Elon Musk: A future worth getting excited about — Tesla Texas Gigafactory interview — TED

[00:00:00] Speaker 1: Elon Musk, great to see you. How are you? Good, how are you? I mean, we're here at the Texas Gigafactory the day before this thing opens. It's been pretty crazy out there. Thank you so much for making time. You're welcome. I would love you to help us cast our minds, I don't know, 10, 20, maybe 30 years into the future, and help us try to picture what it would take to build a future that's worth getting excited about. You've often said, the last time you spoke at TED, you said that was really just a big driver. You talk about lots of other reasons to do the work you're doing, but fundamentally, you want to think about the future and not think that it sucks. Yeah, absolutely. [00:00:44] Speaker 2: I think, in general, there's a lot of discussion of this problem or that problem, and a lot of people are sad about the future and that they're pessimistic, and I think this is not great. I mean, we really want to wake up in the morning and look forward to the future. We want to be excited about what's going to happen. And life cannot simply be about solving one miserable problem after another. [00:01:22] Speaker 1: So if you look forward 30 years, you know, the year 2050 has been labeled by scientists as this kind of, almost like this doomsday deadline on climate. There's a consensus of scientists, a large consensus of scientists, who believe that if we haven't completely eliminated greenhouse gases or offset them completely by 2050, effectively we're inviting climate catastrophe. Do you believe there is a pathway to avoid that catastrophe? And what would it look like? [00:01:55] Speaker 2: Yeah, so I am not one of the doomsday people, which may surprise me. I actually think we're on a good path. But at the same time, I want us to caution against complacency. So long as we are not complacent, as long as we have a high sense of urgency about moving towards a sustainable energy economy, then I think things will be fine. So I can't emphasize that enough. So long as we push hard and are not complacent, the future is going to be great. Don't worry about it. I mean, worry about it, but if you worry about it, ironically, it will be a self-unfulfilling prophecy. So there are three elements to a sustainable energy future. One is obviously sustainable energy generation, which is primarily wind and solar. There's also hydro, geothermal. I'm actually pro-nuclear. I think nuclear is fine. But it's going to be primarily solar and wind as the primary generators of energy. The second part is you need batteries to store the solar and wind energy because the sun doesn't shine all the time, the wind doesn't blow all the time. So you need a lot of stationary battery packs. And then you need electric transport. So electric cars, electric planes, boats. And then ultimately, it's not really possible to make electric rockets, but you can make the propellant used in rockets using sustainable energy. Right. So ultimately, we can have a fully sustainable energy economy. And it's those three things: solar wind, stationary battery pack, electric vehicles. So then what are the limiting factors on progress? The limiting factor really will be battery cell production. So that's going to really be the fundamental rate driver. And then whatever the slowest element of the whole lithium-ion battery cell supply chain from mining and the many steps of refining to ultimately creating a battery cell and putting it into a pack, that will be the limiting factor on progress towards sustainability. All right. [00:04:16] Speaker 1: So we need to talk more about batteries because the key thing that I want to understand, like there seems to be a scaling issue here that is kind of amazing and alarming. You have said that you have calculated that the amount of battery production that the world needs for sustainability is 300 terawatt hours of batteries. [00:04:38] Speaker 2: That's the end goal. Very rough numbers. And I certainly would invite others to check our calculations because they may arrive at different conclusions. But in order to transition not just current electricity production but also heating and transport, which roughly triples the amount of electricity that you need, it amounts to approximately 300 terawatt hours of installed capacity. [00:05:06] Speaker 1: So we need to give people a sense of how big a task that is. I mean, here we are at the Gigafactory. You know, this is one of the biggest buildings in the world. What I've read, and tell me if this is still right, is that the goal here is to eventually produce 100 gigawatt hours of batteries here a year. [00:05:31] Speaker 2: We'll probably do more than that, but yes, hopefully we get there within a couple of years. Right. [00:05:38] Speaker 1: But I mean, so that is… [00:05:40] Speaker 2: 0.1 terawatt hours. [00:05:42] Speaker 1: But that's still 1/100 of what's needed. How much of the rest of that 100 is Tesla planning to take on between, let's say, between now and 2030, 2040, when we really need to see the scale up happen? [00:06:00] Speaker 2: I mean, these are just guesses. I mean, so please, people shouldn't hold me to these things. It's not like this is like some… What just happened is I'll make some like, you know, best guess, and then people will, in five years, there'll be some jerk that writes an article, Elon said this would happen, and it didn't happen. He's a liar and a fool. It's very annoying when that happens. So these are just guesses. This is a conversation. Right. Like, I think Tesla probably ends up doing 10% of that, roughly. Yes. [00:06:36] Speaker 1: Let's say 2050, we have this amazing, you know, 100% sustainable electric grid made up of, you know, some mixture of the sustainable energy sources you talked about. Yeah. That same grid probably is offering the world really low-cost energy, isn't it? Compared with now. Yeah. And I'm curious about, like, should people… Are people entitled to get a little bit excited about the possibilities of that world? [00:07:06] Speaker 2: People should be optimistic about the future. Humanity will solve sustainable energy. It will happen. If we are, you know, continue to push hard, the future is bright and good from an energy standpoint. And then it will be possible to also use that energy to do carbon sequestration. It takes a lot of energy to pull carbon out of the atmosphere just because in putting it in the atmosphere, it released energy. So now, you know, obviously in order to pull it out, you need to use a lot of energy. But if you've got a lot of sustainable energy from wind and solar, you can actually sequester carbon. So you can reverse the CO2 parts per million of the atmosphere and oceans. And also, you can really have as much fresh water as you want. Earth is mostly water. We should call Earth water. It's 70% water by surface area. Now, most of that's seawater, but it's still, it's like, we just have to be on the bit that's land. Right. [00:08:07] Speaker 1: And with energy, you can turn seawater into… Yes. Irrigating water or whatever water you need. Yes, absolutely. [00:08:13] Speaker 2: Yeah. At very low cost. Things will be good. Things will be good. [00:08:17] Speaker 1: Yes, things will be good. And also, there's other benefits, right, to this non-fossil fuel world where the air is cleaner and… [00:08:22] Speaker 2: Yes, exactly. Yeah, yeah. Because like, when you burn fossil fuels, there's all these like, side reactions and toxic gases of various kinds. And like, sort of, little particulates that are bad for your lungs. Like, there's all sorts of bad things that are happening that will go away. Okay. And the sky will be cleaner and quieter. [00:08:45] Speaker 1: The fuel's going to be good. I want us to switch now to think a bit about artificial intelligence. But the segue there, you mentioned how annoying it is when people haul you up for bad predictions in the past. So, I'm possibly going to be annoying now. But I'm curious about your timelines and how you predict and how come some things are so amazingly on the money and some aren't. So, when