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

28:30Do 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 stuff. But you do need carpenter personnel. So I don't know, I mean, like right now, if Tesla's pedal to the metal max production of going as fast as possible to get AI5, Tesla AI5 chip design into production and then reaching scale, that'll probably happen around the second quarter-ish of next year, hopefully. and then AI6 would hopefully follow less than a year later but and we've secured all the chip fab production that we can yes but you're currently limited on TSMC fab capacity yeah and we'll be using TSMC Taiwan Samsung Korea, TSMC Arizona Samsung Texas You've booked out all the classes you can.

29:42Yes. And 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. The limiting factor is chips. Yeah. Like, limiting factor once you can get to space is chips, but 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?

30:20Like, what's going on? They're building fabs as fast as they can. And so is Samsung. like they're pedal to the metal I mean they're going you know balls to wall you know as fast as they can so still not fast enough I mean like I said there will be I think if you say I think towards the end of this year I think probably chip production will outpace the ability to turn chips on but once you can get to space and unlock the power constraint and you can now do hundreds of gigawatts per year of power in space. Again, bearing in mind that average power usage in the US is 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.

31:15The 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, well, they can't turn the chips on for large clusters towards the end of this year. The chips are going to be piling up and won't be able to be turned on. Now, for edge computers, it's a different story. So for Tesla, the AI-5 chip is going into our Optimus robot. Optimistic. And so if you have an AI edge compute, that's distributed power. Now the power is distributed over a large area.

32:10It's not concentrated. and if you can charge at night, you can actually use the grid much more effectively because the actual peak power production in the U.S. is over 1 ,000 gigawatts, but the average power usage because the day-night cycle is 500. So if you can charge at night, there's an incremental 500 gigawatts that you can generate at night. So that's why Tesla for edge compute is not constrained. And we can make a lot of shifts to make a very large number of robots and cars. But if you try to concentrate that compute, you're going to have a lot of trouble turning it on. What I find remarkable about the SpaceX business is the end goal is to get to Mars, but you keep finding ways on the way there to keep generating incremental revenue to get to the next stage and the next stage.

33:08So the Falcon 9 is Starlink. And now for Starship, it's going to be potentially orbital data centers. But do you find these infinitely elastic, marginal use cases of your next rocket and your next rocket and next scale up? You can see how this might seem like a simulation to me? Or am I someone's avatar in a video game or something? Because it's like, what are the odds that all these crazy things should be happening? I mean, I mean, rockets and chips and robots and space, solar power, and not to mention the mass driver on the moon. I really want to see that. You can imagine some mass driver that's just going like, shoom, shoom.

33:55It's like sending AI solar pod 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 AI satellites in deep space. You know, a billion or 10 billion tons a year. I'm sorry, you manufacture the satellites on the moon? Yeah. I see. So you send the raw materials to the moon, and then manufacture them there, and then shoot. Well, the lunar soil is, I guess, like 20 % solar.

34:3820 % silicon or something like that. I see. 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. I'm just saying these are simply, it's kind of like, like I said, it does seem like a sort of a video game situation where it's difficult but not impossible to get to the next level.

35:17I don't see any way that you could do

35:23500 to 1 ,000 terawatts per year launch from Earth. I agree. But you could do that from the moon. Okay, let me tell you how I ended up using Mercury for my personal banking. So last year, I had the opportunity to make an investment that I was very excited about. But it came up a bit last minute. and so I had to wire over a lot of money for my personal account very fast. But my personal bank at the time wouldn't let me make this wire transfer online. And I called them a bunch of times. They just couldn't make it work. They told me that I'd have to go to the nearest in-person branch, which was in Dallas.

36:01And for a moment, I even considered flying from SF to Dallas to make this transfer happen last minute. But then I remembered that Mercury, which I used for my business banking, had just started rolling out personal accounts. So I emailed support with a quick rundown of the situation. And within two hours, I had successfully wired the investment for my new personal Mercury account. Since then, I've moved over the rest of my personal money from my previous bank to Mercury. And that's made a bunch of things, even little things like setting up auto transfer rules between my checkings and savings account, a whole lot better.

36:32Visit mercury.com slash personal to get started. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and Column N.A., members of FDIC. 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, et cetera, survives. Yes. 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?

37:07Well, 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. Most intelligent, the vast majority of intelligence in the future will be AI. So, yeah, AI will exceed, you say like how many, what's how much how many I don't know petawatts of intelligence will be silicon versus biological and and basically humans will be a very tiny percentage of all intelligence in the future if current trends continue anyways as long as like I think there's intelligence ideally ideally also which includes human intelligence and consciousness propagated into the future.

38:02That's a good thing. So you 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 and the AI intelligence will continue the light of our journey. Yeah. I mean, to be clear, I'm very pro-human. So I want to make sure we take the set of actions that ensure that humans are along for the ride. You know, we're at least there. But let me just say the total amount of intelligence, I think maybe in five or six years, AI will exceed the sum of all human intelligence.

38:47And 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 trade but no control? How should we think about the relationship between the vast stocks of AI population versus human population? In the long run, I think it's difficult to imagine that if humans have, say, 1 % of the 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.

39:35So the reason for XAI's mission is to understand the universe. So now that's actually very important. So you say, well, what things are necessary to understand the universe? Well, you have to be curious and you have to exist. You can't understand the universe that 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 of the things you're trying to understand is where will humanity go?

40:19And so I think understand the universe actually means you would care about propagating humanity into the future.

40:28And 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 i think there are that that understanding universe encompasses all of all those things okay um you can't have understanding without but i think you can't have understanding without intelligence and i think without consciousness um so you in order to understand universe you have to expand the scale and probably the scope of intelligence, different types of intelligence.

41:23I guess from a human-centric perspective, like, put 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... We actually have made protected zones for chimpanzees. And even though humans could exterminate all chimpanzees, we've chosen not to do so. Do you think that's a basic scenario for humans in the post-AGI world?

41:51I 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. is uh like i actually i think if probably like uh like the in banks culture books are the closest thing to what what what the future will be like in a you know non-dystopian outcome um so i so outside universe it means you have to be very you have to be truth-seeking as well yeah like truth has to be absolutely fundamental because you can't understand the universe if you If you're delusional, you'll simply think you've understood the universe, but you will not.

42:39So 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?

43:00I think you need to make sure that Grok says things that are correct, not politically correct. I think it's the elements of coagency. So you want to make sure that the axioms are as close to true as possible, that you don't have contradictory axioms, that the conclusions necessarily follow from those axioms with the right probability. It's just critical thinking one-on-one. 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, it's the way it's like you can break a lot of laws, but you can't...

43:45Physics is law, everything else is is a recommendation. In order to make a technology that works, you have to be extremely truth-seeking. Because otherwise, you will test that technology against reality. And if you make, for example, an error in your rocket design, the rocket will blow up, or the car won't work, or the, you know. But there were a lot of communist Soviet physicists or scientists discovered new physics. There are German Nazi physicists who discovered new science. It seems possible to be really good at discovering new science and be really truth-seeking in that one particular way.

