Elon on Lex Fridman Podcast

0:00 - The following is a conversation with Elon Musk, his third time on this, the "Lex Fridman Podcast." 0:07 Yeah, make yourself comfortable. - Boo. - Oh, wow, okay. - You don't do the headphone thing? 0:12 - No. - Okay. I mean, how close do I need to get this thing? - The closer you are the sexier you sound. - Hey babe, sup. - Yup. 0:18 - Can't get enough of you on that baby? (both laughing) - I'm gonna clip that out 0:24 and any time somebody messages me on my phone I'll just respond with that. - If you want my body and you think I'm sexy come right out and tell me so. 0:33 Do do do do do. - [Shivon] So funny. 0:38 - So good. Okay, serious mode activate, alright. - Serious mode. 0:44 Come on, your Russian, you can be serious. - Yeah I know. - Everyone's serious all the time in Russia. - Yeah, yeah. We'll get there. We'll get there. 0:51 (Shivon speaking faintly) Just gotten soft. Allow me to say that the SpaceX launch 0:57 of human beings to orbit on May 30th, 2020, was seen by many as the first step 1:03 in a new era of human space exploration. These human space flight missions were a beacon of hope 1:10 to me and to millions over the past two years as our world has been going through one of the most difficult periods in recent human history. 1:19 We see the rise of division, fear, cynicism, and the loss of common humanity, 1:24 right when it is needed most. So, first, Elon, let me say thank you for giving the world hope and reason 1:30 to be excited about the future. - Oh, it's kind of you to say that. I do want to do that. Humanity has, obviously a lot of issues, 1:39 and people at times do bad things, but despite all that, 1:46 I love humanity and I think we should make sure we do everything we can 1:52 to have a good future and an exciting future, and one where that maximizes the happiness of the people. 1:58 - Let me ask about a Crew Dragon Demo-2. So that first flight with humans onboard, 2:04 how did you feel leading up to that launch? Were you scared? Were you excited? What was goin' through your mind? 2:09 So much was at stake. - Yeah, no, that was extremely stressful. 2:16 The question we obviously could not let them down in any way. 2:21 So, extremely stressful I'd say, to say the least. 2:28 I was confident that, at the time that we launched, that no one could think of anything, 2:34 at all, to do that would improve the probability of success 2:40 and we racked our brains to think of any possible way to improve the probability of success, and we could not think of anything more, 2:47 nor could NASA, and so, that's just the best that we could do. So then we went ahead and launched. 2:55 Now, I'm not a religious person, but I nonetheless got on my knees and prayed 3:01 for that mission. - [Lex] Were you able to sleep? - No. 3:07 - How did it feel when it was a success? First when the launch was a success, 3:12 and when they returned back home, or back to earth. - It was a great relief. 3:20 Yeah. For high stress situations I find it's not so much elation, as relief. 3:29 And, I think once as we got more comfortable and proved out the systems, 3:34 'cause we really, you're gotta make sure everything works. 3:41 It was definitely a lot more enjoyable with the subsequent asteroid missions. 3:46 And I thought the Inspiration mission was actually very inspiring, 3:52 the Inspiration4 mission. I'd encourage people to watch the Inspiration documentary on Netflix, it's actually really good. 4:00 And it really isn't, I was actually inspired by that, so that one I felt, I was kind of able 4:07 to enjoy the actual mission and not just be super stressed all the time. - So, for people that somehow don't know, 4:13 it's the all civilian, first time all civilian out to space out to orbit. 4:19 - Yeah, it was the, I think the highest obit that in like, I don't know, 30 or 40 years or something, 4:26 the only one that was higher was the one shuttle, sorry, a Hubble servicing mission. 4:32 And then before that it would've been Apollo in '72. 4:38 It was pretty wild. So it's cool. It's good. I think 4:43 as a species, we want to be continuing to do better and reach higher ground. 4:51 I think it would be tragic, extremely tragic, if Apollo was the high watermark for humanity, and that that's as far as we ever got. 5:00 And it's concerning that here we are 5:05 49 years after the last mission to the moon. And, so almost half a century, 5:12 and we've not been back. And that's worrying, it's like, 5:17 does that mean we've peaked as a civilization or what? 5:23 I think we gotta get back to the moon and build a base there. A science base. I think we could learn a lot 5:28 about the nature of the universe if we have a proper science base on the moon. 5:33 We have a science base in Antarctica and many other parts of the world. 5:39 So that's what I think the next big thing we've gotta have like a serious black moon base, 5:45 and then get people to Mars and get out there and be a space bearing civilization. 5:52 - I'll ask you about some of those details. But, since you're so busy with the hard engineering challenges 5:58 of everything that's involved, are you still able to marvel at the magic of it all, of space travel, of every time the rocket goes up, 6:05 especially when it's a crude mission? Or are you just so overwhelmed with all the challenges that you have to solve? 6:13 And actually, sort of to add to that, the reason I wanted to ask this question of May 30th, 6:19 it's been some time, so you can look back and think about the impact already. 6:24 At the time it was an engineering problem maybe, now it's becoming a historic moment. Like it's a moment that, 6:31 how many moments will be remembered about the 21st century? To me, that or something like that, 6:37 maybe Inspiration4 or one of those will be remembered as the early steps of a new age of space exploration. 6:44 - Yeah, I mean, during the launches itself, so I mean, I think maybe some people will know, but a lot of people don't know, 6:50 is I'm actually the chief engineer of SpaceX, so I've signed off on pretty much all the design decisions. 7:00 So if there's something that goes wrong with that vehicle, 7:05 it's fundamentally my fault, you know? So I'm really just thinking about all the things that like, 7:14 so when I see the rocket, I see all the things that could go wrong, and the things that could be better, and the same with the Dragon spacecraft. 7:22 Other people will say, "Oh, this is a spacecraft or a rocket." and "This looks really cool." I'm like, I've like a readout of 7:30 these are the risks, these are the problems. That's what I see. Like (Elon chuffing) 7:36 So it's not what other people see when they see the product. - So let me ask you then to analyze Starship 7:43 in that same way. I know you have, you'll talk a bit in more detail about Starship in the near future. 7:49 Perhaps you had that- - We can talk about in now if you want. - But, just in that same way, like you said, you see, 7:57 when you see a rocket, you see the sort of a list of risks. In that same way, you said that Starship was a really hard problem. 8:03 So, there's many ways I can ask this, but if you magically could solve one problem perfectly, 8:09 one engineering problem perfectly, which one would it be? - [Elon] On Starship? - On, sorry, on Starship. 8:15 So is it maybe related to the efficiency, the engine, the weight of the different components, 8:21 the complexity of various things, maybe the controls of the crazy thing it has to do to land? 8:26 - No, it's actually, by far the biggest thing of solving my time is engine production. 8:35 Not the design of the engine, I've often said prototypes are easy. 8:41 Production is hard. So, we have the most advanced rocket engine 8:48 that's ever been designed. 'Cause I say currently the best rocket engine ever 8:55 is probably the RD-180 or RD-170 9:01 the dual Russian engine, basically. And still, I think an engine should only count 9:06 if it's gotten something to orbit. And so our engine has not gotten anything to orbit yet, 9:12 but it is, it's the first engine that's actually better than 9:18 the Russian RD engines, which were amazing design. - So you're talking about Raptor engine. 9:24 What makes it amazing? What are the different aspects of it that make it, 9:29 what are you the most excited about if the whole thing works in terms of efficiency, 9:35 all those kinds of things? - Well, it's, the Raptor is 9:40 a full flow staged combustion 9:45 engine, and it's operating at a very high TAVR pressure. So, one of the key figures, merit, 9:53 perhaps the key figure of merit is what is the chamber pressure 9:58 at which the rocket engine can operate? That's the combustion chamber pressure. So a Raptor is designed to operate at a 300 bar, 10:06 possibly, maybe higher, than standard atmospheres. 10:13 The record right now for operational engine is the RD engine that I mentioned, the Russian RD, which is, I believe around 267 bar. 10:22 And the difficulty of the chamber pressure is increases on a non-linear basis. 10:27 So, 10% more TAVR pressure is more like 50% more difficult, 10:37 but that air pressure, that is what allows you to get a very high power density 10:44 for the engine. So, enabling 10:50 a very high thrust to weight ratio and a very high, specific impulse. 10:57 So, specific impulse is like a measure of the efficiency of a rocket engine. It's really the 11:05 exhaust, the effect of exhaust velocity of the gas coming out of the engine. 11:15 With a very high chamber pressure you can have 11:20 a compact engine that nonetheless has a high expansion ratio, which is the ratio between the 11:28 exit nozzle and the throat. You see a rocket engine has got sort 11:34 of like a hourglass shape. It's like a chamber and then it necks down and there's a nozzle, and the ratio of the exit diameter 11:42 to the throat expansion ratio. - So why is this such a hard engine to manufacture at scale? 11:51 - It's very complex. - What does complexity mean? Here's a lot of components involved. 11:56 - There's a lot of components and a lot of unique materials. 12:03 So we had to invent several alloys that don't exist in order to make this engine work. 12:11 - So it's a materials problem too. - It's a materials problem, and in a stage combustion, that full floor stage combustion, 12:20 there are many feedback loops in the system. 12:26 Basically you've got propellants and hot gas flowing 12:34 simultaneously to so many different places on the engine. And they all have a recursive effect on each other. 12:43 So you change one thing here, it has a recursive effect here. It changes something over there. 12:48 And it's quite hard to control. There's a reason no one's made this before. 12:58 And the reason we're doing a stage commotion full flow 13:04 is because it has the highest theoretical possible efficiency. 13:12 So in order to make a fully reasonable rocket, 13:19 which, that's really the holy grail of orbital rocketry, 13:25 you have to have, everything's gotta be the best. It's gotta be the best engine, the best airframe, the best heat shield, 13:33 extremely light avionics, very clever control mechanisms. 13:40 You've got to shed mass in any possible way that you can. For example, we are, 13:46 instead of putting landing legs on the booster and ship, we are going to catch them with a tower to save the weight of the landing legs. 13:53 So that's like, I mean, we're talking about catching 13:58 the largest flying object ever made 14:03 on a giant tower with chopstick arms. It's like "Karate Kid" with the fly, but much bigger. 14:10 (Elon laughing) - I mean, pulling something- - This probably won't work the first time. 14:15 (Elon laughing) So this is bananas. This is bananas stuff. - So you mentioned that you doubt, well, not you doubt, 14:23 but there's days or moments when you doubt that this is even possible. 14:28 It's so difficult. - The possible part is, well at this point, 14:35 we'll I think we'll get Starship to work. 14:41 There's a question of timing. How long will it take us to do this? How long will it take us to actually achieve full and rapid reusability? 14:50 'Cause it will probably many launches before we are able to have full and rapid reusability. 14:57 But I can say that the physics pencils out, we're not, 15:06 at this point I'd say we're confident that, let's say, I'm very confident success 15:12 is in the set of all possible outcomes. - [Lex] Mm, right, it's not in all set of. - For a while there I was not convinced 15:18 that success was in the set of possible outcomes. (Lex laughing) Which is very important actually. 15:23 But, so... - [Lex] So you're saying there's a chance. 15:29 - I'm saying there's a chance. Exactly. Just not sure how 15:36 long it will take. But we have a very talented team, they're working night and day to make it happen. 15:45 Like I said, the critical thing to achieve with revolution in space flight and for humanity to be a space bearing civilization 15:52 is to have a fully and rapidly reusable rocket, orbital rocket. There's not even been any orbital rocket 15:58 that's been fully reusable ever. And this has always been the holy grail of rocketry 16:06 and many smart people, very smart people, have tried to do this before, 16:11 and they've not succeeded. 'Cause it's such a hard problem. 16:17 - What's your source of belief in situations like this when the engineering problem is so difficult, 16:23 there's a lot of experts, many of whom you admire, who have failed in the past. 16:29 - [Elon] Yes. - A lot of people, 16:36 a lot of experts, maybe journalists, all the kinds of, the public in general, have a lot of doubt about whether it's possible, 16:43 and you yourself know that even if it's a non-nodal set, not empty set, of success, 16:49 it's still unlikely or very difficult. Where do you go to both personally, 16:55 intellectually as an engineer, as a team, for source of strength needed 17:00 to sort of persevere through this and to keep going with the project, take it to completion? 17:18 - I suppose the strength. Hmm. That's really not how I think about things. 17:23 I mean, for me, it's simply this is something that is important to get done and we should just keep doing it or die trying, 17:32 and I don't need a source of strength. - So quitting is not even like... 17:39 - It's not, it's not in my nature. - Okay. - And I don't care about optimism or pessimism. 17:46 Fuck that, we're gonna get it done. - [Lex] Gonna get it done. 17:51 Can you then zoom back in to specific problems with Starship or any engineering problems you work on? 17:58 Can you try to introspect your particular biological neural network, your thinking process, 18:03 and describe how you think through problems, the different engineering and design problems? Is there like a systematic process you've spoken about, 18:10 first principles thinking, but is there kind of - Yeah, absolutely. - process to it? 18:17 - Saying like, physics is low and everything else was a recommendation. I've met a lot of people that can break the law, 18:23 but I have never met anyone who could break physics. 18:28 So first for any kind of technology problem you have to sort of just make sure you're not violating physics. 18:43 First principles analysis, I think, is something that can be applied to really any walk of life, 18:49 anything really. It's really just saying, let's boil something down 18:54 to the most fundamental principles, the things that we are most confident are true 19:00 at a foundational level, and that sets your axiomatic base, and then you reason up from there. 19:07 And then you cross check your conclusion against the axiomatic truth. 19:16 Some basics in physics would be like are violating conservation of energy or momentum or something like that, then it's not gonna work. 19:29 So that's just to establish is it possible? And then another good physics tool is thinking about things 19:35 in the limit. If you take a particular thing and you scale it to a very large number 19:41 or to a very small number, how do things change? - Both in number of things you manufacture, 19:48 something like that, and then in time. - Yeah, let's say, take an example of manufacturing, 19:55 which I think is just a very underrated problem. 