it comes to predicting sales of Tesla vehicles, for example, I mean, you've kind of been amazing. I think in 2014, when Tesla had sold that year 60,000 cars, you said, "2020, I think we will do half a million a year." Yeah, we did almost exactly half a million. You did almost exactly half a million. You were scoffed in 2014 because no one since Henry Ford with the Model T had come close to that kind of growth rate for cars. You were scoffed and you actually hit 500,000 cars and 510,000 or whatever. But five years ago, last time you came to TED, I asked you about full self-driving. And you said, "Yup, this very year, I'm confident that we will have a car going from L.A. to New York without any intervention." [00:10:00] Speaker 2: Yeah, I don't want to blow your mind, but I'm not always right. [00:10:05] Speaker 1: So, what's the difference between those two? Why has full self-driving in particular been so hard to predict? [00:10:13] Speaker 2: I mean, the thing that really got me, and I think it's going to get a lot of other people, is that there are just so many false storms with self-driving. Where you think you've got the problem, have a handle on the problem, and then, nope, it turns out you just hit a ceiling. Because if you were to plot the progress, the progress looks like a log curve. So, it's like a series of log curves. Most people don't know what a log curve is, I suppose. [00:10:45] Speaker 1: Show the shape of your hands. [00:10:47] Speaker 2: It goes up sort of a fairly straight way, and then it starts tailing off, and you start getting diminishing returns. And you're like, "Uh-oh, it was trending up, and now it's sort of curving over." And you start getting to these, what I call, local maxima, where you don't realize basically how dumb you were. And then it happens again. So, ultimately, these things, in retrospect, they seem obvious, but in order to solve full self-driving properly, you actually just have to solve real-world AI. Because you say, "What are the road networks designed to work with?" They're designed to work with a biological neural net, our brains, and with vision, our eyes. And so, in order to make it work with computers, you basically need to solve real-world AI and vision. Because we need cameras and silicon neural nets in order to have self-driving work for a system that was designed for eyes and biological neural nets. I guess when you put it that way, it's sort of like quite obvious that the only way to solve full self-driving is to solve real-world AI and sophisticated vision. [00:12:16] Speaker 1: What do you feel about the current architecture? Do you think you have an architecture now where there is a chance for the logarithmic curve not to tail off any time soon? [00:12:26] Speaker 2: Well, I mean, admittedly, these may be infamous last words, but I actually am confident that we will solve it this year. That we will exceed the probability of an accident, at what point do you exceed that of the average person? Right. I think we will exceed that this year. What are you seeing behind the scenes that gives you that confidence? We're almost at the point where we have a high-quality unified vector space. In the beginning, we were trying to do this with image recognition on individual images. But if you look at one image out of a video, it's actually quite hard to see what's going on with that ambiguity. But if you look at a video segment of a few seconds of video, that ambiguity resolves. So the first thing we have to do is tie all eight cameras together so they're synchronized. So all the frames are looked at simultaneously and labeled simultaneously by one person. Because we still need human labeling. So at least they're not labeled at different times by different people in different ways. So it's sort of a surround picture. Then a very important part is to add the time dimension. So that you're looking at surround video and you're labeling surround video. And this is actually quite difficult to do from a software standpoint. We had to write our own labeling tools and then create an auto labeling. software to amplify the efficiency of human labelers because it's quite hard to label video. In the beginning, it was taking several hours to label a 10-second video clip. This is not scalable. So basically, what you have to have is you have to have surround video. And that surround video has to be primarily automatically labeled with humans just being editors and making slight corrections to the labeling of the video. And then feeding back those corrections into the future auto labeler. So you get this flywheel eventually where the auto labeler is able to take in vast amounts of video. And with high accuracy, automatically label the video for cars, lane lines, drive space. [00:14:46] Speaker 1: What you're saying is that you think that, I mean, the result of this is that you're effectively giving the car a 3D model of the actual objects that are all around it. It knows what they are and it knows how fast they are moving. And the remaining task is to predict what the quirky behaviors are. That, you know, that when a pedestrian is walking down the road with a smaller pedestrian that maybe that smaller pedestrian might do something unpredictable or like things like that, that you have to build into it before you can really call it safe. [00:15:24] Speaker 2: You basically need to have memory across time and space. So what I mean by that is if you, because you can't, the memory can't be infinite because it's using up a lot of the computer's RAM basically. So you have to say, how much are you going to try to remember? But like if it's very common for things to be occluded. So like if you talk about say a pedestrian walking past a truck where you saw the pedestrian start on one side of the truck, then they, then they're occluded by the truck. Right. But you need to think, you would know intuitively, okay, that pressure is going to pop out the other side most likely. Right. And the computer doesn't know until it's still. So you need to slow down. [00:16:09] Speaker 1: I mean, a skeptic is going to say that every year for the last five years, you've kind of said, well, no, this is the year. I mean, we're confident that we're there in a year or two or, you know, like it's, it's always been about that, that far away. But you're, we've got a new architecture now. You're, you're, you're seeing enough improvement behind the scenes to, to make you not certain, but, but pretty confident that this, by the end of this year, what in most, not in every city, in every circumstance, but in many cities and circumstances, basically the, the, the car will be able to drive without interventions, safer than a human. [00:16:43] Speaker 2: Um, yes. I mean, the, the, the car currently drives me around Austin most of the time with no interventions. Um, so it's not like, um, and, and, and, and we, we have, uh, over a hundred thousand people in our, uh, uh, full self-driving beta program. Uh, so you can look at the videos that they post online. Um, okay, great. Um, and, uh, some of them are great. [00:17:04] Speaker 1: And some of them are a little terrifying. I mean, occasionally the car seems to sort of like veer off and scare the hell out of people. Um, but, um, it's still better, but, but you, but the, but you're behind the scenes, looking at the data, you're seeing enough improvement to, to, to, to believe that at this year timeline is real. [00:17:23] Speaker 2: Yes. That's what it seems like. I mean, like, you know, we could be here talking again in a year. It's like, well, yeah, another year went by and it didn't happen. But I think this, I think this is the year. [00:17:33] Speaker 1: And so in general, when, when people talk about Elon, Elon time, I mean, it sounds like, like you can't just have a general rule that if you predict that something will be done in six months, actually what you, what we should imagine is it's going to be a year or it's like two X or three X. It depends on the type of prediction. Some things, I guess things involving software at, you know, AI, whatever are fundamentally harder to predict than, than others. Is