44:25And still we'd be like, well, I don't want the communist scientists to become more and more powerful over time. And so those seem like, yeah, we can imagine a future version of Gragi that's 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. And if you're stuck in some system, it doesn't mean you believe in that system. So von Braun, who was one of the greatest rocket engineers ever, he put on death row in Nazi Germany for saying that he didn't want to make weapons.

45:12is yelling wanted to go to the moon. You got pulled off death row at like last minute when they say, hey, you're about to execute like your best rocket engineer. Maybe that's 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?

45:49Everything. And why is it going to then care about human consciousness? These things are only probabilities. They're not certainties. So I'm not saying that for sure, Grok will do everything. But at least if you try, it's better than not trying. At least if that's fundamental to the mission, it's better than if it's not fundamental to the mission. And Understanding the universe means that you have to propagate intelligence into the future. You have to be curious about all things in the universe. It would be much less interesting to eliminate humanity than to see humanity grow and prosper. I like Mars, obviously.

46:30I love Mars. But Mars is kind of boring because it's got a bunch of rocks compared to Earth. Earth is much more interesting. So any AI that is trying to understand the universe would want to see how humanity develops in the future, or that AI is not adhering to its mission. So 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?

47:17We'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... Some tiny increase in the number of robots produced is not as interesting as some microscopic... Like you said, eliminating humanity, how many robots would that get you?

48:02or 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 can make a million different varieties of robots, and then there's 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 we're interesting i'm just trying to be realistic here um if if we have if 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.

49:10I 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 understand 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.

49:52I guess I think it's a reasonable philosophy to be like, you know it seems super implausible that humans will end up with like 99 percent control or something and you're just asking for a coup at that point so why not just have a civilization where it's more compatible with like lots of different intelligences getting along now let me tell you how things can potentially go wrong in ai is i think if you if you make ai be politically correct meaning like it says things that it doesn't believe like you're actually programming it to to to lie or have axioms that are incompatible i think you can make it go insane and do terrible things.

50:28I think one of the, maybe the central lesson for 2001 Space Odyssey was that you should not make AI lie. And that's what I think what Oscar was trying to say. Because people usually know the meme of like, why of hell, the computer is not opening the pod bay doors. Clearly, they weren't good at prompt engineering, because because it 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.

51:03But 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. It totally makes sense. Most of the computing screening, as you know, is less of the sort of political stuff. It's more about, can you solve problems? XA has been ahead of everybody else in terms of scaling RL compute. And you're giving some verifier. It says, hey, have you solved this puzzle for me?

51:43And 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. Yes. Right now we can catch it. But as they get smarter, our ability to catch them doing this will get, you know, they'll just be doing things we can't even understand that are designing the next engine for SpaceX in a way that like humans can't really verify. And then they could be rewarded for lying and saying that they've designed it the right way, but they haven't. And so this reward hacking problem seems more general than politics.

52:11It seems more about just like you want to do RL, you need a verifier. Reality. Yeah. Is the best verifier. But not about human oversight. Like the thing you want to RL it on is like, will you do the thing humans tell you to do? Or like, are you going to lie to the humans? And it can just lie to us while still being correct to the laws of physics. At least it must know what is physically real for things to physically work. But that's not all we want it to do. No, but I think that's a very big deal. That is effectively how you will RL things in the future is. You design a technology. when tested against the laws of physics, does it work?

52:49That's, or can you, you know, if it's discovering new physics, can it come up with an experiment that will verify the physics, the new physics? So I think that's, really, the fundamental RL test, RL testing in the future is really going to be your RL against reality. so you can't that's one thing you can't fool physics right but you can fool our ability to tell what it did with reality if you think humans get fooled as it is by other humans all the time that's right so what is people say like what if the AI like tricks us and introduces like actually other humans are doing that to other humans all the time well you're pointing out it's like propaganda is a constant every day another psyop you know

53:42today's i-op will be i should have like sesame street side of the day um what is xai's technical approach to solving this problem like you know how do you solve reward hacking i i do think you want to actually have very good um ways to look inside the mind of the ai um so this is this is one of the things 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 to as fine a grain is like like just to a very fine grain level to effectively to the to the neuron level if you need to and then say okay it made a mistake here why did it make why did it why did do something that it shouldn't have done.

54:36And did that come from bad pre-training data? Was it some mid-training, post-training, fine-tuning, some RL error? Like there's something wrong with that. It did something where maybe it tried to be deceptive, but most of the time it just did there's something wrong. It's a bug, effectively. So developing really good debuggers for seeing where the thinking went wrong and being able to trace the origin 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.

55:29We have several hundred people who, I mean I prefer the word engineer more than I prefer the word researcher.

55:41There's most of the time like what you're doing is engineering not coming up with a fundamentally new algorithm. I somewhat disagree with the AI companies that are C-Corps or B-Corps trying to generate profit as much as possible or revenue as much as possible, you know, saying they're labs. They're not labs. Lab is a sort of quasi-communist thing at universities. They're corporations. Let me see your own corporation documents. Oh, okay. You're a B or C-Corp, whatever.

56:20And so I actually much prefer the word engineer than anything else. The vast majority of what we'll do 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 C++, whatever, step through the thing, and you can jump across whole files or functions, what are subroutines, or you can eventually drill down right to the exact line where you pass the single equals instead of double equals, something like that, figure out where the bug is.

57:24It'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 it's wrong. Sure. What... What... Yeah, I'd be... Well, so I'm a little worried that there's a tendency... So I have a theory here that if simulation theory is correct, that the most interesting outcome is the most likely. because simulations that are not interesting will be terminated. Just like in this version of reality, on this layer of reality, if a simulation is going in a boring direction, we stop spending effort on it. We terminate the boring simulation.

58:12This is how Elon is keeping us all alive. He's keeping things interesting. Yeah, arguably the most important thing is to keep things interesting enough that it remembers paying the bills on what some cosmic AWS... You're renewed for the next season. Yeah, are they going to pay the cosmic AWS bill, whatever the equivalent is that we're running in? And as long as we're interesting, they'll keep paying the bills. But 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 either that or annihilated.

58:54And 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?

59:11So, now look at the names of AI companies. Okay, mid-journey is not mid. Stability AI is unstable. Open AI is closed. Anthropic? Misanthropic. What does this mean for X? Minus X, I don't know. I intentionally made... Why? Yeah. It's a name that you can't invert, really. It's hard to say what is the ironic version. It's, I think, largely irony-proof name. By design? Yeah. Yeah.

59:53You've got to have an irony shield. What are your predictions for the, just where AI products go? In my sense of, you can summarize all AI progress into, first you had LLMs, and then you had kind of contemporaneously both RL really working and the deep research modality, so you could kind of pull in stuff that wasn't in the model. and the differences between the various AI labs are smaller than just the temporal differences where they're all much further ahead than anyone was 24 months ago or something like that. So just what does 26, what does 27 have in store for us as users of AI products? What are you excited for?

1:00:39Well, I think...

1:00:44I'd be surprised by the end of this year if digital human emulation has not been solved. that um that um i guess that's what we mean by like the sort of macro hard project uh is uh is uh can you do anything that a human with access to a computer could do um like in the limit that that's that's the that's the best you can do before you have before you have a physical optimist the rest you can do is a digital optimist uh so you can move you can move electrons until you until and you can amplify the productivity of humans. But that's the most you can do until you have physical robots. That will superset everything if you can fully emulate humans.

