? 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 tells us to develop for the car, applies very well to the robot, which is primarily vision in. The car takes some vision, but it actually also is listening for sirens. It's taking in the initial measurements. It's GPS signals, a whole bunch of other data,

combining that with video, it's primarily video, and then outputting the control command. So like your Tesla is taking in one and a half gigabytes a second video, and outputting 2 kilovytes a second of control outputs with the video at 36 hertz and the control frequency at 18. 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 Robotoxy 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. 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 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're just thinking as like a bitstream, AI is really mostly compression and correlation of two butt 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 like, you don't care about the details of the leaves and the tree on the side of the road,

but you care a lot about the road signs and the traffic lights and the pedestrians. And even whether, you know, someone in another car is looking at you or not looking at you. Like some of these details matter a lot. So it is essentially, it's got to turn that. the car is going to turn that one and a half gigabytes a second, ultimately into two kilobytes a second of control outputs. So many stages of compression, 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 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. 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. We're 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 optimizes

that don't work and then get the data that way. So between the increased degrees of freedom and far sparser data, yes, that's a good point. How will you use the sort of Tesla engine of intelligence to train the optimist mind? Now, you're actually, you're highlighting an important limitation and difference between cars. It's like we do have, we'll soon have like 10 million cars in the road. And so it's hard to duplicate. that like massive training fly wheel.

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 Optimus Academy so they can do self-play in reality. So we're actually pulling that out. So we can have at least 10,000 optimist robots, maybe 20 or 30,000 that are doing self-play and testing different tasks.

and then the 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. I actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks, and then 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 sum to real gap.

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 GROC is like doing the slower planning and then like the motor policy is the lower level. Yeah. What will the sort of synergy between these things be? Yeah, so GROC would orchestrate the behavior of the Optimus robots

So let's say you wanted to build a factory Then Grach could organize the optimist robots Give them, assign them tasks To build the factory for To produce whatever you want

Don't you need to merge XAI and Tesla then Because these things end up so What are we saying only about All the company discussions. Well, we're one more going to sit, Elon. What are you waiting to see before you say, we want to manufacture 100,000 optimists? Is it like...

Optimine. Since we're defining the prop an noun, we could define the plural of the proper noun, too. So we're going to prop an noun the plural, and so it's optimize. 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 mass manufacturing of Gen 3? No, we're moving towards that. We're going forward with the mass manufacturing. But you think current hardware is good enough that you are going to, you just want to deploy as many as possible now? I mean, it's very hard to scale up production. I said.

But yeah, but I think Optimus 3 is the right version of the robot to, you know, to produce maybe something on the order of like a million units a year. I think you'd want to go to Optimus 4 before you went to 10 million units here. Okay, but you can do a million year at Optimus 3? Yeah, I mean, it's very hard just bullet manufacturing. Yes. So like manufacturing, like the output per unit time is always follows an S-Cove. So it starts off, agonizingly slow, then it has this sort of exponential increase, then linear,

then a, you know, logarithmic outcome until you sort of eventually asymptote at some number. But Optimus initial production will be, it's going to be a stretched out S-Cove 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 for physics first principles. It's not taken from a catalog. These are custom designed everything, literally everything. I don't think there's a single thing that. How far down the does that go?

I mean, I guess we're not making custom capacitors yet, maybe. But there's nothing you can pick out of a catalog at any price. So it just means that the Optimus Scove, the units per output per unit time, how many autonomous robots do you make per day, whatever, is going to initially ramp slower than a price. where you have an existing supply chain. But it will get to a million. When you see these Chinese humanoids like Unitary or whatever sell humanoid for like 6K or 13K,

do you just like, are you hoping to get your Optimus's 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 it to sell for solo and can we match that? Well, Optimus, our Optimus is designed to have a lot of intelligence and to have the same electron rectangle dexterity if not higher than a human. So the energy does not have that. And it's also, I mean, it's quite a, 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 a saturator. So we've got, we've got, you know, it's 511.

You know, this is 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. But more capable. Yeah. But not a lot more. I mean, like the thing is, over time, as optimist robots built options robots, the cost will drop very quickly. And what will these first billion optimists, optimi, do, like what will their highest

and best use be? I think you would start off with simple tasks that you would start off with simple tasks that you you can count on them doing well. But in the home or in factories, like? The best use for robots in the beginning will be any continuous operations, so only 24 by 7 operation, because they can work continuously.

What fraction of the work at a gigafactory that is currently done by humans? Could a Gen 3 do? I'm not sure, maybe it's like 10, 20%. Maybe more, I don't know, that's it. We would use, we would not like, reduce our head count. We would, for sure. It can increase our head count, to be clear. But we would increase our output. So the, the, the, um, the units produced per human, like

total number of humans at Tesla will increase, but the, um, 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 number of humans will increase. as well. 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 solar tariffs. Yeah. And you think they're a bad idea because, you know, we can't scale up solar in the US. Well, just electricity upward in the US needs to scale up. Right. You can without like good power sources. You just need to get it somehow. Yeah. Where I was going with this is,

if you were in charge, if you were set in all the policies, what else would you change? So you change to solar tariffs as well? Yeah, I would say anything that is a limiting factor for electricity, basically address provided, it's not like very bad for the environment. So presumably some permitting reforms and stuff as well will be in there. 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. removing, permitting roadblocks. And I'm not saying all tariffs are bad. I'm just saying because I think... Solar tariffs. Yeah, yeah. I mean, sometimes if another country is subsidizing the output of something, then you have to have countervailing tariffs to protect domestic industry