there an element that you actually deliberately make aggressive prediction timelines to drive people to be ambitious and without that nothing gets done? [00:18:06] Speaker 2: Well, I, I generally believe, um, in terms of internal, uh, timelines that we want to set, set the most aggressive timeline that we can. Um, uh, because there's sort of like a law of gases expansion where, for schedules where whatever time you set, it's, it's not going to be less than that. It's very rare that it'll be less than that. Um, and as far as my predictions are concerned, um, what, what tends to happen in the media is that they will report all the wrong ones and ignore all the right ones. Um, and, uh, or, or, you know, when, when, when writing an article about me, I've had a long career in multiple industries. If you, if you list my sins, I sound like the worst person on earth, but if you put those against the, my, you know, the things I've done right, it makes much more sense. Uh, you know, so it's actually like the, the longer you do anything, the, the more mistakes that you will make cumulatively, which if you sum up those mistakes will sound like, uh, I'm the worst predictor ever. But for example, for a Tesla vehicle growth, uh, I, I said, I think we're doing 50% and we've, we've, we've done 80%. Yes. Uh, so, uh, but they don't mention that one. Uh, so I mean, I'm not sure what my exact track record is on predictions. They're more optimistic than pessimistic, but they're not all optimistic. Um, some of them, uh, are exceeded, uh, probably more or later. Um, but they, uh, they, they, they do come true. It's very rare that they do not come true. It's sort of like, uh, you know, uh, you know, if, if, if there's some radical technology prediction, uh, the, the point is not that it was a few years late, but that it happened at all. That's the, that's the more important part. [00:19:45] Speaker 1: So it's, it feels like at some point in the last year, seeing the progress on understanding that your, that the AI, the Tesla AI understanding the world around it led to a kind of an aha moment. Because you've really surprised people recently when you said probably the most important product development going on at Tesla this year is this robot Optimus. Yes. Many companies out there have tried to put out these robots. They've been working on them for years and so far, no one has really cracked it. There's no mass adoption robot in people's homes. There are some in, in manufacturing, but it like, I would say that no one's kind of really cracked it. What is it something that happened in the development of full self driving that gave you the confidence to say, you know what, we could do something special here. Yeah. [00:20:37] Speaker 2: Yeah, exactly. So, you know, it took me a while to sort of realize this, that, that, that in order to solve self driving, you really needed to solve real world AI. Um, and at the point at which you solve real world AI for a car, which is really a robot on four wheels, uh, you can then generalize that to a robot on legs as well. The, the two hard parts, I think like it's not, obviously companies like Boston Dynamics have shown that it's possible to make, uh, uh, quite compelling, sometimes alarming. Robots. Right. Um, you know, so, so, so this is from a sensors and actuators standpoint, it's certainly, uh, been demonstrated by, by many that it's possible to make a humanoid robot. The thing that the things that are currently missing are, uh, enough intelligence to, enough intelligence for the robot to navigate the real world and do useful things, um, without being, uh, explicitly instructed. So, so the missing things are basically real world, uh, intelligence and, uh, scaling up manufacturing. Um, those are two things that Tesla is very good at. And, uh, so then we, we basically just need to design the, the, uh, specialized actuators and sensors that are needed for a humanoid robot. People have no idea. This is, this is going to be bigger than the car. So let's dig into exactly that. [00:21:50] Speaker 1: I mean, in one way it's actually an easier problem than full self-driving because you, instead of an object going along at 60 miles an hour, which if it gets it wrong, someone will die. This is an object that's engineered to only go, what? Three or four or five miles an hour. Yeah. [00:22:06] Speaker 2: Walking speed basically. [00:22:07] Speaker 1: And so a mistake isn't, there aren't lives at stake. There might be embarrassment at stake. Yeah. [00:22:12] Speaker 2: As long as the AI doesn't take it over and, uh, uh, right. And go to rest in our sleep or something. [00:22:17] Speaker 1: Right. Um, but, um, um, so talk about, I mean, I, I think the first applications you're, you've mentioned are probably going to be manufacturing, but eventually the vision is to, to have these available for people at home. Correct. If you had a robot that really understood the 3d architecture of your house and knew where every object in that house was or was supposed to be and could recognize all those objects. I mean, that, that's kind of amazing, isn't it? Like, like that, the kind of thing that you could ask a robot to do would be what? Like tidy up. Yeah. [00:22:54] Speaker 2: Um, Absolutely. Or make, make dinner, I guess. Uh, I guess, uh, mow the lawn. [00:22:59] Speaker 1: Take, take a cup of tea to grandma and show her family pictures. [00:23:03] Speaker 2: Yeah. Exactly. Take care of like grandmother and make sure. Yeah, exactly. [00:23:09] Speaker 1: I mean, it could recognize, obviously recognize everyone in the home. Yeah. Could play catch with your kids. Yes. [00:23:14] Speaker 2: I mean, obviously we need to be careful that this doesn't, uh, become a dystopian situation. Um, um, like I think one of the things that's going to be important is to have, uh, a localized ROM chip, uh, on the robot that cannot be updated, uh, over the air. Uh, where if you, for example, were to say stop, stop, stop, that would, if anyone said that, then the robot would stop, you know, type of thing. Um, and that's not updatable remotely. Um, I think it's going to be important to have safety features like that. [00:23:41] Speaker 1: Yeah. [00:23:42] Speaker 2: That, that sounds wise. And I do think there should be a regulatory agency for AI. Right. I've said this for many years. I don't, I don't love being regulated, but I, you know, I think this is an important thing for public safety. [00:23:51] Speaker 1: Look, let's come back to that, but I'm, I'm just, I, I don't think many people have really sort of taken seriously the notion of, you know, a robot at home. I mean, at the, at the start of the computing revolution, you know, Bill Gates said, there's going to be a computer in every home. And people at the time said, yeah, whatever. Right. Who would even want that? Right. Now we have a computer in our pocket. Do you think there will be basically like in say, say 2050 or whatever, that like a, a, a robot in most homes is, is what they will be. And people will, will, will. Yeah. I think there probably will. Yeah. You'll have your own butler basically. [00:24:22] Speaker 2: Yeah. You'll have your sort of buddy robot probably. Yeah. [00:24:26] Speaker 1: I mean, how much of a buddy do you like, do you, do you, do you, how, how many applications do you thought is that, you know, can you have a romantic partner, a sex partner? I mean. A lot of learning people out there. [00:24:35] Speaker 2: It's probably inevitable. I mean, I did promise the internet that I'd make cat girls. We could make a robot cat girl. I mean, I mean, I mean, it's. [00:24:43] Speaker 1: Be careful what you promise the internet, you know. [00:24:46] Speaker 2: Yeah. So, yeah, I, I guess, uh, it'll be what, what, whatever people want really, you know, so. [00:24:52] Speaker 1: What, what sort of timeline should we be thinking about of the first, the first models that are actually made and sold? [00:25:00] Speaker 2: Well, you know, the, the, the first units that, that we intend to make are, um, for jobs that are dangerous, boring, repetitive and things that people don't want to do. And, uh, you know, I, I