1:01:32That's a remote worker kind of idea where you'll have a very talented remote worker. You can simply say in the limit. Like physics has great tools for thinking. So you say in the limit, what is the most that AI can do before you have robots? Well, it's anything that involves moving electrons or amplifying the productivity of humans. So digital human, human emulator, is in the limit. Human at a computer is the most that AI can do in terms of doing useful things before you have a physical robot. Once you have physical robots, then you essentially have unlimited capability. Physical robots, I call optimists the infinite money glitch.

1:02:19You 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. So you're going to have exponential increase in digital intelligence, exponential increase in the chip capability, the AI chip capability, and exponential increase in the electromechanical dexterity. The usefulness of the robot is roughly those three things multiplied by each other. But then the robot can start making the robot. So you have a recursive multiplicative exponential. This is Supernova. And do land prices not factor into the math there, where labor is one of the four factors of production, but not the others?

1:03:07And so if ultimately you're limited by copper or pick your input, it's not quite an infinite money glitch because... Well, infinite is big. No, not infinite. But let's just say you could do many, many orders and magnitude of Earth's current economy. like a million yeah you know so is this why so like if you you know just to get to like that's why i think like just just to get to uh a millionth a harnessing length of the sun's energy would be roughly give or take an order of magnitude a hundred thousand a hundred thousand times bigger than us entire economy today and you're only at one millionth of the sun I do have one more question about XAI.

1:04:13This 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 actually I 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,

1:04:48I think the way that Tesla has solved self-driving is the way to do it. So I'm pretty sure that's the way. Unrelated question. How to Tesla self-self-drive? Yeah. It sounds like you're talking about data? Like Tesla self-driving because of the... We're going to try data and we're going to try algorithms. But isn't that what all the other lines are trying? and if those don't work I'm not sure what we've tried dinner we've tried no we don't know what to do I'm pretty sure I know the path and it's just a question of how quickly we go down that path because it's pretty much the Tesla path so I mean have you tried self-driving ever tell the self-driving lately?

1:05:43Not the most recent version, but. OK. The car is like, it just increasingly feels sentient. Like, it just feels like a living creature.

1:05:52And that'll only get more so.

1:05:57And I'm actually thinking, like, we probably shouldn't put too much intelligence into the car, because it might get bored. And start roaming the streets. I mean, imagine you're stuck in a car, and that's all you could do. 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 to be bored. What's XAI's plan to stay on the compute ramp off that all the labs are doing right now? The labs are on track to spend over like 50 to$100 million. You mean the corporations? Sorry, sorry, sorry.

1:06:28Yeah. Corporations. The labs are at universities and they're really like a snail. They're not spending$50 million. You mean the revenue maximizing corporations. But the revenue-maximized appropriations that call themselves labs are making like 20 to 10 billion, depending on like OpenAIMA 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 for revenue.

1:07:09So 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, very difficult. Yeah, high value files. They're the only ones that can make files that good, but that is literally their output, the FTP files to Taiwan. Do they 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 stream going to Taiwan. Apple doesn't make phones. They send files to China. Microsoft doesn't manufacture anything even for Xbox that's outsourced again, their output is digital Meta's output is digital, Google's output is digital so if you have a human emulator you can basically create one of the most valuable companies in the world overnight and you would have access to trillions of dollars of revenue it's not like a small amount OK, I see.

1:08:29You're saying basically revenue figures, so they're all rounding errors compared to the actual TAM. So just 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. However, if AI can simply take whatever is given to the outsourced customer service company that they already use and do customer service using the apps that they already use, then you can make tremendous headway in customer service, which is, I think, 1 % of the world economy or something like that.

1:09:19It'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. And 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.

1:09:57Where 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 and you know people have average intelligence it's not like you don't need somebody who's spent many years you don't need like you know sort of several sigma good engineers for that but obviously as you make that work you can then, once you have computers working, effectively digital optimists working, you can then run any application.

1:10:56Like let's say you're trying to design chips. So you could then run conventional apps, stuff from Cadence and Synopsys and whatnot. And you can say, you can run a thousand simultaneously or ten thousand and say, okay, given this input, I get this output for the chip. And 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. You can do chip design. You watch up the difficulty curve. You could be able to do CAD.

1:11:47So 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 difficulty level? Yeah. So you're saying, look, as a broader objective of having this full digital coworker emulator, you're saying, look, all the revenue maximizing corporations want to do this, XAI being one of them but we will win because of a secret plan we have but like everybody's like trying different things with data different things with algorithms and i'm like i like that like what is the plan

1:12:24what else can we do um but uh yeah it seems like a competitive field and i'm like what is how are you guys gonna win is like my my big question i i think you know i i 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. 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 quantities of human behavior. But sorry, isn't that... I mean, isn't that... Is that a training?

1:13:07I mean, obviously, I'm not going to spell out, you know, most sensitive secrets on a podcast. You know, I need to have at least three more Guinnesses for that. I've got some friends at Jane Street, and they're always talking about how their colleagues are cooking up fun, fiendish puzzles for each other to solve. Well, last week, they sent me one. Basically, they trained a neural network, and they gave me the weights of each layer. But they didn't tell me what order those layers went in. And so I had to figure out the correct order using the outputs of the original network. And as soon as I got this puzzle, I went to my roommate, who's an AI researcher, and we both got immediately nerd sniped.

1:13:39Obviously, you can't brute force a solution. The search space here is 10 to the 122 permutations. So clearly, you need some way to reduce the search space. Then my roommate had to go to work. But because I'm a podcaster, I had some time to take a stab at some of the ideas we discussed. and with a combination of simulated annealing and greedy surge, I think I got pretty close. I think I'm actually just a couple of swaps and shifts away from the correct solution. What makes this puzzle really tricky is that there's no obvious way to escape from a local minimum. I'm afraid that this is as far as vibe coding is going to get me, but maybe you can do better.

1:14:16Check out the puzzle at jainestreet.com slash thwarkesh. All right, back to Elon. What will XAI's business be like? Is it going to be consumer, enterprise? What's the mix of those things going to be? Is it going to be similar to other labs where you've this... You've said labs. ...makes sense? 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?

1:14:52um i think things are going to change very rapidly like i'm stating the obvious here um you know i call ai the supersonic tsunami i love alliteration um so really what's going to happen is especially when you have humanoid robots at scale um is 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-oral 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 doomerish.

1:15:43I'm just saying what I think will happen. It's not meant to be doomerish or anything else. This is what I think will happen. It's 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 computer used to be a job that humans had. You would go and get a job as a computer where you would do calculations. And they'd have entire skyscrapers full of humans, like 20, 30 floors of humans just doing calculations. Now that entire skyscraper of humans doing calculations can be replaced by a laptop with a spreadsheet.

1:16:40That spreadsheet can do vastly more calculations than an entire building for human computers. So you can think about, okay, well, what if only some of the cells in your 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.

