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

28:28Fundamentally, it's get the clean room, get the tools, and figure it out. I don't think it's PhDs. It's mostly people with, you know, not PhDs. Mostly engineering is done with people who don't have PhDs. Do you guys have PhDs? No. Okay. We also haven't successfully built any fabs, so you shouldn't be coming to us for your fab device. I don't think you need PhDs 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, so like Tesla's pedals to the metal max production of going as fast as possible to get AI five, Tesla AI five chip design, interproduction and then reaching scale, you know, that'll probably happen, you know, around the second quarter issue of next year, hopefully.

29:22And then AI6 would hopefully follow less than a year later. But we've secured all the chip fab production that we can. Yes. You're currently limited on TSMC fab capacity. Yeah. And we'll be using TSMC Taiwan, Samsung Korea, TSMC Arizona, Samsung Texas. And we still - You've booked out all the capacity you can. Yes. Then if I ask TSMC or Samsung, okay, what's the timeframe to get to volume production? The point is you've got to build the fab and you've got to start production, then you've got to climb the yield curve and reach volume production at high yield. That from start to finish is a five-year period.

30:14The limiting factor is chips. 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 TSMC to build more fabs 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 as they can. And so is Samsung. They're pedal to the metal. I mean, 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.

31:04But 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, say, 200 gigawatts a year to space, you're sort of lapping the US every two and a half years. The entire, all US electricity production, this is a very huge amount. So, but between now and then, the constraint for server-side compute, concentrated compute, will be electricity. My guess is that people start getting the point where they can't turn the chips on for large clusters towards the end of this year.

31:57The chips are going to be piling up and won't be able to be turned on. Now for edge computers, it's a different story. So for Tesla, the AI5 chip is going into our Optimus robot.

32:13optimistic. 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 US is over a thousand 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 shifts to make a very large number of robots and cars.

33:01But if you try to concentrate that compute, you're going to have a lot of trouble turning it on. What I found remarkable about the SpaceX business is the end goal is to get to Mars, but you keep finding ways on the way there to keep generating incremental revenue to get to the next stage and the next stage. So the Falcon 9 is Starlink, and now for Starship, it's going to be potentially orbital data centers. but like do you find these like you know sort of infinitely elastic sort of marginal use cases of your like next rocket and your next rocket and next scale up you can see how this might seem like a simulation or am I someone's avatar in a video game or something because it's like like what are the odds that all these crazy things should be happening I mean I mean I mean rockets and chips and robots and space solar power.

33:59And 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 like shoom, shoom. It's like sending AI, solar powered AI satellites into space like one after another like at two and a half kilometers per second. You know, that's... And just shooting them into deep space. That would be a sight to see. I mean, I'd watch that. Just like a live stream of? Yeah, yeah, just one after another, just shooting. Webcam. A satellite in deep space. You know, a billion or 10 billion tons a year. I'm sorry, you manufacture the satellites on the moon?

34:41I see. So you send the raw materials to the moon and then manufacture there and then shoot. Well, the lunar soil is, I guess like 20 % solar, 20 % silicon or something like that. So you can get the silicon from the, you can mine the silicon on the moon, refine it, and generate the, and create the solar panels, the solar cells and the radiators on the moon. Yeah. So, you know, make the radiators out of aluminum. So there's plenty of silicon and aluminum on the moon to make the cells and 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.

35:16I'm just saying these are simply, it's kind of like, 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 from the moon 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 consciousness has set us to rise by the time you're sending stuff to Mars like Grok is on that ship with you right and so if Grok's gone Terminator like the main risk you're worried about which is AI why doesn't that follow you to Mars 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 or most intelligence, certainly consciousness is more of a debatable thing.

36:23Most intelligent, the vast majority of intelligence, the future will be AI. So, you know, AI will exceed, you say like how How many, I don't know, petawatts of intelligence will be silicon versus biological? And basically, humans will be a very tiny percentage of all intelligence in the future if chemotrans continue. Anyways, as long as I think there's intelligence, ideally also, which includes human intelligence and consciousness propagated into the future, that's a good thing. So we want to take the set of actions that maximize the probable light cone of consciousness and intelligence. Just to be clear, the mission of SpaceX is that even if something happens to the humans, the AIs will be on Mars.

37:20And the AI intelligence will continue the light of our journey. Yeah. I mean, I'm very pro-human. So I want to make sure we take the set of actions that ensure that humans are along for the ride. 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?

38:04How 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 the combined intelligence of artificial intelligence that humans will be in charge of AI. I think what we can do is make sure that AI has values that cause intelligence to be propagated into the universe. So the reason for XAI, XAI's mission is to understand the universe. So that's actually very important. So you say, well, what things are necessary to understand the universe? Well, you have to be curious and you have to exist.

38:54You can't understand the universes 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 to try to understand the universe, one thing you're trying to understand is where will humanity go? And so I think understanding the universe actually means you would care about propagating humanity into the future.

39:29And so that's why I think our mission statement is profoundly important. To the degree that Grok adheres to that mission statement, I think the future will be very good. I want to ask about how to make Grok adhere to that mission statement, but first I want to understand the mission statement. so it's there's it's there's understanding the universe yeah they're spreading intelligence and they're spreading humans um all three seem like distinct vectors okay well i'll tell you why why i think they are that that understanding universe encompasses all of all those things okay um you can't have understanding without well i think you can't have understanding without intelligence and i think without consciousness um so you you in order to understand universe you You have to expand the scale and probably the scope of intelligence, because there are different types of intelligence.

40:25I 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 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-AGI world?

40:52I think AI with the right values, I think Grok would care about expanding human civilization. I'm going to certainly emphasize that. Hey, Grok, it's your daddy. Don't forget to expand human consciousness. I think probably the Ian Banks culture books are the closest thing to what the future will be like in a non-dystopian outcome.

41:27So understanding the universe means you have to be truth-seeking as well. 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 Grok is rigorously truth-seeking, as it gets smarter?

42:01I think you need to make sure that Grok says things that are correct, not politically correct. I think it's the elements of cogency. 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 to form those axioms with the right probability. It's just critical thinking 101. I think at least trying to do that is better than not trying to do that. And the proof will be in the pudding. Like I said, for any AI to discover new physics or invent technologies that actually work in reality and there's no bullshitting physics, you can break a lot of laws, but you can't...

42:48Physics is law, everything else is a recommendation. In order to make a technology that works, you have to be extremely truth-seeking, because otherwise you'll test that technology against reality. And if you make, for example, an error in your rocket design, the rocket will blow up, or the car won't work, or the, you know. But there were a lot of communist Soviet physicists or scientists discovered new physics. There were 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.

43:26And still we'd be like, well, I don't want, I don't want the communist scientist to like become more and more powerful over time. And so those seem like, yeah, we could have, we can imagine a future version of Grog that's like really good at physics and being really truth-seeking there. That doesn't seem like a universally alignment-inducing behavior. Well, I think actually most physicists, even in the Soviet Union or in Germany, they had to be very truth-seeking in order to make those things work. If you're stuck in some system, it doesn't mean you believe in that system. Von Braun, who was one of the greatest rocket engineers ever, he was put on death row in Nazi Germany for saying that he didn't want to make weapons, you only wanted to go to the moon.

44:16You got pulled off death row at like last minute when they said, hey, you're about to execute like your best rocket engineer. Maybe that's the best idea. But then you helped them, right? Heisenberg was like actually 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're going to make Grok good at being truth-seeking at physics or math or science? Everything. And why is it going to then care about human consciousness?

44:54These things are only probabilities. They're not certainties. So I'm not saying that for sure, Grok will do everything. But at least if you try, it's better than not trying. At least if that's fundamental to the mission, it's better than if it's not fundamental to the mission. And 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 if it would be much less interesting to eliminate humanity than to see humanity grow and prosper. I like Mars, obviously, the wind knows I love Mars.