think we'll have like an interesting prototype, uh, sometime this year. We, we might have something useful next year, but I think quite likely within at least two years. Uh, and then we'll see rapid growth year over year of the usefulness of the humanoid robots, um, and decrease in cost and scaling up production. [00:25:29] Speaker 1: Initially just selling to businesses, uh, or when, when do you picture your, your sell, your start selling them where you can buy your parents one for Christmas or something? I'd say less than 10 years. Yeah. How, how, how, how, how, help me on the economics of this. So what, what, what do you picture the cost of one of these being? [00:25:47] Speaker 2: Well, I think the cost is actually not going to be a crazy high, um, like less than a car. Initially things will be expensive because it'll be new technology at low production volume. The complexity and cost of a car is greater than that of a humanoid robot. Um, so I would expect that it's going to be less than a car or at least equivalent to a cheap car. [00:26:07] Speaker 1: So even if it starts at 50 K within a few years, it's down to 20 K or lower or whatever to, and, and maybe for home, they'll get much cheaper still, but, but, but, but think about the economics of this. If you can replace a $30,000, $40,000 a year worker, which you have to pay every year with a one time payment of $25,000 for a robot that can work longer hours. Yeah. A pretty rapid replacement of certain types of jobs. How worried should the world be about that? [00:26:38] Speaker 2: I wouldn't worry about the, the sort of putting people out of a job thing. Um, I think we're actually going to have, and already do have a massive shortage of labor. So I, I think, I think we'll, we will have, um, uh, not, not people out of work, but actually still a shortage labor even in the future. Uh, but the, this really will be a world of abundance. Any goods and services, uh, will be available to anyone who wants them. That it'll be so cheap to have goods and services. It'll be ridiculous. [00:27:11] Speaker 1: And presumably it should be possible to imagine a bunch of goods and services that can't profitably be made now, but could be made in, in that world courtesy of, of legions of robots. Yeah. [00:27:23] Speaker 2: Um, it, it will be a world of abundance. The only scarcity that will exist in the future is that which we decide to create ourselves as humans. [00:27:32] Speaker 1: Okay. So AI is, is allowing us to imagine a differently powered economy that, that will create this abundance. What are you most worried about going wrong? [00:27:42] Speaker 2: Well, like I said, you know, AI and robotics will, will bring, um, bring out what might be termed the age of abundance. Um, other people have used this word. Um, and, and, and that this is my prediction will be an age of abundance, um, for everyone. Um, the, the, the, the, I guess there's, uh, the, the dangers would be the artificial general intelligence or digital super intelligence, uh, decouples from a collective human will and, uh, goes in a direction that for some reason we don't like of whatever, whatever direction it might go. Um, you know, that's sort of the, sort of the idea behind neural link is to try to more tightly couple, uh, collective human world to, uh, the, to, to digital, uh, super intelligence. Um, uh, and also along the way solve, uh, and, uh, a lot of, um, uh, brain injuries and spinal injuries and that kind of thing. So, uh, even if it doesn't succeed in the greater goal, it will, I think it will succeed in, in the, uh, goal of alleviating, uh, brain and spine damage. [00:28:48] Speaker 1: So, the, the, the spirit there is that if we're going to make these AIs that are so vastly intelligent, we ought to be wired directly to them so that we, we ourselves can have those superpowers more, more directly. But that doesn't seem to avoid the risk that those superpowers might, um, turn ugly in an unintended way. No, I think it's a risk. [00:29:08] Speaker 2: I agree. I, I, I don't, I, I'm not saying that I have some, uh, certain, uh, answer to that risk. I'm, I'm, I'm just saying like, like maybe one of the things that would be good for ensuring that, uh, the future is one that we want is to more tightly couple, uh, the human world, collective human world to digital intelligence. Um, the, the, the issue that we face here is that we're already, um, a cyborg, if you think about it. The computers, uh, are an extension of ourselves. Um, and when we die, there's like, we have like a digital ghost. You know, all of our text messages and social media and emails. And it's, it's quite eerie actually when someone dies and, and they're, but everything online is still there. But, but you say like, what, what's the limitation? What, what is it that, um, inhibits human machine symbiosis? It's the data rate. When you communicate, especially with a phone, you're moving your thumbs. Right. Very slowly. So you're like moving your two little meat sticks. Right. At, at a rate that's maybe 10 bits per second, optimistically a hundred bits per second. And computers are, are communicating at the gigabit, uh, level and beyond. [00:30:26] Speaker 1: Have you seen evidence that the technology is actually working? That you've got, you've got a richer sort of higher bandwidth connection, if you like, uh, between, uh, external electronics and a brain than has been possible before? Uh, yeah. [00:30:38] Speaker 2: So the, I mean, the, the fundamental principles of, uh, of reading neurons, uh, sort of doing read, write on neurons with tiny electrodes, um, have been demonstrated for decades. Um, so it, it's not like, uh, this is, uh, the, the concept is new. The problem is that there's no product, uh, that works well that you can go and, uh, and buy. So it's, it's all sort of in research labs. Right. Um, and it's, it's not, it's, uh, like there's, there's always like some cord sticking out of your, your head and it, it's quite gruesome. And it's, it's really, um, there's, there's no good product, uh, that, that, that actually does a good job and is high bandwidth and safe and something you'd actually, that you could buy and would want to buy. So, um, but, but in it, the, the, the, the way to think of the neural device is kind of like, um, a Fitbit or an Apple watch, um, that's, uh, where we, we, we, we take out a sort of a small section of skull about the size of a quarter, um, replace that with that. Um, replace that with, uh, what in many ways really is very much like, um, uh, a Fitbit, Apple watch or, or some kind of smartwatch thing. Um, uh, and, um, and, but, but with, with tiny, tiny wires, very, very, very tiny wires, wires so tiny, it's hard to even see them. Um, and it's very important to have very tiny wires that you, uh, when they're implanted, they don't, they don't damage the brain. [00:32:10] Speaker 1: How far are you from putting these into humans? [00:32:13] Speaker 2: Um, I, I, I, well, we, we have, um, put in our FDA application, uh, uh, to have the, uh, aspirationally do, do the first, uh, human implant this year. [00:32:23] Speaker 1: The first uses will be for neurological injuries of different kinds. Yes. But rolling the clock forward and imagining when, when people are actually using these for their own enhancement, let's say, and for the enhancement of the world. How clear are you in your mind as to what it will feel like to have one of these inside your head? [00:32:45] Speaker 2: Well, I, I do want to emphasize we're, we're, we're at a, at an early stage. And so, um, it really will be many years before we have anything, uh, approximating a high bandwidth, uh, uh, neural interface, uh, that allows for, uh, AI human symbiosis. Um, and for, for, for many years, we will just be solving, uh, brain injuries and spinal injuries from probably a decade. Um, and that this is not something that will suddenly one day, it'll, we'll have this incredible, uh, sort of, uh, whole brain interface. Um, it's, um, it's, it's gonna be, like I said, at least a decade of, of really just solving, um, uh, brain injuries and, and spinal injuries. Um, and, and really, I