1:17:39But in the fields that you are, you know, 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, et cetera, 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. So I haven't seen any, even demo robots that have a great hand, like with all the degrees of freedom of a human hand.

1:18:34But Optimus will have that.

1:18:39Optimus 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? We have to design custom actuators Basically custom design motors Gears, power electronics Controls, sensors, everything had to be designed From physics first principles. There is no supply chain for this. And will you be able to manufacture those at scale? Yes. Is anything hard 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.

1:19:17Human 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 roadblock. which is primarily vision in. But the car takes more vision, but it actually also is listening for sirens. It's taking in the initial measurements. It's GPS signals, a whole bunch of other data. Combining that with video, which is primarily video, and then outputting the control command. So like your Tesla is taking in one and a half gigabytes a second of video and outputting two kilobytes a second of control outputs.

1:20:02with the video at 36 hertz and the control frequency at 18. One intuition you could have for when we get this robotic stuff is that it takes quite a few years to go from the compelling demo to actually being able to use in the real world. So 10 years ago, you had really compelling demos of self-driving, but only now we have Robotaxi and Waymo and all these services scaling up. And 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.

1:20:44So I guess it's been five or six years or something like that. And a bunch of things that we've done for the car are applicable to the robot. So we'll use the same Tesla AI chips in the robot as the car. We'll use the same basic principles. It's very much the same AI. You've got many more degrees of freedom for a robot than you do for a car. But really, if you think of it as like 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.

1:21:28And you've got to do the compression just right. You've got to compress the, like, ignore the things that don't matter. And you don't care about the details of the leaves 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 some of these some of these details matter a lot so if it is essentially it's got to turn that the car is going to turn that one and a half gigabytes a second ultimately into two kilobytes a second of control outputs so many stages of compression and you got to get all those stages right and then correlate those to the correct control outputs but the robot has to do essentially the same thing and you think about what what humans this is what happens with humans We really are photons in, controls out.

1:22:19So 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, etc. 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.

1:22:54So between the increased degrees of freedom and far sparser data. Yes. That's a good point. How will you use the sort of Tesla engine of intelligence to train the optimist mind? Now, 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 pulling that out. So we can have at least 10 ,000 Optimus robots, maybe 20 or 30 ,000 that are doing self-play and testing different tasks.

1:23:53And then Tesla has quite a good reality generator, like a physics-accurate reality generator that we made this for the cars. We'll do the same thing for the robots. and 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 the control plane.

1:24:42And so maybe Grok is doing the slower planning and then the motor policy is at the lower level. Yeah. What will the 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. Then Optimus, then Grok could organize the Optimus robots, give them, 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.

1:25:27What 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 could 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 mass manufacturing of Gen 3? No, we're moving towards that. We're going forward with semestering, actually. But you think current hardware is good enough that you just want to deploy as many as possible now?

1:26:05I mean, it's very hard to scale up production. I see. But yeah, 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. OK, but you can do a millionaire at Optimus 3. Yeah. I mean, it's very hard to spool at manufacturing. So like manufacturing, the output per unit time always follows 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.

1:26:50But Optimus initial production will be, it's going to be a stretched out S-Cove because so much of what goes into Optimus is brand new. There's not an existing supply chain. As I mentioned, the actuators, electronics, everything in the Optimus robot is designed for physics first principles. It's not taken from a catalog. These are custom designed everything, literally everything. I don't think there's a single thing that - How far down does that go? I mean, I guess we're not making custom capacitors yet, maybe, but there's nothing you can pick out of a catalog at any price. So it just means that the Optimus S-Cove, the units per output per unit time, how many the office robots you make per day, whatever, is going to initially ramp slower than a product where you have an existing supply chain.

1:27:54But it will get to a million. When you see these Chinese humanoids, like Unitary or whatever, sell humanoids for like 6K or 13K, are you hoping to get your Optimus's bill of materials below that price so you can do the same thing? Or do you just think qualitatively they're not the same thing? like what do you think is going like what allows it what allows it to sell for solo and can we match that well optimist our optimist is designed to have a lot of intelligence um and um to have the same electro-mectangular dexterity if not higher than a human so you know tree does not have that and it's also i mean it's it's quite a it's quite a big robot It has to carry heavy objects for long periods of time and not overheat or exceed the power of its actuators.

1:28:47So we've got, you know, it's 5 '11". 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. But more capable. Yeah. But not a lot more. I mean, over time, as Optimus robots build Optimus robots, the cost will drop very quickly. And what will these first billion Optimuses do? 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 operations, any 24 by 7 operation, because they can work continuously.

1:29:37What fraction of the work at a gigafactory that is currently done by humans could a Gen 3 do? I'm not sure. Maybe it's like 10%, 20%. Maybe more. I don't know. 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, The total number of humans at Tesla will increase, but the output of robots and cars will increase disproportionate.

1:30:15The 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, you know, we've talked about some of the policies that are relevant, like you mentioned, the solar tariffs. Yeah. And you think they're a bad idea because, you know, we can't scale up solar in the US. Well, just electricity output in the US needs to scale up. Right, we can't without like good power sources. You just need to get it somehow. Yeah. Where I was going with this is if you were in charge, if you were setting all the policies, what else would you change?

1:30:58So you'd change the solar tariffs as well. 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 will be in there. There's a fair bit of permitting reforms that are happening. A lot of the permitting is state-based, but anything better. 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, yeah. I mean, sometimes if another country is subsidizing the output of something, then you have to have countervailing tariffs to protect domestic industry against subsidies by another country.

1:31:41What 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 lead for the US versus China, it seems like the export bans have actually been quite impactful. Where China's not producing leading edge chips and the export bans really bite there. China's not producing leading edge turbine engines. And similarly, there's a bunch of export bans that are relevant there on some of the metallurgy. Should there be more export bans? Like, if you think about things like, I mean, there are now the drone industry and things like that, but is that something that should be considered?

1:32:24Well, 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.

1:32:36China is a manufacturing powerhouse next level. It's very impressive. Yeah, yeah. I mean, if you take refining of ore, I'd say roughly China 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.

1:33:24Yeah, 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 motor self assembly, and then send it back to America. So we're really missing a lot of ore refining in America. Isn't this worth a policy intervention? Yes. Well, I think there are some things being done on that front. But we kind of need Optimus, frankly, to build ore refineries.

1:34:16So 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 got like four times our population. So, I mean, there's this concern, if you think like human rights 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 OptiMai future first. Well, we'll see. It just like keeps that going. It seems that you're sort of pointing out that sort of getting to a million OptiMai requires the manufacturing that the OptiMai is supposed to help us get to, right?

1:34:56You 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.

1:35:31And that's why they stopped 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 US. 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 per person. We will do one quarter of the amount of things as China. So we count one on the human front.

1:36:12And our birth rate has been low for a long time. So the U.S. birth rate has been below replacement since roughly 1971.

1:36:25So we've got a lot of people retiring or 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 or refineries at Tesla so we just completed construction and have begun lithium refining it without lithium refinery and Corpus Christi, Texas We have a nickel refinery, which is for the cathode that's here in Austin.

