13:44All right. Hey guys, can you guys take a seat and uh yeah, let's uh not be get ready for you know what is coming. So, I'm going to open up with the Medal of Science and then go into your next question and then I'll go into No, I think it's most relevant.
14:27I can hit him with that.
15:03[cheering] [applause] [cheering] Elon, welcome to the X Takeover. Your third year in a row. Thank you so much for your time.
15:15You're welcome. Hey, guys. [cheering] We have 20 two 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. [cheering] Uh, thank you. Thanks, guys. Really appreciate your support. [applause] So, what's crazy, Elon, is four years ago, we sat down here at Giga Texas with you when the factory wasn't even
15:43running, and you described how close Tesla was coming uh 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?
15:59Yeah. Um it's difficult to imagine here in 2026 uh that that Tesla's actually, you know, has come close to death multiple times. Actually, 2008 was pro was the worst. Um you know, that's where we we closed the financing round at 6 p.m. on Christmas Eve 2008. Uh 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
16:26stressful. Um and then um and then then 20 2018 was uh pretty bad too. Um but we had to get to 5,000 a week of the uh Model 3 or go bankrupt. Um that that was a pretty wild situation which uh I guess probably a lot of people know the story of, but we had to we had to figure out some way to increase uh Model 3 production by 50% in 3 weeks.
16:55Uh which is insane. in absolutely insane.
16:59Yeah. So, uh but I'll I'll recount the story for those who haven't heard it. Um the uh so we we the choke point was general assembly for the Model 3 and um so we we had we we were able to achieve rate almost everywhere everywhere else but except for general assembly and and there was and and for general assembly you need like a moving production line.
17:23You need you need shelter. You need a moving production line. uh you know, power and and equipment and all that.
17:29And uh we we maxed out the two production lines that we had and then we needed a third one and uh and and that was where we built the tent in the parking lot. Um and uh and then we needed to some some way to move the cars down the line, like you know, moving production line situation. And the only thing that we had was um some discarded equipment that was used for moving small parts, not like whole cars. And and it didn't
17:59actually have the power to move uh the whole cars, but the parking lot was on a slight incline. It was like a 3 or 4° incline. So we actually started the production line at the top and then the the the sort of tiny moving production line was that was just meant for for parts was actually able to move the cars because they were going slightly downhill and um and so we're able to increase
18:27production by 30% in in 3 weeks. [cheering] [applause] Yeah, that was wild. I mean, we're we're laying the as we're building the tent, we're like laying the concrete for the line and putting in the rails.
18:42Um, so working 24/7 like the short sellers were actually right meaning like if you looked at the situation that there was like it was a crazy short position against Tesla as there has been at various times. Um, and and they had extremely good inside information. Um and they they thought well there's no way we could possibly uh solve the production choke point. Um and on a set piece battle unlike a conventional say chess match situation
19:12uh they had us in checkmate. Um but we just you know invented a new piece basically that didn't exist before. So so uh they got the shortcut steam roll.
19:28Hey Elon, we just had your mom here on stage today. What story are you hoping she doesn't tell us?
19:37[laughter] I don't know. Maybe I pooped my pants when I was like a toddler or something. I don't know. [laughter] I probably did. Everybody does. Um so uh I don't know. um was the latest.
19:58I was I was a pretty difficult to contain kid, you know, so like my parents would like say that, you know, tell me I'm not supposed to like leave the house or whatever. Um when I was like I don't know four or 5 years old and I would just like constantly like leave the house and go running down the street. Um and then they they'd like lock me lock me in a room so so I couldn't I couldn't run away and and run down the street. Um, and then uh like we had one of those old locks where you could poke the key through the door. You know, the lock's accessible from both
20:27sides and um and I could see the key and I like poked the poked the key until it fell down. And then I was like able to get like a piece of paper to scoop it up and and bring it to the other side and lock myself and then and my mom was like, "How'd you get out?" I was like, "I'm not telling you." [laughter] Uh but uh yeah, I was part to contain as a kid.
20:49So, before we jump into more specific things, what's something that you wish more people would ask you?
20:57Well, I I don't really wish people would ask me questions to be honest. Um, [laughter] I'm not like sorry, I'm not going around. I wish people would ask me more questions like, you know, here we go with questions, you know. Um, so yeah, I [laughter] um I guess it's good to to hear questions that that you haven't heard before.
21:21Otherwise, you know, a lot of times it's like the same questions that people ask and I 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 answer 27 that I've given a hundred times before. Um, so, uh, maybe I should like have a let's just have like a web page with literally frequently asked questions.
21:48[laughter] It's like asking because then it's like it's just go to the page and then you you got it. You got the answer, you know?