think you can solve a very wide range of, of brain injuries, including severe depression, uh, morbid obesity, uh, sleep, uh, uh, potentially schizophrenia. Like a lot of things that cause great stress to people, uh, restoring, uh, memory in, in older people. [00:33:51] Speaker 1: If you can pull that off, that is, that, that's the app I will sign up for. Absolutely. I would, please hurry, actually. Yeah. [00:33:59] Speaker 2: Um, I, I mean, the, the, the, the emails that we get at Neuralink, um, are heartbreaking. Um, I mean, they, they, they'll send us just tragic, you know, you know, where someone was, was sort of in the prime of life and, and, and they had an accident on a, uh, motorcycle and, and, and someone who's 25 is, is, you know, it can't even feed themselves. And, and this is something we could fix. Um. [00:34:24] Speaker 1: But, but you have, you have said that AI is one of the things you're most worried about and that Neuralink may be one of the ways where we can keep abreast of it. [00:34:34] Speaker 2: Uh, I, yes, it's, it's, there's, there's, there's the, the short-term thing, which I think is helpful on an individual human level, um, with, with injuries. And then the long-term thing is an attempt to, uh, address the civilizational risk of, of AI by, uh, bringing, bringing, um, digital intelligence and biological intelligence, uh, closer together. I mean, if you think of how the brain works today, there are really kind of two layers to the brain. There's the limbic system and the, the, the cortex. You've got the kind of animal brain where, which is kind of like the fun part, really. Um. [00:35:08] Speaker 1: That's where most of Twitter operates, by the way. [00:35:10] Speaker 2: Um, yeah. I mean, we're, I think, like, Tim Owen said this. Like, we're, we're like, we're like somebody, uh, you know, stuck a computer on a monkey. Right. Um, you know, so we're, we're like, if, if you gave a monkey a computer, that's our cortex. But we still have a lot of monkey instincts. Right. So, uh, which we then try to rationalize this. No, it's not a monkey instinct. It's something more important than that. But it's often just really a monkey instinct. We're in this, we're, we're, we're just monkeys with a computer stuck, stuck in our brain. Um, so, um, but, but even though the cortex is sort of the smart or the intelligent part of the, the brain, the thinking part of the brain, um, people are quite, I've not yet met anyone who wants to delete their limbic system or their cortex. They're quite happy having both. Right. Both parts of their brain and, um, and people really want their, their phones and their computers, which are really the, the tertiary, the third part of, of, of your intelligence. It's just that it's, it's like, so the, the bandwidth, the, the rate of communication with that tertiary layer is, uh, is slow. Um, and it's just a very tiny stroll to, to this tertiary layer. And, and we want to make that tiny straw of the big highway. Um, and I, I'm definitely not saying that this is going to solve everything. Or this is, you know, it's the only thing, it's, it's, it's, it's something that, that, that might be helpful. Um, and, and, and worst case scenario, I think we, we solve some important brain injury, spinal injury issues, and that's still a great outcome. Right. [00:36:38] Speaker 1: Best case scenario, we may discover new human possibility, telepathy you've spoken of in a way. A connection with, with, with, with a loved one, you know, full memory. Um, um, and, and much faster thought process than maybe, all these things. It's very cool. If AI were to take down earth, we need a plan B. Let's, let's shift our attention to, to, to space. We, we, we spoke last time we talked about reusability and you, you had just demonstrated that spectacularly for the first time. Since then, you've gone on to build this monster rocket, Starship. Starship, which kind of changes the rules of the game in spectacular ways. Tell us about Starship. [00:37:21] Speaker 2: Yes. Starship is extremely fundamental. So the holy grail of rocketry or space transport is full and rapid reusability. This has never been achieved. The closest that anything's come is our Falcon 9 rocket, where we are able to recover the first stage, the boost stage, which is probably about 60% of the cost of the vehicle of the whole launch, maybe 70%. And we've now done that over 100 times. So with Starship, we will be recovering the entire thing. That's, or at least that's the goal. Right. And moreover, recovering it in such a way that it can be immediately reflown. Whereas with Falcon 9, we still need to do some amount of refurbishment to the booster and to the ferry or nose cone. But with Starship, the design goal is immediate reflight. Right. So you just refill propellants and go again. And this is gigantic. Just as it would be in any other mode of transporting. [00:38:33] Speaker 1: And the main design is to basically take, what, 100 plus people at a time, plus a bunch of things that they need to Mars. So first of all, talk about that piece. What is your latest timeline? One, for the first time, a Starship goes to Mars, presumably without people, but just equipment. Two, with people. Two, with people. Three, the sort of, okay, 100 people at a time, let's go. [00:39:03] Speaker 2: Sure. And just to put the cost thing into perspective, the expected cost of Starship putting 100 tons into orbit is significantly less than what it would have cost, or what it did cost to put our tiny Falcon 1 rocket into orbit. Just as the cost of flying a 747 around the world is less than the cost of a small airplane, you know, a small airplane that was thrown away. So, it's really pretty mind-boggling that the giant thing costs way less than the small thing. So it doesn't use sort of exotic propellants or things that are difficult to obtain on Mars. It uses methane as fuel, and it's primarily oxygen, so sort of roughly 77, 78% oxygen by weight. And Mars has a CO2 atmosphere and has water ice, which is CO2 plus H2O, so you can make CH4, methane, and O2 oxygen on Mars. [00:40:11] Speaker 1: Presumably one of the first tasks on Mars will be to create a fuel plant that can create the fuel for the return trips of many starships. [00:40:20] Speaker 2: Yes. And actually, it's mostly going to be an oxygen plant, but it's because it's 78% oxygen, 22% fuel. But the fuel is a simple fuel that is easy to create on Mars and many other parts of the solar system. So basically, and it's all propulsive landing, no parachutes, nothing thrown away, has a heat shield that's capable of entering on Earth or Mars. We could even potentially go to Venus, but you don't want to go there. Venus is hell, almost literally. But it's a generalized method of transport to anywhere in the solar system, because the point at which you have a propellant depot on Mars, you can then travel to the asteroid belt, and to the moons of Jupiter, and then to Saturn, and ultimately anywhere in the solar system. [00:41:19] Speaker 1: Right. But your main focus, and SpaceX's main focus, is still Mars. Like, that is the mission, that is where most of the effort will go. Or are you actually imagining a much broader array of uses, even in the coming, you know, the first decade or so of uses of this? Where we could go, for example, to other places in the solar system to explore, perhaps NASA wants to use the rocket for that reason? [00:41:53] Speaker 2: Yeah, NASA is planning to use a starship to return to the moon, to return people to the moon. And so we're very honored that NASA has chosen us to do this. So, but I'm saying it is a generalized, it's a general solution to getting anywhere in the greater solar system. It's not suitable for going to another star system, but it is a general solution for transport anywhere in the solar system. [00:42:25] Speaker 1: Before it can do any of that, it's got to demonstrate it can get into orbit, you know, around Earth. What's your latest advice on the timeline for that? [00:42:35] Speaker 2: It's looking promising for us to have an orbital launch attempt in a few months. So, we're actually integrating the, we'll be