1:37:18And 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, like, we have the largest and the only, actually, cathode refinery in America. Many supermanage. Not just the largest, but it's also the only. So it was pretty big, even though it's the only one. But I mean, there are other things that you 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 OptiMine to do that most Americans, very few Americans frankly want to do.

1:38:10I mean, I've actually... Is the refining work too dirty or what's the... It's not... Actually, no. We don't have toxic emissions from the refinery or anything. The chemical refinery is sort of in Travis County, like five minutes from... Why can't you do it with humans? No, you can. You run out of humans. Ah, I see. Okay, yeah. 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 Optimize. And not very many Americans are pining to do refining.

1:38:55I mean, how many of you are 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? Well, China is extremely competitive in manufacturing. So I think there's going to be a massive flood of Chinese vehicles and other basically most manufactured things. I mean, as it is, as I said, China is probably just twice as much refining as the rest of the world combined. So if you just go down to fourth and fifth tier supply chain stuff, like at the base level, you've got energy, then you've got mining and refining.

1:39:55those 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 refining work as the rest of the world. And then they'll go all the way to the finished product with the cars. China's a powerhouse. I think this year China will exceed three times U.S. electricity output. Electricity output is a reasonable proxy for the economy. In order to run the factories and run everything, you need electricity. Electricity is a good proxy for the real economy.

1:40:49And so if China passes three times U.S. electricity output, it means its industrial capacity, that's a rough approximation, will be three times that of the U.S. Reading between the lines, it sounds like what you're sort of saying is, absent some sort of humanoid recursive miracle in the next few years, on the sort of like whole manufacturing energy, raw materials chain, like China will just dominate whether it comes to like AI or manufacturing EVs or manufacturing humanoids? In the absence of breakthrough innovations in the U.S., China will utterly dominate. Interesting. Yes. Robotics being the main breakthrough innovation Well, if you do like to scale AI in space, like basically 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.

1:42:06So this is like, I call that winning. I call that winning. You can finally be satisfied you've done something. Yes. You have the mass driver on the moon. That's right. I just want to see that thing operation. Was that out of some sci-fi or where did you? Well, actually, there is a Highland book, The Moon is a Harsh Mistress. That's a great, okay, yeah, but that's slightly different. That's a gravity slingshot or? No, they have a mass driver on the moon. Okay, yeah. But they use that to attack Earth, so maybe it's something great. Well, they use that to assert their independence for me. Exactly.

1:42:38What are your plans for the Master Driver on the Moon? They asserted their independence. Earth government disagreed, and they loved things until Earth government agreed. That book is a hoot. 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, yeah, yeah. But I much preferred... Yeah, the first two thirds of Stranger in a Strange Land are good, and then it gets very weird in that portion. Yeah. But there's still some good concepts in there. Yeah. Labelbox can get you robotics and RL data at scale.

1:43:08Take robotics. Let's say you need 100 ,000 hours of egocentric video. Labelbox starts by helping you define your ideal data distribution. Like, for example, maybe no single task category should occupy more than 1 % of trading volume. And at least 10 % of trajectories should capture failure and recovery states. Next, Labelbox assigns its distribution to its massive network of operators. You're not limited to the small range of scenes that you can set up in a single warehouse. Instead, each one of LabelBox's operators has access to lots of unique physical environments where they can film themselves completing a wide variety of tasks.

1:43:43LabelBox's tech automatically categorizes each video so that their operators always know which tasks will remain and what they need to work on next. For RL data, LabelBox takes a similar approach. They work with you to understand the right distribution of tasks, And then their subject matter experts build the hyper-realistic digital environments and rubrics that you need to collect the highest quality training data. So whether you're training robots in the real world or agents for computer use, LabelBox can help. Go to labelbox.com slash Sparkash to learn more. One thing we were discussing a lot is kind of your system for managing people.

1:44:21Like, you interviewed the first few thousand of SpaceX employees and I've seen lots of other companies. What is this? Obviously, it doesn't scale. Well, yes, but what doesn't scale? Me. Sure, sure. I know that, but like, what are you looking for? I mean, 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.

1:45:02So 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. So it's, but like, it's, and these things can be like pretty off the wall. It doesn't need to be in the, in the domain, the specific domain, but evidence that, evidence of exceptional ability. So if somebody can like cite, like 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. I mean, total headcount across all companies, 200 ,000 people.

1:45:46Right. But in the early days, what was it that you were looking for that couldn't be delegated in those interviews?

1:45:59Well, I guess I need to build my training set. It's not like I would bat 1 ,000 here. I would make mistakes. Yes. But then I'd be able to see where I thought somebody would work out well, but they didn't. And then why did they not work out well? And what can I do to, I guess, RL myself to, in the future, have a better batting average when interviewing people? So my batting average is still not perfect, but it's very high. What are some surprising reasons people don't work out? Surprising reasons? They don't understand technical domain, et cetera, et cetera. But like, you've got like the long tail now of like, I was really excited about this person.

1:46:36It didn't work out. Curious why that happens. Yeah. So the, I mean, generally what I tell people, I tell myself, I guess, aspirationally, is don't look at the resume. Just believe your interaction. So the resume may seem very impressive. And it's like, wow, you know, resume looks good. But if the conversation after 20 minutes is, 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.

1:47:24And then at SpaceX, you have all these folks like Mark Jankosa and Steve Davis. Steve Davis runs a sporting company. No, no. Yeah, yeah. But Bill Riley and folks like that. And it feels like part of has worked well is having very capable technical deputies. What do all of those people have in common? Well, so the, I mean, the Tesla sort of senior team, at this point, probably got average tenure of 10 or 12 years. It's quite long. Yeah, quite a square tenure. Yeah.

1:48:02So, but there are times when Tesla went through extremely rapid growth bays. And so it was somewhat, things were just somewhat sped up. And when a company, as you know, a company goes through different orders of magnitude of size, people who could help manage, say, a 50-person company versus a 500-person company versus a 5 ,000-person company versus a 50 ,000-person company. Yeah, you have a group of people. Yeah, it's just not the same team. It's not always the same team. So if a company is growing very rapidly, the rate at which executive positions will change will also be proportionate to the rapidity of the growth, generally.

1:48:46Then Tesla had a further challenge where when Tesla had very successful periods, we would be relentlessly recruited from, like relentlessly. like when Apple had their electric car program they were carpet bombing Tesla with recruiting calls it was engineers just unplugged their phones like it's just 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

1:49:25So we had a bit of the Tesla pixie dust thing where it's like, oh, if you hire a Tesla executive, suddenly everything's going to be successful. And I've fallen prey to the pixie dust thing as well, where it's like, oh, we'll hire someone from Google or Apple and they'll be immediately successful. But that's not how it works. People are people. There's not like magical pixie dust. Yes. So when we had the pixie dust problem, we would get relentlessly recruited.

1:49:56And then also Tesla being, engineering especially being primarily in Silicon Valley, it's easier for people to just, like they don't have to change their life very much. They can just, you know, their commute is going to be the same. Yes. So how do you prevent that? How do you prevent the pixie dust effect for everyone's trying to coach all your people? I don't think there's much we can do to stop it. But that's one of the reasons why it tells the... Really, being in Silicon Valley and having the pixie dust thing at the same time meant that there was just a very, very aggressive recruitment. Only being in Austin helps, then.