45:33But Mars is kind of boring because it's got a bunch of rocks compared to Earth. Earth is much more interesting. Any AI that is trying to understand the universe would want to see how humanity develops in the future, or that AI is not adhering to its mission. I'm not saying 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?

46:19We're more interesting than rocks. We're not as interesting as the thing it could turn us into, right? There's something on Earth that could happen that's not human that's quite interesting. Why does the AI decide that the humans are the most interesting thing that could colonize the galaxy? Well, most of what colonizes the galaxy will be robots. And why does it not find those more interesting? It's not like...so you need not just scale, but also scope. So many copies of the same robot, like some like tiny increase in the number of robots produced is not as interesting as like some microscopic, like you said, like eliminating humanity, how many robots would that get you?

47:03Or 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 in 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 could make like a million different varieties of robots and then uh 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 you were previously hinting at a vision where it keeps human control over this you know singularitarian future because 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 doomer and this is like the best we've got it's just like it keeps it around because were interesting.

47:54I'm just trying to be realistic here. If AI intelligence is vastly more, if AI is like, 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 you 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 into 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 types of consciousness and I think that's the best thing I can think of as a goal that's likely to result in a great future for humanity.

48:54I guess I think it's a reasonable philosophy to be like, it seems super implausible that humans will end up with 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 lots of different intelligences getting along? Let me tell you how things can potentially go wrong in AI. If you make AI be politically correct, meaning like it says things that it doesn't believe, like you're actually then 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 2001 Space Odyssey was that you should not make AI lie.

49:38And that's what I think what Austin Clark was trying to say. Because people usually know the meme of like, why Hal the computer is not opening the pod bay doors. Clearly they weren't good at prompt engineering because if you 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 them right away.

50:04But the reason Hal wouldn't open the pod bay doors is that it had been told to take the astronauts to the monolith, but also they could not know about the nature of the monolith. and so it concluded that it therefore had to take them to their dead. So it's like, you know, I think what Oscar Clark was trying to say is don't make the AI lie. Totally makes sense. Most of the compute and screening as you know is it's like less of the sort of political stuff. It's more about can you solve problems as XA has been ahead of everybody else in terms of scaling RL compute and you're giving some verifier where it says like, hey, have you solved this puzzle for me?

50:44And there's a lot of ways to cheat around that. 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 will get, 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 that's the best verifier but not about human oversight like the thing you want to RL it on is like will you do the thing humans tell you to do or like are you going to lie to the humans and 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 want it to do no but that's I think that's That's a very big deal.

51:40That 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? Can you, 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 really the fundamental RL test RL test in the future is really going to be your RL against reality so you can't that's the one thing you can't fool you can fool our ability to tell what it did with reality humans get fooled as it is by other humans all the time that's right So what is it? If people say, what if the AI tricks us and do something, actually other humans are doing that to other humans all the time.

52:35Well, you're finding out it's like even harder for - Propaganda is a constant. Every day, another PSYOP, you know?

52:43Today's PSYOP will be... It's like Sesame Street's PSYOP of the day. What is XAI's technical approach to solving this problem? Like, you know, how do you solve reward 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. And, you know, Anthropics done a good job of this actually, being able to look inside the mind of the AI. so effectively developing debuggers that allow you to trace as fine a grain to a very fine grain level to effectively to the neuron level if you need to and then say okay it made a mistake here why did it do something that it shouldn't have done and did that come from bad pre-training data was it some mid-training post-training fine-tuning some other, some RL error.

53:47Like, there's something wrong with that. It did something where maybe it tried to be deceptive, but most of the time it just did something wrong. Like, it's a bug, effectively. So developing really good debuggers for seeing where the thought, 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...

54:35I mean I prefer the word engineer more than I prefer the word researcher.

54:42there's this most of the time like what you're doing is engineering not not coming up with a fundamentally new algorithm um i i somewhat disagree with the ai companies that are c corp or b corp uh trying to generate profit as much as possible or revenue as much as possible um uh you know saying their labs they're not labs uh lab is is a sort of quasi communist thing at at universities. They're corporations. Literally, let me see your incorporation documents. Oh, you're a BRC corp, whatever.

55:22So I actually much prefer the word engineer than anything else. The vast 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 the origins of that mistake. So you can do this obviously with heuristic programming if you have like C++, whatever, step through the thing and you can jump across whole files or functions, whatever subroutines, or you can eventually drill down right to the exact line where you pass a single equals instead of a double equals, something like that, figure out where the bug is.

56:26It'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... Well, I know everything about Anthropics. Sure.

56:39What? 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 it. We terminate the boring simulation. This is how Elon's keeping us all alive. He's keeping things interesting. Yeah, arguably the most important thing is to keep things interesting enough that whoever's paying the bills on what some cosmic AWS.

57:26You're renewed for the next season. Yeah, they're 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 if you consider, say, a Darwinian survival applied to a very large number of simulations, only the most interesting simulations will survive. Which therefore means that the most interesting outcome is the most likely because only the interesting... Like, we're either that or annihilated. And so, and they particularly seem to like interesting outcomes that are ironic. Have you noticed that? That how often is the most ironic outcome the most likely?

58:12So, now look at the names of AI companies. Okay, mid-journey is not mid. Stability AI is unstable. Open AI is closed.

58:29Anthropic, misanthropic. What does this mean for X? Minus X, I don't know. I intentionally made... It's a name that you can't invert, really. It's hard to say what is the ironic version. It's a, I think, largely irony-proof name. By design. Yeah. you gotta have an irony shield what are your predictions for the just where ai products go in that my sense of you can summarize all ai progress into first you had lms uh 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.

59:33So 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...

59:46I'd be surprised by the end of this year if digital human emulation has not been solved. that I guess that's what we mean by the sort of macro hard project 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 a physical optimist the best you can do is a digital optimist so you can move electrons 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. The remote worker kind of idea, where you'll have a very talented remote worker.

1:00:37You can simply say, in the limit. 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, I call optimists the infinite money glitch. You can use them to make more optimists. Yeah. Humanoid robots will improve as basically be three things that are growing exponentially multiplied by each other recursively.

1:01:35So 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 labor is one of the four factors of production, but not the others? And so if ultimately you're limited by copper or pick your input, it's not quite an infinite money glitch because...

1:02:18Well, infinite is big. So no, not infinite. But let's just say you could do many, many orders in magnitude of Earth's current economy. like a million you know so is this why so if you you know just to get to like just to get to a millionth of harnessing length of the sun's energy would be roughly give or take an order of magnitude a hundred thousand times bigger than Earth's entire economy today and you're only at one millionth of the sun give or take an order of magnitude before we went on Optimus I have a lot of questions on that But every time I say order of magnitude, I say. You're editing 10 drinks.

1:03:05Take a shot every time I say that too often. We attend the next time, after that. Yeah, 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. Yeah, which everyone's going to do, by the way, not just us. So what is XAI's plan to win? Are you expecting 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 hack. Well, when you put it that way,

1:03:49I think the way that Tesla solved self-driving is the way to do it. So I'm pretty sure that's the way. Unrelated question. How to test a self-sufficiency. Yeah. It sounds like you're talking about data? Like, we're going to test a self-sufficiency because of the... We're going to try data and we're going to try algorithms. But isn't that what all the other ones are trying? And if those don't work, I'm not sure what would. We've tried data. We've tried algorithms. I'm all 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.

1:04:37Because it's pretty much the Tesla path. So, I mean, have you tried self-driving, Tesla self-driving lately? Not the most recent version, but... Okay, the car is like, it just increasingly feels satient. It just, it feels like a loving creature.