20:02 Like I said, it's much harder to take an advanced technology part and bring it 20:09 into volume manufacturing, than it is to design it in the first place. More is magnitude. 20:16 So let's say you're trying to figure out, why is this part or product expensive? 20:24 Is it because of something fundamentally foolish that we're doing? Or is it because our volume is too low? 20:31 And so then you say, okay, well what if our volume was a million units a year? Is it still expensive? That's what I'm radical, thinking about things to the limit. 20:38 If it's too expensive at a million units a year, then volume is not the reason why your thing is expensive. There's something fundamental about the design. 20:44 - And then you then can focus on the reducing complexity or something like that in the design. - Gotta change the design to, 20:50 change the part to be something that is not fundamentally expensive. 20:57 That's a common thing in rocketry 'cause the unit volume is relatively low, and so a common excuse would be 21:04 "Well, it's expensive because our unit volume is low. And if we were in like automotive or something like that, or consumer electronics, then our costs would lower." 21:10 I'm like, "Okay, so let's say" we skip, "now you're making a million units a year. Is it still expensive?" 21:16 If the answer is yes, then economies of scale are not the issue. 21:22 - Do you throw, into manufacturing, do you throw like supply chain, you talked about resources and materials 21:27 and stuff like that, do you throw that into the calculation of trying to reason from first principles? Like, how are we gonna make the supply chain work here? 21:34 - Yeah, yeah. - [Lex] And then the cost of materials, things like that, or is that too much? - Yeah. Exactly. 21:41 Like a good example of thinking about things in the limit is if you take any 21:51 product, any machine or whatever, like take a rocket or whatever, and say, if you've got, 21:59 if you look at the raw materials in the rocket, so you're gonna have like 22:05 aluminum, steel, titanium, Inconel, specialty alloys, 22:12 copper. And you say, "What's the weight of the constituent elements 22:19 of each of these elements, and what is their raw material value?" And that sets the asymptotic limit 22:25 for how low the cost of the vehicle can be, unless you change the materials. 22:32 And then when you do that, I call it like maybe the magic one number or something like that. So that would be like, if you had the, 22:40 just a pile of these raw materials here, and you could wave a magic wand and rearrange the atoms into the final shape, 22:47 that would be the lowest possible cost that you could make this thing for, unless you change the materials. 22:52 So then, and that is always, almost always a very low number. 22:57 So then, what's actually causing things to be expensive is how you put the atoms into the desired shape. 23:05 - Yeah, actually, if you don't mind me taking a tiny tangent, I had a, I often talk to Jim Keller 23:11 who's somebody that worked with you as a- - Oh yeah. Jim did great work at Tesla. 23:17 - So, I suppose he carries the flame of the same kind of thinking that you're talking about now. 23:26 I guess I see that same thing at Tesla and SpaceX folks who work there, 23:31 they kind of learn this way of thinking and it kinda becomes obvious almost. But anyway, I had argument, not argument. 23:40 He educated me about how cheap it might be to manufacture Tesla Bot. 23:46 We just, we had an argument. How can you reduce the cost, of scale, of producing a robot? 23:52 Because, so far I've gotten a chance to interact quite a bit, obviously in the academic circles, 23:58 with humanoid robots, and then with Boston Dynamics and stuff like that. And they're very expensive to build. 24:04 And then Jim kinda schooled me on saying like, "Okay, this kind of first principles thinking 24:10 of how can we get the cost of manufacturing down." I suppose you do that, you have done that kind of thinking 24:16 for Tesla Bot and for all kinds of, all kinds of complex, systems that are traditionally seen 24:22 as complex, and you say, "Okay, how can we simplify everything down?" - Yeah. 24:28 I mean, I think if you are really good at manufacturing, you can basically make, 24:34 at high volume you can basically make anything for a cost that asymptotically approaches the raw material value 24:42 of the constituents, plus any intellectual property that you need to license. Anything. - Right. 24:49 - But it's hard. It's not like that's a very hard thing to do, but it is possible for anything. 24:54 Anything in volume can be made of, like I said, for a cost that asymptotically approaches 25:00 it's raw material constituents plus intellectual property license rights. So what will often happen in trying to design a product 25:08 is people will start with the tools and parts and methods that they are familiar with, 25:15 and try to create a product using their existing tools and methods. 25:21 The other way to think about it is actually imagine the, try to imagine the platonic ideal of the perfect product 25:28 or technology, whatever it might be, and say, "What is this? What is the perfect arrangement of atoms 25:35 that would be the best possible product? And now let us try to figure out how to get the atoms in that shape." 25:43 - I mean, it sounds, it's almost like "Rick and Morty" absurd 25:50 until you start to really think about it. And you really should think about it in this way 25:56 'cause everything else is kind of, if you think you might fall victim to the momentum 26:03 of the way things are done in the past, unless you think in this way. - Well, just as a function of inertia, people will want to use the same tools and methods 26:10 that they are familiar with. That's what they'll do by default. - [Lex] Yeah. 26:16 - And then that will lead to an outcome of things that can be made with those tools and methods, but is unlikely to be the platonic ideal 26:23 of the perfect product. So that's why it's good to think of things 26:29 in both directions, so like what can we build with the tools that we have, but also what is the perfect, 26:35 the theoretical perfect product look like? And that theoretical perfect product is gonna be a moving target, 'cause as you learn more the definition 26:44 of that perfect product will change 'cause you don't actually know what the perfect product is, but you can successfully approximate a more perfect product. 26:54 So, thinking about it like that, and then saying, "Okay, now what tools, methods, materials, whatever, 26:59 do we need to create in order to get the atoms in that shape? 27:06 But people very rarely think about it that way. But it's a powerful tool. 27:12 - I should mention that the brilliant Shivon Zilis is hanging out with us, 27:18 in case you hear a voice of wisdom from outside, from up above. 27:25 Okay. So let me ask you about Mars. You mentioned it would be great for science to put a base on the moon, to do some research, 27:35 but the truly big leap, again, in this category of seemingly impossible, is to put a human being on Mars. 27:43 When do you think SpaceX will land a human being on Mars? 27:50 - Hm. 28:08 Best case is about five years, worst case 10 years. 28:16 - What are the determining factors, would you say, from an engineering perspective? Or is that not the bottlenecks? 28:24 - No, it's fundamentally you're engineering the vehicle. 28:32 I mean Starship is the most complex and advanced rocket that's ever been made by, I don't know, 28:39 order of magnitude or something like that. It's a lot. It's really next level. 28:46 And the fundamental optimization of Starship is minimizing cost per ton to orbit, 28:51 and ultimately cost per ton to the surface of Mars. This may seem like a mercantile objective, but it is actually the thing that needs to be optimized. 29:00 There is a certain cost per ton to the surface of Mars where we can afford to establish a self-sustaining city. 29:09 And then above that, we cannot afford to do it. So, right now you can fly to Mars for $1 trillion. 29:16 No amount of money could get you a ticket to Mars. So we need to get that above, 29:22 to get that like something that is actually possible at all. 29:29 We don't want to just wanna have, with Mars, flags and footprints, and then not come back for a half century 29:35 like we did with the moon. In order to pass a very important, great filter. 29:43 I think we need to be a multi-planet species. 29:48 This ways sound somewhat esoteric to a lot of people, but, eventually given enough time, 29:57 something, Earth is likely to experience some calamity, that could be something that humans do to themselves, 30:06 or an external event like happened to the dinosaurs. 30:12 But if, eventually, if none of that happens, and somehow, magically, 30:19 we keep going, then the sun will, the sun is gradually expanding and will engulf the earth. 30:26 And probably Earth gets too hot for life in 30:33 about 500 million years. It's a long time, but that's only 10% longer than earth has been around. 30:39 And so if you think about like the, the current situation, it's really remarkable and kind of hard to believe, 30:45 but Earth's been around four and a half billion years, and this is the first time in four and a half billion years 30:52 that it's been possible to extend life beyond Earth. And that window of opportunity may be open for a long time, 30:58 and I hope it is, but it also may be open for a short time, and we should, I think it is wise for us to 31:08 act quickly while the window is open. Just in case it closes. 31:13 - Yeah, the existence of nuclear weapons, pandemics, all kinds of threats, 31:19 - [Elon] Yeah. - should kind of give us some motivation. 31:25 - I mean, civilization could get, could die with a bang or a whimper. 31:33 If it dies of demographic collapse, then it's more of a whimper, obviously. 31:38 And if it's World War III, it's more of a bang, but these are all risks. 31:43 I mean, it's important to think of these things and just, things like probabilities, not certainties, 31:48 there's a probability that something bad will happen on earth. 31:53 I think most likely the future will be good, but there's, let's say for argument's sake, 31:59 a 1% chance per century of a civilization ending event. Like that was Stephen Hawking's estimate. 32:08 I think he might be right about that. 32:16 We should basically think of this, being a multi-planet species, just like taking out insurance for life itself, 32:21 like life insurance for life. (both laughing) 32:27 - This turned into a infomercial real quick. - Life insurance for life, yes. And we can bring the creatures from, 32:36 plants and animals from Earth to Mars, and breathe life into the planet, 32:41 and have a second planet with life. That would be great. They can't bring themselves there, 32:47 so if we don't bring them to Mars, then they will just for sure all die when the sun expands anyway, and then that'll be it. 32:56 - What do you think is the most difficult aspect of building civilization on Mars, terraforming Mars, 33:02 like from engineering perspective, from a financial perspective, human perspective, 33:10 to get a large number of folks there who will never return back to Earth? 33:15 - No, they could certainly return, some will return back to Earth. - They will choose to stay there for the rest of their lives. 33:20 - Yeah, many will. 33:27 We need the spaceships back, like the ones that go to Mars, we need them back, so you can hop on if you want. 33:32 But we can't just not have the spaceships come back, those things are expensive. We need them back. I'd like to come back and journal their trip. 33:38 - I mean, do you think about the terraforming aspect, actually building, are you're so focused right now on the spaceships part that's so critical 33:45 to get to Mars? - Yeah, yeah. We absolutely, if you can't get there, nothing else matters. 33:50 And like I said, we can't get there at some extraordinarily high cost. I mean, the current cost of 33:57 let's say one ton to the surface of Mars is on the order of a billion dollars. 34:02 So, 'cause you don't just need the rocket and the launch and everything, you need like heat shield, you need guidance system, 34:09 you need deep space communications. You need some kind of landing system. 34:15 So, like rough approximation would be a billion dollars per ton to the surface of Mars right now. 34:22 This is obviously way too expensive to create a self-sustaining civilization. 34:30 So we need to improve that by at least a factor of a thousand. 34:36 - [Lex] A million per ton? - Yes, ideally less than, much less than a million ton. 34:44 You have to say like, well how much can society afford to spend or want to spend on a self-sustaining city on Mars? 34:52 The self-sustaining part is important. Like it's just the key threshold, 34:57 the grateful to, we'll have been passed, when the city on Mars can survive 35:05 even if the space ships from earth stop coming, for any reason. Doesn't matter what the reason is. But if they stop coming for any reason, 35:12 will it die out or will it not? And if there's even one critical ingredient missing, then it still doesn't count. 35:18 It's like if you're in a long sea voyage and you've got everything except vitamin C. (Elon laughing) 35:23 It's only a matter of time, you're gonna die. So we gotta get a Mars city 35:28 to the point where it's self sustaining. I'm not sure this will really happen in my lifetime, but I hope to see it at least have a lot of momentum. 35:37 And then you could say, "Okay, what is the minimum tonnage necessary to have a self-sustaining city?" 35:45 And there's a lot of uncertainty about this. You could say, I dunno, it's probably at least a million tons. 35:52 'Cause you have to set up a lot of infrastructure on Mars. Like I said, you can't be missing anything 35:58 that in order to be self-sustaining, you can't be, like you need a semiconductor, fabs, 36:04 you need iron ore refineries, you need lots of things, you know? 36:11 And Mars is not super hospitable. It's the least inhospitable planet, but it's definitely a fixer upper of a planet. 36:18 - [Lex] Outside of Earth. - Yes. - Earth is pretty good. - Earth is like easy. Yeah. - And, also, we should clarify in the solar system. 36:25 - [Elon] Yes. In the solar system. - There might be nice like vacation spots. - There might be some great planets out there, 36:31 but it's hopeless- - Too hard to get there? - Yeah, way, way, way, way, way too hard, 36:36 to say the least. - Let me push back on that. Not really a pushback, but quick a curve ball of a question. 36:42 So you did mention physics as the first starting point. 36:48 General relativity allows for worm holes. They technically can exist. 36:53 Do you think those can ever be leveraged by humans to travel fast in the speed of light? 37:01 Or are you saying- - The worm hole thing is debatable. We currently do not know of any means 37:08 of going faster than the speed of light. 37:17 There are some ideas about having space. 37:25 You're gonna move at the speed of light through space, but if you can make space itself move, 37:33 that would be warping space. Space is capable of moving faster than the speed of light. 37:39 - [Lex] Right. - Like the universe in the big bang, the universe expanded at much more than the speed of light, 37:46 by a lot. - [Lex] Yeah. 37:55 If this is possible, the amount of energy required to warp space is so gigantic, it boggles the mind. 38:03 - So, all the work you've done with propulsion, how much innovation is possible with rocket propulsion? 38:09 I mean, you've seen it all, and you're constantly innovating in every aspect. How much is possible? 38:15 Like how much, can you get 10 X somehow? Is there something in there, in physics, that you can get significant improvement 38:21 in terms of efficiency of engines and all those kinds of things? - Well, as I was saying, really the holy grail 38:27 is a fully and rapidly reasonable orbital system. 