against subsidies by another country. What else would you change? I don't know if there's that much that the government can actually do. One thing I was wondering is, it seems like the, for the... policy goal of creating a lease for the US versus China, it seems like the export bans have actually been quite impactful, where China is not producing leading edge chips and the export bands really bite there. China's not producing leading edge turbine engines, and similarly, there's a bunch of export bands that are relevant there on some of the metallurgy. Should there be more

export bans? Like, 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? 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. China is a manufacturing powerhouse next level. It's very impressive. Yeah, yeah. I mean, if you take refining of ore, I'd say roughly China, there's twice as much ore refining on average as the rest of the world combined.

And I think there's some areas like, say, refining gallium, which goes into solar cells. I think there are like 98% of gallium refining. So China is actually very advanced manufacturing in, I'd say most areas. It seems like there is discomfort with this supply chain dependence, and yes, nothing is really happening on it. Supply chain depends. It depends on say like the galleon refining that you're saying. Yeah, yeah, there's a, there's a, there's a, all the rare earth stuff and... Yeah, rare earth which are, as you know, not rare.

Yeah. Like we actually do rare earth or mining in the US. Send the rock, we put it on it on a train and then put on a boat to China that goes another train and goes to the rare earth refining refinerers in China who then refine it, put it into a magnet, put into a motor service assembly, and then set it back to America. So the thing, we're really missing a lot of ore refining in America. Isn't this worth a policy intervention? Yes.

Well, I think there are some things being done on that front. But we kind of need optimists, frankly, to build ore refineries. So you think the main advantage of China has is the abundance of skilled labor. And that's the thing Optimus fixes. But also we need the... Times got like four times our population. But we need...

So, I mean, there's this concern if you think like human rights or the future that like... Right now, if it's the skilled labor for manufacturing, that's determining who can build more humanoids. You know, China has more of those. It manufactures more humanoids.

Therefore, it gets the optimized future first. Well, we'll see. It just like keeps that special going. It seems that you're sort of pointing out that sort of getting to a million optimi requires the manufacturing

that the optimi is supposed to help us get to, right? You can close that recursive loop pretty quickly. With a small number of optimi.

Yeah. So you close the recourse of recourse loop to help the robots build the robots. And then we can try to get to tens of millions of year.

Maybe if you start getting to hundreds of millions 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 a population.

Right. And frankly, America's 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 frankly, just my observation is the average work ethic in China is higher than in the U.S. So it's not just that there's four times the population, but the amount of work that people put in is higher. So you can try to rearrange the humans, but you're still one quarter of the, you know, assuming that productivity is the same, which I think actually might not be. China might have advantage on productivity per person. 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 the U.S. birth rate's been below replacement since roughly 1971. So we've got a lot of people retiring or more people dying than, we're close to sort of more people domestically dying than being born. So we definitely can't win on the human front, but we might have a shot at the robot front. Are there other things that you have wanted to manufacture in the past? but they've been too labor intensive or too expensive, that now you can come back to and say, oh, we can finally do the, whatever,

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 without lithium refinery in Corpus Christi, Texas. We have a nickel refinery, which is for the cathode,

that's here in Austin. And these are the largest, this is the largest cathode refinery, largest lithium refinery, largest nickel and lithium refinery outside of China. And it's like the, you know, the cathode team would say, like, we have the largest and the only, actually, cathode refinery in America.

Many supermatives, 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, you could do a lot more refineries and help America be more competitive on refining capacity. So there's basically a lot of work for the optimality to do that most Americans, very few Americans, frankly, want to do. I mean, I've actually... Is the refining work too dirty or what's the... It's not... actually, no, we don't...

There's not... We don't have toxic emissions from the refinery or anything. The capital refiner is what... Right. Sort of in Travis County, like five minutes from... Why can't you do it with humans? No, you can. You just run out of humans.

Ah, I see, okay, yeah. Like, no matter what you do, you have one quarter of number of humans in America and China. So if you have them do this thing, they can't do the other thing. So, so then, then, um... Well, how do you build this refining capacity? Well, you can do it with the optimale. And not very many Americans are pining to do refining. I mean, how many of you're running to?

Very few. Very few pined to refine. You know, BYD is reaching Tesla production or sales in quantity. What do you think happens in global markets is? Chinese production and EV skills up? Well, China's 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, China's probably just twice as much refining as the rest of the world combined. Yeah. So if you go, you know, if you just go down to like fourth and fifth tier supply chain stuff, like at the base cell, we've got energy, then you've got mining and refining. Those those foundation layers are, like I said, as a rough guess, Chinese are twice as much refining the rest of the world combined. So any given thing is going to have Chinese content because China is doing twice as much refining work as the rest of all. And then they'll go all the way to the finished product with the cars. China is a powerhouse.

I mean, I think this year China will exceed three times U.S. electricity output. electricity output is a reasonable proxy for the economy. So in order to run the factories and run everything, you need electricity. So electricity is a good proxy for the real economy. And so if China passes three times the U.S. electricity output, it means it's industrial capacity. as a rough approximation is three times that will be three times out of the US. 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. In the absence of, of, um, breakthrough innovations in the U.S., China will utterly dominate. Interesting. Yes.