integrating the engines into the booster for the first orbital flight starting in about a week or two. And the launch complex itself is ready to go. So, assuming we get regulatory approval, I think we could have an orbital launch attempt within a few months. [00:43:10] Speaker 1: And a radical new technology like this, presumably there is real risk on those early attempts. A hundred percent, yeah, yeah. [00:43:16] Speaker 2: Like, I mean, the joke I make all the time is that excitement is guaranteed. Success is not guaranteed, but excitement certainly is. [00:43:23] Speaker 1: But the last I saw in your timeline, you've slightly put back the expected date to put the first human on Mars until 2029, I want to say. [00:43:33] Speaker 2: Yeah, I mean, so, let's see, I mean, we are, we have built a production system for Starship, so we're making a lot of ships and boosters. [00:43:43] Speaker 1: How many are you planning to make, actually? [00:43:45] Speaker 2: Well, we're currently expecting to make a booster and a ship roughly every, well, initially roughly every couple of months and hopefully by the end of this year, one every month. So, it's giant rockets, but a lot, and a lot of them, just in terms, talking in terms of rough orders of magnitude, in order to create a self-sustaining city on Mars, I think the, we'll need something on the order of a thousand ships. And we just need a, we just need a Helen of, of, of, of Sparta, I guess on the, on Mars or something. [00:44:19] Speaker 1: This is, this is not in most people's heads, Elon. [00:44:21] Speaker 2: The planet that launches a thousand ships. [00:44:23] Speaker 1: That's, that's nice. But this is not in most people's heads, this picture that you have in your mind that, so there's, there's, there's basically a two-year window. You can only really fly to Mars conveniently every two years. Yes. You are still, you are picturing that during, during the 2030s, every couple years, something like a thousand starships take off, each containing a hundred or more people. Yes. I mean, that, that, that picture is just completely mind-blowing to me. [00:44:50] Speaker ?: Yes. [00:44:50] Speaker 1: That, that sense of this armada of humans going to. [00:44:54] Speaker 2: Yeah, like Battlestar Galactica, the fleet departs. [00:44:56] Speaker 1: And you think that, that it can basically be funded by people spending maybe a couple hundred grand on a ticket to. Yeah. To Mars. Is that, is that price about where, where it has been? [00:45:06] Speaker 2: Well, I think if you say like what's, what's, what's required in order to get enough people and enough cargo to Mars to build a self-sustaining city. Um, and it's where you have an intersection of sets of people who want to go, because I think only a small percentage of humanity will want to go. Um, and can afford to go or get sponsorship in some manner, uh, that intersection of sets, I think needs to be a million people or something like that. Um, and so it's what, what, what can a million people afford or get sponsorship for, or, uh, because I think governments will also pay for it. And, um, people could take out loans and, but, but, but I think at the point at which, um, you say, okay, okay. Like if, if moving to Mars costs, or for argument's sake, um, a hundred thousand dollars, then I think, um, you know, almost anyone can, can work, uh, and save up and, and, and eventually have a hundred thousand dollars and be able to go to Mars if they want. We want to make it available to anyone who wants to go. Yeah. Um, so, uh, it, and, and, and very important to emphasize that Mars, especially in the beginning will not be luxurious. It will be dangerous, uh, cramped, difficult, hard work. It's kind of like that Shackleton ad for going to the Antarctic, um, which I think is actually not real, but, but it sounds real and it's cool. Yeah. It's sort of like the, the, the, the sales pitch for going to Mars is it's, it's, it's dangerous. It's cramped. Uh, you might not make it back. Uh, it's difficult. It's hard work. That's the sales pitch. [00:46:41] Speaker 1: Right. But you will make history. [00:46:43] Speaker 2: Yeah. [00:46:44] Speaker 1: But it'll be glorious. [00:46:45] Speaker 2: Right. [00:46:46] Speaker 1: Also on that kind of launch rate, you're talking about over two decades, you could get your million people to, to Mars, essentially. Whose city is it? Is it NASA city? Is it SpaceX? [00:46:56] Speaker 2: It's the people of Mars, the city. Uh, the, the, the, the reason for this, I mean, I have to say like, what, what, we feel like, well, why, why, why, why do this thing? It's, I think this is important, uh, for maximizing the probable lifespan of, of humanity or consciousness. Human civilization could come to an end for external reasons like a giant meteor or super volcanoes or, uh, extreme climate change, uh, or, or, uh, World War III. Right. Or, you know, you, uh, any, any, any, any number of reasons, um, and, and, um, but the probable lifespan of, of, of, of civilizational consciousness as we know it, um, which we should really view as this very delicate thing, like a small candle in a vast darkness. That's, that's, that's caught, that, that, that is what appears to be the case. Um, we're in this vast darkness of space, um, and there's this little candle of consciousness that's only really come about, um, after four and a half billion years. Yeah. And, um, it could just go out. [00:48:00] Speaker 1: I think that's powerful. And I think, I think a lot of people will be inspired by that vision. And, and the reason, so the reason you need the million people is because they, there has to be enough people there to do everything that you need to survive. [00:48:10] Speaker 2: It's really like the critical threshold is, um, if the ships from earth stop coming for any reason. Right. Um, does it, does the, the Mars city die out or not? Right. And, and so we have to pass that. That's, you know, people talk about like these, the sort of the great filters, the things that perhaps, um, uh, you know, we talk about the Fermi paradox and where are the aliens and like, well, maybe the aliens didn't, there is various great filters that the aliens didn't pass. And, and so they've eventually just ceased to exist. And one of the great filters is becoming a multi-planet species. So we want to pass that filter. Um, and, and, uh, I'll be long dead before this is, uh, you know, a real, real thing before, before it happens, but I'd like, I'd like to at least see us make a great progress in this direction. [00:49:06] Speaker 1: Given how tortured the earth is right now, how much we're beating each other up. Shouldn't there be discussions going on with everyone who is dreaming about Mars to try to say, we've got a once, once in a civilization's chance to make some new rules here. Is that, should someone be trying to lead those discussions to figure out what it, what it means for this to be the people of Mars' city? [00:49:33] Speaker 2: Well, I think ultimately this will be up to the people of Mars to decide what, uh, how they want to rethink society. Yet there's, there's certainly risk there, um, and, uh, hopefully the people of Mars will be, uh, more enlightened and will not fight amongst each other too much. Uh, I mean, I have some recommendations, but which people of Mars may choose to, uh, listen to or not. And I would advocate for more of a direct democracy, not a representative democracy, um, and laws that are short enough for people to understand. Um, and, uh, where it is, is harder to create laws than to, uh, get rid of them. [00:50:12] Speaker 1: Coming back a bit nearer term, I'd love you to just talk a bit about some of the other possibility space that Starship, um, seems to have created. Yeah. So given, given suddenly we've got this ability to move a hundred tons plus into orbit. [00:50:27] Speaker 2: Yes. [00:50:28] Speaker 1: So we've just spent, we've just launched the James Webb telescope, which is an incredible thing. It's unbelievable. It's an exquisite piece of technology. It's an exquisite piece of technology, but people spent two years trying to figure out how