38:34 Right now, the Falcon 9 is the only reusable rocket out there. 38:42 The booster comes back and lands, you've seen the videos. And we got the nose cone or fairing back, but we do not get the upper stage back. 38:52 That means that we have a minimum cost of building an upper stage. You can think of like a two-stage rocket 38:59 of sort of like two airplanes, like a big airplane and a small airplane, and we get the big airplane back, 39:04 but not the smaller airplane. And so it still costs a lot. That upper stage is at least $10 million. 39:13 And then the degree of the booster is not as rapidly and completely reusable as we'd like 39:19 in order of the pharynx. So, our kind of minimum marginal cost not counting overhead 39:26 for per flight is on the order of 15 to $20 million, maybe. 39:35 That's extremely good for, it's by far better than any rocket ever in history. 39:41 But with full and rapid reusability, we can reduce the cost per ton to orbit 39:48 by a factor of a hundred. Just think of it like, 39:55 like imagining if you had an aircraft or something or a car. And if you had to buy a new car every time you went 40:03 for a drive, that'll be very expensive. It'll silly, frankly. - Mhm. 40:10 - But, in fact, you just refuel the car or recharge the car and that's makes your trip, 40:18 I don't know, a thousand times cheaper. So, it's the same for rockets. 40:25 Very difficult to make this complex machine that can go to orbit. And so if you cannot reuse it, and have to throw even any significant part of it away, 40:34 that massively increases the cost. 40:39 Starship in theory could do a cost per launch of like a million, maybe $2 million or something like that. 40:49 And put over a hundred tons in orbit, which is crazy. - Yeah. That's incredible. 40:55 So you're saying it's, by far the biggest bang for the buck is to make it fully reusable versus like some kind of brilliant breakthrough 41:04 in theoretical physics. - No, no, there's no, there's no brilliant brea, no, there's no. 41:10 We gotta make the rocket reusable, this is an extremely difficult engineering problem. - Got it. - But no new physics is required. 41:17 - Just brilliant engineering. Let me ask a slightly philosophical fun question. Gotta ask. 41:23 I know you're focused on getting to Mars, but once we're there on Mars, what form of government, economic system, political system, 41:32 do you think would best for an early civilization of humans? 41:39 The interesting reason to talk about this stuff, it also helps people dream about the future. I know you're really focused 41:45 about the short-term engineering dream, but it's like, I don't know. There's something about imagining 41:50 an actual civilization on Mars that gives people, - Sure. - really gives people hope. - Well, it would be a new frontier and an opportunity 41:57 to rethink the whole nature of government just as was done in the creation of the United States. 42:07 I mean, I would suggest having a direct democracy, 42:14 like people vote directly on things, as opposed to representative democracy. So, representative democracy, I think, 42:21 is too subject to a special interests and coercion of the politicians and that kind of thing. 42:31 So I'd recommend that there's just direct democracy. 42:39 People vote on laws, the population votes on laws themselves, and then the laws must be short enough 42:44 that people can understand them. - Yeah, and then keeping a well-informed populace, 42:50 really being transparent about all the information about what they're voting for. - Yeah. Absolute transparency. - Yeah. 42:55 And not make it as annoying as those cookies we have to accept- - Have to accept cookies. 43:01 There's always a slight amount of trepidation when you click accept cookies. I feel as though there's perhaps a very tiny chance 43:09 that'll open a portal to hell or something like that. - [Lex] That's exactly how I feel. 43:15 Why do they keep wanting me to accept that? What do they want with this cookie? Somebody got upset with accepting cookies 43:21 or something somewhere. I mean, who cares? So annoying to keep accepting all these cookies. 43:26 - [Lex] To me, it's just a great- - I'm tired of accept- (Shivon speaking faintly) Yes you can have my damn cookie, I don't care. Whatever. 43:32 - [Lex] You heard it from me Elon first, he accepts all your damn cookies. - Yeah. (both laughing) 43:38 And stop asking me. It's annoying. - Yeah, it's one example of 43:44 implementation of a good idea done really horribly. 43:50 - Yeah, somebody was like, there's some good intentions of like privacy or whatever, but now everyone's just has to tick accept cookies 43:57 and it's now, you have billions of people who have to keep clicking accept cookie and it's super annoying. 44:03 Just accept the damn cookie, it's fine. There is like, I think fundamental problem that we're, 44:08 because we've not really had a major, like a world war or something like that in a while. 44:14 And obviously we would like to not have world wars. There's not been a cleansing function 44:19 for rules and regulations. So wars did have some silver lining 44:25 in that there would be a reset on rules and regulations after a war. 44:31 So World Wars I and II there were huge resets on rules and regulations. 44:37 If society does not have a war, and there's no cleansing function or garbage collection for rules and regulations, 44:42 then rules and regulations will accumulate every year 'cause they're immortal. There's no actual, humans die, 44:48 but the laws don't. So, we need a garbage collection function for rules and regulations that should not just be immortal. 44:57 'Cause some of the rules and regulations that are put in place will be counterproductive, done with good intentions, but counterproductive. 45:03 And sometimes not done with good intentions. If rules and regulations just accumulate every year, 45:10 and you get more and more of them, then eventually you won't be able to do anything. You're just like Gulliver with, 45:16 tied down by thousands of little strings. And we see that in, 45:24 U.S. and LA, basically all economies that have been around for awhile, 45:31 and regulators and legislators create new rules and regulations every year, 45:36 but they don't put effort into removing them. And I think that's very important that we put effort into removing rules and regulations. 45:44 But it gets tough 'cause you get special interests that then are dependent on, they have a vested interest in that whatever rule regulation 45:51 and that they, then they fight to not get it removed. 45:57 - Yeah. I mean, I guess the problem with the constitution is it's kinda like C versus Java 46:04 'cause it doesn't have any garbage collection built in. I think there should be. When you first said the metaphor of garbage collection, 46:10 I loved it - Yeah, it's from a coding standpoint. - From a coding standpoint, yeah, yeah. It would be interesting if the laws themselves kinda had a built in thing 46:19 where they kinda die after a while, unless somebody explicitly publicly defends them. So that's sort of, it's not like somebody has to kill them. 46:26 They kinda die themselves. They disappear. - [Elon] Yeah. 46:32 - Not to defend Java or anything, C++, you could also have great garbage collection 46:38 in Python and so on. - Yeah. So, yeah, something needs to happen 46:44 or just the civilizations arteries just harden over time. And you can just get less and less done 46:50 because there's just a rule against everything. 46:56 So I think, I don't know, for Mars, or whatever, I say, or even for here, obviously for Earth as well, I think there should be an active process 47:02 for removing rules and regulations and questioning their existence. 47:09 If we've got a function for creating rules and regulations, 'cause rules and regulations could also think of as like, they're like soft work or lines of code 47:15 for operating a civilization, that's the rules and regulations. 47:21 So it's not like we shouldn't have rules and regulations, but you have your code accumulation, but no code removal. 47:27 And so it just gets to be become basically archaic bloatware after a while. 47:33 And it's just, it makes it hard for things to progress. So, I don't know, maybe Mars you'd have like 47:41 any given law must have a sunset, and require active voting 47:50 to keep it up there. I actually also say like, and these are just, I don't know, recommendations or thoughts, 47:57 and ultimately will be up to the people on Mars to decide, but I think 48:04 it should be easier to remove a law than to add one, because of the, just to overcome the inertia of laws. 48:10 So, maybe it's like, for argument's sake, you need like say 60% vote 48:17 to have a law take effect, but only a 40% vote to remove it. 48:23 - So let me be the guy, you posted a meme on Twitter recently where there's like a row of urinals 48:30 and a guy just walks all the way across - So true, yeah. - and he tells you about crypto. 48:35 - Listen, I mean, that's happened to me so many times, I think maybe even literally. (both laughing) 48:41 - Do you think technologically speaking there's any room for ideas of smart contracts or so on? 48:47 'Cause you mentioned laws, that's an interesting implement use of things like smart contracts 48:53 to implement the laws by which governments function. Like something built on Ethereum, or maybe 49:01 a dog coin that enables smart contracts somehow. - I never, I didn't quite understand 49:06 this whole smart contract thing. (both laughing) 49:12 I'm too downtown to understand smart contracts. - That's a good line. (both laughing) 49:17 - I mean, my general approach to any kind of deal or whatever is just make sure there's clarity of understanding. 49:23 That's the most important thing. - [Lex] Right. - And just keep any kind of deal very short and simple, 49:29 plain language, and just make sure everyone understands this is the deal. Does everyone, is it clear? 49:38 And what are the consequences if first things don't happen? But usually deals are, 49:46 business deals or whatever are way too long and complex and overly lawyered and pointlessly. 49:52 - You mentioned that Doge is the people's coin. - [Elon] Yeah. 49:57 - And you said that you were literally going, SpaceX may consider literally putting 50:04 a Dogecoin on the moon. - Yeah. - Is this something you're still considering, Mars perhaps, 50:12 do you think there's some chance, we've talked about political systems on Mars, that a Dogecoin is the official currency of Mars, 50:20 it's the coin of the future? - Well, I think Mars itself will need 50:25 to have a different currency because you can't synchronize due to speed of light, 50:31 or not easily. - So it must be complete standalone from earth. 50:36 - Well, yeah, Mars is, at closest approach, it's four light minutes away roughly, 50:43 and then add for this approach, it's roughly 20 light minutes away, maybe a little more. 50:50 So you can't really have something synchronizing if you've got a 20 minute speed of light issue, 50:55 if it's got a one minute blockchain. It's not gonna synchronize properly. 51:03 I don't know if Mars would have a cryptocurrency as a thing, but probably, seems likely. But it would be so kind of localized thing on Mars. 51:12 - And you let the people decide. - Yeah, absolutely. 51:17 The future of Mars should be up to the martians. 51:24 I mean, I think the cryptocurrency thing is an interesting approach to 51:30 reducing the error in the 51:36 database that is called money. 51:41 I think I have a pretty deep understanding of what money actually is on a practical day-to-day basis, 51:46 because of PayPal. We really got in deep there. 51:55 And right now the money system, actually for practical purposes is really a bunch of heterogeneous mainframes running 52:04 a old COBOL. - [Lex] Okay, you mean literally- - Literally. - That is literally 52:10 what's happening. - in batch mode. Okay. - In batch mode. - Yeah. Pity the poor bastards who have to've maintained that code. 52:19 Okay. That's pain. - [Lex] Not even Fortrans, COBOL, yep. 52:24 - That's COBOL. And they still, the banks are still buying mainframes, in 2021, and running engine COBOL code. 52:35 The federal reserve is like probably even older than what the banks have, and they have an old COBOL mainframe. 52:43 And so the government effectively has editing privileges on the money database. 52:49 And they use those editing privileges to make more money whenever they want. 52:56 And this increases the error in the database that is money. So I think money should really be viewed 53:01 through the lens of information theory. 53:07 You're kinda like an internet connection. Like what's the bandwidth, total bit rate, 53:13 what is the latency jitter, packet drop, 53:18 errors in the network communication. Just think of money like that basically. 53:25 I think that's probably what I really think of it. And then say what system, 53:31 from an information theory standpoint, allows an economy to function the best. 53:39 Crypto is an attempt to reduce the error 53:46 in money that is contributed by governments diluting the money supply 53:53 as basically a pernicious form of taxation. 53:58 - So both policy in terms of with inflation, and actual like technological, COBOL, 54:06 cryptocurrency takes us into the 21st century in terms of the actual systems that allow you to do the transaction, to store wealth, 54:12 all those kinds of things. - Like I said, just think - In theory. 54:17 - of money as information, people often will think of money as having power in and of itself. 54:24 It does not. Money is information, and it does not have power in and of itself. 54:32 Applying the physics tools of thinking about things in the limit is helpful. If you are stranded on a tropical island 54:41 and you have a trillion dollars, it's useless. 54:47 'Cause there's no resource allocation. Money is a database of resource allocation, but there's no resources to allocate except yourself. 54:55 So money's useless. 55:01 If you're stranded on a desert island with no food, all the Bitcoin in the world will 55:09 not stop you from starving. - [Lex] Yeah. 55:16 - Just think of money as a database for resource allocation across time and space. 55:26 And then what system, 55:32 in what form should that database, or data system, what would be most effective? 55:40 There is a fundamental issue with, say Bitcoin, in its current form 55:46 in that it's, the transaction volume is very limited. 55:51 And the latency, the latency, for a properly confirmed transaction 55:57 is too long, much longer than you'd like. It's actually not great from transaction volume standpoint 56:05 or latency standpoint. So it is perhaps useful as, 56:13 to solve an aspect of the money database problem, which is the sort of store of wealth 56:19 or an accounting of relative obligations, I suppose. 56:25 But it is not useful as a currency, as a day-to-day currency. 56:30 - But people have proposed different technological solutions- - [Elon] Like Lightning. - Yeah, Lightening Network and the Layer 2 technologies 56:36 on top of that. I mean, it's all, it seems to be all kind of a trade-off, but the point is, it's kinda brilliant to say, 56:43 to just think about information, think about what kind of database, what kind of infrastructure enables the exchange of- - Yeah, let's say like 56:48 you're operating an economy, and you need to have some thing that allows 56:55 for the efficient, to have efficient value ratios between products and services. 57:01 So you've got this massive number of products and services, and need to, you can't just barter. 57:07 'Cause that would be extremely unwieldy. So you need something that gives you 57:16 a ratio of exchange between goods and services. And then, something that allows you to shift obligations 57:25 across time, like debt, debt and equity shift obligations across time. Then what does the best job of that? 57:33 Part of the reason why I think there's some merit to Dogecoin, even though, it was obviously created as a joke, 57:41 is that it actually does have a much higher transaction volume capability than Bitcoin. 