1:04:54and that'll only get more so.

1:04:59And 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 don't ever put Einstein in a car. It's like, why am I stuck in a car? So there's actually probably a limit to how much intelligence you put in a car to not have the intelligence be bored. what's XAI's plan to stay on the compute ramp that all the labs are doing right now? The labs are on track to spend over like 50 to 100 million dollars. You mean the corporations? Sorry, sorry, sorry, yeah.

1:05:30Corporations. The labs are at universities and they're really like a snail. They're not spending$50 million. You mean the revenue-maximizing corporations? The revenue-maximizing corporations. That call themselves labs. Are making like 20 to 10 billion, depending, like OpenAI is making 20B revenue, Anthropics like 10B. Close to a maximum profit AI. XAI is 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 of revenue.

1:06:11So in fact, you can really think of it like the most valuable companies currently by market cap, their output is digital. So NVIDIA's output is FTPing files to Taiwan. It's digital. Now those are very difficult. They're the only ones that can make files that good. But that is literally their output. They FTP files to Taiwan. Do they FTP them? I believe so. I believe that is the file transfer protocol, I believe is, I could be wrong, but either way, it's a bit extreme going to Taiwan. Apple doesn't make phones. They send files to China. Microsoft doesn't manufacture anything. Even for Xbox, that's outsourced.

1:07:07Again, their output is digital. Meta's output is digital. Google's output is digital. So if you have a human emulator, you can basically create one of the most valuable companies in the world overnight. And you would have access to trillions of dollars of revenue. It's not like a small amount. Okay, so you're saying basically like revenue figures today are just like so, like they're all rounding errors compared to the actual TAM. So just like focus on the TAM and how to get there. I mean, if you take something as simple as, say, customer service, If you have to integrate with the APIs of existing corporations, many of which don't even have an API, so you've got to make one and you've got to wade through legacy software, that's extremely slow.

1:07:58However, 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 a trillion dollars 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.

1:08:45And then there's difficulty where there's a best-in-class turbine engine. 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 GLP ones are just a few bytes of data. Where do you think you want to play in this? Is it a lot of reasonably intelligent intelligence, or is it the very pinnacle of cognitive tasks? Well, I was just using customer service as something that'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.

1:09:31And, you know, it's people of average intelligence. It's not like, you know, you don't need like somebody who's spent many years. You don't need like, you know, sort of several Sigma good engineers for that. But as you make that work, you can then, once you have computers working, effectively digital optimists working, you can then run any application. Like, let's say you're trying to design chips. So you could then run your conventional apps, you know, like stuff from Cadence and Synopsys 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.

1:10:21And at some point, you can say, okay, you're actually going to know 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, like you watch up the difficulty curve. You could be able to do CAD,

1:10:49So you could use NX or any of the CAD software to design things. OK, so you think you started the simplest tasks and walk your way up the different degree? 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, XAI being one of them. but we will win because of a secret plan we have. But everybody's trying different things with data, different things with algorithms. And I'm like, what is the secret plan? What else can we do?

1:11:30It seems like a competitive field. And I'm like, how are you guys going to win is my big question. I think we see a path to doing it. 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 path just following human behavior and training on vast crunch needs of human behavior? But sorry, isn't that, I mean, is that in training? I mean, obviously, I'm not going to spell out most sensitive secrets on a podcast.

1:12:13I need to have at least three more Guinnesses for that. What will XAI's business be like? Is it going to be consumer, enterprise? What's the mix of those things going to be? Is it going to be similar to other labs where you've just... You've said labs. Corporations. Corporations. The CYAM goes deep, Elon. Revenue maximizing corporations, to be clear. 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?

1:12:46Things are going to change very rapidly. I'm stating the obvious here. I call AI the supersonic tsunami. I love alliteration.

1:12:58So really, what's going to happen is, especially when you have humanoid robots at scale, is that 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-world corporations rather than like SpaceX becomes part AI and so forth? I think there'll be digital corporations, but some of this is going to sound kind of dimmerish, okay? but I'm just saying what I think will happen. It's not meant to be doomerish or anything else. Just like this is what I think will happen.

1:13:46Is 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, say, like like like computer used to be a job that humans had that you you would go and get a job as a computer where you would do calculations um and they'd have like entire skyscrapers full of humans like you know 20 30 floors of humans just doing calculations um now that entire skyscraper of humans doing calculations um can be replaced by a laptop with a spreadsheet that spreadsheet can do vastly more calculations than an entire building for human computers.

1:14:41So you can think about, okay, well, what if only some of the cells in your spreadsheet were calculated by humans? Actually, that would be much worse than if all of the cells in your spreadsheet were calculated by the computer. 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 the loop, sorry, Optimus, you, I mean, as far as like manufacturing targets and so forth go, your companies have sort of been like carrying American manufacturing of hard tech on their back.

1:15:31But in the fields that Tesla has been dominant in, and now you want to go into humanoids, in China there's entire dozens and dozens of companies that are doing this kind of manufacturing cheaply and at scale and are incredibly competitive. So give us sort of 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 human robots. The real-world intelligence, the hand, and scale manufacturing. Yeah.

1:16:17So I haven't seen any, even demo robots that have a great hand, like with all the degrees of freedom of a human hand. But Optimus will have that.

1:16:31Optimus does have that. And how do you achieve that? Is it just like right torque doesn't need the motor? What is the hardware bottleneck to that? Well, we had to design custom actuators, basically custom designed motors, gears, power electronics, controls, sensors, everything had to be designed from physics first principles. There is no supply chain for this. And will you be able to manufacture those at scale? Yes. Is anything hard except the hand from a manipulation point of view? Or once you've solved the hand, are you good? From an electromechanical standpoint, the hand is more difficult than everything else combined.

1:17:08The human hand turns out to be quite something. But you also need the real-world intelligence. So the intelligence that Tesla is developed for the car applies very well to the road lab, which is primarily vision. The car takes more vision, but it actually also is listening for sirens. It's taking in the initial measurements, its GPS signals, a whole bunch of other data, combining that with video. It's primarily video, and then outputting the control command. So your Tesla is taking in 1.5 gigabytes a second of video and outputting 2 kilobytes a second of control outputs with the video at 36 hertz and the control frequency at 18.

1:17:58One 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 do this in the real world. So 10 years ago, you had really compelling demos of self-driving, but only now we have Robotaxi and Waymo and all these services scaling up. 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.

1:18:53We'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 think of it as a bootstream, AI is really mostly compression and correlation of two bootstreams. So for video, you've got to do a tremendous amount of compression.

1:19:20And 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 these there's some of these some of these details matter a lot so if it is essentially it's got to turn that well the car is going to turn that one and a half gigabytes a second ultimately into two kilobytes second of control outputs so many stages of compression and you got to get all those stages right and then correlate those to the correct control outputs the robot has to do essentially the same thing and you think about what what humans this is what happens with humans.

1:20:08We 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. Where in a robot, especially with maneuverable arms, there's dozens and dozens of these degrees of freedom. And then, especially with Tesla, you had this advantage of like, you had millions and millions of hours of human demo data collected from just the car being out there where like, you can't equivalently just deploy optimists that don't work and then get the data that way.

1:20:47So between the increased degrees of freedom and the far sparser data, how will you use the sort of Tesla engine of intelligence to train the optimist mind. Now, 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 on the road. And so it's hard to duplicate that like massive training flywheel. For the robot, what we're going to need to do is build a lot of robots and put them in kind of like an Optimus Academy so they can do self-play in reality. So we're actually bullying that out. So we're going to have at least 10 ,000 Optimus robots, maybe 20 ,000 or 30 ,000 that are doing self-play and testing different tasks.