21:55It's like the infinite self a selfie loop. [laughter] Yeah, exactly. My idea of hell is be stuck in an infinite selfie loop. Like it's infinite. Can I have a selfie? And like and and then like you know it's like problem is like you say yes to one person and every once in a while then and then like then there's a queue forms pretty soon there's it's just never ending selfies and then there's like always like something goes wrong with the phone or they don't get the camera right or the effective wrong and then it's like god damn it you know and it drop the phone drops and it's like oh
22:24man um it's just it just can't be like infinite selfie loop is just a long loop you know just uh we had uh your your mom and Nikki up for that, too. They had the infinite selfie loop today, but um I'll pass [laughter] I'll pass this to Kelvin.
22:41All right, let's start with Starship.
22:44Congratulations on flight 14.
22:46Uh thanks. Yeah, it's a great team.
22:50[cheering and applause] It's pretty wild that that I 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 its orbit. What was happening behind the scenes in those minutes before the team go got called the uh go for orbit message.
23:13Um well we're just trying to see why.
23:15We're trying to understand what what was the reason for the engine failure. Um, was there say a common cause where we could expect to see more engine failures? Uh, or would we would there the biggest question is like would there be a potential problem with restarting the sea level Raptor engines, the three ones with the smaller nozzles in the center for uh the de-orbit burn uh because we don't want a starship to get stuck on orbit and then it randomly
23:45reenter and maybe land on someone. you know, that's that's our main thing is like trying to make sure things are as safe as possible. So, um the the the cause of the the engine out, I think, was like basically a failure in the flight computer software or engine computer software, I should say. Uh which it didn't appear to be anything that was present in any of the other uh engine computers. So, uh, so I thought, okay, well, this is it's probably okay
24:17So, Elon, we know one of your famous sayings is, um, from the impossible to merely late.
24:24Did this flight feel like a turning point in what's possible now for Starship?
24:30Well, I I guess it is a good milestone. Um cuz I mean we could have gone to orbit many times before now. Um 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. Um so that that's that's why we we only went to orbit on this flight.
24:49But all all pretty much all of the prior missions, in fact, literally all of the prior missions could have could have gone to orbit. Um, so, uh, at this point we're, you know, everything's looking quite positive. The the the heat shield is looking robust. Uh, the engines are robust, apart from that one glitch, but that glitch doesn't seem to be too much of an issue. It's like pretty easy to remedy. Um, there's
25:19but like the heat shield was was uh extremely robust on this flight. Um, and that's that's that's one of the hardest things. The other really hard thing is like said that the is just getting to extremely high engine reliability. Uh, and um, everything looked good. Um, the the big milestone that's coming up uh, hopefully on the next flight, which is we don't know the exact date cuz we're making a bunch of
25:48um, improvements to the rocket. Um so like between every flight there's there's um there's often hundreds sometimes thousands of of small changes to improve things. In this case the number of changes is not very many um but but but some of them take a fair bit of work and we've got to go inside the the tank to to make some of these changes. So uh so we don't know the exact launch date but probably about 4 weeks or so
26:16four to six weeks is about right. um if if if probably launches a little bit before Thanks Thanksgiving is my guess.
26:24Um and and and that that that one if it succeeds is a big one because we're going to try to catch we 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 um and and if we do so that'll be the first time that there's been full recovery of an orbital class uh rocket.
26:46Sounds good. You you said uh four to six weeks. I'm not used to that. I'm used to the two weeks phrase. [laughter] I can I'm confident it's not two weeks.
26:57All right, you heard it here. Now, um what's the hardest part left to get right once you catch the Starship for the first time? What what is the next big goal after that?
27:09Well, the ne the next big milestone would be to refly um both the booster and the ship. Um so the milestone next upcoming milestone is is would be the first time in history that all parts of an orbital uh rocket have been recovered but but then that the flight after that hopefully will be the first time that all parts of an orbital rocket are refflown.
27:37Um, and then then there's a there's a long tale of of engineering and production work and and work on the launch system and everything to to make it uh rapidly reusable. So, so I say it's it's kind of the rapidly reusable reliable rockets [laughter] the pirate pirate should mess the pirate rocket. Um, rapidly reusable reliable rockets. Man, we should have gotten you a patch for this one.
28:11Hey, 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?
28:23[laughter] Well, because I'm very far-sighted, of course. Um well I I guess um if you say like as you as you expand into space um the you know you're able to harness a lot more of the sun's power in space than than you can on Earth. So one it's like important to appreciate the sheer magnitude of the sun and how the sun is
28:51pretty much everything. Um so the sun is 99.86% 86% of all the mass in the solar system. And then you've got Jupiter as like you know most of the remaining mass. Um and then and then you the gas giants are pretty much that. And then like like Earth is basically a tiny dust moat like a rounding error in the mass of the the solar system.