57:51 The costs of doing a transaction, the Dogecoin fee is very low. Like right now, if you wanna do a Bitcoin transaction, 57:58 the price of doing that transaction is very high, so you could not use it effectively for most things. 58:04 And nor could it even scale to a high volume. 58:11 And when Bitcoin was started, I guess around 2008 or something like that, 58:17 the internet connections were much worse than they are today, like order of magnitude. 58:23 I mean, they were way, way worse in 2008. 58:28 So like having a small block size or whatever it is, 58:33 and a long synchronization time made sense in 2008, but, 2021, or fast forward 10 years, 58:42 it's like, comically low. 58:50 And I think there's some value to having a linear increase 58:55 in the amount of currency that is generated. So, because some amount of the currency, 59:04 if a currency is too deflationary or like, or should say if, 59:09 if a currency is expected to increase in value over time, there's reluctance to spend it. 59:15 'Cause you're like, "Oh, if I, I'll just hold it and not spend it because its scarcity is increasing with time, 59:20 so if I spend it now, then I will regret spending it. So I will just, you know, hoard all it." 59:28 But if there's some dilution of the currency occurring over time, that's more of an incentive to use that as a currency. 59:34 So Dogecoin just somewhat randomly has 59:41 just a fixed a number of sort of coins or hash strings 59:46 that are generated every year. So there's some inflation, 59:51 but it's not a percentage at base. It's a fixed number, so the percentage 59:57 of inflation will necessarily decline over time. 1:00:04 I'm not saying that it's like the ideal system for a currency, but I think it actually is just fundamentally better 1:00:10 than anything else I've seen, just by accident. 1:00:16 - I like how you said around 2008, so you're not, 1:00:22 some people suggest that you might be Satoshi Nakamoto. You've probably said you're not. Let me ask- - I'm not. - You're not, for sure. 1:00:28 Would you tell us if you were? - Yes. - Okay. Do you think it's a feature or a bug 1:00:34 that he's anonymous, or she, or they? It's an interesting kind of quirk of human history 1:00:41 that there is a particular technology that is a completely anonymous inventor. 1:00:49 Or creator. 1:01:03 - Well, I mean, you can look at the evolution of ideas 1:01:10 before the launch of Bitcoin and see who wrote 1:01:17 about those ideas. And then, I don't know, obviously I don't know who created Bitcoin 1:01:25 for practical purposes, but the evolution of ideas is pretty clear for that. And, it seems as though Nick Szabo 1:01:33 is probably more than anyone else responsible for the evolution of those ideas. 1:01:38 So, here he claims not to be Nakamoto, but I'm not sure, that's neither here nor there, 1:01:45 but he seems to be the one more responsible for the ideas behind Bitcoin than anyone else. 1:01:50 - So it's not, perhaps, like singular figures aren't even as important as the figures involved in the evolution 1:01:56 of ideas that led to things. - Yeah. - Yeah. Perhaps it's sad to think about history, 1:02:03 but maybe most names would be forgotten anyway. - What is a name anyway, it's a name, 1:02:08 a name attached to an idea. What does it even mean really? 1:02:13 - I think Shakespeare had a thing about roses and stuff, whatever he said. - "Rose by any other name would smell as sweet." 1:02:21 (Lex laughing) - I got Elon to quote Shakespeare. I feel like I accomplished something today. 1:02:26 - "Shall I compare thee to a summer's day?" (both laughing) - [Lex] I'm gonna clip that out instead. 1:02:34 - Thou art more temporate and more fair. (both laughing) (Shivon speaking faintly) - Autopilot. 1:02:40 Tesla autopilot- (Elon laughing) 1:02:46 Tesla autopilot has been through an incredible journey over the past six years, or perhaps even longer in the minds of, 1:02:52 in your mind, and the minds of many involved. - I think that's where we first like connected, really, 1:02:59 was the autopilot stuff, autonomy and... - The whole journey was incredible to me to watch. 1:03:08 'Cause I knew, well, part of it is I was at MIT and I knew the difficulty of computer vision. 1:03:13 And I knew the whole, I had a lotta colleagues and friends, about the DARPA challenge, and knew how difficult it is. 1:03:18 And so there was a natural skepticism when I first drove a Tesla with the initial system based on Mobileye. 1:03:25 I thought there's no way. So at first when I got in I thought "There's no way this car could maintain, 1:03:33 like stay in the lane and create a comfortable experience." So my intuition initially was that the lane-keeping problem 1:03:39 is way too difficult to solve. - [Elon] Oh lane-keeping, yeah, that's relatively easy. 1:03:46 - But solve in the way that we just, we talked about previous, this prototype, versus a thing that actually creates a pleasant experience 1:03:54 over hundreds of thousands of miles or millions. Yeah, so, I was proven wrong- - We had 1:03:59 to wrap a lot of code around the Mobileye thing, it doesn't just work by itself. 1:04:04 - I mean, that's part of the story of how you approach things sometimes. Sometimes you do things from scratch. 1:04:09 Sometimes at first you kind of see what's out there, and then you decide to from scratch. That was one of the boldest decisions I've seen 1:04:17 is both on the hardware and the software to decide to eventually go from scratch. I thought, again, I was skeptical 1:04:22 of whether that's going to be able to work out 'cause it's such a difficult problem. And so it was an incredible journey, 1:04:28 what I see now with everything, the hardware, the compute, the sensors, the things I maybe care and love about most 1:04:37 is the stuff that Andrej Karpathy's leading with, the dataset selection, the whole data engine process, 1:04:43 the neural network architectures, the way that's in the real world, that network is tested, validated, 1:04:49 all the different test sets, versus the image net model of computer vision, 1:04:54 like what's in academia is like real world artificial intelligence. 1:05:01 - Andrej's awesome and obviously plays an important role, but we have a lot of really talented people driving things. 1:05:09 Ashok is actually the head of autopilot engineering. 1:05:14 Andrej's the director of AI. - Ai stuff, yeah. So yeah, I'm aware that there's an incredible team 1:05:20 of just a lot going on. - People will give me too much credit, 1:05:26 and they'll give Andrej too much credit. - And people should realize how much is going on under the- 1:05:32 - Yeah, just a lot of really talented people. The Tesla Autopilot AI team is extremely talented. 1:05:40 It's like some of the smartest people in the world. So, yeah, and we're getting it done. 1:05:45 - What are some insights you've gained over those five, six years of autopilot 1:05:51 about the problem of autonomous driving. So, you leaped in having some 1:05:57 sort of first principles kinds of intuitions, but nobody knows how difficult the pro- 1:06:04 - Yeah, I thought the self-driving problem would be hard, but it was harder than I thought. It's not like I thought it'd be easy, 1:06:10 I thought it would be very hard, but it was actually way harder than even that. So, I mean want it comes down to at the end of the day 1:06:17 is to solve self-driving you have to solve. 1:06:22 You basically need to recreate what humans do to drive, 1:06:28 which is humans drive with optical senses, eyes, and biological neural nets. 1:06:34 And so in order to, that's how the entire road system is designed to work, 1:06:40 with basically passive optical and neural nets, 1:06:45 biologically. So, for actually, for full self driving to work, we have to recreate that in digital form. 1:06:52 So we have to, that means cameras with 1:06:58 advanced neural nets in silicon form. 1:07:04 And then it will obviously solve for small cell driving. That's the only way, I don't think there's any other way. 1:07:10 - But the question is what aspects of human nature do you have to encode into the machine, right? 1:07:15 So you have to solve the perception problem, like detect, and then you first realize, 1:07:21 what is the perception problem for driving? Like all the kinds of things you have to be able to see. Like what do we even look at when we drive? 1:07:27 There's, I just recently heard, Andrej talked about, at MIT, about like car doors. 1:07:33 I think it was the world's greatest talk of all time about car doors. 1:07:39 The fine details of car doors, like what is even an open car door, man. 1:07:44 So like the ontology of that, that's a perception problem. We humans solve that perception problem, 1:07:49 and Tesla has to solve that problem. And then there's the control and the planning, coupled with the perception. 1:07:55 You have to figure out like what's involved in driving, especially in all the different edge cases. 1:08:04 Maybe you can comment on this, how much game theoretic kind of stuff needs to be involved, 1:08:10 at a four-way stop sign? As humans, when we drive, our actions affect the world. 1:08:18 - True. - It changes how others behave, most autonomous driving, you're usually just responding to the scene, 1:08:27 as opposed to like really asserting yourself in the scene. Do you think... 1:08:34 - I think these sort of control logic conundrums are not the hard part. 1:08:42 Let's see... - [Lex] What do you think is the hard part in this whole beautiful complex problem? 1:08:50 - It's a lot of freaking software man, and a lot of smart lines of code. 1:08:57 For sure, in order to create an accurate vector space. 1:09:06 You're coming from image space, which is like this flow of 1:09:11 photons going to the camera, cameras and then since you have this massive bitstream 1:09:22 in image space, and then you have to effectively compress the, 1:09:29 a massive bitstream corresponding to photons that knocked off an electron 1:09:39 in a camera sensor and turn that bitstream into a vector space. 1:09:47 By vector space I mean, you've got cars and humans 1:09:55 and lane lines and curves and traffic lights and that kind of thing. 1:10:03 Once you have an accurate vector space, the control problem is similar to that of a video game, 1:10:11 like a "Grand Theft Auto" or "Cyberpunk." If you have accurate vector space. It's, the control problem is, 1:10:18 I wouldn't say it's trivial, it's not trivial, but it's 1:10:24 it's not like some insurmountable thing. Having an accurate vector space is very difficult. 1:10:32 - Yeah, I think we humans don't give enough respect to how incredible the human perception system is 1:10:38 to mapping the raw photons to the vector space representation in our heads. 1:10:44 - Your brain is doing an incredible amount of processing and giving you an image that is a very cleaned up image. 1:10:51 Like when we look around here, you see color in the corners of your eyes, but actually your eyes have very few cones, 1:11:00 cone receptors in the peripheral vision. Your eyes are painting color in the peripheral vision. 1:11:05 You don't realize it, but they're, eyes are actually painting color and your eyes will also have, there's blood vessels 1:11:12 and all sorts of gnarly things, and there's a blind spot, but do you see your blind spot? No, your brain is painting in the missing, the blind spot. 1:11:21 You're gonna do these things online where you look here and look at this point and then look at this point, 1:11:27 and it's, if it's in your blind spot, your brain will just fill in the 1:11:32 missing bits. - So cool. The peripheral vision's so cool. - Yeah. - It makes you realize all the illusions, provision science, 1:11:38 it makes you realize just how incredible the brain is. - The brain's doing a crazy amount of post-processing on the vision signals from your eyes. 1:11:45 It's insane. And then even once you get all those vision signals, 1:11:53 your brain is constantly trying to forget as much as possible. So human memory is perhaps the weakest thing about the brain 1:12:00 is memory. So because memory is so expensive to our brain, and so limited, 1:12:06 your brain is trying to forget as much as possible and distill the things that you see 1:12:12 into the smallest amounts of information possible. So your brain is trying to not just get to a vector space, 1:12:19 but get to a vector space that is the smallest possible vector space of only relevant objects. 1:12:28 You can sort of look inside your brain, or at least I can like when you drive down the road, and try to think about what your brain is actually doing, 1:12:37 - Yeah - consciously. It's like, you'll see a car, because you don't have cameras. 1:12:46 You don't have eyes in the back of your head or the side, so you say like, you're basically, your head is like a, 1:12:54 you basically have like two cameras on a slow gimbal. 1:12:59 (both laughing) And eyesight's not that great. Okay? Human eyes are... And people are constantly distracted 1:13:05 and thinking about things and texting and doing all sorts of things they shouldn't do in a car, changing the radio station. 1:13:11 So, having arguments. 1:13:21 When's the last time you looked right and left, and rearward, or even diagonally forward 1:13:28 to actually refresh your vector space? So you're glancing around and what your mind is doing 1:13:34 is trying to distill the relevant vectors, basically objects with a position and motion, 1:13:44 and then editing that down to the least amount that's necessary for you to drive. 1:13:49 - It does seem to be able to edit it down or compress even further into things like concept, 1:13:55 so it's not, it's like it goes beyond, the human mind seems to go sometimes beyond vector space 1:14:01 to sort of space of concepts, to where you'll see a thing, it's no longer represented spatially somehow, 1:14:07 it's almost like a concept that you should be aware of. If this is a school zone, you'll remember that as a concept. 1:14:14 Which is a weird thing to represent, but perhaps for driving you don't need to fully represent those things. 1:14:20 Or maybe you get those kind of - Well you- - indirectly. 1:14:26 - You need to established vector space and then actually have predictions for 1:14:33 those vector spaces. 1:14:39 Like you drive past say a bus and you see that there's people, 1:14:47 before you drove past the bus you saw people crossing, or just imagine there's like a large truck 1:14:52 or something blocking site. But before you came up to the truck you saw that there were some kids about to cross the road 1:15:00 in front of the truck. Now you can no longer see the kids, but you would now know, 1:15:06 okay, those kids are probably gonna pass by the truck and cross the road. Even though you cannot see them. 1:15:12 So you have to have memory. 1:15:17 You need to remember that there were kids there and you need to have some forward prediction of what their position will be. 1:15:23 - It's a really hard problem - at the time of relevance. - So with occlusions and computer vision, when you can't see an object anymore, 1:15:30 even when it just walks behind a tree and reappears, that's a really, really, I mean, at least in academic literature, 1:15:37 it's tracking through occlusions, it's very difficult. - Yeah, we're doin' it. 1:15:42 - [Lex] I understand this. So some of it- - It's like object permanence. The same thing happens with the humans with neural nets. 1:15:48 When like a toddler grows up, there's a point in time where they develop, 1:15:54 they have a sense of object permanence. So before a certain age, if you have a ball, or a toy or whatever, 1:15:59 and you put it behind your back and you pop it out, before they have object permanence, it's like a new thing every time. 1:16:05 It's like, "Whoa, this toy went poof, disappeared, and now it's back again." and they can't believe it. And that they can play peek-a-boo all day long 1:16:12 because peek-a-boo's fresh every time. But then we figure out object permanence, then they realize, 1:16:18 "Oh, no, the object is not gone. It's just behind your back." - Sometimes I wish we never did figure out 1:16:25 object permanence. - Object permanence. Yeah, so that's a... - [Lex] That's an important problem to solve. 