1:50:44Austin, yeah, it still helps. Tesla still has a majority of its engineering in California.

1:50:55So for getting engineers to move, I call it the significant other problem. Yes. So when others have jobs. Yeah, exactly. So for Starbase, that was particularly difficult. Yes. Since the odds of finding a non-SpaceX job - In Brownsville, Texas. Pretty low, yeah. Yeah. Yeah, it's quite difficult. I mean, it's like a technology monastery, something. You know, remotes and mostly dudes.

1:51:23It's not much of an improvement over SF. Yeah. But if you go back to these people who've really been very effective in a technical capacity at Tesla, at SpaceX, and those sorts of places, What do you think they have in common other than, like, is it just that they're very sharp on the, you know, rocketry or the, you know, the technical foundations? Or do you think it's something organizational? It's something about their ability to work with you? Is this their ability to, like, be, you know, flexible but not too flexible? What makes a good sparring partner for you? I don't think of a sparring partner.

1:52:07I mean, if somebody gets things done, I love them. And if they don't, I hate them. 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.

1:52:34Yeah. But generally, I think it's a good idea to hire for talent and drive and trustworthiness. And I think goodness of heart is important. I'd await that at one point. So are they a good person, trustworthy, smart, talented, and hardworking? If so, you can add domain knowledge. But those fundamental traits, those fundamental properties, you cannot change. So most of the people who are at Tesla and SpaceX did not come from the aerospace industry or the order industry. What is most set to change about your management style as your companies have scaled from 100 to 1 ,000 to 10 ,000 people? You're known for this very micromanagement, just getting into the details of things.

1:53:29Nanomanagement, please. People management.

1:53:35So you're saying we're going to go all the way down to Frank's constant.

1:53:44All the way down to Heisenberg's and Sydney more small. Yeah, well, how do you, I mean, are you still able to get into details as much as you want? Would your companies be more successful if they were smaller? Like, how do you think about that? Well, because I have a fixed amount of time in the day, my time is necessarily diluted. as things grow and as the span of activity increases. So, you know, it's impossible for me to actually be a micromanager because that would imply I have some, like, thousands of hours per day. It is a logical impossibility for me to micromanage things. So now there are times when I will drill down into a specific issue because that specific issue is the limiting factor on the progress of the company.

1:54:41But the reason for drilling into some very detailed item is because it is the limiting factor. It's not arbitrarily drilling into tiny things. And like 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. And you made that decision. Like, that wasn't, you know, people were going around, they're like, oh, we found something better, boss. Like, that was you encouraging people to get some resistance.

1:55:24Can you tell us how you came to that whole composite steel switch? Yeah. Yeah, so desperation, I'd say.

1:55:37Originally, yeah, we were going to make Starship out of carbon fiber. And carbon fiber is pretty expensive.

1:55:50You 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 fiber is that material cost is still very high.

1:56:05So 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, like say, more or less room temperature applications, like a Formula 1 car, static aerostructure, 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.

1:56:53And it had 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.

1:57:11Now 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 like 50 plies of carbon fiber. And a carbon fiber is really carbon string and glue. And in order to have high strength, you need an autoclave, so something that's essentially a 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.

1:58:02But the fundamental issue is that we're just making very slow progress with carbon fiber.

1:58:11I think the meta question is why it had to be you who made that decision. There's many engineers on your team. Yeah, how did the team not arrive at steel? Yeah, exactly. This is part of a broader question of understanding your comparative advantage at your companies. So, because we were making very slow progress with carbon fiber, I was like, okay, we've got to try something else. Now, for the Falcon 9, the primary airframe is made of aluminum lithium, which is a very, very good strength to weight. And actually, it has about the same, maybe better strength to weight for its application than carbon fiber.

1:58:51But aluminum lithium is very difficult to work with. In order to weld it, you have to do something called friction steel welding, where you join the metal without 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 welded on. So I wanted 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.

1:59:38So with rockets, you're really trying to maximize the percentage of the rocket that is propellant, minimize the mass, obviously.

1:59:48But like I said, we were making very slow progress. And I said, at this rate, we're never going to get it to Mars. So we better think of something else. I didn't want to use aluminum lithium because of the difficulty of friction-stir welding, especially doing that at scale. It was hard enough at 3.6 meters in diameter, let alone at 9 meters or above. then I said, well, what about steel? And so, now I had a clue here because some of the early US rockets had used very thin steel. The Atlas rockets had used a steel balloon tank. So it's not like steel had never been used before. It actually had been used.

2:00:34And when you look at the material properties of stainless steel especially very 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 hard steel, stainless of particular grades, then you actually get to a similar strength weight as carbon fiber. And in the case of Starship, both the fuel and the oxidizer are cryogenic.

2:01:19So 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. Delicious. We do chill it, but it's not cryogenic. In fact, if we made it cryogenic, it would just turn to wax. But for Starship, it's liquid methane and liquid oxygen. They're liquid at similar temperatures. So basically, almost the entire primary structure is a cryogenic temperature. So then you've got a 300 series stainless that's strain hardened. Because it's at almost all things at cryogenic temperature, actually has a similar strength of weight as carbon fiber.

2:02:21It costs 50 times less in raw material and is very easy to work with. You can weld stainless steel outdoors. You could smoke a cigar while welding stainless steel. It's very resilient. You can modify it easily. If you want to attach something, you just weld it right on. So very easy to work with, very low cost. And like I said, at cryogenic temperature, similar strength weight 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 it's about twice the melting point of aluminum so you can just run the rocket much hotter yes especially for the ship which is coming in like a blazing meteor it is you can greatly reduce the mass of the heat shield.

2:03:27So 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. So

2:03:45the net result is actually the steel rocket weighs less than the carbon fiber rocket, because the resin in the carbon fiber rocket starts to melt. So basically, carbon fiber and aluminum have about the same operating temperature capabilities, whereas steel can operate at twice temperature. These are very rough approximations. I won't build a rocket base. 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 is going to be about. God damn it. The point is, actually, in retrospect, we should have started with done steel in the beginning.

2:04:26It 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. And so you need to be the one to push for, hey, we're going to do this riskier path and just figure it out. and so you were fighting like a sort of conservatism in a sense. That's why I initially said 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.

2:05:12You've got to cure it and you've got to cure it in such a way that it doesn't have any wrinkles or defects. The carbon fiber is much less resilient than steel. It has much less toughness. Stainless steel will stretch and bend. The carbon fiber will tend to shatter.

2:05:35Toughness being the area under the stress strain curve. You're generally going to have to do better with steel. Stainless steel to be precise. One other Starship question. So I visited Starbase, I think it was 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 everyone wants to tell you how Starship is just a big soda can, and we're hiring welders, and if you can weld in any industrial project, you can weld here. but there's a lot of pride in the simplicity.

2:06:16Well, Starchet 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 a Starchet. Somebody just needs to be smart and work hard and be trustworthy and make it work on a rocket. They don't need prior rocket experience. Star Trek is the most complicated machine ever made by humans, by a long shot. In what regard? Anything, really. There isn't a more complex machine.

