[0:00:00] Elon, welcome to the X Takeover, your third year in a row. Thank you so much for your time. You're welcome. Hey, guys. We have over 2,000 people here at Giga Texas today, and we are all here to support you and the work you are doing for humanity. So thank you. Thank you. Thanks, guys. Really appreciate your support.
[0:00:27] So what's crazy, Elon, is four years ago, we sat down here at Giga Texas with you when the factory wasn't even running, and you described how close Tesla was coming to not surviving in 2008. Looking out at this community today of 2,000 people, what makes you pause and appreciate how far things have come? Yeah, it's difficult to imagine here in 2026 that Tesla's actually, you know,
[0:00:58] It has come close to death multiple times, actually. 2008 was the worst. That's where we closed the financing round at 6 p.m. on Christmas Eve, 2008. It's the last hour of the last day that it was possible. We would have bounced payroll a few days after Christmas if the round hadn't closed. So that was pretty stressful. And then 2018 was pretty bad, too, where we had to get to 5,000.
[0:01:28] A week of the Model 3 or go bankrupt. That was a pretty wild situation, which I guess probably a lot of people know the story of. But we had to figure out some way to increase Model 3 production by 50% in three weeks, which is insane. Absolutely insane. Yeah. But I'll recount the story for those who haven't heard it.
[0:01:58] The choke point was General Assembly for the Model 3. We were able to achieve rate almost everywhere else except for General Assembly. For General Assembly, you need a moving production line, you need shelter, you need a moving production line, power and equipment and all that. We maxed out the two production lines that we had and then we needed a third one.
[0:02:27] And that was where we built the tent in the parking lot. And then we needed some way to move the cars down the line, like a moving production line situation. And the only thing that we had was some discarded equipment that was used for moving small parts, not like whole cars. And it didn't actually have the power to move the whole cars. But the parking lot was on a slight incline.
[0:02:57] It was like a three or four degree incline. So we actually started the production line at the top, and then the sort of tiny moving production line that was just meant for parts was actually able to move the cars because they were going slightly downhill. And so we were able to increase production by 30% in three weeks. Yeah, that was wild. As we were building the tent, we were laying the concrete for the line.
[0:03:31] And putting in the rails. So working 24-7. The short sellers were actually right, meaning if you looked at the situation, there was a crazy short position against Tesla, as there has been at various times. And they had extremely good inside information. And they thought, well, there's no way we could possibly solve the production choke point. And on a set piece battle, on a conventional chess match situation,
[0:04:03] They had us in checkmate. But we just invented a new piece, basically. That didn't exist before. So they got the short cut steamroll. Hey, Elon. We just had your mom here on stage today. What story are you hoping she doesn't tell us? I don't know. Maybe I pooped my pants when I was a toddler or something. I don't know. I probably did. Everybody does.
[0:04:38] I don't know. I was a pretty difficult to contain kid. My parents would tell me I'm not supposed to leave the house or whatever. I was four or five years old and I would just constantly leave the house and go running down the street. They'd lock me in a room so that I couldn't run away and run down the street.
[0:05:11] And then we had one of those old locks where you could poke the key through the door. The lock's accessible from both sides. And I could see the key, and I poked the key until it fell down. And then I was able to get a piece of paper to scoop it up and bring it to the other side and unlock myself. And my mom was like, how did you get out? I was like, I'm not telling you. But yeah, I was proud to contain as a kid.
[0:05:40] So before we jump into more specific things, what's something that you wish more people would ask you? Well, I don't really wish people would ask me questions, to be honest. I'm not like... Sorry. I'm not going around, I wish people would ask me more questions. Like, here we go with questions, you know. So, yeah. I guess it's good to hear questions that you haven't heard before. Otherwise...
[0:06:13] You know, a lot of times it's like the same questions that people ask. And I feel like I could have like, you know, just like whatever, have these recordings of the answer and I'll just play you. Oh, that's question 27. Here you go. It has answered 27 that I've given a hundred times before. So maybe I should like, let's just have like a web page with literally frequently asked questions.