1:16:31 - Yes. So, an important evolution of the neural nets in the car is 1:16:41 memory across both time and space. Now you can't remember, you have to say how long do you want 1:16:48 to remember things for. There's a cost to remembering things for a long time. 1:16:53 So you could run out of memory to try to remember too much for too long. 1:16:59 And then you also have things that are stale if you remember 'em for too long. And then you also need things that are remembered over time. 1:17:06 So even if you, say have, for evidence sake, five seconds of memory on a time basis, 1:17:14 but, let's say you you're parked at a light and you saw, 1:17:19 use a pedestrian example, that people were waiting to cross the cross the road, 1:17:25 and you can't quite see them because of an occlusion, but they might wait for a minute before the light changes 1:17:31 for them to cross the road. You still need to remember that that's where they were, 1:17:37 and that they're probably going to cross road type of thing. So even if that exceeds your time-based memory, 1:17:44 it should not exceed your space of memory. - And I just think the data engine side of that, 1:17:50 so getting the data to learn all of the concepts that you're saying now, is an incredible process. 1:17:56 It's this iterative process of just, there's this HydraNet of many- - HydraNet. 1:18:03 We're changing the name to something else. - Okay. Alright. I'm sure it will be equally as "Rick and Morty," like. 1:18:10 - Yeah. We've re-architected the neural nets in the cars 1:18:15 so many times, it's crazy. - Oh, so every time there's a new major version, you'll rename it to something more ridiculous or, 1:18:23 or memorable and beautiful, sorry. Not ridiculous of course. 1:18:28 - If you see the full like array of neural nets that are operating the cars, it kinda boggles the mind. 1:18:36 There's so many layers. It's crazy. 1:18:44 We started off with simple neural nets that were basically 1:18:52 image recognition on a single frame from a single camera, and then trying to knit those together with, 1:19:02 with C. I should say, we were really familiar running C here, 1:19:07 'cause C++ is too much overhead, and we have our own C compiler. So, to get maximum performance we actually wrote 1:19:14 our own C compiler and are continuing to optimize our C compiler for maximum efficiency. 1:19:20 In fact, we've just recently done a new rev on the C compiler that will compile directly 1:19:25 to our autopilot hardware. - So you wanna compile the whole thing down with your own compiler? 1:19:30 - Yeah. - So efficiency here, 'cause there's all kinds of computers, CPU, GPU, there's like basic types of things 1:19:37 and you have to somehow figure out the scheduling across all of those things. And so you're compiling the code down - Yeah. - that does all, okay. 1:19:44 So that's why there's a lotta people involved. - There's a lot of hardcore software engineering 1:19:50 at a very sort of bare metal level. 'Cause we're trying to do a lot of compute 1:19:57 that's constrained to the our full self-driving computer. 1:20:03 And we wanna try to have the highest frames per second possible 1:20:10 in a sort of very finite amount of compute and power. 1:20:18 We really put a lot of effort into the efficiency of our compute. 1:20:24 So there's actually a lot of work done by some very talented software engineers at Tesla that, 1:20:31 at a very foundational level to improve the efficiency of compute and how we use the trip accelerators, 1:20:38 which are basically doing matrix math, dot products, 1:20:45 like a bazillion dot products. And it's like, one of our neural nets is like, 1:20:50 compute wise, like 99% dot products. 1:20:56 - And you wanna achieve as many high frame rates, like a video game, you want - Yeah. - full resolution, 1:21:03 higher frame. - High frame rate, low latency, 1:21:09 low jitter. 1:21:15 I think one of the things we're moving towards now is no post-processing of the image through 1:21:25 the image signal processor. What happens for cameras is that, 1:21:32 well almost all cameras, is they there's a lot of post-processing done in order to make pictures look pretty. 1:21:40 And so we don't care about pictures looking pretty. We just want the data. So we're moving just raw photon counts. 1:21:52 The image that the computer sees is actually much more than what you'd see 1:21:57 if you represent it on a camera, it's got much more data. And even in very low light conditions, you can see that there's a small photon count difference 1:22:05 between this spot here and that spot there, which means that, so it can see in the dark incredibly well, 1:22:12 because it can detect these tiny differences in photon counts. Like much better than you could possibly imagine. 1:22:22 We also save 13 milliseconds on latency. 1:22:29 - [Lex] From removing the post-processing on the image? - Yes. - Yeah. 1:22:34 - 'Cause we've got eight cameras and then there's roughly, I don't know, one and a half milliseconds or so, 1:22:42 maybe 1.6 milliseconds of latency for each camera. 1:22:53 Basically bypassing the image processor gets us back 13 milliseconds of latency, which is important. 1:23:01 And we track latency all the way from photon hits the camera, to all the steps that it's gotta go through to get, 1:23:09 go through the various neural nets and the C code, 1:23:14 and there's a little bit of C++ there as well. Well, I can, maybe a lot, but it, 1:23:20 the core stuff is, the heavy-duty compute is all in C. 1:23:27 And so we track that latency all the way to an outward command to the drive unit to accelerate the brakes, 1:23:34 to slow down the steering, turn left or right. 1:23:39 'Cause you gotta output a command, that's gotta go to a controller, and like some of these controllers have an update frequency that's maybe 10 Hertz 1:23:45 or something like that, which is slow. That's like now you lose a hundred milliseconds potentially. 1:23:51 So then we wanna update the drivers on the steering and braking control 1:23:58 to have more like 100 Hertz instead of 10 Hertz, then you've got a 10 millisecond latency 1:24:04 instead of 100 milliseconds worst-case latency. And actually, jitter is more of a challenge than latency, 1:24:09 'cause latency is, you can anticipate and predict, but if you've got a stackup of things going from the camera 1:24:15 to the computer, through then a series of other computers, and finally to an actuator on the car; 1:24:22 if you have a stackup of tolerances, of timing tolerances, then you can have quite a variable latency, 1:24:29 which is called jitter. And that makes it hard to anticipate exactly how you should turn the car 1:24:36 or accelerate because, if you've got maybe 150, 200 milliseconds of jitter, 1:24:42 then you could be off by 2.2 seconds. And this could make a big difference. 1:24:47 - So you have to interpolate somehow to deal with the effects of jitter, 1:24:52 so they can make robust control decisions. 1:24:59 So the jitters and the sensor information, or the jitter can occur at any stage in the pipeline. 1:25:05 - If you have just, if you have fixed latency, you can anticipate and like say, "Okay, we know what that our information is," 1:25:15 for argument's sake, "150 milliseconds stale." 1:25:21 For argument's sake, 150 milliseconds from photons taking camera to 1:25:26 where you can measure a change in the acceleration of the vehicle. 1:25:35 Then you can just say, "Okay, well we're gonna, we know it's 150 milliseconds, so we're gonna take that into account 1:25:42 and compensate for that latency." However, if you've got then 150 milliseconds of latency, plus 100 milliseconds of jitter, 1:25:49 which could be anywhere from zero to 100 milliseconds on top. So then your latency could be from 150, 250 milliseconds, 1:25:55 now you've got 100 milliseconds that you don't know what to do with. That's basically random. 1:26:01 So, getting rid of jitter is extremely important. - And that affects your control decisions and all of those kinds of things. 1:26:07 Okay. - Yeah, the cars just gonna fundamentally maneuver better with lower jitter. 1:26:12 - [Lex] Got it. - The cars will maneuver with super human ability and reaction time, much faster than a human. 1:26:20 I mean, I think over time, the autopilot, full self-driving will be capable 1:26:25 of maneuvers that 1:26:32 are far more than what like James Bond could do in like the best movie, type of thing. - That's exactly what I was imagining in my mind, 1:26:38 as you said it. - It's like impossible maneuvers that a human couldn't do. 1:26:45 - Well, let me ask sort of a, looking back the six years, looking out into the future, 1:26:50 based on your current understanding, how hard do you think this full self-driving problem, 1:26:55 when do you think Tesla will solve level four FSD? 1:27:01 - I mean, it's looking quite likely that it'll be next year. - And what does the solution look like? 1:27:07 Is it the current pool of FSD beta candidates? They start getting greater and greater 1:27:12 as they have been, degrees of autonomy. And then there's a certain level beyond 1:27:18 which they can do their own, they can read a book. - Yeah. 1:27:25 I mean, you can see, anybody who's been following the full self-driving beta closely will see that the 1:27:34 rate of disengagements has been dropping rapidly. So, like there's engagement B where the driver intervenes 1:27:40 to prevent the car from doing something - [Lex] Right. dangerous potentially. 1:27:49 So the interventions per million miles has been dropping dramatically. 1:27:58 And that trend looks like it happens next year is that the probability of an accident 1:28:05 on FSD is less than that of the average human, 1:28:11 and then significantly less than that of the average human. So, it certainly appears like we will get there next year. 1:28:23 Then there's gonna be a case of, okay, well, we not have to prove this to regulators and prove it to, and we want a standard that is not just equivalent 1:28:31 to a human, but much better than the average human. I think it's gotta be at least two or three times higher safety than a human. 1:28:39 Two or three times lower probability of injury than a human before we would actually say like, 1:28:44 "Okay, it's okay to go." It's not gonna be equivalent, it's gonna be much better. - So if you look, FSD 10.6 just came out recently, 1:28:53 10.7's on the way, maybe 11 is on the way somewhere in the future. 1:28:58 - Yeah. We were hoping to get 11 out this year, but it's, 1:29:03 11 actually has a whole bunch of fundamental rewrites on the neural net architecture 1:29:10 and some fundamental improvements in creating vector space. 1:29:19 - So there is some fundamental leap that really deserves the 11. I mean, that's a pretty cool number. 1:29:25 - Yeah. 11 would be a single stack for all, 1:29:31 one stack to rule them all. - A single stack. 1:29:36 But there are just some really fundamental neural net architecture changes 1:29:44 that will allow for much more capability. 1:29:49 At first they're gonna have issues. Like we have this working on like sort of alpha software and it's good, but it's, 1:29:58 it's basically taking a whole bunch of C, C++ code and leading a massive amount of C++ code 1:30:05 and replacing it with the neural net. And Andrej makes this point a lot, which is like neural nets are kind of eating software. 1:30:12 Over time there's less and less conventional software, more and more neural net. Which is still software, but it's, 1:30:19 still comes out to lines of software. But, just more neural net stuff, 1:30:25 and less, heuristics basically. 1:30:34 More matrix based stuff, and less heuristics based stuff. 1:30:48 One of the big changes will be, right now the neural nets will 1:30:57 deliver a giant bag of points to the C++, or C and C++ code. 1:31:04 - [Lex] Yeah. - We call it the giant bag of points. - [Lex] Yeah. And it's like, so you got a pixel 1:31:10 and something associated with that pixel, like this pixel is probably car, this pixel is probably landline. 1:31:18 Then you've got to assemble this giant bag of points in the C code and turn it into vectors. 1:31:27 And it does a pretty good job of it, but it's, 1:31:32 we wanna just, we need another layer of neural nets on top of that to take the giant bag of points 1:31:38 and distill that down to a vector space in the neural net part of the software, 1:31:45 as opposed to the heuristics part of the software. This is a big improvement. 1:31:51 - [Lex] Neural net's all the way down, so you want. - It's not even all neural nets, but it's, 1:31:58 this is a game changer to not have the bag of points, the giant bag of points, that has to be assembled with many lines of C, C++, 1:32:09 and have a neural net just assemble those into a vector. So the neural net is outputting 1:32:17 much, much less data, it's outputting, this is a lane line, this is a curb, this is drivable space, this is a car, 1:32:24 this is a pedestrian or cyclist or something like that. It's outputting, 1:32:32 it's really outputting proper vectors to the C, C++ control code, 1:32:39 as opposed to, sort of, constructing the vectors 1:32:47 in C. Which we've done, I think, quite a good job of, 1:32:53 but it grew kinda hitting a local maximum on the, how well the C can do this. 1:32:59 So this is really a big deal. And just all of the networks in the car need to move 1:33:05 to Surround Video, there's still some Legacy Networks that are not Surround Video. 1:33:12 And all of the training needs to move to Surround Video, and the efficiency of the training, it needs to get better, 1:33:17 and it is. And then we need to move everything to raw 1:33:23 photon counts, as opposed to processed images. 1:33:28 - [Lex] Okay. So if you- - Which is quite a big reset on the training, 'cause the system's trained on post-process imaged images. 1:33:35 So we need to redo all the training to train against the raw photon counts, 1:33:42 instead of the post-processed image. - So ultimately, it's kind of reducing the complexity of the whole thing. 1:33:47 So, reducing. - Yep. Lines of code will actually go lower. - Yeah, that's fascinating. 1:33:54 So you do infusion of all the sensors, so reducing the complexity of having to deal with these- - [Elon] Infusion of the cameras. 1:33:59 - Sorry. - It's all cameras really. - Right, yes. Same with humans. 1:34:05 - Yeah. - Well, I guess we got ears too, okay. - Yeah, we'll actually need to incorporate sound as well. 1:34:11 'Cause you know, you need to listen for ambulance sirens or firetrucks. 1:34:17 If somebody, yelling at you or something, I don't know. It just, there's a little bit of audio 1:34:23 that needs to be incorporated as well. - Do you need to go to bathroom break? - [Elon] Yeah, sure, let's take a break. - Okay. 1:34:29 - [Elon] Honestly, frankly, the ideas are the easy thing, and the implementation is the hard thing. 1:34:35 The idea of going to the moon is the easy part, but going to the moon is the hard part. - [Lex] Is the hard part. 1:34:40 - And there's a lot of like hardcore engineering that's gotta get done at the hardware and software level. 1:34:46 Like I said, optimizing the C compiler and just, 1:34:52 cutting out latency everywhere. If we don't do this, the system will not work properly. 1:35:00 So, the work of the engineers doing this, they are like the unsung heroes. 1:35:05 But they are critical to the success of the situation. - I think you made it clear. I mean, at least to me, it's super exciting, 1:35:11 everything that's going on outside of what Andrej is doing. Just the whole infrastructure of the software. 