Robotics being the main breakthrough innovation. Well, if you do, like to scale AI in space, like basically need the humanoid robots, you need real world AI, you need a million tons a year to orbit. Let's just say, like, if we get the mass driver on the moon going, my favorite thing, then I think... We'll have solved all our problems. Yeah. So this is like, I call that winning. I call that winning.

You can finally be satisfied. You've done something. Yes. You have the master driver on the moon. I just want to see that thing in operation. Was that out of some sci-fi or where did you... Well, actually, there is a highland book. The moon is a harsh...

That's true. Okay. Yeah, but that's slightly different. That's a gravity slingshot? No, they have a master driver on the moon. Okay. Yeah. But they use that to attack Earth, so maybe it's something great.

Well, they use that to their independence for me. What are your plans for the master driver on the moon? They asserted their independence. Earth government disagreed and they loved things until the Earth government agreed. That book is a huge. I found that book much better than his other one that everyone reads, Stranger and a Strange Land. Yeah, Grok comes from Stranger in a Strange Land. Yeah, but I much preferred.

Yeah. If the first two-thirds of strangers' strandlines are good, and then it gets very weird in the third portion. Yeah. But there's still some good concepts in there. Yeah. Labelbox can get your robotics and URL data at scale. Take robotics.

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Go to labelbox.com slash Sparkash to learn more. One thing we were discussing a lot is kind of your system for managing people. Like, you interviewed the first few thousand of SpaceX employees and I've seen with lots of other companies. Well, it doesn't scale. Well, yes, but what doesn't scale? Me. Sure, sure. I know that, but like, what are you looking for?

Literally, it's not enough hours in the day. It's impossible. But what are you looking for that someone else who's good at interviewing and hiring people? What's the genusiqua? Well, at this point, I think I've got, I might have more training data. on evaluating technical talent especially, but talented all kinds, I suppose, but technical talent especially, given that I've done so many technical interviews and then seen the results. Technical interviews, seeing the results.

So my training set is enormous and as a very wide range. Generally the thing I ask for are bullet points for evidence of exceptional ability. So it's But like It's And these things can be like pretty off the wall It doesn't need to be 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 a good sign

But why do you have to be the one to determine that? No, I don't It can't be It's impossible Right I mean total employee It had count across all companies 200,000 people

Right But in the early days, what was it that you were looking for that couldn't be delegated in those interviews? Well, I guess I'd need to build my training sets. It's not like 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 did they not work out well? And what can I do to, I guess, RL myself to in the future, how.

a better batting average when interviewing people. So my batting average is still not perfect, but it's very high. What are some surprising reasons people don't work out? Surprising reasons. Like, you know, they don't understand technical domain, et cetera, et cetera. But like, you've got like the long tail now of like, I was really excited about this person. It didn't work out.

Curious why that happens? Yeah, so the, I mean, generally when I tell people, or tell myself, I guess, aspirationally, is don't look at the resume. Just believe your interaction. So the resume may seem very impressive and it's like, wow, you know, resume looks good. But if the conversation after 20 minutes

that conversation is not wow, you should believe the conversation, not the paper. I feel like part of your method is that, you know, there was this meme in the media a few years back about Tesla being a revolving door of executive town. Because actually, I think when you look at it, 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 Junkosa and Steve Davis and Steve Davis runs a boring company.

No, no. 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? Well, so the, I mean, it Tesla is sort of senior team. At this point, it's probably got average tenure of 10 or 12 years. It's quite long.

Yeah. So, but there are times when Tesla went through extremely rapid, and extremely rapid growth base. And so it was somewhat, things were just somewhat sped up. And when a company, as I'm, as you know, the company goes through, different orders and magnitude of size, you know, people there 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 company.

Yeah, you agree with people. Yeah, it's just not the same team. It's not always the same team. So if a company is growing very rapidly, the rate at which executive positions will change will also be proportionate to the rapidity of the growth, generally. then Tesla had a further challenge

where when Tesla had very successful periods we would be relentlessly recruited from like relentlessly like when Apple had their electric car program they were carpet bombing Tesla

with recruiting calls engineers just unplug their phones I'm trying to get worked on here yeah If I get one more call from an Apple recruiter, but they're opening off without any interview with me double the compensation at Tesla. So we had a bit of the Tesla pixie dust thing

where it's like, oh, if you hire a Tesla executive, suddenly you're going to, everything's going to be successful. And I've fallen prey to the pixie dust thing as well where it's like, oh, we'll hire someone from Google or Apple and they'll be immediately successful, but that's not how it works. You know, people are people. It's not like magical pixie dust.

Yes. So when we had the pixie dust problem, we would get relentlessly recruited. 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.

They can just get, you know, their community is going to be the same. Yes. So how do you prevent that? How do you prevent the pixie dust effect for everyone's trying to coach all your people? I don't think we can, I don't think as much we can do to stop it. But that's like, that's one of the reasons why it tells a, really being in Silicon Valley and having the pixie dust thing at the same time meant that there was just a very, very aggressive

recruitment. Only being in Austin helps then. 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 called the significant other problem. Yes. So others have jobs. Yeah, yeah, exactly.