to fold up this thing. Yes. It's a three ton, it's a three ton telescope. [00:50:43] Speaker 2: We can make it a lot easier if you've got more volume and mass. [00:50:45] Speaker 1: Well, so, but let's, but let's ask a different question, which is what, what, what, how much more powerful a telescope could someone design based on using Starship, for example? [00:50:58] Speaker 2: Um, I mean, I, I mean, roughly I'd say it's probably an order of magnitude more resolution, um, if you've got a hundred tons and a thousand cubic meters volume, which is roughly what we have. [00:51:08] Speaker 1: Yeah. And what about other explorations through the solar system? I mean, I'm, I'm, you know. [00:51:13] Speaker 2: Europa. Well, Europa, so. Big, big, big question mark. Right. [00:51:17] Speaker 1: So, so there's an ocean there, right? Yeah. And what you really want to do is to drop a submarine into the ocean. [00:51:21] Speaker 2: Yeah. I mean, maybe there's like some squid civilization, uh, under the cephalopod civilization under the ice of Europa. That would be pretty interesting. [00:51:28] Speaker 1: I mean, you know, if, if you could take a submarine to Europa and we see pictures of this thing being devoured by a squid. Yeah. That would honestly be the happiest moment of my life. Pretty wild. [00:51:38] Speaker 2: Yeah. [00:51:39] Speaker 1: That would be, what, what, what are the possibilities that are out there? Like, cause it feels like if, if you're going to create a thousand of these things, they can only fly to Mars every two years. What are they doing the rest of the time? It feels like there's this, this. Right. some explosion of possibility that I don't think people are really thinking about. I mean, I don't know. [00:51:59] Speaker 2: We've certainly got a long way to go. As you alluded to earlier, we've, we still have to get to orbit. Uh, and, and then, um, after we get to orbit, we have to, um, really prove out and refine, uh, full and rapid reusability. Uh, that'll take a moment. Um, and, um, but, but I do think we will solve this. I, I'm, I highly confident we will solve this. At this point. [00:52:24] Speaker 1: Um, do you ever wake up with the fear that there's going to be this Hindenburg moment for SpaceX where? [00:52:30] Speaker 2: We've had many Hindenburg. Well, we've, we've never had Hindenburg moments with people, uh, which is very important. Big difference. Right. There is. We've blown up quite a few rockets. So there's, we have a, uh, there's a whole compilation online that we've put together and others put together. Uh, it's showing rockets are hard. Um, I mean, the sheer amount of, uh, energy going through rocket is boggles the mind. So. [00:52:51] Speaker 1: Yeah. [00:52:52] Speaker 2: You know, getting out of Earth's gravity well is difficult. We have a strong gravity and a thick atmosphere. And, and, um, and, and Mars, which is, uh, less than 40% of it's, it's like 37% of Earth's gravity, um, and has a thin atmosphere. Uh, the ship alone can go all the way from the surface of Mars to the surface of Earth. Um, whereas getting to Mars requires a giant booster and orbital refilling. [00:53:16] Speaker 1: So, Elon is, uh, so think more about this incredible array of things that you're involved with. I keep seeing these synergies, to use a horrible word, um, between them, you know, for example, the robots you're building from Tesla could possibly be pretty handy on Mars. Sure. Um, doing some of the dangerous work and so forth. I mean, maybe there's a scenario where your city on Mars doesn't need a million people. It needs half a million people and half a million robots. Sure. And, um, that's a possibility. Maybe the boring company could play a role helping create some of those subterranean, um, dwelling spaces that you might need. Yeah. Um, back on planet Earth, it seems like a partnership between boring company and Tesla could offer an unbelievable deal to a city to say, we, we will create for you a 3D network of tunnels populated by robo taxis. [00:54:13] Speaker 2: Yeah. [00:54:14] Speaker 1: That will offer fast, low cost transport to anyone. You know, full self-driving may or may not be done this year. And then, and in some cities, like some like Mumbai, I'm, I suspect won't be done for a decade. Some places are more challenging than others. [00:54:27] Speaker 2: Yes. [00:54:28] Speaker 1: But today, today with what you've got, you could put a 3D network of tunnels under there. [00:54:34] Speaker 2: Oh, if we're just in a tunnel, that's the sole problem, basically. [00:54:37] Speaker 1: Exactly. Full self-driving is itself a problem. So, so to me, there's, there's amazing synergy there. Um, you're with, with, with, uh, Starship, you know, Gwynn Shotwell talked about by 2028 having from city to city, you know, transport on planet Earth. [00:54:51] Speaker 2: Yeah. This is a real possibility. It's, it's a, uh, yeah. Uh, the, the, the, the fastest way to get from one place to another, if it's a long distance, is, is a rocket. It's, uh, it's basically an ICBM. Right. With the. But it has to land. Landing delete the nuke. [00:55:07] Speaker 1: Because it's an ICBM, it has to land probably offshore. Yes. Because it's loud. So, so why not have a tunnel that then connects to the city? Yeah. That'd be cool. Sure. And, and, and Neuralink. I mean, if you're going to go to Mars, having a telepathic connection with loved ones back home, even if there's a time delay. [00:55:27] Speaker 2: I mean, I'm not, I, I, I, if these are not intended to be connected, by the way, they're just, but I, there should certainly could be some synergies. Yeah. [00:55:35] Speaker 1: Surely there is a growing argument that you should actually put all these things together into one company and just, just have a company devoted to creating a future that's exciting and let a thousand flowers bloom. Have you, have you been thinking about that? [00:55:52] Speaker 2: I mean, it is tricky because Tesla is a publicly traded company and the, the investor base of Tesla and SpaceX and, and certainly Boring Company and Neuralink are quite different. And Boring Company and Neuralink are, are, are, are tiny companies. Just. Right. By comparison. The audience may, yeah. Tesla's got 110,000 people. Uh, SpaceX, I think is around 12,000 people. Uh, Boring Company and Neuralink are, uh, both under 200 people. So, uh, they're little, little tiny companies, but they will probably get bigger in the future. They will get bigger in the future. Uh, it's not that easy to sort of combine these things. Um. [00:56:32] Speaker 1: Traditionally, you've said that for SpaceX especially, you don't, you wouldn't want it public because public investors wouldn't support, um, the craziness of the idea of going to Mars or whatever. And you want to, you know. Yeah. [00:56:44] Speaker 2: Making life multi-planetary is, is outside of the, the normal, uh, time horizon of Wall Street analysts. Right. [00:56:52] Speaker 1: To say the least. I think something's changed though. Um, what's changed is that Tesla is now so powerful and so big and, and throws off so much cash that you actually could connect the dots here. Just tell the public that X billion dollars a year, whatever your number is, will be diverted to the Mars mission. I, I suspect you'd have massive interest in that company. And it might, it might unlock a lot more possibility for you now. [00:57:21] Speaker 2: Yeah. I mean, I would like to give the public access to, uh, ownership of SpaceX, uh, but I mean, the thing that, like the, the overhead associated with public company, uh, is high. Um, so the, I mean, as a public company, you're just constantly sued. It does occupy like a fair bit of, uh, you know, time and effort to, uh, deal with these things. [00:57:48] Speaker 1: Right. But you would still only have one public company. It would be bigger and, um, have more things going on. But instead of being on four boards, you'd be on one. [00:57:57] Speaker 2: I'm actually not even on the, the Neuralink or Boring Company boards. Oh, oh wow. Yeah. And I, I don't really attend the SpaceX board meetings. We only have two a year and I, I just