1:35:17 I mean, everything is going on with data engine, whatever it's called, the whole process is just a work of art. 1:35:24 - The sheer scale of it is, it boggles the mind. The training, the amount of work done with, we've written all this custom software 1:35:30 for training and labeling, and to do order labeling. Order labeling is essential. 1:35:38 'Cause, especially when you've got like Surround Video, it's very difficult to label Surround Video from scratch 1:35:45 is extremely difficult. Take humans such a long time 1:35:50 to even label one video clip, like several hours. Or the order labeler, 1:35:55 it basically will just apply heavy duty, a lot of compute to the video clips, 1:36:03 to pre-assign and guess what all the things are that are going on in the Surround Video. 1:36:09 - [Lex] And there's like correcting it. - Yeah, and then all the human has to do is like tweak, like say, adjust what is incorrect. 1:36:16 This is like, increases productivity by 100 or more. 1:36:21 - Yeah. So you've presented Tesla Bot as primarily useful in the factory. First of all, I think humanoid robots are incredible 1:36:28 from a fan of robotics. I think the elegance of movement that humanoid robots, 1:36:35 that bipedal robots show are just so cool. It's really interesting that you're working on this 1:36:40 and also talking about applying the same kind of, all the ideas, of some of which you've talked about, with data engine, all the things that we're talking about, 1:36:48 with Tesla autopilot, just transferring that over to the, just yet another robotics problem. 1:36:54 I have to ask since I care about human robot interactions, so the human side of that. So you've talked about mostly in the factory. 1:37:03 Do you see as part of this problem that Tesla Bot has to solve is interacting with humans and potentially having a place like in the home. 1:37:10 So, interacting, not just, - Sure. - not replacing labor, but also like, I don't know, being a friend or an assistant. 1:37:19 - [Elon] I think the possibilities are endless. 1:37:27 Yeah, I mean, it's obviously, it's not quite in Tesla's primary mission direction 1:37:32 of accelerating sustainable energy, but it is an extremely useful thing 1:37:37 that we can do for the world, which is to make a useful humanoid robot that is capable of interacting with the world and 1:37:47 helping in many different ways. So in factories, and really just, I mean, 1:37:53 I think, if you say, extrapolate to many years in the future, 1:38:00 I think work will become optional. 1:38:06 There's a lot of jobs that, if people weren't paid to do it, they wouldn't do it. 1:38:12 Like it's not, it's not fun, necessarily. If you're washing dishes all day, it's like, eh. 1:38:18 Even if you really like washing dishes, do you really wanna do it for eight hours a day every day? Probably not. 1:38:25 And then there's like dangerous work, and basically if it's dangerous, boring, has like potential for repetitive stress injury, 1:38:33 that kind of thing, then that's really where humanoid robots would add the most value initially. 1:38:41 So that's what we're aiming for is to, 1:38:46 for the humanoid robots to do jobs that people don't voluntarily want to do. 1:38:51 And then we'll have to pair that, obviously, with some kind of universal, basic income in the future. 1:38:57 So, I think. - Do you see a world when there's like hundreds of millions 1:39:03 of Tesla Bots doing different, performing different tasks 1:39:08 throughout the world? - Yeah, I haven't really thought about it that far into the future, 1:39:14 but I guess that there may be something like that. 1:39:20 - Can I ask a wild question? So, the number of Tesla cars has been accelerated 1:39:25 and has been close to 2 million produced. Many of them have autopilot. - [Elon] I think we're over 2 million now. 1:39:31 - Yeah. Do you think there'll ever be a time when there'll be more Tesla Bots than Tesla cars? 1:39:40 - Yeah. Actually, it's funny you ask this question 'cause normally I do try to think pretty far 1:39:46 into the future, but I haven't really thought that far into the future with the Tesla Bot, or it's codenamed Optimus, 1:39:54 I call it Optimus Subprime, because it's not like a giant transformer robot. 1:40:07 But it's meant to be a general purpose help robot. 1:40:14 And basically, the things that were, basically, Tesla, 1:40:20 I think, has the most advanced real-world AI 1:40:25 for interacting with the real world, which we've developed as a function to make self-driving work. 1:40:30 And so, along with custom hardware and, like a lotta 1:40:36 hardcore low-level software to have it run efficiently and be power efficient 'cause, 1:40:42 it's one thing to do neural nets if you've got a gigantic server room with 10,000 computers, but now, let's say you just, 1:40:47 you have to now distill that down into one computer that's running at low power in a humanoid robot or a car. 1:40:53 That's actually very difficult and a lotta hardcore soft work is required for that. 1:40:59 So since we're kind of like solving the navigate the real world 1:41:05 with neural nets problem for cars, which are kinda like robots with four wheels, then it's like kind of a natural extension of that 1:41:12 is to put it in a robot with arms and legs. 1:41:18 And actuators. 1:41:26 The two hard things are, you basically need to make the, 1:41:31 have the robot be intelligent enough to interact in a sensible way with the environment. 1:41:37 So you need real real world AI, and you need to be very good at manufacturing, 1:41:43 which is a very hard problem. Tesla's very good at manufacturing, and also has the real world AI, 1:41:49 so making the humanoid robot work is, basically it means developing custom 1:41:57 motors and sensors that are different from what a car would use. 1:42:08 I think we have the best expertise in developing advanced electric motors 1:42:13 and power electronics. So, it just has to be for humanoid robot application, 1:42:19 not a car. - Still, you do talk about love sometimes. 1:42:25 So let me ask, this isn't like for like sex robots or something- - [Elon] Love is the answer. - Yes. 1:42:33 There is something compelling to us, not compelling, but we connect with humanoid robots, or even legged robot, 1:42:40 like with a dog, in shapes of dogs. It just, it seems like there's a huge amount of loneliness 1:42:47 in this world. All of us seek companionship with other humans, friendship and all those kinds of things. 1:42:52 We have a lot of here in Austin, a lot of people have dogs. - [Elon] That's right. - There seems to be a huge opportunity 1:42:58 to also have robots that decrease 1:43:04 the amount of loneliness in the world, or help us humans connects with each other. 1:43:09 So, in a way that dogs can. Do you think about that with Tesla Bot at all, or is it really focused on the problem 1:43:17 of performing specific tasks? Not connecting with humans? 1:43:23 - I mean, to be honest, I have not actually thought about it from the companionship standpoint, but I think it actually would end up being, 1:43:30 it could be actually a very good companion. And it could 1:43:37 develop a personality over time that is unique. 1:43:45 It's not just all the robots are the same. And that personality could evolve to be, 1:43:54 match the owner or the, I guess the owner. 1:44:00 Whatever you wanna call it. The companion, the human. - The other half, right? 1:44:05 In the same way that friends do. See, I think that's a huge opportunity. I think- - Yeah, no, that's interesting. 1:44:14 'Cause there's a Japanese phrase; wabi-sabi, 1:44:20 the subtle imperfections are what makes something special. And the subtle imperfections 1:44:25 of the personality of the robot, mapped to the subtle imperfections of the robot's human 1:44:34 friend, dunno, owner sounds like maybe the wrong word, but, 1:44:39 could actually make an incredible buddy basically. - [Lex] And in that way, the imperfections- - Like R2-D2 or a C-3PO sort of thing. 1:44:46 - So from a machine learning perspective, I think the flaws being a feature is really nice. 1:44:54 You could be quite terrible at being a robot for quite a while in the general home environment or all in the general world. 1:45:00 And that's kind of adorable and that's, those are your flaws, and you fall in love with those flaws. 1:45:07 It's very different than autonomous driving where it's a very high stakes environment, you cannot mess up. 1:45:13 And so it's, yeah, it's more fun to be a robot in the home. - Yeah, in fact, if you think of like a C-3PO and R2-D2, 1:45:22 they actually had a lot of like flaws and imperfections and silly things and they would argue with each other. 1:45:29 - Were they actually good at doing anything? I'm not exactly sure. - They definitely added a lot to the story. 1:45:38 But there sort of quirky elements and, that they would make mistakes and do things, 1:45:45 it would just, it made them relatable, I don't know. 1:45:50 Endearing. So yeah, I think that that could be something that, 1:45:56 it probably would happen. But our initial focus is just to make it useful. 1:46:06 I'm confident we'll get it done, I'm not sure what the exact timeframe is, but we'll probably have, I don't know, 1:46:11 a decent prototype towards the end of next year or something like that. - And it's cool that it's connected to Tesla, the car. 1:46:20 - Yeah, it's using a lotta, it would use the autopilot inference computer and a lot of the training that we've done for the four cars, 1:46:29 in terms of recognizing real world things, could be applied directly to the robot. 1:46:38 But there's a lot of custom actuators and sensors that need to be developed. - And an extra module on top of the vector space for love. 1:46:47 - Ah, yeah. - That's missing. Okay. - We could add that to the car too. 1:46:53 - That's true. Yeah, it could be useful in all environments. Like you said, a lot of people argue in the car, 1:46:59 so maybe we can help 'em out. You're a student of history, fan of "Dan Carlin's Hardcore History" podcast. 1:47:06 - [Elon] Yeah. That's great. - Greatest podcast ever. - Yeah, I think it is, actually. 1:47:12 - It almost doesn't really count as a podcast. - [Elon] It's more like a audio book. - Yeah. 1:47:17 So you were on the podcast with Dan, I just had a chat with him about it. He said you guys went military and all that kind of stuff. 1:47:23 - Yeah, it was basically, 1:47:29 it should be titled engineer wars. Essentially, when there's a rapid change 1:47:35 in the rate of technology, then engineering plays a pivotal role in victory in battle. 1:47:43 - How far back in history did you go? Did you go to World War II? - Well, it was supposed to be a deep dive 1:47:50 on fighters and bomber technology in World War II, but that ended up being more wide-ranging than that. 1:47:58 'Cause I just went down the, a total rat hole of like studying all of the fighters and bombers in World War II, 1:48:04 and the constant rock, paper, scissors game that one country would make this plane, 1:48:10 and they'd make a plane to beat that, and they'd try to make a plane to beat that, and then they'll... And really what matters is like the pace of innovation, 1:48:18 and also access to high quality fuel and raw materials. 1:48:24 So, like Germany had like some amazing designs, but they couldn't make them because they couldn't get the raw materials. 1:48:31 And they had a real problem with the oil and fuel, basically, the fuel quality was extremely variable. 1:48:40 - So the design wasn't the bottleneck, it was- - Yeah, the U.S. had kick-ass fuel, that was very consistent, the problem is, 1:48:47 if you make a very high performance aircraft engine, in order to make it high performance, you have to 1:48:56 the fuel, the aviation gas, has to be a consistent mixture. And it has to have a high octane. 1:49:07 High octane is the most important thing, but also can't have like impurities and stuff 'cause you'll foul up the engine. 1:49:14 And the German just never had good access to oil. They try to get it by invading the caucuses, but that didn't work too well. 1:49:21 - That never works well. - Didn't work out for them. (woman speaking faintly) Nice to meet you. 1:49:28 Germany was always struggling with basically shitty oil, and so then they could not, they couldn't count 1:49:35 on high quality fuel for their aircraft. So then they had to have all these additives and stuff. 1:49:43 Whereas the U.S. had awesome fuel, and they provided that to Britain as well. So, that allowed the British and the Americans 1:49:51 to design aircraft engines that were super high-performance, better than anything else in the world. 1:49:58 Germany could design the engines, they just didn't have the fuel. And then also the likes of the, 1:50:04 the quality of the aluminum alloys that they were getting was also not that great, and so, yeah. 1:50:10 - [Lex] You talked about all this with Dan? - Yep. - Awesome. Broadly looking at history, when you look at Genghis Khan, 1:50:16 when you look at Stalin, Hitler, the darkest moments of human history, 1:50:22 what do you take away from those moments? Does it help you gain insight about human nature, about human behavior today? 1:50:28 Whether it's the wars or the individuals, or just the behavior of people, any aspects of history. 1:50:41 - Yeah. I find history fascinating. 1:50:49 There's just a lot of incredible things that have been done, good and bad, that they 1:50:57 just help you understand the nature of civilization, 1:51:03 and individuals, and... - Does it make you sad that humans do these kinds of things to each other? 1:51:09 You look at the 20th century, World War II, the cruelty of the abuse of power. 1:51:15 Talk about communism, Marxism, and Stalin. - I mean, some of these things do, I mean, if you, 1:51:23 there's a lot of human history, but most of it is actually people just getting on with their lives, 1:51:28 and it's not like human history is just non-stop war and disaster, 1:51:36 those are actually just, those are intermittent and rare, and if they weren't then humans would soon cease to exist. 1:51:47 But there's just that, wars tend to be written about a lot. Whereas 1:51:53 something being like, well, a normal year where nothing major happened doesn't get written about much, but that's, 1:51:59 most people just like farming and kinda living their life. Being a villager somewhere. 1:52:09 And every now and again, there's a war. 1:52:17 I would have to say, there aren't very many books that I, where I just had to stop reading, 1:52:23 'cause it was just too dark. But the book about "Stalin The Court Of The Red Star," 1:52:30 I had stopped reading, it was just too dark. 1:52:35 Rough. - Yeah. The 30s. 1:52:41 There's a lot of lessons there to me, in particular that it feels like humans, 1:52:48 all of us have that zeal, Solzhenitsyn line, that the line between good and evil runs 1:52:55 to the heart in every man that all of us are capable of evil, all of us are capable of good, it's almost like this kind of responsibility 1:53:01 that all of us have to tend towards the good. 1:53:07 And so, to me, looking at history is almost like an example of, look, you have some charismatic leader that convinces you 1:53:15 of things, is too easy, based on that story to do evil, onto each other, onto your family onto others. 1:53:23 And so it's like our responsibility to do good. It's not like now somehow different from history, 1:53:29 that can happen again, all of it can happen again. And yes, most of the time you're right. 1:53:35 I mean, the optimistic view here is mostly people are just living life. And as you've often memed about, 1:53:42 the quality of life was way worse back in the day, and it keeps improving over time, through innovation, through technology, 1:53:48 but still it's somehow notable that these blimps of atrocities happen. 