So for Starbase, that was particularly difficult. Since the odds of, you know, finding a non-sperspace job. In Brownsville, Texas. still Texas. Pretty low, yeah. Yeah. Yeah, it's quite difficult. I mean, it's like a technology monastery,

so I think. You know, remote and mostly do. But again, if you go back... It's not much of an improvement of R.SF. Yeah. If you go,

but if you go back to these people who've really 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? What makes a good sparring partner for you? I don't think about a sparring partner. I mean, if somebody gets things done, I love them. if they don't. 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 not about mapping to my idiosyncratic preferences. I'll certainly try not to have it be mapping to my idiosyncratic preferences. So, yeah. Yeah. But generally, I think it's a good idea to hire for talent and drive and trustworthiness.

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

are at Tesla and SpaceX did not come from the aerospace industry or the order industry. What is most set to change about your management style as your companies have scaled from 100 to 1,000 to 10,000 people? You're known for this very micromanagement, just getting into the details of things. Nano management, please. Pico management. Femto management. So you're saying we're going to go all the way down to fly spot. We're going to go all the way down Planks constant. Well, way down to Heisenberg's and simply more small. 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 think about that? Well, because I have a fixed amount of time in the day, my time is necessarily diluted as things grow. And as the span of activity increases. So, you know, it, it's a very much. It's impossible for me to actually be a micromanagement because that would imply I have some like thousands of hours per day. It is a logical impossibility 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.

But the reason for drilling into some very detailed item is because it is the limiting factor. It's not arbitrarily drilling into tiny things. And like it, obviously, from a time standpoint, it is physically impossible for you arbitrarily going to tiny things that don't matter. And that would result in failure. But sometimes the tiny things are decisive in victory. famously you switched the Starship design from composites to steel

and you made that decision like that wasn't a 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 yeah so desperation I'd say

the originally yeah we're We were going to make Starship out of common fiber. And common fiber is pretty expensive. Like 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 common fiber is that material cost is still very high. So it's about 50 times, particularly if you go for high strength.

specialized carbon fiber that can handle cryogenic oxygen. It's like, quote, roughly 50 times the cost of steel. 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 applications, you know, like say more of this room temperature applications like a Formula One car, static error structure or any kind of era structure really is going to you're going to probably be better off with carbon fiber. Now the problem is that we were trying to make this enormous rocket

out of carbon fiber and our progress was extremely slow. And it's been picked in the first place just because it's light? Yes. Like at first glance, like most people would think that the choice for making something light would be carbon fiber. Now the thing is that when you make something very enormous at a carbon fiber and then you try to have the carbon fiber be efficiently cured, meaning not room temperature cure

because sometimes you've got like 50 plies of carbon fiber. And a 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 album's got to be bigger than the rock wood. So we're trying to make the order clave that's bigger than any autoclave that's ever existed. Or do room-trapshire cure, which takes a long time and has issues. But the final issue is that we're just making very slow progress with carbon fiber. So 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 part of a broader question of understanding your comparative advantage at your companies. So because we were making very slow progress with carbon fiber, I was like, Okay, we've got to try something else. Now, for the Falcon line, 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 better strength to 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 steel welding, where you join the metal without 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. 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 common fiber that had very good mass properties. So with rockets, you're really trying to maximize the percentage of the rocket that is propellant,

minimize the mass, obviously. And the, but like I said, we're making very slow progress. And as at this rate, we're never going to get to Mars. So we better to think of something else. I didn't want to use aluminum lithium because of the difficulty of friction steel welding, especially doing that at scale. It was hard enough at 3.6 meters in diameter, let alone at 9 meters or above. Then I said, well, what about steel?

Now, I had a clue here because some of the early US rockets had used very thin steel. Atlas Rockets used a steel balloon tank. So it's not like steel never been used before. It actually had been used. And when you look at the material properties of stainless steel, especially if it's been like full hard, strain-hardened stainless steel, at cryogenic temperature,

the strength weight is actually similar to carbon fiber. So if you look at the material property, 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 hot steel stainless, of particular grades, then you actually get to a similar strength weight as carbon fiber. 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 pure, like a very pure form of jet fuel,

which is, but that is roughly room temperature, although we do actually, we do actually chill it slightly below, we chill it like a beer. Deliciously. Yeah, we do chill it, but it's not cryogenic. In fact, if we made it cryogenic, it would just turn to wax.

So, but for starship, it's liquid methane and liquid oxygen. They, they are liquid at similar temperatures. So basically almost the entire primary structure is a cryogenic temperature. So then you've got a 300 series stainless that's strain hardened because it's almost all things a cryogenic temperature actually has similar strength of weight as carbon fiber. But costs 50 times less normal material. is very easy to work with.

You can weld stainless steel outdoors. You could smoke a cigar while welding stainless steel. It's very resilient. You can modify it easily. If you want to attach something, you just weld it right on. So very easy to work with very low cost. And like I said, at cryogenic temperature, similar strength to weight to carbon fiber, then when you factor in that, that we don't need, we don't, we have a much reduced

heat shield mass because the melting point of steel is much greater than the melting point of aluminum. 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, which is coming in like a blazing meteor. It is, the, you can greatly reduce. the mass of the heat shield so you can cut the mass of the windward part of the heat

shield maybe in half and you don't need any heat shielding on the on the leeward side. So the net result is actually the steel rocket weighs less than the carbon fiber rocket because the resin in the carbon fiber rocket, starts to melt. So basically, common fiber and aluminum have about the same operating temperature capabilities.