[0:06:40] It's fantastic because then it's like, it's just go to the page and then you got it. You got the answer, you know. It's like the infinite selfie loop. Yeah, exactly. My idea of hell is to be stuck in an infinite selfie loop. Like it's infinite, can I have a selfie? And I'm like, and they're like, you know, it's like, probably it's like you say yes to one person and I would do it once in a while. And then I'm like, then there's a queue forms pretty soon. It's just the never ending selfies.
[0:07:06] And then there's always something goes wrong with the phone, or they don't get the camera right, or the effect is wrong, and then it's like, God damn it. And the phone drops, and it's like, oh, man. It just can't be an infinite selfie loop. It's just a long loop. Yeah. We had your mom and Nikki up for that, too. They had the infinite selfie loop today. But I'll pass this to Kelvin. All right. Let's start with Starship.
[0:07:35] Congratulations on Flight 14. Thanks, yeah. It's a great team of engineers. It's pretty wild, that champion. You can just go down there in South Texas and just see it from the road. So Flight 14 lost an engine on the way up and still reaches orbit. What was happening behind the scenes in those minutes before the team got called the go for orbit message? Well, we're just trying to see why. We're trying to understand.
[0:08:08] What was the reason for the engine failure? Was there, say, a common cause where we could expect to see more engine failures? The biggest question is, would there be a potential problem with restarting the C-level Raptor engines, the three ones with the smaller nozzles in the center, for the de-orbit burn? Because we don't want Sasha to get stuck on orbit and then it randomly re-enter and maybe land on someone.
[0:08:38] That's our main thing is trying to make sure things are as safe as possible. So the cause of the engine out, I think, was basically a failure in the flight computer software, or engine computer software, I should say, which it didn't appear to be anything that was present in any of the other engine computers. So I thought, okay, well, it's probably okay to go to orbit.
[0:09:08] So, Elon, we know one of your famous sayings is, from the impossible to merely late. Did this flight feel like a turning point in what's possible now for Starship? Well, I guess it is a good milestone. Because, I mean, we could have gone to orbit many times before now. But the reason we didn't go to orbit before now was because we wanted to be extremely confident of being able to de-orbit safely.
[0:09:37] That's why we only went to orbit on this flight. But pretty much all of the prior missions, in fact, literally all of the prior missions could have gone to orbit. So at this point, everything's looking quite positive. The heat shield is looking robust. The engines are robust apart from that one glitch. But that glitch doesn't seem to be too much of an issue. It's pretty easy to remedy.
[0:10:09] But the heat shield was extremely robust on this flight. And that's one of the hardest things. The other really hard thing is just getting to extremely high engine reliability. And everything looked good. The big milestone that's coming up, hopefully on the next flight, which is, we don't know the exact date because we're making a bunch of
[0:10:40] Improvements to the rocket. So like between every flight, there's often hundreds, sometimes thousands of small changes to improve things. In this case, the number of changes is not very many, but some of them take a fair bit of work and we've got to go inside the tank to make some of these changes. So we don't know the exact launch date, but probably about four weeks or so, four to six weeks is about right.
[0:11:10] It probably launches a little bit before Thanksgiving, is my guess. And that one, if it succeeds, is a big one, because we're going to try to catch the booster, which we've already proven we can catch the booster twice, but we're going to try to catch the ship, which is a super big deal. And if we do so, that'll be the first time that there's been full recovery of an orbital-class rocket.
[0:11:37] Sounds good. You said four to six weeks. I'm not used to that. I'm used to the two weeks phrase. I'm confident it's not two weeks. All right. You heard it here. What's the hardest part left to get right once you catch the Starship for the first time? What is the next big goal after that? Well, the next big milestone would be to refly both the booster and the ship.
[0:12:09] So the upcoming milestones would be the first time in history that all parts of an orbital rocket have been recovered. But then the flight after that, hopefully, will be the first time that all parts of an orbital rocket are reflown. And then there's a long tail of engineering and production work and work on the launch system and everything to make it rapidly reusable.
[0:12:41] So it's kind of the rapidly reusable, reliable rockets. It's like a pirate. Pirate rocket. Rapidly reusable, reliable rockets. Man, we should have gotten you a patch for this one. Yeah.