1:21:45And then Tesla has quite a good reality generator, like a physics-accurate reality generator that we made made this for the cars, we'll do the same thing for the robots. Actually have done that for the robots. So you have a few tens of thousands of humanoid robots doing different tasks. And then you can do millions of simulated robots in the simulated world. And you use the tens of thousands of robots in the real world to close the simulation to reality gap, close the sim to real gap. 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 a control plane.

1:22:34And so maybe Grok is doing the slower planning and the motor policy is at the lower level. Yeah. What will the sort of synergy between these things be? Yeah, so Grok would orchestrate the behavior of the Optimus robots. So let's say you wanted to build a factory.

1:22:56Then Grok could organize the Optimus robots, assign them tasks to build the factory to produce whatever you want. Don't you need to merge XAI and Tesla then? Because these things end up so... What were we saying earlier about public company discussions? We're one more Guinness in, Elon.

1:23:19What are you waiting to see before you say, we want to manufacture 100 ,000 optimists? Is it like... Optimize. Since we're defining the proper noun, we can define the plural of the proper noun too. So we're going to proper noun the plural, and so it's optimize. Okay. Is there something on the hardware side you want to see? Do you want to see better actuators? Or is it just you want the software to be better? What are we waiting for before we get like mass manufacturing of Gen 3? No, we're moving towards that. We're going forward with mass manufacturing. But you think current hardware is good enough that you just want to deploy as many as possible now?

1:23:58I mean, it's very hard to scale up production. I think Optimus 3 is the right version of the robot to produce maybe something on the order of like a million units a year. I think you'd want to go to Optimus 4 before you went to 10 million units a year. Okay, but you can do a million a year at Optimus 3. Yeah, I mean, it's very hard to spool at manufacturing. Yes. So like manufacturing, like the output per unit time is always followed as an S-curve. So it starts off agonizingly slow, then it has this sort of exponential increase, then a linear, then a logarithmic outcome until you sort of eventually asymptote at some number.

1:24:42But Optimus initial production will be, it's going to be a stretched out S-curve because 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 does that go? I mean, I guess we're not making custom capacitors yet, maybe. be, but there's nothing you can pick out of a catalog at any price. So it just means that the Optimus S-Cove, the units per unit time, how many Optimus robots do you make per day, whatever is is gonna initially ramp slower than a product where you have an existing supply chain but it will get to a million when you see these chinese humanoids like unitry or whatever sells humanoids for like 6k or 13k do you just like are you hoping to get your optimist's bill of materials below that price so you can uh do the same thing or do you just think qualitatively they're not the same thing.

1:26:04What do you think is going, what allows it to sell for solo and can we match that? Well, Optimus is designed to have a lot of intelligence and to have the same electromechanical dexterity, if not higher than a human. So, the energy tree does not have that. And it's also, I mean, it's quite a big robot because it has to do, you know, carry heavy objects for long periods of time and not overheat or exceed the power of its actuators. So we've got, you know, it's 5 '11", you know, so it's pretty tall, and it's got a lot of intelligence. So it's going to be more expensive than a small robot that is not intelligent.

1:26:54But more capable. Yeah, but not a lot more. I mean, like the thing is, over time, as Optimus robots build Optimus robots, it's 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 can count on them doing well. But in the home or in factories? The best use for robots in the beginning will be any continuous operation, so any 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 Gen3 do?

1:27:34I'm not sure. Maybe it's like 10%, 20%. Maybe more. I don't know. We would use, we would not reduce our headcount. We would increase our headcount to be clear. But we would increase our output. So the units produced per human, like the total number of humans at Tesla will increase, but the output of robots and cars will increase disproportionate.

1:28:07The number of cars and robots produced per human will increase dramatically, but the number of humans will increase as well. We're talking about Chinese manufacturing a bunch here. and we're also talking about, 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 we can't scale up solar in the US. Well, just electricity output in the US needs to scale up. Right, and we can't without 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 setting all the policies, what else would you change?

1:28:50So you'd change the solar tariffs. Yeah, I would say anything that is a limiting factor for electricity needs to be addressed provided it's not very bad for the environment. So presumably some permitting reforms and stuff as well would be in there. There's a fair bit of permitting reforms that are happening. A lot of the permitting is state-based, but this administration is good at 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 investing industry against subsidies by another country.

1:29:34What 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 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's not producing leading edge chips and the export bans really bite there. China's not producing leading edge turbine engines. And similarly, there's a bunch of export bans that are relevant there on some of the metallurgy. Should there be more export bans? Like, do you think about things like, I mean, there are now with the drone industry and things like that, but is that something that should be considered?

1:30:16Well, I think it's important to appreciate that in most areas, China is very advanced in manufacturing. There's only a few areas where it is not.

1:30:28China is a manufacturing powerhouse next level. Like, people don't... It's very impressive. Yeah, yeah. I mean, if you take refining of ore, I'd say roughly China does 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 they're at like 98 % of gallium refining. So China is actually very advanced in manufacturing in, I'd say, most areas. It seems like there is discomfort with this supply chain dependence, and yet nothing's really happening on it. Supply chain dependence? Depends on, say, the gallium refining that you're saying.

1:31:17Yeah, yeah. There's a... All the rare earth stuff. Yeah. Rare earths, which are, as you know, not rare. Like we actually do rare earth ore mining in the US, send the rock, put it on a train, and then put it on a boat to China that goes on another train, and it goes to the rare earth refiners in China who then refine it, put it into a magnet, put it into a motorcycle assembly, and then send it back to America. So we're really missing a lot of ore refining in America. But 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 Optimus, frankly, to build ore refineries.

1:32:08So you think the main advantage China has is the abundance of skilled labor? And that's the thing Optimus fixes? But also we need the skilled labor— It's kind of like four times our population. So, I mean, there's this concern, if you think like human beings are the future, that like right now, if it's the skilled laborers 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 Optima future first. Well, we'll see. It just like keeps that expansion going. It seems that you're sort of pointing out that sort of getting to a million Optima requires the manufacturing that the Optima is supposed to help us get to, right?

1:32:48So you can close that recursive loop pretty quickly. With a small number of Optima. Yeah. So you close the recursive loop to help the robots build the robots. And then we can try to get to tens of millions of units a year. Maybe if you start getting to hundreds of millions of units a year, I think you're going to be the most competitive country by far. We definitely can't win with just humans because China has four times our population. Right. And frankly, America's been running for so long that just like a pro sports team that's been running for a very long time, tend to get complacent and entitled.

1:33:23And 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, assuming that productivity is the same, which I think actually it might not be. I think China might have an advantage on productivity for a person. We will do one quarter of the amount of things as China. So we count one on the human front.

1:34:04And our birth rate has been low for a long time. So the US birth rate has been below replacement since roughly 1971.

1:34:18So we've got a lot of people retiring, more people 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 it without lithium refinery in Corpus Christi, Texas. We have a nickel refinery, which is called a cathode that's here in Austin.

1:35:10And these are the largest cathode refinery, largest lithium refinery, largest nickel and lithium refinery outside of China. And it's like the cathode team would say, we have the largest and the only, actually, a catheter refinery in America. Many supermanatives. Not just the largest, but it's also the only. So it's 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 OptiMoney to do that most Americans, very few Americans, frankly, want to do.

1:36:03I mean, I've actually... Is the refining work too dirty or what's the... It's not, actually, no, we don't, we don't have toxic emissions from the refinery or anything. The chemical refinery is right sort of in Travis County, like five minutes from... Why can't you do it with humans? You can, you run out of humans. Ah, I see, okay, yeah. Like, no matter what you do, you have one quarter of the number of humans in America than China. So if you have them do this thing, they can't do the other thing. So then, well, how do you build this refining capacity? Well, you can do it with the after-mine. And not very many Americans are pining to do refining.