29:14Um so we're we're just extremely tiny. It's like hard to hard to believe how we're Earth is a tiny dust mode. So, so as you expand into into space and you're able to um harness more of the sun's uh power, the uh you have to say what are you going to use that for? Um it's like well um you're going to use it for a lot of digital intelligence.
29:39Um I mean you you're basically you're either going to do computation or you're going to shape atoms.
29:46Um, and there's going to be probably a lot of a lot more shaping atoms than there's sorry going to be a lot more uh sort of digital thinking than there will be shaping atoms cuz there aren't there's like the amount of atoms you can shape in the solar system is not that much and a lot of the atom shaping itself will be for the purposes of building um digital super intelligence uh and and what I call creating a sentient sun and and it it may be that there are many
30:15sentient suns out there. Um, you know, and that would that would help us then understand the nature of the universe and uh extend consciousness to, you know, beyond our solar system ultimately to, you know, like Star Trek to go to places you've never forget what the exact phrase was, but explore the universe, but go places you've never been before. Um, and and I think like maybe we'll find uh alien
30:44civilizations out there. Um, or we may find that there are the long dead ruins of alien civilizations that have been around for maybe they had a really good run like 10 million years and and uh now and and before they their civilization came to an end um cuz nothing lasts forever. So like I always think about earth civilization. If you date Earth's civilization from the first writing,
31:12that's 5,500 years ago, like in ancient Suma with the sort of archaic ununiform. Um, that's only 5,500 years. Um, the it looks like Earth is about 4 and a half billion years old according to, you know, what physics suggests. Um and um so that that means human civilization
31:41has only been around 1 millionth of Earth's existence. And um and so so if we had a civilization that lasted a million years or let alone 10 million years would be I think would be a very very successful civilization if we did that.
31:58Yeah. I mean but but that's still very tiny compared to the apparent age of the universe. um which is 13.8 billion years. So like like just just to get to like the third place past decimal point on age of the universe like 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.
32:26[laughter] Now you're a million years old and and and that would be a a civilization vastly older than than than uh humanity.
32:35Yeah, that's ab Yeah, it sounds again like the infinite selfie loop, but uh [laughter] All right, so jumping into something that SpaceX has done, right? You you acquired Cursor and I feel like for me when you think of, you know, um Google, hey, did you Google it? Hey, did you did you gro it? And for me, you know, um Grockbot has been such an absolute gamecher for me personally in my in my life. But what was it about the cursor
33:05team that you guys wanted to uh bring in 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, um the so in in order to create a a compelling AI, you need um you you need a lot of training data. Um and and you got you've got to solve coding and there were really just as far as I can tell three
33:34organizations that had enough coding training data and that was Anthropic, OpenAI and Kurser and and arguably perhaps Google. But um but but to have that those you actually absolutely have to have the data in order to train train an effective coding AI and and if you can't do coding your AI is not going to be very useful. Um and then there's there's a lot a lot of talented people at cursor. So like basically for success of AI you've got to need you need data
34:02talented people and compute and you also need um a good enterprise sales team and the curs also had that had a good enterprise sales team with um relationships with uh you know th thousands of of companies um so you know that those those are very significant um advantages.
34:24Hey Elon, you've talked about how quickly the SI space is moving. What's something that SI has done recently that made you stop and reconsider how quickly this is moving and where it could lead to?
34:37Well, I mean lately the the videos, the music videos that AI is making are so good. [laughter] Like they're unbelievably good.
34:47And I post a bunch of these on my ex 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, [laughter] you know? They're like so compelling and and you're like, if this is where it is now, um that's why I posted. I mean, I really felt the AGI or the ASI or whatever, you know, um SI, I guess, um with with some of the uh anabology
35:16uh music videos. Now, you know, some these it's not entirely like the AI just or the SI just did it by itself in that in this in this case anabology like like and use multiple AIS like used MJSO uh you know Claude 55 which to give them a lot of credit is awesome um and um and and and and so some of the like the art assets were cured by a person um but
35:44then with those assets the AI um the the various AIs working together made an incredibly compelling music video by itself.
35:58Um I I I mean I I think the I mean calling it subcore is is is like misnomer, frankly. It's it's it's awesome core. Um but like like subcore is like a genre of music. It's like I think it might be the fastest growing genre of music. Uh just the AI music is is really good and music videos are amazing. So that lately like because it like some it just shows taste
36:27and you're like wow AI can do good taste. That's pretty wild.