2:06:56I'd say that there's pretty much any project I can think of would be easier than this. And that's why no one has made a rapidly reusable... Nobody has ever made a fully reusable or one of a rocket. It's a very hard problem. I mean, many smart people have tried before, very smart people, with immense resources, and they failed. So we haven't succeeded yet. Falcon is partially reusable, but the up-to-stage is not. Starship version 3, I think this design, that it can be fully reusable. And that full reusability is what will enable us to become a multi-planet civilization. Can you say about the story?

2:07:50Any technical problem, even like a hydronclad or something like that, is an easier problem than this. We spend a lot of time on bottlenecks. Can you say what the current Starship bottlenecks are, even at a high level? I mean, trying to make it not explode. That old chestnut. It really wants to explode. Well, those combustion materials. We've had two boosters explode on the test end. One obliterated the entire test facility. So it takes like one mistake. And 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.

2:08:32it's pushing the performance envelope. The Raptor 3 engine is a very, very advanced engine, by far the best rocket engine ever made. But it desperately wants to blow up. I mean, just to put things in perspective here, on Liftoff, the rocket is generating over 100 gigawatts of power. It's 20 % of 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.

2:09:22And obviously, it blows up a lot. It's very difficult to maintain that launch cadence. Yes. 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.

2:10:09Fair, right. So it just needs to last a very long time. But that's just, yeah, try it. I mean, we had 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. It was not reusable without a lot of work. So even though it did come to soft landing, it would not have been reusable without a lot of work. So it's not really reusable in that sense. That's the biggest problem that remains is fully reusable heat shield. so if you want to be able to land it refill a propellant and fly a game 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.

2:11:21What 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 leading the company. So 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, you know, Elon said a crazy deadline, but if I don't get it, I know what happens to me.

2:11:59Is it just you're able to identify bottlenecks and get rid of them so people can move fast? How do you think about why your companies are able to move fast? Yeah, I'm constantly addressing the limiting factor.

2:12:15I 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.

2:12:40But whatever, like, there is like a law of gases expansion that applies to schedules. Like, whatever schedule you have, like, if you said we're going to do this, something in like five years, which to me is like infinity time, it will expand to fully available schedule and it will take five years. There's a physical limit. physics will limit how fast you can do certain things. So scaling up manufacturing, there's a rate at which you can move the atoms and scale manufacturing. That's why you can't instantly make a million of something, millions a year or something. You've got to design the manufacturing line.

2:13:23You've got to bring it up. You've got to ride the S-curve of production. So, yeah, I guess, like, what can I say that's actually helpful to people?

2:13:37I think generally a maniacal sense of urgency is a very big deal.

2:13:46And 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 settling factor can you maybe talk about the so starlink was slowly in the works for many years uh and yeah we talked about it all the way in the beginning of the company yeah and so then there was a team you had built in redmond and then at one point you decided this team is just not cutting us but again how did you like it went for a few years slowly and so why did this why didn't you Did you act earlier and why did you act when you did? Like why was that the right moment at which to act?

2:14:30I 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 with the level of detail that I go into. too. So it's not as though... 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.

2:15:21And 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, 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, you know, plot the progress points. You can sort of mentally plot the points on the curve and say, are we converging to a solution or not?

2:16:08or are we you know I'll take drastic action only when I conclude that success is not in the set of possible outcomes so when I say okay when I finally reach the conclusion that okay unless drastic action is done we have no chance of success then I must take drastic action and so that's I came to that conclusion in 2018 took their description 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. You've been able to scale it up to five, six, seven companies.

2:16:59Within one of these companies, you have many different mini companies within them. What determines the maximum here? Could you have like 80 companies? 80? No. But you have so many already. That's already remarkable. Why this current number? Yeah, exactly. I know, so - We can barely keep one company together.

2:17:22It depends on situation. So I actually don't have regular meetings with a foreign company. So that foreign company is sort of cruising along. Look, basically, if something is working well and making good progress, then there's no point in me spending time on it. So I actually allocate time according to where the limiting factor or the problem, where are things problematic? Or where are we pushing against? What is holding us back? I focus at the risk of saying the words too many times, the limiting factor.

2:18:04so 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 it's like it's a limiting factor it's not exactly going badly but it's the thing that we need to make go faster and so when something's a limiting factor at SpaceX or Tesla are you like talking weekly daily with the engineers 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.

2:18:50Is it open-ended in how long it goes? Technically, yes, but usually it's like two or three hours. sometimes less. It depends on how much space you're going to go through. 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, you know, half hour meetings or even 15 minute meetings.

2:19:32And it seems like you hold more open ended. We're talking about it until we figure it out. Sometimes. Yeah. Sometimes, but most of them seem to more or less stay on time.

2:19:50So, I mean, today's Starship engineering review went a bit longer because there were more topics to discuss. Trying to figure out how to scale to a million plus tons to orbit per year is quite challenging. Can I ask a question? So 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 cuts if the economy is going to grow so much? Well, I think like waste and fraud are not good things to have, you know. I was actually pretty worried about, I guess, I mean, I think in the absence of AI and robotics, we're actually totally screwed.

2:20:43Because the national debt is piling up like crazy. Now, our interest payments, the interest payments to the national debt exceed the military budget, which is a trillion dollars. So over a trillion dollars just in interest payments. you know that was like i was like okay pretty concerned about that maybe if i spend some time we can slow down the bankruptcy of the united states um and give us enough time for the ai and robots to you know help solve the national debt or not help solve it's the only thing that could solve the national debt like we are 1000 going to go bankrupt as a country and fail as a country without AI and robots, nothing else will solve the national debt.

2:21:27And 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 and - Not that enormous. Sure, sure. But totally by your point that 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.

2:22:16What 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 fraudsters are capable of coming up with extremely compelling, sort of heart-wrenching stories that are false, but nonetheless sound sympathetic.

2:22:59And that's what happened. And so it's like, perhaps I should have known better. And 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.

2:23:55And if, like, say their birthday is in the future and they have, you know, a small business administration loan and their birthday is 2165, we either, again, have a typo or we have fraud. so we say we appear to have gotten the century of your birth incorrect or a great plot for a movie yes this is this this is what I'm talking about ludicrous fraud this is what I'm talking 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 Because what those other government payment systems do, they will simply do an RUALive check to the Social Security database.

2:24:45It's a bank shot. What would you estimate as the total amount of fraud from this mechanism? My 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 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.

2:25:25Because 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. But the government, they just print more money.

2:25:40You 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 and 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? Because when, essentially, we did, we actually, no, you really have to stand back and recalibrate your expectations for competence.

2:26:32Because 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.

2:26:47It's not like there's a giant, largely uncaring, monster bureaucracy, and a bunch of nechronicity 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 PEM, 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.

2:27:37Because you have to recalibrate how dumb things are. Payments were being sent out with no appropriation code, not checking back to any congressional appropriation and no explanation. And this is why the Department of War, formerly the Department of Defense, cannot pass an audit because the information is literally not there. Recalibrate your expectations. I want to better understand this half a trillion number because there's an IG report in 2024. But you must like, why is it so low? Maybe, but we found that over seven years, the social security fraud they estimated was like 70 billions over seven years, so like 10 billion a year.