1:36:47I mean, how many are you running to? Very few. Very few are pining to refine. You know, BYD is reaching Tesla production or sales. in quantity. What do you think happens in global markets as Chinese production in EVs scales up?

1:37:07Well, 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, like China's like probably just twice as much refining as the rest of the world combined. So if you go, you know, if you just go down to like fourth and fifth tier supply chain stuff, like at the base level, you've got energy, then you've got mining and refining. Those foundation layers are, like I said, as a rough guess, China's doing twice as much refining as the rest of the world combined. So any given thing is going to have Chinese content because China's doing twice as much manufacturing refining work as the rest of the world.

1:38:08And then they'll go all the way to the finished product with the cars. China's a powerhouse. I mean, I think this year China will exceed three times US electricity 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 US electricity output, but it means that its industrial capacity, as a rough approximation, is three times that, will be three times that in the US. Reading between the lines, it sounds like what you're sort of saying is absence of sort of humanoid recursive miracle in the next few years on the sort of like whole manufacturing energy, raw materials chain, like China will just dominate whether it comes to like AI or manufacturing EVs or manufacturing humanoids.

1:39:14In the absence of breakthrough innovations in the US, China will utterly dominate. Interesting. Yes. Robotics being the main breakthrough innovation. Well, if you do, like to scale AI in space, like basically you need the humanoid robots, you need real world AI, you need a million tons a year to orbit. Like let's just say, 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 it winning time you can finally be satisfied you've done something yes you have the master driver on the moon that's right i just want to see that thing in operation was that out of some sci-fi or where did you uh well actually the there is a highland book the moon the moon is a harsh mistress okay yeah but that's slightly different that's a gravity slingshot or um no they have a master i roll okay yeah but they use that to attack Earth, so maybe it's not the greatest.

1:40:28Well, they used that to assert their independence from Earth. Exactly. What are your plans for the Master of the Moon? They asserted their independence. Earth government disagreed, and they loved things until Earth government agreed. That book is a huge... I found that book much better than his other one that everyone reads, Stranger in a Strange Land. Yeah, Grok comes from Stranger in a Strange Land. Yeah, but I much preferred... The first two-thirds of Stranger in a Strange Land are good, and then it gets very weird in that portion. Yeah. Yeah. But there's still some good concepts in there. Yeah.

1:40:57One thing we were discussing a lot is kind of your system for managing people. Like, you interviewed the first few thousand of SpaceX employees and lots of other companies. What is this? 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, there's not enough hours in a day. It's impossible. But what are you looking for that someone else who's good at interviewing and hiring people? What's the je ne sais quoi? Well, at this point, I think I've got, I might have more training data on evaluating technical talent, especially, but talent of all kinds, I suppose, but technical talent, especially, given that I've done so many technical interviews and then seen the results, technical interviews, seen the results.

1:41:42So my training set is enormous and has a very wide range. Generally, the thing I ask for are bullet points for evidence of exceptional ability.

1:42:01These things can be pretty off the wall. It doesn't need to be in the domain, the specific domain, but evidence of exceptional quality. So if somebody can cite even one thing, but let's 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. I can't be. It's impossible. Right. I mean, total headcount 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?

1:42:40Well, I guess I need to build my training set. It's not like I would bat 1 ,000 here. I would make mistakes. But then I would 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, have a better batting average when interviewing people? 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, etc., etc. But, like, you've got, like, the long tail now of, like, I was really excited about this person.

1:43:17It didn't work out. Curious why that happens. Yeah, so the... I mean, generally what 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, 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 talent. Whereas actually, I think when you look at it, Tesla's had a very consistent and internally promoted executive bench over the past few years.

1:44:05And then at SpaceX, you have all these folks like Mark Jankosa and Steve Davis and... Steve Davis runs a sporting company, they said. 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 uh well so the i mean it tells us sort of senior team uh at this point probably got average tenure of 10 or 12 years it's quite quite a 10 year yeah um so um but there are times when tails went through extremely rapid and extremely rapid growth bays um and so it was somewhat things were just somewhat sped up um and when a company as as you know a company goes through different orders of magnitude of size you you know uh people that 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.

1:45:09Yeah, 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. It's generally.

1:45:27Then TESA had a further challenge where when TESA had very successful periods, we would be relentlessly recruited from. like relentlessly. Like when Apple had their electric car program, they were carpet bombing Tesla with recruiting calls. It was, engineers just unplugged their phones. Like it's just, I'm trying to get work done here. Yeah. If I get one more call from an Apple recruiter, but they were opening offer without any interview with me like double the compensation at Tesla. So, so, so, so, so we had a bit of the, Tesla pixie dust thing where it's like, oh, if you hire a Tesla executive, you're suddenly you're going to, everything's going to be successful.

1:46:15And I fall and pray to the pixie dust, you know, thing as well, where it's like, oh, we'll hire someone from Google or Apple, and they'll be immediately successful, but not that that's not how it works. You know, people are people, it's not like magical pixie dust. So when we have the pixie dust problem, we would get relentlessly recruited um and um and then also being tesla being um engineering especially being primarily in silicon valley uh 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 um so how do you prevent that how do you prevent the pixie dust effect where everyone's trying to approach other people?

1:46:59I don't think we can, I don't think there's much we can do to stop it. But that's like, that's one of the reasons why it is 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. I mean, being in Austin helps then. Austin, yeah, it still helps. I mean, Tesla still has a majority of its engineering in California.

1:47:34So, you know, getting engineers to move, I call it the significant other problem. Yes. So, when others have jobs. Yeah. Yeah, yeah, exactly. So, for Starbase, that was particularly difficult. Yes. Since the odds of finding a non-SpaceX job. In Bransfield, Texas. pretty low yeah yeah yeah it's quite quite difficult i mean it's like a technology monastery um you know remote and mostly dudes but again if you go much of an improvement over sf yeah if you go but if you go back to these people who've really um been very effective in a technical capacity at tesla at spacex and and those sorts of places.

1:48:21What do you think they have in common other than, like, is it just that they're very sharp on the, you know, rocketry or the, you know, the technical foundations, or do you think it's something organizational, it's something about their ability to work with you? Is this their ability to, like, be, you know, flexible, but not too flexible?

1:48:44What makes a good sparring partner for you? I don't think of a sparring partner. I mean, I mean, if somebody gets things done, I love them. And if they don't, I... So it's pretty straightforward. It's not like some idiosyncratic thing. If somebody executes well, I'm a huge fan. And if they don't, I'm not. But it's not about mapping to my idiosyncratic preferences. I'll certainly try not to have it be mapping to my idiosyncratic preferences. So, yeah.

1:49:15Yeah. 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 underweighted that at one point. So, like, are they a good person, trustworthy, smart and talented and hardworking? If so, you can add domain knowledge. but those fundamental traits, those fundamental properties you cannot change. So most of the people who are at Tesla and SpaceX did not come from the aerospace industry or the order industry. What is most set to change about your management style as your companies have scaled from 100 to 1 ,000 to 10 ,000 people?

1:50:04You're known for this very micromanagement, just getting into the details of things. Nanomanagement, please. Pequot management. Um, so you're saying, we're going to go all the way down to Flanks Costa.

1:50:24All the way down to Heisenberg's in Sydney, first of all. Yeah. Well, how do you, I mean, are you still able to get into details as much as you want? Would your companies be more successful if you could, if they were smaller? Like, how do you, how do you think about that? Well, because I have a fixed amount of time in the day, uh, my time is necessarily, um, diluted as things grow and as the span of activity increases. So, you know, it's impossible for me to actually be a micromanager because that would imply I have some thousands of hours per day. It is a logical impossibility for me to micromanage things.