Whereas steel can operate at twice temperature. These are very rough approximations. People will... I won't build a rocket paper. What I'm just like, people are saying, oh, he said it's twice, it's actually 0.8. I don't show up, assholes. That's what the main comment's going to be about.

Oh, damn it. Okay, the point is, actually, in retrospect, we should have started, we're done, steel, the beginning. It was dumb not to do steel. Okay, but to play this back to you, what I'm hearing is that steel was a riskier or less proven path other than the early US rockets versus carbon fiber was like 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 were fighting like a sort of conservatism in a sense. That's why I initially said that the issue is that we weren't making it passed off We're 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 way that it doesn't have any wrinkles or defects. The carbon fiber is much less resilient than steel. It has less toughness.

Like stainless steel will stretch and bend, the column 5 will tend to shatter. So topness being the area under the stress strain curve, so that you're generally going to have to do better with steel. A stainless steel, to be precise. One of their 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, you know, everyone wants to tell you how Starship is just a big soda can and, you know, we're hiring welders. And, you know, if you can weld in any industrial project, you can weld here.

But there's a lot of pride in the simplicity. Well, exactly Star Ship was a very complicated rocket. So that's what I'm getting at. Is, are things simpler or are they complex? I think maybe what they're trying to say is that you don't have to have prior experience in the rocket industry to work on star ship. You know, 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. Star Trek is the most complicated machine ever made by humans, by a longshund.

In what regards? Anything, really. There isn't a more complex machine. 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 over a rocket. It's a very hard problem. I mean, many smart people have tried before, very smart people with immense resources and they failed.

And we haven't succeeded yet. You know, Falcon is partially reusable, but the up the stage is not. Starship version 3, I think this design, that it can be fully reusable. And that full reusability is what will enable us to become a multi-planet civilization. Can you say about the circles? I don't, like, I said, I could. Any technical problem, even like a hydrant cladder or something like that, it's easier

following this. We spent a lot of time on bottlenecks. Can you say what the current Starship Battlenacks are, even at a high level? I mean, trying to make it not explode. Generally. That old chestnut. Really wants to explode. All those combustible.

We've had two boosters explode on the test stand. One obliterated the entire test facility. So it takes one mistake. And I mean, the amount of energy contained in Starshop is insane. So is that why it's harder than Falcon? It's because it's just more energy? It's a lot of new technology. It's pushing the performance envelope.

The Raptor 3 engine is a very, very advanced engine. By far the best recognition ever made. But it desperately wants to blow up. I mean, just put things in perspective here. On Lyftolf, the rocket is generating over 100 gigawatts of power. It's 20% of US, the trust of science. It's insane. It's a great comparison.

While not exploding. Sometimes. Sometimes. I was discussed. Yeah, so I was like, how does it not explode? There's thousands of ways that it could explode and only one way that it doesn't. So we wanted to not merely not explode, but fly reliably. on a daily basis, like once a per hour.

And obviously, you know, blows up a lot. It's very difficult to maintain that. Of launch cadence. Yes. And then, I'm going to say, like, what's the single biggest remaining problem for Starship? It's having the heat shield be reusable, such that no one has ever made a reusable orbital heat shield. So the heat shield's got to make it through. the ascent phase without shocking a bunch of tiles.

And then it's going to come back in and also not lose a bunch of tiles or overheat the main airframe. Isn't that hard? It's kind of fundamentally a consumable? Well, yes, but your brake pads on your car are also consumable, but the last very long time. Fair. So it just needs to last very long time. That's just, yeah, try to.

I mean, we have brought the ship back and had it do a soft landing in the ocean. I've done that a few times. But it lost a lot of tiles. You know, it was not reusable without a lot of work. So even though it did land, it did come to a soft landing, it would not have been reusable without a lot of work. So it's not really reusable in that sense. That's the biggest problem that remains is fully reusable heat shield. So if you want to be able to land it, refoil, propellant, and fly a game.

You can't do this laborious inspection of 40,000 tiles, everything. 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 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? I don't know. But like today you said you had a bunch of SpaceX meetings. Like what is it that you're doing there that's like keeping that? That's adding urgency.

Yeah, yeah, yeah. Well, I don't know. I guess the urgency is going to come from where I was leading the company. of my sets of urgency. I've like a maniacal sense of urgency. So that maniacal sense urgency projects through the rest of the company. Is it because of consequences? They're like, if, you know, Elon said a crazy deadline, but if I don't get it, I know what happens to me. Is it just you're able to identify bottlenecks and get rid of them so people can move fast? Like, how do you think about why your companies are able to move fast?

Yeah, I'm constantly addressing the limiting factor. So, I mean, on the deadlines front, I mean, 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 except 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, like if you said we're going to do this something in like five years, which to me is like infinity time, it will expand to fully available schedule and it will take five years. You know, like there's like this, there's a physical limit.