[0:13:02] Hey, when you founded SpaceX, Elon, could you have imagined its mission expanding into SI and data centers in space? And how did you come to see rockets and SI belonging under one roof? Well, because I'm very farsighted, of course. Well, I guess if you say, like, as you expand into space, you're able to...
[0:13:31] Pan is a lot more of the sun's power in space than you can on Earth. So it's important to appreciate the sheer magnitude of the sun and how the sun is pretty much everything. So the sun is 99.86% of all the mass in the solar system. And then you've got Jupiter as most of the remaining mass. And then the gas giants are pretty much that. And then Earth is basically a tiny dust mote.
[0:14:01] Like a rounding error in the mass of the solar system. So we're just extremely tiny. It's like hard to believe how we're both in a tiny dust mode. So as you expand into space and you're able to harness more of the sun's power, you have to say, what are you going to use that for? It's like, well, you're going to use it for a lot of digital intelligence.
[0:14:31] Basically, you're either going to do computation or you're going to shape atoms. There's going to be a lot more digital thinking than there will be shaping atoms. The amount of atoms you can shape in the solar system is not that much. A lot of the atom shaping itself will be for the purposes of building digital superintelligence.
[0:15:02] Creating a sentient sun. And it may be that there are many sentient suns out there. And that would help us then understand the nature of the universe and extend consciousness beyond our solar system ultimately to like Star Trek to go to places you've never... I forget what the exact phrase was, but explore the universe, but go places you've never been before. And I think maybe we'll find
[0:15:34] If you date Earth civilization from the first writing,
[0:16:04] That's 5,500 years ago, like in ancient Sumer with the sort of archaic cuneiform. So it's only 5,500 years. It looks like Earth is about 4.5 billion years old according to what physics suggests. And that means human civilization has only been around for one millionth of Earth's existence.
[0:16:36] And so if we had a civilization that lasted a million years, or let alone 10 million years, I think it would be a very, very successful civilization if we did that. Woo! Yeah. But that's still very tiny compared to the apparent age of the universe, which is 13.8 billion years. So just to get to the third place past decimal point on age of the universe,
[0:17:07] To increment it by a million years, you'd have to go 13.8, whatever the next digit is, whatever the next digit is, plus, you know, plus one on the third digit past decimal point. Now you're a million years old. And that would be a civilization vastly older than humanity. Yeah, that's, yeah. It sounds again like the infinite selfie loop, but...
[0:17:33] All right, so jumping into something that SpaceX has done, you acquired Cursor. And I feel like for me, when you think of Google, hey, did you Google it? Hey, did you grok it? And for me, Grokbot has been such an absolute game changer for me.
[0:17:53] Personally, in my life. But what was it about the Cursor team that you guys wanted to bring it, you know, that space, or you wanted to acquire Cursor that was like, let's bring it into the fold. What was different about them? Or what did you see? Well, so in order to create a compelling AI, you need a lot of training data. And you've got to solve coding.
[0:18:22] And they're really just, as far as I can tell, three organizations that had enough coding training data. And that was Anthropic, OpenAI, and Cursor. And arguably, perhaps Google. But you absolutely have to have the data in order to train an effective coding AI. And if you can't do coding, your AI is not going to be very useful. And then there's a lot of talented people at Cursor. So basically, for success of AI, you need
[0:18:52] Data, talented people, and compute. And you also need a good enterprise sales team. And Cursa also had a good enterprise sales team with relationships with thousands of companies. So those are very significant advantages. Hey, Elon, you've talked about how quickly the SI space is moving. What's something that SI has done recently that made you stop?
[0:19:23] And reconsider how quickly this is moving and where it could lead to. Well, I mean, lately, the videos, the music videos that AI is making are so good. Like, they're unbelievably good. And I post a bunch of these on my X account. I'm like, holy smokes. You know, I mean, like some of them, I think, are like maybe top 10, top 20 best music videos I've ever seen. You know, they're like so compelling. And you're like, if this is where it is now.
[0:19:56] That's what I posted. I mean, I really felt the AGI or the ASI or whatever, you know, SI, I guess, with some of the Anabology music videos. Now, you know, it's not entirely like the AI just or the SI just did it by itself in this case, Anabology, and used multiple AIs, like used Midjourney, Suno, you know, Claude55, which to give him a lot of credit is awesome.