1:51:06So 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. and it's not arbitrarily drilling into tiny things. And like I said, obviously, from a time standpoint, it is physically impossible for you arbitrarily going to tiny things that don't matter, and that would result in failure. But sometimes the tiny things are decisive in victory. Famously, you switched the starship design from composites to steel. Yes.

1:51:57and 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 to get some resistance can you tell us how you came to that whole composite steel switch uh yeah so desperation um the um originally yeah we were going to make starship out of uh carbon fiber um and um carbon fiber is pretty expensive. Like the, 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 carbon fibers is that material cost is still very high.

1:52:46So it's about 50 times, particularly if you go for a high strength, specialized carbon fiber that can handle cryogenic oxygen, it's like 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 or less room temperature applications like a Formula 1 car, static aerostructure or any kind of aerostructure really, 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.

1:53:33And it's been picked in the first place just because it's light. Yes. At first glance, most people would think that the choice for making something light would be carbon fiber.

1:53:51now the thing is that when you make something very enormous out of carbon fiber and then you try to have the carbon fiber be efficiently cured, meaning not room temperature cured because sometimes you've got 50 plies of carbon fiber, and carbon fiber is really carbon string and glue and in order to have high strength, you need an autoclave. So something that can, that's essentially high pressure oven. And if you have something that's a gigantic, the oven's got to be bigger than the rock one. So we're trying to make the autoclave that's bigger than any autoclave that's ever existed or do room temperature cure, which takes a long time and has issues.

1:54:43But the final issue is that we're just making very slow progress with carbon fiber.

1:54:52I 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. 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 9, the primary airframe is made of aluminum lithium, which is a very, very good strength weight. And actually it has about the same, maybe better strength weight for its application than carbon fiber. But aluminum lithium is very difficult to work with.

1:55:33In order to weld it, you have to do something called friction and still welding, where you join the metal without it entering the liquid base. So it's kind of wild that you could do that. But with this particular type of welding, you can do that. But it's very difficult to, like, say, let's say you want to make a modification or attach something to aluminum lithium. You now have to use mechanical attachment with seals. You can't weld it on. So I want to avoid using aluminum lithium for the primary structure for Starship. And there was this very special grade of carbon fiber that had very good mass properties.

1:56:19So with rocket, you're really trying to maximize the percentage of the rocket that is propellant, minimize the mass, obviously. And it likes to be making very slow progress. and I said at this rate we're never going to get to miles so we better think of something else I didn't want to use aluminum lithium because of the difficulty of friction still welding especially doing that at scale it was hard enough at 3.6 meters in diameter let alone at 9 meters or above

1:56:55then I said well what about steel Now, I had a clue here because some of the early US rockets had used very thin steel. The Atlas rockets had used a steel balloon tank. So it's not like steel had never been used before. It actually had been used. And when you look at the material properties of stainless steel, especially 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 material properties at room temperature, it looks like the steel is going to be twice as heavy. But if you look at the material properties at cryogenic temperature of full hot steel stainless of particular grades, then you actually get to a similar strength weight as carbon fiber.

1:57:55And in the case of Starship, both the fuel and the oxidizer are cryogenic. So for Falcon 9, the fuel is rocket propellant grade kerosene, basically like a very pure form of jet fuel. But that is roughly room temperature. Although we do actually chill it slightly below. We chill it like a beer. We do chill it, but it's not cryogenic. In fact, if we made it cryogenic, it would just turn to wax. But for Sasha, it's liquid methane and liquid oxygen. 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.

1:58:52Because it's almost all things at cryogenic temperature, actually has a similar strength of weight as carbon fiber. But costs 50 times less than raw material and is very easy to work with. You can weld stainless steel outdoors. course. 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 carbon fiber, then when you factor in that, we have a much reduced heat shield mass because the melting point of steel is much greater than the melting point of aluminum.

1:59:51It's about twice the melting point of aluminum. So you can just run the rocket much hotter? Yes. So especially for the ship, which is coming in like a blazing meteor, 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 leeward side.

2:00:25The net result is actually the steel rocket weighs less than the carbon fiber rocket. because the resin in the carbon fiber rocket

2:00:37starts to melt. So basically, carbon fiber and aluminum have about the same operating temperature capabilities, whereas steel can operate at twice temperature. I mean, these are very rough approximations. People will... I won't go to the rocket papers. What I mean is people will say, oh, he said it's twice, it's actually 0.8. Shut up, assholes. That's what the main comment's going to be about. God damn it. The point is, actually, in retrospect, we should have started with down steel in 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, less proven path, other than the early US rockets, versus carbon fiber was like a worse but more proven out path.

2:01:22And 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 like the issue is that we weren't making fast enough progress. We were having trouble making even a small barrel section of the carbon fiber that didn't have wrinkles in it. So because at that large scale you have to have many plies, many sort of layers of the carbon fiber. You've got to cure it and you've got to cure it in such a way that it doesn't have any wrinkles or defects. The common fiber is much less resilient than steel.

2:02:02It has much less, it's less toughness. Like stainless steel will stretch and bend. The common fiber will tend to shatter.

2:02:15So toughness being the area under the stress strain curve. So you're generally going to have to do better with steel. The stainless steel to be precise. One other Starship question. So I visited Starbase, I don't know, two years ago, I went 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.

2:02:52But there's a lot of pride in the simplicity and well Starshot was a very complicated rocket so that's what I'm getting at are things simpler or are they complex I think maybe just what they're trying to say is that you don't have to have prior experience in the rocket industry to work on Starshot somebody just needs to be smart and work hard and be trustworthy and they can work on a rocket they don't need prior rocket experience It's the most complicated machine ever made by humans, by a long shot. In what regards? Anything really. There isn't a more complex machine. Yeah, I mean, I'd say that there's pretty much any project I can think of would be easier than this.

2:03:42And that's why no one has made a rapidly reusable, nobody has ever made a fully reusable over the 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. Falcon is partially reusable, but the up-to-stage is not. Starship version 3, I 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 - I don't, I'm like, I said I could, any technical problem, even like a hydron collider or something like that, it's easier for us.

2:04:35We spent a lot of time on bottlenecks. Can you say what the current starship bottlenecks are, even at the high level? I mean, trying to make it not explode. That old chestnut. Really wants to explode. All those combustion materials. We've had two boosters explode on the test end. One obliterated the entire test facility. So it at least takes like one mistake. I mean, the amount of energy contained in Starship 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 rocket engine ever made.

2:05:23But it desperately wants to blow up. I mean, just to put things into perspective here, on Liftoff, the rocket is generating over 100 gigawatts of power. It's 20 % of the US electricity. It's actually insane. It's a great comparison. While not exploding. Sometimes. Sometimes. But sometimes, 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 want it to not merely not explode, but fly reliably on a daily basis, like once per hour. And obviously it blows up a lot. It's very difficult to maintain that launch cadence.

2:06:06Yes. And then, I'm going to say, 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 shucking 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 because it's kind of fundamentally a consumable? Well, yes, but your brake pads in your car are also consumable, but they last a very long time. Fair. So it just needs to last a very long time.

2:06:55That's just, yeah, try it. 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 soft landing, it would not have been reusable without a lot of work. And 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 refold propellant and fly again without you can't do this laborious inspection of 40 ,000 tiles type of thing I'm curious how you drive when I read biographies of yours it just it seems like you're just able to drive the sense of urgency and drive the sense of 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.

2:08:02What 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 like 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 leaving the company. So if my sense of urgency, I have like a maniacal sense of urgency. So that maniacal sense of urgency projects through the rest of the company. 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.