Like that, like physics will limit how fast you can do certain things. Like so like scaling up manufacturing, there's like 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 of something, million years a year or something. You've got to design the manufacturing line. You could bring it up. You've got to ride the S-curve of production. So yeah, I guess like what can I say that's that's actually helpful to people? I think generally a maniacal sense of urgency is a very big deal. So and it's a very big deal. And it's and it's 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. Can you maybe talk about the, so Starlink was slowly in the works for many years. Yeah, we talked about it all the way in the beginning of the company. Yeah, and so then there was a team you had built in Redmond, and then at one point you decided this team is just not cutting us. But again, how did you, like, it went for a few years slowly. And so why did this, why didn't you act earlier and why did you act when you did?

Like, why was that the right moment of which to act? 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 that manufacturing company that goes to the 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. 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 I say these days. Yeah, exactly. Very Gen Z of you. How do you prevent it's advanced administration? You just call them randomly? Like, no, just go around the room.

Everyone provides an update. Okay. So, I mean, it's a lot of information to keep you ahead because you've got a, 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 the 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 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 and so that's I came up that conclusion in 2018 into drastic action and fix the problem. 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 the relevant bottlenecks are, so you can do these reviews with people.

Yeah. 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? 80? No. But like you have so many already.

That's already remarkable. Why this current number? Yeah, exactly. I know so. We barely keep one company together. It depends on situation. So I actually don't, don't have regular meetings with foreign company. So that the boring company is sort of cruising along.

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? I focus

at the risk of saying the words too many times the limiting factor. So, So basically, if something's going, 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. And so if something is going.

Or not even badly. It's like if something's a limiting factor. It's a limiting factor, exactly. It's not exactly going badly, but it's the thing that's, it's the thing that we need to make go faster to. And so when 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? Most things that a learning factor are weekly, and some things are twice weekly. So the AI-5 chip review is twice weekly, and so it's every Tuesday and Saturdays is the chip review.

Is it open-ended and how long it goes? Technically, yes, but usually it's like two or three hours. Sometimes less. It depends on how much. much if they should go through. Yeah. That's another thing. I'm just trying to tease out the differences here because the outcome seemed quite

different. And so I think it's interesting to note 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.

Sometimes. Yeah. Yeah, sometimes. But most of them seem to more or less stay on time. So, I mean, today's Starship engineer interview went a bit longer because there were more topics to discuss. they're trying to figure out how to scale 2 million plus tons to over per year is quite challenging can answer a question so you said about optimist and AI that they're going to result in double-judged growth rates within a matter of years oh like the economy yeah yes I think that's right

what was the point of the doge cuts if the economy is going to grow so much well I think like Waste and fraud are not good things to have, you know. 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 national debt exceed the military budget, which is a trillion dollars. So 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. 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 we'd like to Well we just need

We need enough time to Get build the AI and robots To And not go bankrupt before then I guess the thing I'm curious about is When Doge starts you have this enormous Ability to enact Reform and

Not that enormous 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. I'm like the president. And very hard to cut to even

to cut things that are obvious waste and fraud, 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 like who's complaining. Like 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, they're like, you're killing baby panders. And we're like, meanwhile, there's no baby fenders 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's what happened. And so it's like, perhaps I should have known better. And I thought, wait, let's take a, let's try to cut some amount of waste and poor from the government. Maybe there shouldn't be, you know, 20 million people walked as alive in Social Security who are definitely dead.

and over the age of 115. The oldest American is 114. So it's safe to say if somebody is 115 and marked us alive in the Social Security database, there's either a typo. Somebody should call them and say, we seem to have your birthday wrong or we need to mark you as dead.

One of the two things. Very intimidating call to get. Well, it seems like a reasonable thing. And 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, again, have a typo or we have fraud. So we say we appear to have gotten the century of your birth incorrect.

Or a great plot for a movie. Yes. Yes. When I remember ludicrous fraud, this is when I'm about ludicrous fraud, this is my ludicrous fraud. Were those people getting payments? Some were getting payments from Social Security. 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 would simply do an are you a live check to the Social Security database? It's a bank shot. What would you estimate is like the total amount of fraud from this mechanism? My guess is, and by the way, way that the government accountability office has done these estimates before. I'm not the only one who is not coming out of this. In fact, I think they did, the GAO did analysis a rough estimate

of fraud during the Biden administration and calculated at roughly half a trillion dollars. So don't take my word for it. Take it a report issued during the Biden administration. How about that? From this social security mechanism? It's one of many. It's important to appreciate that the government does not, is, is very ineffective at stopping forward because it's not like, like if it was a company, like stopping forward, you've got a motivation because it's affecting the earnings of your company. But the government just, they just print more money. So it's not, like you need caring and competence.

And these are in short supply at the federal level. Yeah, I was sorry. 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 a DMV that can print money. So was it not possible? At least the state-level DMVs need to, the states more or less need to stay within their budget that go bankrupt. But the federal government just prints more money. Was it not possible if there's a cashier half a trillion of fraud?

Why was it not possible to cut all that? Because when as soon as 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. Buy the microphones. Yeah, yeah, exactly. So, so you, if you don't have, it's not like there's a giant, largely uncaring monster bureaucracy.

It's not even, it's, and a bunch of inaccuracy computers that are just sending payments. Like one of the things that, that the Doge team did there was, and sounds so simple, 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 PAMS, like payment accounts master or something like that, there's $5 trillion 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. Because you see you have to recalibrate how dumb things are. Payments were being sent out with no appropriation. code, not checking back to any congressional appropriation, and no explanation.