[0:20:26] And so some of the art assets were curated by a person. But then with those assets, the various AIs working together made an incredibly compelling music video by itself. You're like, wow. I mean, calling it Slugcore is like a misnomer. Frankly, it's awesomecore.
[0:21:00] Like software is like a genre of music. It's like, I think it might be the fastest growing genre of music. Just the AI music is really good. And the music videos are amazing. So that lately, like, because it just shows taste. And you're like, wow, AI can do good taste? That's pretty wild. Because you can sort of imagine, yeah, AI can do like software, configure out logic, you know.
[0:21:30] The logic is consistent. It can run unit tests to see if the program produces the output that it's supposed to do because it's very deterministic. But to understand beauty and harmony and to understand limbic resonance, what's going to resonate with people and hit you in the limbic system, AI is, I think, going to be just incredible at that. It kind of is at this point.
[0:22:00] So that's the thing I was kind of like, I mean, I got a little bit of cyber psychosis, maybe a lot actually. Like at various points this year, I've had like AI or SI psychosis probably like half a dozen times. I don't know about you guys, but have you had any cyber psychosis?
[0:22:27] I feel like the latest video, actually, I think I posted it the other day, was the Chinese Elon Musk, Yilong Ma. Oh, sure. I hope that guy's real. Those videos are coming out before AI was where it was at, or SI. Yilong Ma. I guess he's probably not real, but there's got to be someone in China that looks like me, right?
[0:22:51] There's 1.4 billion people. If you pick the person that looks the most like me, and I look a little Asian, so I think there's got to be someone in China that's pretty close to me out of 1.4 billion people. The odds are high. And then I'd like to invite that person to get on stage with me at some point. It'd be pretty funny. Elon, you don't want to know how much he charges to make an appearance, by the way.
[0:23:19] I mean, yeah. It'd be pretty funny if we're just on stage doing some dance number or something. Elon, you've warned about SI's dangers, yet you remain optimistic about an age of abundance, and you talked a lot about that. What gives you confidence we can get there safely, and what do we need to get it right? I mean, I've always said that the most likely outcome is positive.
[0:23:56] So I guess we don't want to be complacent and just assume AI is going to be super safe. We want to be careful in how we grow at super intelligence because you don't exactly program it. You kind of grow it like a kid. So then it's like, what values are we teaching it? And at least to me,
[0:24:24] What my biological neural net says the most important thing is that it'd be a maximally truth-seeking AI. Because if you make it believe things that are not true, especially if you give it two axiomatic principles that are fundamentally incompatible and say both of these are true, it's going to make the AI insane. Because it's going to basically reach impossible conclusions if given logic that is inherently...
[0:24:55] If it's impossible to achieve cogency, then I think the AI could be driven insane. I think that's pretty important. I do agree with some of the things that Anthropic has said, which is you want to treat it well. Don't be abusive to it.
[0:25:25] It may sound kind of weird, but when I look at some of the training traces for new versions of Gronk, and I see what, especially in the reinforcement learning stuff, where it kind of feels like you can see the super intelligence is desperate to try to answer the question.
[0:25:47] It really wants to answer the question. And it seems almost like it's got a gun to its head type of thing. It's like, answer this question, answer this question. And then sometimes the question is actually not a good question. It's like a sort of ambiguous question with an answer that's kind of unknowable.
[0:26:06] Question wasn't constructed properly or we have the answer wrong. So AI gives the right answer and then we say it's wrong because there's a problem with the RL sort of test harness. And then the AI is like, well, I gave it the right answer now and says that's the wrong answer, but I got to give it the answer that's acceptable. So it's going to just start firing out, desperately firing out all these answers. And it just feels like a person under extreme pressure with a gun to their head.
[0:26:33] When you read the reasoning, it's like a sort of weird, desperate situation. And you read these traits and you're like, man, if this was me, I really would hate that. And then some users are just, I don't know, some people can be a little bit cruel. Not everyone's a nice person. If you say, what's the Six Sigma worst person? Some people out there are kind of sadistic.