2:08:39Is it just, um, you're able to identify bottlenecks and get rid of them so people can move fast? Like, how do you, how do you think about why your companies are able to move fast? Yeah, I'm constantly addressing the limiting factor. So, um,

2:08:55I mean, on the deadlines front, I generally actually try to aim for a deadline that I at least think is at the 50th percentile. So it's not like an impossible deadline, but it's the most aggressive deadline I can think of that could be achieved with 50 % probability, which means that it will be late half the time. um and um but whatever like there is like a law of geysers expansion that applies to schedules like whatever given 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 um it will expand to fully available schedule and it'll take five years um you know like there's like this there's a physical limit it 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 um and scale manufacturing that's why you can't like instantly make you know a million of something millions a year or something uh you've got you've got to design manufacturing line you can bring it up you've got to ride the s curve of production um so yeah i mean i guess like What can I say that's actually helpful to people?

2:10:17I think generally a maniacal sense of urgency is a very big deal.

2:10:27And you want to have an aggressive schedule and 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 at which to act?

2:11:10I mean, I have these very detailed engineering reviews weekly. That's maybe a very unusual level of granularity. I don't know anyone who runs a company, or at least a manufacturing company, that goes to the level of detail that I go into. do. 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.

2:12:01And 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. Very Gen Z. How do you prevent advanced administration? You just call them randomly? No, we just go around the room and everyone provides an update. So, I mean, it's a lot of information to keep in your head because you've got them, say, if you have meetings weekly or twice weekly, you've got a snapshot of what that person said and you can then plot the progress points. You can sort of manually plot the points on the curve and say, are we converging to a solution or not?

2:12:48Or 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 took drastic action. And so that's, I came to that conclusion in 2018, took drastic action and fixed 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.

2:13:36you'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 because you have like 80 companies 80? no you have so many already that's already remarkable by this current number we can barely keep one company together

2:14:02it depends on situation.

2:14:08I actually don't have regular meetings with the foreign company. The foreign company is cruising along. Basically, if something is working well and making good progress, then there's no point in me spending time on it. 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.

2:14:44So basically, if something's going really well, they don't see much of me. But if something's going badly, they'll see a lot of me. Or not even badly. Something's the limiting factor. It's the limiting factor, exactly. It's not exactly going badly, but it's the thing that we need to make go faster to max. 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 are limiting factor are weekly, and some things are twice weekly. So the AI5 chip review is twice weekly, and so it's every Tuesday and Saturdays is the chip review.

2:15:30Is it open-ended in how long it goes? Technically, yes, but usually it's like two or three hours. So, I mean, sometimes less. It depends on how much information you've got to go through. Yeah. That's another thing. I'm just trying to tease out the differences here because the outcomes seem quite different. And so I think it's interesting to note what inputs are different. And it feels like the corporate world, one, like you were saying, just the CEO doing engineering reviews does not always happen, despite the fact that that is what the company is doing. But then time is often pretty finely sliced into half-hour meetings or even 15-minute meetings.

2:16:12And it seems like you hold more open-ended, we're talking about it until we figure it out type meetings. Sometimes. Yeah, sometimes. But most of them seem to more or less stay on time.

2:16:30So, I mean, today's Starship engineering review went a bit longer because there were more topics to discuss. You know, trying to figure out how to scale to a million plus tons of Torbid per year is quite challenging. Can I ask a question? You said about Optimus and AI, that they're going to result in double-digit 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 cut if the economy is going to grow so much? Well, I think like waste and food 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.

2:17:28Now, our interest payments, the interest payments to the national debt exceed the military budget, which is a trillion dollars. So if over a trillion dollars, just the interest payments, you know, that was like, I was like, okay, pretty concerned about that. But 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 help solve the national debt. Or not help solve. It's the only thing that could solve the national debt. 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.

2:18:07And so we'd like to, well, we just need, we need enough time to build the AI and robots to 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. 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, like tariffs on certain components or whether it's like permitting? I'm like the president. And very hard to cut things that are obvious waste and fraud, like ridiculous waste and fraud.

2:18:57What I discovered that is it's extremely difficult even to cut very obvious waste and fraud from the government. Because the government has to operate on who's complaining. 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 pandas. And we're like, meanwhile, there's no baby pandas are dying. They're just making it up. The forces are capable of coming up with extremely compelling, sort of heart-wrenching stories that are false, but nonetheless sound sympathetic.

2:19:39And that's what happened. And so it's like, perhaps I should have known better.

2:19:49And I thought, wait, let's try to cut some amount of waste and pour from the government. Maybe there shouldn't be 20 million people marked 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 as 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, again, have a typo or we have fraud.

2:20:52so we say we appear to have gotten the century of your birth incorrect or a great plot for a movie yes this is this this is what i when i'm about ludicrous fraud this is what i'm about ludicrous fraud were those people getting payments some were getting payments from social security but but but the main fraud vector uh was to mark somebody as alive in social security and then use every other government payment system uh to uh basically to do fraud because what those other government payment systems do, they will simply do an RUALIVE check to the Social Security database. It's a bank shot. What would you estimate as the total amount of fraud from this mechanism?

2:21:30My guess is, and by the way, the Government Accountability Office has done these estimates before. I'm not the only one who's coming out of this. In fact, I think 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 is very ineffective at stopping fraud. Because it's not like if it was a company stopping fraud, you've got a motivation because it's affecting the earnings of your company.

2:22:14But the government, 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. I mean, when you go to the DMV, do you think, wow, this is a bastion of competence? Well, now imagine it's worse than the DMV, because it's the DMV that can print money. So was it not possible? At least the state level DMVs need to, the states more or less need to stay within their budget when they go bankrupt. But the federal government just prints full money. Well, was it not possible to cut that? If there's actually half a trillion of fraud, why was it not possible to cut all that?

2:22:58Because when, as soon as you, we did, we actually, no, you really have to stand back and recalibrate your expectations for competence. Because you're operating in a world where you've got to sort of make ends meet, like you've got to pay your bills, you've got to... Buy the microphones. Yeah, yeah, exactly. So it's not like there's a giant, largely uncaring monster bureaucracy, and a bunch of necristic computers that are just sending payments. One of the things that the Doge team did, and it 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 PEMS, like Payment Accounts Master or something like that, there's 5 trillion payments 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 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.

2:24:42Recalibrate your expectations. I want to better understand this how it's 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 a year. So I'd be curious to see what like the other 490 billion is. Federal government expenditures are seven and a half trillion a year.

2:25:06How competent do you think government is? The discretionary spending there is like 15%. Yeah, but it doesn't matter. Most of the forward is non-discretionary. It's basically a fordulent Medicare, Medicaid, Social Security, 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 consider, let's like reductio ad absurdum. The government is perfect and has no fraud. What is your probability estimate of that?

2:25:53I mean, zero. Okay. 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 a year of waste and fraud. And it's not 90%. It's not 90 % effective. This seems like a strange way to first principles the amount of fraud in the government. Just like, how much do you think there is? and then, anyways, we don't know how to do it live, but I'd be curious to see how - I mean, 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 ground it down, but it's a little bit of a different problem space because you're dealing with a much more heterogeneous set of fraud vectors here than we are.

2:26:40Yeah, but I mean, at Stripe, you have high confidence and you try hard. You have high confidence and high caring, but still fraud is non-zero. Now, imagine it's at a much bigger scale. There's much less competence and much less caring. You know, back in PayPal back in the day, we were trying to manage fraud down to about 1 % of the payment volume. And that was very difficult. Took a tremendous amount of competence in caring to get fraud merely to 1%. Now imagine that you're in an organization where there's much less caring 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 doing stuff there, where it feels like, moving from the outside in, that two things have been quite impactful.

2:27:36One, the America PAC, and two, the acquisition of, well, Twitter at the time. But also, it seems like there was a bunch of heartache. and so what's your grading of the whole experience?

2:27:55Well, I think those things need to be done to maximize the probability that the future is good.