And this is why the Department of War, formerly the Department of Defense, cannot pass an audit because the information is literally not there. Recalibrate your expectations. I want to better understand this how much really a number, because there's an IG report in 2024. How, you must like, why is it so low? 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 in error. So I'd be curious to see what like the other 490 billion is.

Federal government expenditure is a 7.5 trillion a year. Yeah. What percentage, how competent do you think of much is? The discretionary spending there is like 15%. Yeah, but it doesn't matter. Most of the fraud is non-discretionary. It's basically a forulent Medicare, Medicaid, Social Security, you know, disability, there's a zillion government payments.

Yeah. 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 know if there's fraud. Let's like a reducte or out of certum. The government is perfect and has no fraud. What is your probability estimate of that?

I mean, zero. Okay. So then would you say that fraud and waste, that the government is 90%? That also would be quite generous. But if it's only 90%, that means that there's $750 billion here of waste and fraud. And it's not 90%. It's not 90% effective. 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, see how. You know a lot about fraud at Stripe. People are constantly trying to do fraud. 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 we're dealing with a much more heterogeneous set of fraud vectors here than we're. Yeah, but I mean, I mean, at Stripe, you have high confidence and you try hard. You have high competence and high caring, but still fraud is non-zero.

Now imagine it's at a much bigger scale, there's much less competent and much less carrying. You know, PayPal back in the day, we try to manage fraud down to about 1% of the payment volume. And that was very difficult. It took a tremendous amount of competence in carrying to get fraud merely to 1%. Now imagine that your own organization where there's much less carrying and much less competence. It's going to be much more than 1%. how do you feel now looking back on kind of politics and

and doing stuff there where it feels like moving from the outside in the two things have been quite impactful one the America pack 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 Well, I think those things needed to be done to maximize the probability that the future is good. So politics generally is very tribal, and it's very tribal. And people lose their objectivity usually with politics. They generally have trouble seeing the good on the other side or the bad in their own side. that's generally how it goes. 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 simply believe that everything their tribe does is good and anything the other political tribe does is bad. And persuading them otherwise is almost impossible. So anyway, but I think overall, those actions acquiring Twitter getting trouble elected

even though it makes a lot of people angry I think those actions are good for civilization how does it feed into the future you're excited about well America needs to be strong enough to last long enough to extend life to other planets

and to 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, say, communism or some situation where the state was extremely oppressive, that would mean that we might not be able to become multi-planetary. And we might, the state might, you know, stamp out our progress in AI robotics. How do you feel about, you know, Optimus, GROC, et cetera, are going to be leveraged by,

and not just yours, any revenue maximizing company's 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 gar-reel should, like, should, you know, should AI models be made to do whatever the government that has contracted them out to do, ask them to do, should like, should should Grod get to say like actually even the military wants to do X? No, the GROC will not do that. I think probably the biggest danger of AI, maybe the biggest danger of fail for AI and robotics going wrong is government.

Interesting. You know, I mean, the way of the, 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, it is, it is, it is, government is just. the biggest corporation with a monopoly on violence. 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. 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 have better morality than the government. So I actually think it's, you know, That is a thing to be worried about. It's like, if the government should not, like, the government could potentially use AI and robotics to suppress the population. Like that is a serious concern. As a guy building AI in robotics, how do you, how do you prevent that? Well, I think that like if you have a limited government, 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 as a government, then you're probably going to have a better outcome than if you have more government. Roblox will be available to all governments, right? Not about all governments. I mean, it's difficult to predict the, like I can say, like, what's the end point or like what is what is many years in the future, but it's difficult to predict the sort of path along that way. Like if civilization progresses, AI will vastly exceed the sum of all human intelligence, and there will be far more robots than humans.

Along the way, what happens? It's very difficult to predict. I mean, it seems like one thing you do is just say you are not allowed to, whatever government index you're not allowed to use optimists to do X, Y, Z, just write out like a policy. I mean, I think you treated recently that GROC 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. I mean, yeah, we can do what is what we're I mean, technically, I mean, if the policies pass a law, then and they can enforce that law, then it's hard to

not do that law, you know, 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. I guess 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... You think I'll be the boss of the government. Or you will get the, you will like the, I mean already, it's the case with SpaceX that for

things that are crucial to the like the government really cares about getting certain satellites up in space, whatever. like it needs SpaceX. It is the, it is the, um, a necessary contractor. And you are in the process of building more and more of the, um, uh, the technological components of the future that, that will have analogous role in different industries. And you could have this ability to like set some policy that, um, you know, suppressing classical liberalism in any way.

I, my companies will not help in it in any way with that or, you know, some policy like that. I will do my business to ensure that anything that's within my control maximizes the good outcome for humanity. I think anything else would be short-sighted because obviously I'm part of humanity, so I like humans. Pro-human, pro-human. You've mentioned that Dojo 3 will be used for space-based compute. You really read my what I say. I don't know if you know Twitter.

I know you lot. You're a lot of followers. They did give away. How do you... How do you... How do you... How do you... How do you design this chip for space?

What changes? Well, I guess you want to design it to be more radiation tolerant and run at a higher temperature. So you get... You know, roughly if you increase the operating temperature by 20th set in degrees Kelvin,

you can cut your radiation. or 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 bitflips. So like most of what happens for radiation is like random bitflips. But like if you've got like, you know, a multi-trily parameter model and you get a few bit flips doesn't matter.