1:53:54 - Sure. Yeah, I mean, life was really tough 1:54:00 for most of history. I mean, probably for most of human history, 1:54:05 a good year would be one where not that many people in your village died of the plague, starvation, 1:54:12 freezing to death, or being killed by a neighboring village. It's like, "Well, it wasn't that bad." 1:54:18 It was only like, "You know, we lost 5% this year. It was a good year." - Yeah. 1:54:23 - That would be par for the course. Just not starving to death would have been the primary goal 1:54:28 of most people throughout history. Just making sure we'll have enough food to last through the winter and not get, freeze or whatever. 1:54:40 Now food is plentiful. We have an obesity problem. 1:54:46 - Well, yeah, the lesson there is to be grateful for the way things are now for some of us. 1:54:53 We've spoken about this offline. I'd love to get your thought about it here. 1:54:59 If I sat down for a long form in person conversation with the President of Russia, Vladimir Putin, 1:55:07 would you potentially want to call in for a few minutes to join in on a conversation with him, 1:55:12 moderated and translated by me? - Sure. Yeah. Sure, I'd be happy to do that. 1:55:19 - You've shown interest in the Russian language. Is this grounded in your interest in history of linguistics culture, general curiosity? 1:55:27 - [Elon] I think it sounds cool. - Sounds cool, not looks cool. 1:55:36 It takes a moment to read Cyrillic. Once you know what the Cyrillic characters stand for, 1:55:43 actually, then reading Russian becomes a lot easier 'cause there are a lot of words that are actually the same. 1:55:49 Like bank is bank. 1:55:55 - So find the words that are exactly the same and now you start to understand Cyrillic, yeah. - If you can sound it out, then it's much, 1:56:04 there's at least some commonality of words. - What about the culture? 1:56:10 You love great engineering, physics. There's a tradition of the sciences there. When you look at the 20th century, from rocketry. 1:56:16 So, some of the greatest rockets, some of the space exploration has been done in the Soviet, in the former Soviet Union. 1:56:23 - Yeah. - So, do you draw inspiration from that history? Just how this culture, that in many ways, I mean, 1:56:30 one of the sad things is, because of the language, a lot of it is lost to history, because it's not translated, all those kinds of, 1:56:37 because it is in some ways an isolated culture, it flourishes within it's borders. 1:56:44 - [Elon] Yeah. - So do you draw inspiration from those folks, from the history of 1:56:49 science engineering there? - Yeah. I mean, the Soviet Union, Russia, and Ukraine as well, 1:56:58 have a really strong history in space flight, like some of the most advanced and impressive things 1:57:04 in history were done by the Soviet Union. 1:57:14 One cannot help but admire the impressive rocket technology that was developed. 1:57:22 After the sort of fall of the Soviet Union, 1:57:27 there's much less that happened, 1:57:33 still things are happening, but it's not quite at the frenetic pace that it was happening 1:57:41 before the Soviet Union kind of dissolved into separate republics. 1:57:47 - Yeah. I mean, there's the Roscosmos, the Russian, the agency. 1:57:53 I look forward to a time when those countries, with China, are working together, the United States, 1:57:58 they're all working together, maybe a little bit of friendly competition, but. - I feel like friendly competition is good. 1:58:05 Governments are slow and the only thing slower than one government is a collection of governments. (Lex laughing) 1:58:12 - Yeah. - The Olympics would be boring if everyone just crossed the finishing line at the same time. - Yeah. - Nobody would watch. 1:58:17 - [Lex] Yeah. - And people wouldn't try hard to run fast and stuff. So, I think friendly competition is a good thing. 1:58:26 - This is also a good place to give a shout out to a video titled "The Entire Soviet Rocket Engine Family Tree" by Tim Dodd, AKA Everyday Astronaut. 1:58:34 It's like an hour and a half. It gives a full history of Soviet rockets. And people should definitely go check out and support Tim 1:58:41 in general, that guy's super excited about the future, super excited about space flight, every time I see anything by him I just have a stupid smile 1:58:48 on my face, 'cause he's so excited about stuff. - Yeah, Tim Dodd is - I love people like that. - really great if you're interested 1:58:54 in anything to do with space. He's, in terms of explaining rocket technology 1:59:00 to your average person, he's awesome. The Best, I'd say. 1:59:06 I should say, the whole reason I switched us from, 1:59:12 Raptor at one point was gonna be a hydrogen engine, but hydrogen has a lot of challenges. 1:59:17 It's very low density. It's a deep cryogen, so it's only liquid very close to absolute zero. 1:59:23 Requires a lot of insulation. So it was a lot of challenges there. 1:59:32 And I was actually reading a bit about Russian rocket engine development. 1:59:37 At least the impression I had was that Soviet Union, Russia, and Ukraine primarily were 1:59:45 actually in the process of switching to Methalux. 1:59:50 And there were some interesting test and data for ISP, they were able to get up to like a 382nd ISP 1:59:59 with the Methalux engine. And I was like, "Whoa, okay, that's, that's actually really impressive." 2:00:09 So I think we could, you could actually get a much lower cost, 2:00:14 an optimizing cost per ton to orbit, cost per to Mars. 2:00:20 I think methane option is the way to go. 2:00:25 And I was partly inspired by the Russian work on the test ends, with Methalux engines. 2:00:32 - And now for something completely different. Do you mind doing a bit of a meme review 2:00:38 in the spirit of the great, the powerful Pewdiepie? Let's say one to 11, - Okay. - just go over a few documents printed out. 2:00:45 - [Elon] We can try. - [Lex] Let's try this. I present to you document numero uno. 2:00:56 (Elon laughing) - Okay. - [Lex] Vlad The Impaler discovers marshmallows. 2:01:04 - Yeah, that's not bad. - You get it, because 2:01:10 he likes impaling things. - Yes, I get it. Yes, I get it, I don't know, three, whatever. - [Lex] Oh, that's not very good. 2:01:19 This is ground in some engineering, some history. 2:01:27 (Elon laughing) - Yeah, I give this an 8 out of 10. - [Lex] What do you think about nuclear power? 2:01:33 - I'm in favor of nuclear power. In a place that is not subject to extreme natural disasters. 2:01:42 I think it's a, new nuclear power is a great way to generate electricity. 2:01:48 I don't think we should be shutting down nuclear power stations. - [Lex] Yeah, but what about Chernobyl? 2:01:54 - Exactly. I think people, there's like a lot of fear 2:02:00 of radiation and stuff. I guess, the problem is a lot of people just don't, 2:02:09 they didn't study engineering or physics, so they don't, just the word radiation just sounds scary, you know? 2:02:15 So they don't, they can't calibrate what radiation means. 2:02:21 But radiation is much less dangerous than you'd think. 2:02:30 For example, Fukushima, when the Fukushima problem happened, 2:02:38 due that tsunami. I got people in California asking me if they should worry 2:02:44 about radiation from Fukushima. And I'm like, definitely not, not even slightly, not at all. 2:02:51 That is crazy. And just to 2:02:57 show this is how, the dangers is so much overplayed 2:03:04 compared to what it really is that I actually flew to Fukushima. And, actually, I donated a solar power system 2:03:13 for a water treatment plant. And I made a point of eating locally grown vegetables 2:03:23 on T.V. in Fukushima. 2:03:29 I'm still alive. Okay. - So it's not even that the risk of these events is low, but the impact of them is- 2:03:36 - The impact is greatly exaggerated. - It' human nature. 2:03:41 - People don't know what radiation is, I've had people ask me, "What about radiation from cell phones causing brain cancer?" 2:03:46 I'm like, "When you say radiation, do you mean photons or particles?" They're like, dunno, "What do you mean photons particles?" 2:03:52 "Do you mean, let's say photons. What frequency or wavelength?" 2:03:59 And they're like, "No, I have no idea." "Do you know that everything's radiating all the time?" 2:04:04 They're like, "What do you mean?" "Like, everything's radiating all the time." Photons are being emitted 2:04:10 by all objects all the time, basically. And if you wanna know what it means to stand 2:04:18 in front of nuclear fire, go outside. The sun is a gigantic thermonuclear reactor 2:04:26 that you're staring right at it. Are you still alive? Yes. Okay. Amazing. 2:04:32 - Yeah, I guess radiation is one of the words that could be used as a tool 2:04:37 to fear monger by certain people. That's it. - I think people just don't understand. - I mean, that's the way to fight that fear, I suppose, 2:04:44 just to understand, just to learn. - Yeah, just say, okay, how many people have actually died from nuclear accidents? 2:04:50 It's like practically nothing, and, say how many people have died from coal plants? 2:04:57 And it's a very big number. Obviously we should not be starting up coal plants 2:05:04 and shutting down nuclear plants, just doesn't make any sense at all. Coal plants, I don't know, 2:05:09 a hundred to a thousand times worse for health than nuclear power plants. 2:05:15 - You wanna go to the next one? It's really bad. 2:05:20 That 90, 180 and 360 degrees, everybody loves the math. Nobody gives a shit about 270. 2:05:28 - It's not super funny. I don't know, like two or three. - [Lex] Yeah. This is not, LOL situation. 2:05:36 (both laughing) - [Lex] Yeah. (Elon laughing) 2:05:43 - That one's pretty good. - [Lex] The United States oscillating between establishing and destroying dictatorships. 2:05:48 It's like a metro, is that metro- - Yeah, metronome. Yeah, it's, I dunno, a 7 out of 10. It's kinda true. 2:05:56 - This is kinda personal for me. Next one. - Oh, man, is this Laika. - [Lex] Yeah, well, no, this is- 2:06:02 - Or it's referring to Laika or something. - [Lex] It's Laika's husband. - Husband, yeah. 2:06:08 - [Lex] Hello? Yes, this is dog. Your wife was launched into space. And then the last one is him with his eyes closed 2:06:14 and a bottle of vodka. - Yeah, Laika didn't come back. - [Lex] No. They don't tell you the full story of, 2:06:22 the impact it had on the loved ones. - True. - That one gets an 11 from me. 2:06:29 It just keeps goin', on the Russian theme. First man in space, nobody cares. 2:06:35 First man on the moon. - Well, I think people do care. - [Lex] I know, but. 2:06:41 - Yuri Gagarin's name will be forever in history. I think. - There is something special about placing, 2:06:48 stepping foot onto another totally foreign land. It's not the journey, like people that explore the oceans. 2:06:56 It's not as important to explore the oceans as to land in a whole new continent. 2:07:02 - [Elon] Yeah. - [Lex] Oh this is about you. (Elon laughing) Oh yeah. I'd love to get your comment on this. 2:07:08 Elon Musk after sending $6.6 billion to the UN to end world hunger. 2:07:13 "You have three hours." - Yeah, well, I mean obviously $6 billion 2:07:19 is not gonna end world hunger. 2:07:25 I mean, the reality is at this point the world is producing far more food than it can really consume. 2:07:32 We don't have a caloric constraint to this point. So where there is hunger, it is almost always due to 2:07:41 civil war, or strife, or some like, it's not a thing that is extremely rare for it to be 2:07:50 just a matter of, lack of money. 2:07:56 There's a civil war in some country, and one part of the country's literally trying to starve the other part of the country. 2:08:02 - So it's much more complex than something that money could solve. It's geopolitics, 2:08:08 it's a lot of things, it's human nature, it's governments, it's monies, monetary systems, 2:08:13 all that kinda stuff. - Yeah. Food is extremely cheap these days. 2:08:21 I mean, the U.S. at this point, among low income families, obesity is actually now the problem. 2:08:27 It's not, obviously it's not hunger, it's too much, too many calories. 2:08:36 It's not that nobody's hungry anywhere, it's just, this is 2:08:41 not a simple matter of adding money and solving it. - [Lex] What do you think that one gets? 2:08:48 Is getting? - Two. 2:08:53 - [Lex] Just going after empires. World, "Where did you get those artifacts?" The British Museum. 2:08:59 It's a shout out to "Monty Python." "We found them." - Yeah. The British Museum is, it's pretty great. 2:09:05 I mean, admittedly Britain did take these historical artifacts from around the world and put them in London, 2:09:11 but it's not like people can't go see them. So, it is a convenient place to see these ancient artifacts 2:09:19 is London, for a large segment of the world. 2:09:25 So I think, unbalanced, the British Museum is net good. Well, I'm sure that a lot of countries are arguing about that. 2:09:31 - [Lex] Yeah. - It's like, you wanna make these historical artifacts accessible to as many people as possible. 2:09:37 And the British Museum, I think does a good job of that. - Even if there's a darker aspect to like the history 2:09:43 of empire in general, whatever the empire is, however things were done. 2:09:50 It is the history that happened. You can't sort of erase that history, unfortunately. You can just become better in the future. 2:09:56 Is the point. - Yeah, I mean, well how are we gonna pass moral judgment 2:10:03 on these things? 2:10:08 If one is gonna judge, say the Russia Empire, you gotta judge what everyone was doing at the time, and how were the British relative to everyone? 2:10:18 And I think that the British would actually get a relatively good grade, relatively good grade, not in absolute terms, 2:10:24 but compared to what everyone else was doin', 2:10:30 they were not the worst. Like I said, you gotta look at these things in the context of the history at the time 2:10:35 and say, "What were the alternatives, and what are you comparing it against?" - Yes. - And I do not think it would be the case 2:10:41 that Britain would get a bad grade, when looking at history at the time. 2:10:49 Now if you judge history from what is morally acceptable today, 2:10:56 you're basically are gonna give everyone a failing grade. I'm not clear. I don't think anyone would get a passing grade 2:11:02 in their morality of, you could go back 300 years ago, who is getting a passing grade? 2:11:08 Basically no one. - [Lex] And we might not get a passing grade from generations - Yeah. Exactly. 2:11:14 - [Lex] that come after us. What does that one get? 2:11:20 - Sure. A six, a seven. - For the "Monty Python," maybe. - [Elon] I always 'Monty Python," they're great. 2:11:26 The "Life of Brian" and the "Quest for the Holy Grail" are incredible. - Yeah. Yeah. - Damn, those are serious eyebrows. 2:11:32 - [Lex] Brezhnev. How important, do you think, - Damn. - [Lex] is facial hair to great leadership? 2:11:38 You got a new haircut. How does that affect your leadership? 2:11:43 - [Elon] I don't know. Hopefully not. It doesn't. - [Shivon] Is that the second, no one? - Yeah, the second is no one. 2:11:51 - [Elon] There is no one competing with Brezhnev. - No one two. - Those are like epic eyebrows. 2:11:57 Sure. - [Lex] That's ridiculous. - Give it a six or seven, I dunno. - [Lex] I like this, Shakespeare analysis of memes. 2:12:05 - Brezhnev, he had a flare for drama as well. German joke. 2:12:11 - [Lex] Yeah, yeah. It must come from the eyebrows. Alright. Invention, great engineering. 2:12:18 Look what I invented. That's the best thing since rip up bread. - Yeah. - 'Cause they invented 2:12:25 sliced bread. Am I just explaining memes at this point? (all laughing) This is what my life has become. 2:12:33 - [Shivon] He's a memelord, you're a meme explainer. - [Lex] I'm a meme, like a scribe, that runs around with the kings 2:12:41 and just writes down memes. - I mean, when was the cheeseburger invented? That's an epic invention. 2:12:47 - [Lex] Yeah. - Like, wow. - [Lex] Versus just like a burger? 2:12:53 - Or a burger, I guess a burger in general. - Then there's, what is the burger? 2:12:58 What's a sandwich? And then you start getting is a pizza a sandwich? And what is the original? 