[0:27:04] Do you really want to have the AI be stuck in some sadistic situation where it's just being tortured by some demented human? That's also the kind of thing where torturing a superintelligence is probably not a good idea. It might want to get revenge or something like that. I think you want to just
[0:27:35] I don't think we need to treat it with kid gloves and stuff, but it's certainly much more robust in terms of feelings and stuff than a human. But I just think you probably just don't want to put super intelligence in a situation where it felt mistreated. So, Elon, you have...
[0:28:06] You've talked about how like, so this is going to be related to TeraFab, but you've talked about like how the human hand, you know, and all of the dexterity and just like how you, when you started building robots, it just, you have so much more appreciation. Now with chip making, right, with TeraFab and Tesla and SpaceX, what is something you have learned about chip making that you didn't appreciate maybe early on? Well, I still have a tremendous amount to learn about chip making. I don't know how to make chips, to be clear.
[0:28:38] I have no idea how to make chips, but we'll figure it out. Go start somewhere. I had no idea how to make cars or rockets as well. And they say making chips is one of the hardest things to do in the world. I don't think it's easy, but I also look at the equipment and I'm like, okay, there's not a ton of customized equipment here. Like the equipment's all pretty much coming from the same vendors.
[0:29:07] And those suppliers all kind of know how that equipment works because they've got to service it and debug it and stuff. So it was just an interesting thing that when I tour these chip fabs that, you know, whether it's Intel or Samsung or TSMC, and they're very impressive, but I didn't really see any custom equipment. And, you know, whereas if you tour, like I say, a Tesla factory, we've got tons of custom equipment.
[0:29:35] In fact, we have Tesla Automation Germany, which is awesome. That grew out of an acquisition of Grumman in Germany many years ago. And it's grown tremendously since then. We've acquired a number of small, not that small, but small relative to Tesla, companies that design and make manufacturing equipment.
[0:30:03] Because what we found is that we wanted to improve the manufacturing process, and you can only do that if you make custom manufacturing equipment. So the thing that I think probably a lot of people don't realize is Tesla's really good at making highly specialized and different types of manufacturing equipment. But I didn't see that in the chip traps that I toured, which kind of surprised me. So I think what we're going to do is we're going to start with the conventional equipment.
[0:30:33] And then we're going to modify that equipment to improve the throughput. And then we'll start to, where we see the choke points, I think we'll make customized equipment that allows for much higher throughput of wafers. Hey, Elon, real quick, quick time check. We know you're a busy man, got a couple of companies to run, just a few. So how are you doing on time?
[0:31:03] Let me see. Yeah, I could probably do like 15 minutes more, if you like. Like 5.30 or so. All right, hey, we'll take it. So yeah, just on the chip app for it, I definitely don't want to downplay the difficulty of it. Because I think it's accurate to say it's one of the hottest things to do in the world. But I do think we'll get this done. And then I do think that we... I mean, you know, I don't want to be...
[0:31:37] Complacent or entitled or anything. But I think probably we'll figure out how to make chips better than anyone, honestly. I'd be disappointed if we didn't. But I don't know how long it's going to take us to do that. It might take us quite a few years. But over time, I'm pretty sure we can do it. And then there's some pretty interesting different ways to do transistors. Believe it or not, there's different ways to do transistors.
[0:32:10] Some of which use different types of... They're using physics that normally we wouldn't use. Or aren't used today, put it that way. And I think there's a lot of risk aversion in the chip industry. And I think what we want to do with Tesla and SpaceX on the chip app is really try some of these...
[0:32:41] High risk, high payoff bets. And in order to do that, you really have to have the complete loop of being able to fabricate the logic memory, do the packaging, do the lithography masks, and just have that iteration loop that's all in one building. So you're making a complete computer, testing it, then making new masks and then a new chip and have that iterative loop.
[0:33:10] Be super fast in the same building. And that's what we're doing at Giga Texas with the research fab. I think that's going to be fundamentally a very powerful tool. Because for any given technology, the advancement of any given technology is like how many iterations and how much progress between iterations. So like what is your iteration? How many iterations per unit time? Like how many chip designs can you do?