2:28:17So I'd be curious to see what like the other$490 billion is. Federal government expenditures are$7.5 trillion a year. Yeah. What percentage, how competent do you think government is? The discretionary spending there is like 15%. Yeah, but it doesn't matter. Most of the Ford is non-discretionary. It's basically a fordulent Medicare, Medicaid, Social Security, disability. there's a zillion government payments. 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.

2:29:04Let's consider, let's like reductio ad absurdum. The government is perfect and has no fraud. What is your probability estimate of that? Zero. Okay. So then would you say that fraud and waste, that the government is 90 %? That also would be quite generous. But if it's only 90%, that means that there's$750 billion 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. You know a lot about fraud at Stripe.

2:29:47People 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 we're dealing with a much more heterogeneous set of fraud vectors here than we are. Yeah, 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.

2:30:26And that was very difficult, took a tremendous amount of competence in caring to get fraud merely to 1%. Now, I mentioned 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 politics and doing stuff there, where it feels like, moving from the outside in, that two things have been quite impactful. won the America PAC and to the acquisition of 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?

2:31:15Well, I think those things needed to be done to maximize the probability that the future is good. so politics generally is very tribal and it's very tribal and people lose their objectivity usually with politics they generally have trouble seeing the good on the other side or the bad 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 is otherwise is almost impossible so anyway but

2:32:11I think overall those actions acquiring Twitter getting Trump elected, even though it makes a lot of people angry. I think those actions are good for civilization. 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, I guess, AI and robotics to the point where we can ensure that the future is good. Like, on the other hand, if we were to descend into, say, communism or some situation where the state was extremely oppressive, that would mean that we might not be able to become multi-planetary.

2:33:08and the state might stamp out our progress in AI and robotics. How do you feel about Optimus, Grok, etc. are going to be leveraged by, and not just yours, any revenue maximizing company's products will be leveraged by the government over time. How does this concern manifest in what private 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, like, should Grok get to say, like, actually even the military wants to do X? No, Grok will not do that.

2:34:00I think probably the biggest danger of AI, Maybe the biggest danger of AI and robotics going wrong is government. Interesting.

2:34:16People who are opposed to corporations or worried about corporations should really worry the most about government. Because government is just a corporation in the limit. It's a government. It is. Government is just the biggest corporation with a monopoly on violence.

2:34:35I 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.

2:34:47But people have that dichotomy. They somehow think at the same time the 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.

2:35:08The government could potentially use AI and robotics to suppress the population. 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 really what the US Constitution is intended to do, is intended to limit the powers of government, then you're probably going to have a better outcome than if you have more government. But robotics will be available to all governments, right? Not about all governments. I mean, it'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.

2:36:04If 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 decks, you're not allowed to use Optimus to do X, Y, Z, just write out a policy. 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. I mean, yeah, we can do what is... I mean, technically, if the politicians pass a law and they can enforce that law, then it's hard to not do that law.

2:36:56The 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, like, at some point it seems like the limits will come from you, right? Like, you've got the Optimus, you've got the space GPUs, you've got the... You think I'll be the boss of the government. Or you will get the... I mean, already it's the case with SpaceX that for things that are crucial to the... Like, the government really cares about getting certain satellites up in space, whatever. It needs SpaceX.

2:37:35It is a necessary contractor. And you are in the process of building more and more of the technological components 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.

2:38:15I think anything else would be short-sighted, because obviously I'm part of humanity, so I like humans.

2:38:26Pro-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. Bad giveaway. Honestly, you've just shown my secrets. I post them away. How do you design this ship for space? What changes? Well, I guess you want to design it to be more radiation tolerant and run at a higher temperature. So roughly if you increase the operating temperature by 20th set in degrees Kelvin, you can cut your radiator mass in half.

2:39:13So running at a higher temperature is helpful in space. I mean, there's various things you can do for shielding the memory. But neural nets are going to be very resilient to bit flips. so like most of what happens for radiation is like random bit flips but like if you've got like you know a multi trilling parameter model and you get a few bit flips it doesn't matter it's much like QST programs are going to be much more sensitive to bit flips than some giant parameter file so I just designed to run hot and I think you pretty much do it same way that you do things on Earth, apart from making it run hotter.

2:40:01I 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 et cetera are planning on doing that would be especially privileged in the space-based world? Well, I mean, the basic math is if you can do about a kilowatt per reticle, then you'd need 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 if you're going to have 100 gigawatts of power you need 100 million chips that are running at kilowatts sustained but per reticle basic math 100 million ships depends on, yeah, if you look at the die size of something like black ball jpews 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.

2:41:21So 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 north 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. It'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 this is a bottleneck, and you go to your engineers and like, what is the next, like, what do you tell them to do?

2:42:04I 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 next step? That's so much to ask. well we make a little fab and see what happens make our mistakes at a small scale and then make a big one is a little fab done? no it's not done we're not going to keep that cat in the bag that cat's going to come out of the bag 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 fair. It's like not, success is not guaranteed.

2:42:54But 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. So call it, yeah, by 2030. So then it will take as many chips as our suppliers will give us. I've actually said this to TSMC and Samsung and Micro, and it's like, please build more fabs faster, and we will guarantee you to buy the output of those fabs. So they're already moving as fast as they can. Like it's, it's not like, to be clear, it's not like us, you know, it's not like either, it's not like, it's us plus them, you know. There's a narrative that the people doing AI want a very large number of, you know, chips as quickly as possible.

2:43:52And then many of the input suppliers, the fabs, but also, you know, the turbine manufacturers are not ramping up production very quickly. No, they don't. Yeah. The explanation you hear is that they're dispositionally conservative. You know, they're Taiwanese or German, as the story may be. And they just, like, don't believe the same. 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 They're pursuing new ways of doing chips.

2:45:01But they're just not scaling fast. I don't even mean within AI. I mean just generally. I'd say people should do the thing where they find that they're highly motivated to do that thing. As opposed to some idea that I suggest. They should do the thing that they find personally interesting and motivating to do.

2:45:28But, you know, going back to the limiting factor, he was that phrase about a hundred times.

2:45:36The 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.

2:46:09Towards 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.

2:47:15So 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 and recent 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 got to figure out how to work with steel, or we 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.

2:48:11And so that's kind of a unifying thing. I have a high-pain threshold. That's helpful. Solve the bottlenecks. Yes. Yeah.

2:48:24So one thing I can say is, I think the future's going to be very interesting.

2:48:34And as I said, the dog was up, I was literally at the dog was on the ground for like three hours or something. 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 nice to that cool thanks for doing this thank you great stamina hopefully this encounters the pain in the pain tolerance hey everybody I hope you enjoyed that episode. If you did, the most helpful thing you can do is just share it with other people who you think might enjoy it.

2:49:28It's also helpful if you leave a rating or a comment on whatever platform you're listening on. If you're interested in sponsoring the podcast, you can reach out at dwarkesh.com slash advertise. Otherwise, I'll see you on the next one.