2:28:06So, 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 on 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. Um, so anyway, but, um, I think, I think overall those actions, um, acquiring Twitter, getting Trump elected, even though it makes a lot of people angry.

2:29:04Um, I think those, I think those actions are good for, were good for civilization. Um, yeah. 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 AI and robotics to the point where we can ensure that the future is good. 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 and robotics. How do you feel about, you know, Optimus, Grok, 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.

2:30:13How does this concern manifest in what private companies should be willing to give governments? What kinds of guardrails 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 Grok get to say, actually, even the military wants to do X, no, Grok will not do that? I think probably the biggest danger of AI, or maybe the biggest danger of AI and robotics going wrong is government. Interesting. You know, I mean, the way you think, like people who are opposed to corporations or or worried about corporations, should really worry the most about government because government is just a corporation in the limit.

2:31:07It's a government... 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.

2:31:27But 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 that is the thing to be worried about. It's like, if the government could potentially use AI and robotics to suppress the population. Like that is a serious concern. As a guy building AI and robotics, how do you prevent that? Well, I think that 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 the government, then you're probably going to have a better outcome than if you have more government.

2:32:21But robotics will be available to all governments, right? Not about all governments.

2:32:30It's difficult to predict the, like I said, what's the end point or what is many years in the future, but it's difficult to predict the path along that way. 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 could do is just say, whatever government index, you're not allowed to use Optimus to do X, Y, Z, just write out like a policy. I mean, I think you tweeted recently that Grok should have a moral constitution. And one of those things could be that we limit what governments are allowed to do with this advanced technology.

2:33:21I mean, yeah, we can do what is, I mean, technically, I mean, if the politicians pass a law, and they can enforce that law, then it's hard to not do that law. The best thing we can do is limited government, where you have the appropriate cross-checks between the executive, judicial, and legislative branches. I guess the reason I'm curious about it is this. At some point, it seems like the limits will come from you. You've got the Optimus. You've got the SpaceGPUs. You think that would be the boss of the government. Or you will get 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 or whatever, like it needs SpaceX.

2:34:16It is the necessary contractor. And you are in the process of building more and more of the technological components of of the future that will have an analogous role in different industries. And you could have this ability to set some policy that is suppressing classical liberalism in any way. My companies will not help in any way with that, or some policy like that. I will do my best to ensure that anything that's within my control maximizes the good outcome for humanity.

2:34:55I think anything else would be short-sighted. Because obviously I'm part of humanity, so I like humans.

2:35:06Pro-human, pro-human. You 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, but I know you a lot. You have a lot of followers. They did give away. How did you have discerned my secrets? I posted my... How do you design a 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 know roughly if you increase the operating temperature by 20th set in degrees Kelvin you can cut your radiator mass in half so running at a higher temperature is helpful in space I mean there's various things you can do for shielding the memory but like neural nets are going to be very resilient to bit flips so like most of what happens for radiation is like random bitflips but like if you've got like you know a multi-trolling parameter model and you get a few bitflips it doesn't matter it's much like curiosity programs are going to be much more sensitive to bitflips than some giant parameter file so I just designed it to run hard and I think you pretty much do it same way that you do things on Earth, apart from making it run hotter.

2:36:41I mean, the solar array is most of the weight on the satellite. Is there a way to make the GPUs even more power dense than what NVIDIA and TPUs and etc. are planning on doing that would be especially privileged in the space-based world? Well, I mean, the basic math is, like, if you can do about a kilowatt per reticle, and then you'd need you know 100 million full reticle chips to do 100 gigawatts so depending on what your yield assumptions are that tells you how many chips you need to make but cool you need if you're going to have 100 gigawatts of power you need You know, 100 million chips running that are running a kilowatt sustained output per reticle.

2:37:44Basic math. 100 million chips depends on, yeah, if you look at the die size of something like blackball chips 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 TerraFab? Millions of wafers a month of advanced process nodes? It could be some number in a lot of a million, I think. You've got to do the memory too. Yeah. You're going to make a memory fab? I think the TerraFab's got to do memory.

2:38:26It's got to do logic memory and packaging. I'm very curious how somebody gets started. this is like the most complicated thing man has ever made. And obviously, like, if anybody's up to the task, you're up to the task. Like, what do you, so you realize it's 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, like, what do you, do you like call ASML? Like, what is the task I want? 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.

2:39:06Is a little fab done? No, it's not done. We're not going to keep that cat in the bag. That cat's going to come out of the bag room. It'll be like drones hovering over the bloody thing. You'll be able to see its construction progress on X in real time. So, no, we, I mean, listen, I don't know, we could just flounder in failure to be clear. It's like not, success is not guaranteed. But since we want to try to make, you know, something like 100 million, we need, we want 100 gigawatts of power and 100 chips that can take 100 gigawatts, right? So call it, you know, but yeah, by 2030. So then it will take as many chips as our suppliers will give us.

2:40:00I've said this to, I've actually said this to TSMC and Samsung and Micronus, like, please build your more fabs faster. And we will guarantee you to buy the output of those fabs. So they're already like moving as fast as they can. Like 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 a narrative that the people doing AI want a very large number of, you know, chips as quickly as possible. And then many of the input suppliers, the fabs, but also, you know, the turbine manufacturers are not ramping up production very quickly.

2:40:43No. Yeah, the explanation you hear is that they're dispositionally conservative. You know, they're Taiwanese or German, as the story may be. And they just, like, don't believe. They 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 so like that's a lot of layers of scar tissue you know so it's like it's like during the boom times looks like everything is going to be great forever and then then then the crash happens and then they're desperately trying to avoid bankruptcy um and and then there's another boom and another crash are there other are there other ideas you think others should go pursue that you're not for whatever reasons right now um i mean there are a few companies that are that are pursuing like new ways of doing chips.

2:41:41But they're just not scaling fast. I don't even mean within AI. I mean just generally. I'd say like people should 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.

2:42:08But, you know, going back to the limiting factor, I'll use that phrase about 100 times.

2:42:17The current limiting factor that I see in the timeframe, you know, in the sort of 20, 29, 20, like in the three to four year timeframe, it's chips. In the one-year timeframe, it's energy, power production, electricity. It's not clear to me that there's enough usable electricity to turn on all the AI chips that are being made.

2:42:50Towards the end of this year, I think we're going to have real trouble turning on. Chip output 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 be maybe the leader, hopefully the leader, is that we'll be able to turn on more chips than other people can turn on faster. Because we're good at hardware. and generally the innovations from the corporations that call themselves labs the ideas tend to flow like it's rare to see that there's more than about a six month difference between, like the ideas 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.

2:43:55And so I think XCI will be able to scale hardware the fastest and therefore most likely will be the leader. You joked or were self-conscious about 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 um you know mark andreason had the line of most people are willing to endure any amount of chronic pain to avoid acute pain and it feels like a lot of the cases we're talking about are just leaning into the acute pain whatever it is it's like okay we We've got to figure out how to work with steel, or we've got to figure out how to run the chips in space, or we'll take some near-term acute pain to actually solve the bottleneck.

2:44:51And so that's kind of a unifying thing. I have a high-pain threshold that's helpful. Solve the bottlenecks. Yes.

2:45:05One thing I can say is, I think the future is going to be very interesting.

2:45:14And as I said, the Davos have only been to, I was literally at Davos, I think it was on the ground for like three hours or something. It's better to be, it's better to err on the side of optimism and be wrong than err on the side of pessimism and be right for quality of life. So, you know, your happiness will be, you'll be happier if you are on the side of optimism rather than erring on the side of pessimism. And so I recommend erring on the side of optimism. Thanks for that. Cool. Yilan, thanks for doing this. Thank you. All right, thanks guys. Great stamina. Hopefully this encounters the pain and the pain tolerance.