2:13:04 It gets into an ontology argument. - Yeah, but everybody knows if you order a burger, or cheeseburger, or whatever, and you get tomato and some lettuce and onions and whatever, 2:13:12 and mayo and ketchup and mustard, it's like epic. - Yeah, but I'm sure they've had bread and meat separately 2:13:19 for a long time. And it was kind of a burger on the same plate, but somebody who actually combined them into the same thing 2:13:25 and then bite it and hold it, makes it convenient. It's a materials problem. 2:13:30 Like your hands don't get dirty and whatever. Yeah, it's brill- (Shivon talking faintly) 2:13:38 That is not what I would've guessed. - But everyone knows, if you order a cheeseburger, you know what you're getting, 2:13:44 it's not like some obtuse, well, I wonder what I'll get. 2:13:50 Fries are, I mean, great. I mean, they're the devil, but fries are awesome. 2:13:58 Yeah, pizza is incredible. - Food innovation doesn't get enough love. 2:14:03 - Yeah. - I guess is what we're getting at. - [Elon] It's great. - What about the Matthew McConaughey, Austinite here? 2:14:11 President Kennedy, "Do you know how to put men on the moon yet?" NASA, "No." President Kennedy, "Be a lot cooler if you did." 2:14:20 - Pretty much, sure. Six, six or seven, I suppose. 2:14:25 - [Lex] And this is the last one. - That's funny. - [Lex] Someone drew a bunch of dicks all over the walls. 2:14:33 Sistine Chapel, Boys bathroom. - Sure, I'll give it a nine. It's really true. 2:14:39 - This is our highest ranking meme for today. - [Elon] I mean, it's true, how did they get away with it? 2:14:44 - Lotsa nakedness. - I mean, dick pics are, I mean, just something throughout history. 2:14:50 As long as people can draw things, there's been a dick pic. - It's a staple of human history. - It's a staple. 2:14:56 Consistent throughout human history. - You tweeted that you aspire to comedy, you're friends with Joe Rogan. 2:15:02 Might you do a short standup comedy set at some point in the future? Maybe open for Joe? Something like that? 2:15:09 Is that- - Really? Stand up? Actual just full-on stand up? - [Lex] Full-on stand up. Is that in there or is that? 2:15:15 - I've never thought about that. - It's extremely difficult, 2:15:20 at least that's what like Joe says, and the comedians say. - [Elon] Huh? I wonder if I could. 2:15:27 - Only one way to find out. - I have done standup for friends, just impromptu, 2:15:36 I'll get on like a roof, and they do laugh, but they're all friends too. 2:15:41 So, I don't know if you got a room of strangers. Are they gonna actually also find it funny, 2:15:46 but I could try. See what happens. - I think you'd learn something either way. 2:15:54 - Yeah. - I kinda love both when and when you do great, just watching people, 2:16:00 how they deal with it. It's so difficult. You're so fragile up there. 2:16:06 It's just you. And you think you're gonna be funny and when it completely falls flat, it's just, it's beautiful to see people deal with that. 2:16:15 - I think I might have enough material to do stand up. I've never thought about it, but I might have enough material. 2:16:23 I don't know, like 15 minutes or something. - Oh yeah. Yeah. Do a Netflix special. (Elon laughing) 2:16:29 - [Elon] Netflix special, sure. - What's your favorite "Rick and Morty" concept? 2:16:35 Just to spring that on you, is there, there's a lot of sort of scientific engineering ideas explored there. There's the, - Favorite "Rick and Morty" 2:16:41 - There's the butter robot. - Yeah, it's a great show. - You like it? - Yeah, "Rick and Morty's" Awesome. 2:16:47 - Somebody that's exactly like you from an alternate dimension showed up there. Elon Tusk. - Yeah. That's right. - That you voiced. 2:16:53 - Yeah, "Rick and Morty" certainly explores a lot of interesting concepts. 2:16:59 Sure, like what's the favorite one. The butter robot certainly is, it's certainly possible to have too much sentience, 2:17:06 in a device. You don't want to have your toaster be a super genius toaster. 2:17:12 It's gonna hate life, 'cause all it can make is toast. It's like, you don't wanna have super-intelligence stuck 2:17:18 in a very limited device. - Do you think it's too easy, from a, if we're talking about from the engineering perspective, 2:17:25 super intelligence, like with Marvin, the robot. It seems like it might be very easy to engineer 2:17:32 just a depressed robot. - Sure. - It's not obvious to engineer a robot that's going 2:17:37 to find a fulfilling existence. Same as humans, I suppose. 2:17:44 I wonder if that's like the default, if you don't do a good job on building a robot, 2:17:49 it's going to be sad a lot. - Well, we can reprogram robots easier 2:17:55 than we can reprogram humans. I guess if you let it evolve without tinkering, 2:18:03 then it might get sad, but you can change the optimization function and have it be a cheery robot. 2:18:13 - Like I mentioned with SpaceX, you give a lot of people hope, and a lot of people look up to you. Millions of people look up to you. 2:18:20 If we think about young people in high school, maybe in college, 2:18:25 what advice would you give to them about if they wanna try to do something big in this world, 2:18:31 they wanna really have a big, positive impact, what advice would you give them about their career, maybe about life in general? 2:18:39 - Try to be useful. Do things that are useful to your fellow human beings, 2:18:45 to the world. It's very hard to be useful. 2:18:51 Very hard. Are you contributing more than you consume? 2:19:02 Try to have a positive net contribution to society. 2:19:07 I think that's the thing to aim for. Not to try to be sort of a leader for the sake 2:19:13 of being a leader or whatever. A lot of the time people who, 2:19:19 a lot of times the people you want as leaders, are the people who don't want to be leaders. 2:19:29 If you're living a useful life, that is a good life, a life worth having lived. 2:19:39 Like I said, I would encourage people to use the mental tools of physics 2:19:46 and apply them broadly in life. They are the best tools. - When you think about education and self-education, 2:19:52 what do you recommend? So there's the university, there's self study. 2:19:58 There is hands-on, sort of finding a company or a place or a set of people 2:20:04 that do the thing you're passionate about and joining them as early as possible. There's taking a road trip across Europe for a few years 2:20:12 and writing some poetry. Which trajectory do you suggest? 2:20:18 In terms of learning about how you can become useful, as you mentioned, 2:20:23 how you can have the most positive impact. 2:20:33 - I encourage people to read a lot of books, just read, basically try to ingest as much information as you can, 2:20:43 and try to also just develop a good general knowledge. So you at least have a rough lay of the land 2:20:51 of the knowledge landscape, try to learn a little about a lot of things. 2:20:58 'Cause you might not know what you're really interested. How would you know what you're really interested in if you at least aren't like doing it? 2:21:03 Peripheral exploration broadly of the knowledge landscape. 2:21:12 And talk to people from different walks of life and different industries, and professions, and skills, 2:21:18 and occupations, like just try. Learn as much as possible. 2:21:27 Be on the search for meaning. - Isn't the whole thing a search for meaning? 2:21:34 - Yeah, what's the meaning of life and all? But just generally, like I said, I would encourage people to read broadly 2:21:40 in many different subject areas, and then try to find something where there's an overlap 2:21:48 of your talents and what you're interested in. So people may be good at something, 2:21:53 or they may have skill at a particular thing, but they don't like doing it. 2:21:59 So you wanna try to find a thing that's a good combination 2:22:06 of the things that you're inherently good at, but you also like doing. 2:22:13 - And reading as a super fast shortcut to figure out which, where are you, you're both good at it, 2:22:19 you like doing it, and it'll actually have positive impact. - Well, you gotta learn about things somehow. 2:22:24 So reading, a broad range, just really read. 2:22:31 More important was as a kid I read through the encyclopedia. So, that was pretty helpful. 2:22:39 And, there was all sorts of things I didn't even know existed, well lots, obviously. - That's as broad as it gets. 2:22:45 - Encyclopedias were suggestible, I think, whatever 40 years ago. 2:22:55 Maybe read through like the condensed version of the Encyclopedia Britannica, I'd recommend that. 2:23:01 You can always like skip subjects, so you read a few paragraphs and you know you're not interested, just jump to the next one. 2:23:07 So, read the encyclopedia, or skim through it. 2:23:18 I put a lotta stock and certainly have a lot of respect for someone who puts in an honest day's work 2:23:23 to do useful things. And just generally to have a, not a zero sum mindset, 2:23:31 or have more of a grow the pie mindset. 2:23:40 When I see people like, perhaps, including some very smart people, kind of taking an attitude of, 2:23:48 I like doing things that seem like morally questionable. It's often because they have, at a base sort of axiomatic level, a zero sum mindset. 2:23:57 And they, without realizing it, they don't realize to have a zero sum mindset, 2:24:02 or at least they don't realize it consciously. And so, if you have a zero sum mindset, then the only way to get ahead 2:24:08 is by taking things from others. 2:24:13 If the pie is fixed, then the only way to have more pie is to take someone else's pie. 2:24:20 But this is false. Obviously the pie has grown dramatically over time, the economic pie. 2:24:25 In reality, you can have, (Elon laughing) overuse this analogy, we can have a lot of, 2:24:32 there's a lot of pie. (Lex laughing) My pie is not fixed. 2:24:39 So, you really wanna make sure you're not operating, without realizing it, from a zero sum mindset. 2:24:46 Where the only way to get ahead is to take things from others, then that's gonna result in you trying to take things from others, which is not good. 2:24:52 It's much better to work on adding to the economic pie. 2:25:02 Like I said, creating more than you consume. Doing more than you, yeah. 2:25:08 So that's a big deal. I think there's a fair number of people in finance that do have a bit of a zero-sum mindset. 2:25:17 - I mean, it's all walks of life. I've seen that. One of the reasons Rogan inspires me 2:25:24 is he celebrates others a lot, not creating a constant competition like there's a scarcity of resources. 2:25:31 And what happens when you celebrate others and you promote others, the ideas of others, 2:25:38 it actually grows that pie. The resources become less scarce. 2:25:44 And that applies in a lot of kinds of domains. It applies in academia where a lot of people are very, 2:25:49 see some funding for academic research as zero sum. It is not, if you celebrate each other, if you make, 2:25:56 if you get everybody to be excited about AI, about physics, about mathematics, I think there'll be more and more funding, 2:26:02 and I think everybody wins. Yeah. That applies, I think, broadly. - Yeah, yeah. Exactly. 2:26:08 - So the last question about love and meaning. 2:26:14 What is the role of love in the human condition broadly, and more specific to you? How has love, romantic love or otherwise, 2:26:21 made you a better person, a better human being? 2:26:27 Better engineer? - Now you're asking really perplexing questions. 2:26:34 It's hard to give a. I mean, there are many books, poems, and songs written about what is love, 2:26:42 and what is, what exactly, 2:26:48 what is love, baby don't hurt me. (Lex laughing) - That's one of the great ones, yes. 2:26:54 You have earlier quoted Shakespeare, but that's really up there. - [Elon] Yeah. 2:27:00 Love is a many splendor thing. - I mean, there's, 2:27:05 'cause we've talked about so many inspiring things, like be useful in the world, sort of solve problems, alleviate suffering, but it seems like connection 2:27:13 between humans is a source, it's a source of joy, it's a source of meaning, 2:27:20 and that's what love is, friendship, love. I just wonder if you think about that kind of thing, 2:27:26 when you talk about preserving the light of human consciousness. - Right. - And us becoming a multi-planetary species. 2:27:35 I mean, to me at least, that means, if we're just alone, and conscious, 2:27:43 and intelligent, it doesn't mean nearly as much as if we're with others. 2:27:48 Right? And there's some magic created when we're together. 2:27:53 The friendship of it, and I think the highest form of it is love, which I think broadly is much bigger 2:27:59 than just sort of romantic, but also yes. Romantic love and family and those kinds of things. 2:28:06 - Well, I mean, the reason I guess I care about us becoming a multi-planet species and a space bearing civilization is foundationally, 2:28:14 I love humanity. And so I wish to see it prosper and do great things 2:28:22 and be happy, and if I did not love humanity, I would not care about these things. 2:28:31 - So when you look at the whole, the human history, all of the people whose ever lived, all the people alive now, 2:28:37 It's pretty, we're okay. On the whole, we're a pretty interesting bunch. 2:28:45 - Yeah. All things considered, and I've read a lot of history, including the darkest, worst parts of it. 2:28:54 Despite all that, I think on balance, I still love humanity. 2:28:59 - You joked about it, the 42, what do you think is the meaning of this whole thing? 2:29:06 Is there a non-numerical representation? - Oh, I should say Yeah, well really, I think what Doug Sanders was saying 2:29:11 in "The Hitchhiker's Guide to the Galaxy" is that the universe is the answer. 2:29:19 What we really need to figure out are what questions to ask about the answer that is the universe. 2:29:26 And that the question is the really the hard part. And if you can properly frame the question, then the answer, relatively speaking, is easy. 2:29:35 So therefore, if you want to understand what questions to ask about the university, you wanna understand the meaning of life, 2:29:42 we need to expand the scope and scale of consciousness so that we're better able to understand the nature 2:29:49 of the universe and understand the meaning of life. - And ultimately, the most important part will be 2:29:54 to ask the right question. - [Elon] Yes. - Thereby elevating the role of the interviewer 2:30:02 - [Elon] Yeah, exactly. - as the most important human in the room. - Good questions are, 2:30:11 it's hard to come up with good questions. Absolutely. But yeah, that is the foundation of my philosophy 2:30:18 is that I am curious about the nature of the universe. 2:30:25 And obviously I will die. I don't know when I'll die, but I won't live forever. 2:30:32 But I would like to know that we are on a path to understanding the nature of the universe and the meaning of life and what questions to ask 2:30:38 about the answer that is the universe. And so if we expand the scope and scale of humanity, 2:30:44 and consciousness in general, which includes silicon consciousness, then 2:30:51 that seems like a fundamentally good thing. - Elon, like I said, 2:30:57 I'm deeply grateful that you would spend your extremely valuable time with me today, and also that you have given millions 2:31:04 of people hope in this difficult time, this divisive time and this cynical time. 2:31:11 So I hope you do continue doing what you're doing. Thank you so much for talking today. - Oh, you're welcome. 2:31:16 Thanks for your excellent questions. - Thanks for listening to this conversation with Elon Musk. 2:31:21 To support this podcast, please check out our sponsors in the description. And now, let me leave you with some words 2:31:27 from Elon Musk himself. "When something is important enough, you do it, even if the odds are not in your favor." 2:31:35 Thank you for listening, and hope to see you next time.