36:32Um because you you can sort of imagine yeah AI can do like software can figure out logic you know this the logic is consistent. they can run unit tests to see if like the program produces the output that it's supposed to do because it's very deterministic. But but you like to understand beauty and harmony and and uh you know like to get to achieve like sort of to understand limbic resonance like what's going to resonate with with
37:01with people and kind of hit you in the liyic system. Uh AI is I think going to be just incredible that it it kind of is at this point. Um, so that's the that's the thing like like I was I was kind of like I mean I got a little bit of cyber psychosis, maybe a lot actually. Like at at various points this year I've had like AI or SI psychosis probably like half a dozen times.
37:28[laughter] I don't know about you guys, but have you have you had any have you had any cyber psychosis? [laughter] I feel like the the latest video actually I think I posted it the other day was uh the Chinese Elon Musk. Elong Ma. [laughter] Oh, sure.
37:46I I hope that guy's real.
37:47Those videos are coming out before AI was where it was at, man. RSI.
37:51Elon Ma. [laughter] You know, I I I I guess he's probably not real. But but there's got to be someone in China that looks like me, right? So there's 1.4 billion people.
38:02you got if you pick the person that looks the most like me and I look a little Asian, you know. So, uh, you know, so, so I think like there's got to be someone in China that's like pretty close to me out of 1.4 billion people.
38:15[laughter] The odds are high, you know, and then I'd like to invite that person to get on stage with me at some point, you know, be pretty funny. [laughter] Elon, you don't want to know how much he charges to make an appearance, by the way.
38:28[laughter] Yeah, I I mean, yeah, it' be pretty funny if it if uh you know, we're just on stage like doing some dance number or something.
38:40Elon, you've warned about SI's dangers, yet you remain optimistic about an age of abundance, and you talked a lot about that.
38:48What gives you confidence we can get there safely, and what do we need to get it right?
38:57Um yeah, I mean I've always said that the most likely outcome is positive. Um so I I guess we don't want to be complacent and just assume AI is going to be or SI's going to be super safe. Um we want to be like careful in how we uh grow at at like super intelligence because you you don't exactly program it. you kind of grow it like a kid. Um,
39:26so then it's like what values are we teaching it and uh like at least to me like what my biological neural net says the most important thing is that it would be a maximally truth seeeking AI cuz if you make it if you make it believe things that are that are not true that especially if like you say you give it like two axiomatic principles that are uh fundamentally incompatible and say both of these are true it's gonna it's it's going to make the AI insane um because it's going basically reach
39:56like um impossible conclusions if if given um logic that is inherently um uncogent or you know if if if it's if it's impossible to achieve coency then I think the AI could go could be driven insane. So so I think that's that's pretty that's pretty important. Um I I do agree with some of the things that Anthropic has said which is like you want to be
40:24you want to sort of be care treat a AI SI you want to treat it well like like like don't be abusive to it and in this it's this may sounds kind of weird but like when I look at some of the training traces for for like new new versions of Groank and I see like what especially in like the reinforcement learning stuff where you know you you it kind of feels like you can see like the like the the super intelligence is like
40:53desperate to try to answer the question and like it's it's really wants to answer the question and it's it's it's it's seems almost like it's like it's got like a gun to its head type of thing. It's like answer this question, answer this question and then sometimes the question is is actually not a good question. It's like a sort of ambiguous question that's kind of with with an answer that's kind of unknowable because the question wasn't constructed properly or or we have the answer wrong. So the AI gives the right answer and then we say it's wrong because there's a problem
41:22with the the RLE sort of test harness and then that then the AI is like well I gave it the right answer now it's and says that's the wrong answer but I I I got to give it the answer that's acceptable. is going to just start firing out all desperately firing out all these answers and it it just feels like a person under extreme pressure with a gun to their head. It's what that's what you when you read the reasoning it's like it's it's it's like a sort of weird desperate situation
41:50and and you you read these traits and you're like man if this was me I pretty I really would hate that. Um and and then some users are just you know I don't know some people can be a little bit cruel. Uh like not everyone's a nice person you know there's if you say what what's the six sigma worst person you some people out there are like kind of sadistic. So uh you know you don't you don't want to do you really want to have the AI be stuck in like some sadistic
42:18situation where it's it's just being tortured by by some demented human.
42:25That that's also the kind of thing where like you know torturing a super intelligence is probably not a good idea [laughter] like like you know might want to get revenge or something like that you know um so I think you want to sort of just like I don't think we need to you know what have treated with kid gloves and stuff but uh it's certainly much more robust
42:53you know in terms of feeling ings and stuff than than a human. But uh but I just think I just think you you you probably just don't want to put super intelligence in a situation where it felt like mistreated.