It's much curiosity programs are going to be much more sensitive to bitflips than some giant parameter file. So I just designed it run hot. I think you pretty much do it the same way that you do things on Earth, apart from make it run hotter. I mean, the solar array is most of the weight on the satellite.

Is there a way to make the GPUs even more power ends than what Nvidia and TPUs and etc. are planning on doing that would, you know, be especially privileged in the space-based world? Well, I mean the basic math is like if you can do about a kilowattice per reticle and then you'd need, you know, 100 million full radical chips to do 100 gigawatts. So depending on what your yield assumptions are, you know, that tells you how many chips you need to make. But cool, you need, if you want, if you're going to have 100 gigawatts of power, you need, you know, 100 million chips running, that are running a kilowattatt sustained, but per reticle. Basic math.

A hundred million ships. It depends on, yeah, if you look at the dye size of something like blackball GPUs 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. That's the plan with TeraFav. millions away for us a month of advanced process notes.

It could be some number of an worth of a million, I think. You've got to do the memory too. Yeah. You're going to make a memory fad? I think the tar fap's got to do memory. It's got to do logic memory and packaging. 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 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? I want a million wafers a month in 2030 What is the next? That's right Do you like call ASML? Like what is the job?

I think I won. What is the next step? That's so much to ask. Well, we make a little fab And see what happens Make our mistakes at a small scale And then make a big one. Is a little fab done?

No, it was not done. I mean, people would We're not going to keep that cat in the bag. that chat's going to come out of the back roll it'll be like drones hovering over the bloody thing you know you'll be able to see its construction progress on X right

in real time so I mean like I don't know we could just flounder and failure to say it's like not success is not guaranteed but

um since we want to try to make uh you know something like a hundred million We want 100 gigawatts of power and 100 chips that can take 100 gigawatts. Right. So call it, yeah, buy 2030.

So then we'll take as many chips as our suppliers will give us. I've actually said this to TSM and Samsung and Micro and it's like, please build your more fabs faster. And we will guarantee you to buy the output of those faves. So they're already like moving as fast 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 not like it's us plus them, you know. There's an argument 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. No, the explanation you hear is that they're dispositionally conservative, you know, they're Taiwanese or German as the, you know, story may be. And they just like don't believe to say, like, is that really the explanation or is there something else?

Well, I mean, it's reasonable. Like, if somebody's been in, say, the computer memory business for 30 or 40 years. And they've seen cycles. They've seen like boom and bust like 10 times. Yeah. You know, 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 the crash happens and then I desperately trying to avoid bankruptcy. And then there's another boom and another crash. Are there other ideas you think others should go pursue that you're not for whatever reasons right now? I mean, there are a few companies that are pursuing like new ways of during chips. but they're just not scaling fast. I don't even with an AI, I mean just generally. I'd say like people should just do the thing where they find that they're highly motivated to do that thing.

As opposed to, you know, some idea that I suggest. They should do the thing that they find personally interesting and motivating to do. But going back to the limiting factor, So he was that phrase about 100 times. The current limiting factor that I see in the time frame, you know, in the sort of 20, 29, 20, like in the three, three to four year time frame, it's chips. In the one year time frame, it's energy, power production, electricity.

It's not clear to me that there's enough. usable electricity to turn on all the AI shifts that are being made. Towards the end of this year, I think people are going to have real trouble turning on. Chip outward will exceed the ability to turn chips on. What's your plan to deal with that world? Well, we're trying to accelerate electricity production. I guess that's maybe one of the reasons that XAI will, maybe the leader, hopefully the leader,

is that we'll be able to turn on more shifts than other people can turn on faster. Because we're good at hardware. And generally, the innovations from the corporations that call themselves labs, the idea is sent a flow, like it's rare to see that there's like more than about a six-month difference between,

like the idea is, travel back and forth with the people. So I think you sort of hit the hardware wall. And then whichever company can scale hardware, the fastest will be the leader. And so I think XCI will be able to scale hardware the fastest, and therefore most likely will be the leader. You jokes or, you know, are self-conscious about, you know, using 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 and Driesen had the line of most people are willing to injure any amount of chronic pain to avoid acute pain. And it feels like a lot of the cases we're talking about are just leading. into the acute pain whatever it is it's like okay we got to figure out how to you know work with steel or we got to figure out how to run the chips in space or like we'll take some near-term acute pain to actually solve the bottleneck and so that's kind of a unifying thing i have a high fan threshold that's helpful solve the bottlenecks yes um so you know one thing i can say is like uh

I think the future is going to be very interesting. And as I said, the devil's up. It was definitely a double-s-thling. It was on the ground for like three hours or something. It's better to be, it's better to earn the side of optimism and be wrong than earn the side of pessimism and be right

for quality of life. So, you know, your happiness will be, You'll be happier if you are on the side of optimism rather than erring on the side of pessimism. And so I recommend erring on the side of optimism. Nice to that. Cool. Yelan, thanks for doing this. Thank you.

Thanks, guys. Great stamina. Hopefully this encounters the pain and the pain tolerance. Hey, everybody. I hope you enjoyed that episode. If you did, the most helpful thing you can do is just share it with other people who you think might enjoy it. It's also helpful if you leave a rating or comment on whatever platform you're listening on. If you're interested in sponsoring the podcast, you can reach out at thwarcash.com

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