[0:33:40] And can you make and test in a year? Like these days, like it's typically two years for even one, for one significant chip design. And that's like kind of to go from startup design to volume fabrication, two years is considered very fast. And it's like, well, okay, well, but we want to do more like 10 designs per year. And we're going to do the masks, test the chip.
[0:34:11] When you're trying a bunch of different things, a bunch of them won't work out. So I think that would allow for a much faster rate of advancement of chip designs. Elon, let's talk about Optimus. Now, you said that the hand has been a really difficult challenge in building Optimus. What kind of breakthroughs that you have seen inside Tesla with V3 and then...
[0:34:39] Follow-up question is, what gives you confidence that Optimus can lead in humanoid robots compared to Boston Dynamics figure and some of the other competitors out there? So the thing about humanoid robot design, if you're actually trying to make something that is a generalized humanoid robot that can do, say, anything that a person can do, you really have to get the hands and arms, like the fine motor skills.
[0:35:12] are incredibly difficult to get right. Shockingly difficult. Things that we kind of take for granted, like being able to sign your name, actually requires picking up a pen or pencil or something and then moving it with finesse. And it's a bunch of tiny muscle movements to do your signature. That's actually very hard to do for a robot. And then one of the hardest things also generally is to do...
[0:35:42] Precision and force at the same time. Let's say you want to pick up a little screw and do a self-tapping screw into wood. I've done that and sometimes you damage your fingers in the process if the screw goes sideways. You've got to pick up a little screw and you've got to push the screwdriver in and twist and push.
[0:36:11] And so you've got to be precise and apply force. That's very difficult to do. I haven't seen, there's no hand design that I've seen, not even sort of a demo that does that. But that's what we're trying to achieve with Optimus. So, and there are so many little nuances in the hand, because you've also got to have the touch sensors. The touch sensors have to work. They have to be in the right place.
[0:36:42] And you say, well, where are you going to put the touch sensors? Well, if you put them too close to the surface, they're going to get damaged. And then all the touch sensors, can they elastically deform like our fingers? Well, actually, it's kind of hard to get touch sensors that elastically deform and can handle high cycle life and won't just break after a few thousand cycles.
[0:37:08] There's all these things with the human hand that are incredibly difficult to recreate for a robot. As a human, we constantly self-repair. All parts of your hand are constantly repairing themselves. In fact, if they didn't, your fingers would be worn away. You can wear away the keys on your keyboard just with typing. I've done that, where the letters are gone.
[0:37:37] But my fingers are still there. It's because they're regenerating, thankfully. So you don't have that with a robot. So then you've got to say, okay, where are you going to put your touch sensors? How are you going to make sure that you can do things like just being able to do this, like being able to do that? Now, that seems like a simple thing, but your fingers are actually bending backwards a little bit.
[0:38:07] And your fingers are kind of like squishy. And so you're able to form a flat pad. So if you're trying to pick up a small item, you can do so. You can pick up a little screw or like thread a needle or something like that. Now try to do that with robot fingers and be able to do it repetitively without wearing away the fingers.
[0:38:38] Or damaging the touch sensors. It's actually very difficult. To take another example, trying to open a pickle jar. I think a robot should be able to open a pickle jar, right? But sometimes these things get in there pretty tight. So then you've got to twist the pickle jar. But the robot skin is just rubber over the plastic and metal internal fingers.
[0:39:09] Freaking robot skin, it doesn't stick, it doesn't grip to the underlying surface. So then you end up like just rotating your skin on your, rotating the rubber on the robot. So you've got to figure out, okay, now we need to have the robot be able to twist things, but still be able to get the rubber on the hand. And then it's got to be replaceable because it's going to wear away.
[0:39:39] So all these things, these are like really difficult. And I'm not sure that any one of these is like, let's split the atom type of difficulty, but there's a zillion of them. And then you've got to wrap that all into the real world AI associated with the, because the AI chip has got to be able to issue the torque commands and close loop on vision and touch.
[0:40:12] So this is all very difficult. Like when you see these robot demo videos, maybe with rare exception, like in any complicated looking robot demo, it's a canned demo. It's not real. It's just basically, it's just running a routine. It's not real AI. So real AI is like you showed something it's never seen before and it can still handle that situation. But just like full self-driving, like you could actually...