stopped by and chat for an hour. Um, so, uh, the board overhead for a public company is much higher. [00:58:14] Speaker 1: Right. I think some investors probably worry about how your time is being split and they would be, they might be excited by, you know, that that's anyway, I, um, I just, I just woke up the other day thinking, thinking just, there are so many ways in which these things connect. And, and, and, you know, that, that just the note, the simplicity of that mission of building a future that is worth getting excited about might, might appeal to, um, an awful lot of people. Um, Elon, you, uh, reported by Forbes and everyone else as, as now, you know, the world's richest person. [00:58:47] Speaker 2: That's not a sovereign. You know, I think it's fair to say that, uh, if somebody is like the king or the president de facto, uh, king of a country, they're wealthier than I am. [00:58:58] Speaker 1: So, but, but it's just harder to measure, but what people do, so, so $300 billion. I mean, your, your net worth on any given day is rising or falling by several billion dollars. How insane, how insane is that? It's bonkers, yeah. I mean, does that, how do you, how do you handle that psychologically? There aren't many people in the world who have to even think about that. [00:59:19] Speaker 2: I actually don't think about that too much, but the, the, the thing that is actually, uh, more, more difficult and that does make sleeping difficult is that, um, you know, every good hour, uh, or even minute of thinking about, uh, Tesla and, and SpaceX has such a big effect on the company that I really try to work as, as, as much as possible. Uh, you know, to, to the edge of sanity basically, uh, because the, you know, Tesla's getting to the point where, uh, probably we'll get to the point later this year where every good, every high quality minute of thinking, um, uh, is a million dollars to, to, uh, impact on, on Tesla. So, uh, which is insane. Um, so, um, I mean the basic, you know, if, if, if Tesla's doing, you know, of, uh, sort of $2 billion a week, let's say in revenues, sort of $300 million a day, seven days a week, you know, it's, [01:00:26] Speaker 1: it's, if you, if you can change that by 5% in an hour's brainstorm, um, that, that those, those are valuable, that's a pretty valuable hour. [01:00:36] Speaker 2: I mean, there, there are many, many instances where, uh, a half hour meeting, the finale, I was able to improve the financial outcome of the company, um, by a hundred million dollars in a half hour meeting. [01:00:48] Speaker 1: There are many other people out there who, who, who can't stand this world of, of, of billionaires. Like they, they are hugely offended by the notion that an individual can have the same wealth as, as say a billion of, or more of the, the world's poorest people. [01:01:06] Speaker 2: If, if they examine sort of the, sort of, I think there's some axiomatic floors, um, that, that are leading to them, to, to, to that conclusion. If, if, for sure, it would be very problematic if I was consuming, uh, you know, billions of dollars a year in, in personal consumption. But that is not the case. Um, in fact, I don't even own a home right now. Um, I'm literally staying at friends' places. I, if I travel to the Bay Area, which, where most of Tesla engineering is, I, I stay in my, I basically rotate through friends' spare bedrooms. Um, I don't have a yacht. I, I really don't take vacations. Uh, so, um, it's not like, it's not as though there's, um, that, that my postal consumption is, is high. Uh, with, I mean, the one exception is a plane. But if I don't use the plane, then [01:01:50] Speaker 1: I have less hours to work. So, um, I, I personally think you have shown that you are mostly driven by really quite a deep sense of moral purpose. Like you, you've tried, your, your, your, your attempts to solve the climate problem have, have been as powerful as anyone else on the planet that I'm aware of. And I actually can't, can't understand. Personally, I can't understand the fact that you get all this criticism from the left about, oh my God, he's so rich. That's disgusting. Um, when, when climate is their issue, um, philanthropy is, is a topic that some people go to. Philanthropy is a hard topic. Like how, how do you think about that? [01:02:30] Speaker 2: Um, I, I think if you, if you care about the reality of goodness, instead of the perception of it, philanthrop, philanthropy is extremely difficult. Um, SpaceX, Tesla, Neuralink and Boring Company are philanthropy. If you say philanthropy is love of humanity, um, they are philanthropy. They're, Tesla is accelerating sustainable energy. This is a love of, of full philanthropy. Uh, SpaceX is trying to ensure the long-term survival of humanity with multi-planet species. This is love of humanity. Um, you know, Neuralink is, is to help solve, uh, brain injuries and, uh, existential risk with AI, love of humanity. Boring Company is trying to solve traffic, which is health, most people. And, uh, that also, it is like humanity. [01:03:18] Speaker 1: Right. It's like, how, how upsetting is it to you to hear this constant drumbeat of billionaires, my God, Elon Musk, oh my God. Like, is it, do you, do you, do you just shrug that off or does it, does it actually hurt? [01:03:33] Speaker 2: I, I mean, at this point it's water off a duck's back. [01:03:38] Speaker 1: You know, I'd like to, as, as we wrap up now, just pull the camera back and just think you're a father now of seven, uh, surviving kids and, and. [01:03:48] Speaker 2: Well, I, I mean, I'm trying to say a good example because the birth rate on earth is so low that we're facing civilizational collapse unless the birth rate, uh, returns to a sustainable level. [01:04:00] Speaker 1: Yeah. You've talked about this a lot, that depopulation is a big problem and we, we, we, we. [01:04:04] Speaker 2: Yes. People don't understand. Population collapse is, uh, one of the biggest threats to the future of human civilization. And that is what is going on right now. [01:04:11] Speaker 1: How, what, what drives you on a day to day basis to do what you do? [01:04:16] Speaker 2: Uh, I guess the, like, I, I, I really want to make sure that there is a good future for humanity. Um, and that we're on a path to understanding the nature of the universe. Um, the meaning of life, why are we here? How do we get here? Um, and in order to understand the nature of the universe and all these fundamental questions, um, we must expand the scope and scale of consciousness. Uh, certainly it must not diminish or go out. We, we, we certainly, we weren't to understand this. So I, I would say I've been motivated by curiosity more than anything. Um, and, uh, just, uh, desire to think about the future and not be sad, you know? [01:05:01] Speaker 1: Um, and, um, and, and are you, are you not sad? [01:05:05] Speaker 2: I'm sometimes sad. I, I, I mostly I'm, I'm, I'm, I mean, I'm feeling, I guess, relatively optimistic about the future these days. Um, there are certainly, um, some big risks that humanity faces. Uh, I think the, the, the population collapse is a really big deal that, um, I wish more people would, would, would think about. Um, cause the, the birth rate is far below as what's needed to sustain civilization at its current, at its current level. Um, and, uh, you know, there's obviously, um, we, we, we need to take action on climate sustainability, which is, is, is being done. Um, and we need to secure the future of consciousness by being a multi planet species. Um, we, we need to address the, essentially we, it's important to take whatever actions we can think of to address the existential risks that affect the, the future of, of consciousness. [01:05:59] Speaker 1: There's a, there's a whole generation coming through who seem really sad about the future. What, what would you say to them? [01:06:05] Speaker 2: Well, I think if you want the future to be good, you must make it so. Take action to make it good. And it will be. [01:06:15] Speaker 1: Elon, thank you for all this time. Um, that is a beautiful place to end. Thanks for all that you're doing. You're welcome.