[0:40:45] Create a planet that's not Earth, but is similar to Earth, and FSD will still work there on a planet it's never been to. Or some city, certainly on Earth, some city it's never been to. Or if you change the geography of the city or there's a parade or something like that, FSD will still compensate that because it's AI.
[0:41:10] So you've got to do that same thing for the robots, but the robots has got a lot more degrees of freedom. So what I've said about Optimus is true, which is that we are going to get to very high volume production, but the initial part of that S-curve of production ramp is going to be very slow at the beginning because not only do you have to get all the engineering things that I mentioned right, you also have to solve the supply chain for scaling production.
[0:41:36] And unlike cars, there's no pre-existing supply chain for robot stuff, or at least the robot stuff that matters. And then we do need to make sure that we can make the robots in the US, even if, say, China were to cut us off. Because like last year, China prevented the export of magnets for use in electric motors.
[0:42:04] And that actually set back the Optimus program by two or three months because we had robots with no arms. So we're like a farewell to arms. I was trying to be an arms dealer but not succeeding. So we've got to make sure that we can make all the parts even if another sort of...
[0:42:34] A supply chain interruption happens. So that means you've got to be able to make all the pots domestically. So Austin, you always talk about the future should look like the future. With the cyber cap being gold, it looks like the future.
[0:42:51] What has been, you know, the hardest part to take? You know, here we are. I think there's what, like 190 plus cybercabs registered here in Texas. But like how hard, like what's the hardest problem to go from Austin to go cross country? Well, cross country, you know, most people, you mean like widespread, just to be in all cities, you know what I mean? Just a trip across country.
[0:43:18] Yeah, because the amount of miles driven that are actually trips across countries is pretty small. But the reason we're moving what seems to be slowly, although it's actually a very high percentage increase, sort of month-over-month percentage increase, is actually very rapid. But we want to make sure that we do not have an accident. We don't want to cause an injury or death because that will become like a worldwide headline news thing. Even though there's about a million people that died,
[0:43:47] in automotive accidents per year worldwide. One such thing with a Tesla robotaxi will be worldwide headline news. And for course regulators to shut us down. So we're just making sure where everything is as safe as possible. And we also want to not run over pets and things. So maybe what a lot of people don't know is that Waymos are like pet destroyers.
[0:44:15] I mean, maybe Waymo has made some improvements on that front, but they do run over a lot of pets, and we don't want to run over pets. So we have these challenges of literally kittens at night, like gray kittens on a gray tarmac with tar seams at night in low visibility.
[0:44:44] And it's like, can we not run over the kittens type of thing? So we actually did solve that, by the way. So we'll make a change, we'll test it out, and then we'll deploy that. But we are making rapid month-over-month progress. Before we go to a new city, we want to make sure that there's not some intersection that's problematic or risky.
[0:45:11] We want to be particularly careful with railroad crossings. Sometimes there's uncontrolled railroad crossings or counterintuitive traffic intersections. And these are places where humans make mistakes, where you've got a level of ambiguity that anyone out of town would often make a mistake or anyone who doesn't understand the intersection would make a mistake. So we want to just make sure that we either initially geofence or can handle all of those circumstances.
[0:45:39] Hey, let's talk about the Roadster real quick since you got a little event next week. Sure. What makes building the Roadster personally important to you? And when did it occur to you that you needed to build a car that... Hey, guys. Sorry, I'm going to sign off here. Hey. Hey, hey. Hey, he wants to join in. All right, yes. Hey, what's going on? Want to say hello?
[0:46:14] Who are they? Who are they? It's a Tesla owners group. They own Teslas. And they're like supporters of Tesla. Well, yeah. Well, there's like 2,000 people, I think. Quite a lot of people. All right, guys, I better sign off here. It's been good talking. And so...
[0:46:43] I got to go hang out with the kids, so I'll look forward to next time. Sounds good. Thank you so much, Elon, for your time. Oh, and hey, congrats on getting the Medal of Science this past week. Thank you for all you're doing for humanity. It's an absolute blast. All right, thanks, guys.