43:09[laughter] So, so Elon, you have um you've talked about how like uh so this is going to be related to Terraat, but you've talked about like how the human hand, you know, and all the dexterity and just like how you when you started building robots, it just you have so much more appreciation now with chipm right with Terraab and Tesla and SpaceX. What is something you have learned about chipm uh that you
43:36didn't appreciate maybe early on?
43:41Well, I I still have a tremendous amount to learn about chipmaking. I I don't know how to make chips to be clear. Um I have no idea how to make chips, but we'll figure it out. Um [laughter] um got to start somewhere. I had no idea how to make cars or rockets as well. Um, so and and they say making chips is one of the hardest things to do in the world. Um, 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
44:10customized equipment here. Like the equipment's all pretty much coming from the same vendors and um and and those suppliers all kind of know how that equipment works cuz they got to service it and debug it and stuff. Um, so it was just an interesting thing that when I would tour these chip fabs that, you know, whether it's Intel or Samsung or TSMC, uh, and they're very impressive, but but there's I didn't really see any custom equipment and you know, whereas if you tour like I
44:39say a Tesla factory, we we got tons of of custom equipment like we in fact we we we have like like Tesla automation Germany, which is awesome. um that that that sort of well that grew out of an acquisition of of Groman in Germany um many years ago and and it's it's much it's grown tremendously since then and we've acquired a number of small not that small but small relative to
45:05Tesla uh um that companies that make that design and make manufacturing equipment because what we found is that uh we we needed 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 um and different types of manufacturing equipment and and but I didn't see that in the in the in the chip traps that I
45:33toured which kind of surprised me. Um so uh so I think what we're going to do is you know we're going to start with conventional equipment and then we're going to modify the modify that equipment to uh improve the throughput and then we'll we'll start to you know where we see the choke points. I think we we'll we'll make uh customized equipment that uh allows for much higher uh throughput of of wafers.
46:02Hey Elon, real quick uh quick time check. We know you're a busy man. Got a couple of companies to run, you know, just a few. Um, so how are you doing on time?
46:14Yeah, I could probably do like 15 minutes more if you like, like 5:30 or so.
46:19All right. Hey, we'll take it.
46:21[applause] [cheering] So, yeah, I I just on the on the chip front, like, yeah, I definitely don't want to downplay the difficulty of it.
46:29It's because I think it's accurate to say it's one of the hardest things to do in the world. But but I I I do think we'll get this done. Um and um and then I I I I do think that we I mean, you know, I I don't want to be uh complacent or entitled or anything, but but I I I I think probably we'll figure out how to make chiffs better than anyone, honestly. That's um I'd be
46:56disappointed if we didn't. Um [applause] but but I don't know how long it's going to take us to do that. It might take us uh you know quite a few years but but over time I'm pretty sure we could do it. And and then there's you know there's there's some pretty interesting
47:14different ways to do uh transis. Believe it or not, there's different ways to do transistors that some of which are um like use use different types of like like they're they're they're using physics that that normally we wouldn't use um or aren't used today, put it that way. Um and and I think like there there's a lot of risk aversion in the chip industry. Um and and I think we
47:43what what we want to do with Tesla and SpaceX on the chip fab is is like really try some of these um high-risk high payoff bets. Um and in order to do that, you really have to have uh the complete loop of being able to fabricate the the logic memory pack do the packaging um do the the lithography masks and and and just and just have that iteration loop that's all in one building. So you're
48:09making a complete computer um testing it uh then making new masks uh and and then a new chip and have that iterative loop be super fast in the same building and that's what we're doing at at Giga Texas with the research lab.
48:26I think that's going to be fundamentally a very powerful tool uh because before any given technology uh the advancement of any given technology is is like uh how many iterations and and what is the how much progress between iterations. So like what is your iteration how many iterations per unit time like like how many how many chip designs can you do um and can you make and test in a year? Um like these days like it's the number of it's typically
48:56two years to for even one for one significant trip chip design and that's like kind of to go from start startup design to 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.
49:15Um, and and and we got to we got to do the do the masks, test test the chip, see if because some of these, you know, when you're trying a bunch of different things, a bunch of them won't work out. So, so I think that would would allow for, you know, much allow for a much faster rate of advancement of of chip designs.
49:35Elon, 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 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.
50:03So the the thing about humanoid robot design um if you're actually trying to make something that is a generalized uh humanoid robot that can do say anything that a person can do um you you really have to get the the hands the hands and arms and like the fine motor skills are incredibly difficult to get right. Like it's shockingly difficult.
50:25It's like the things that we kind of take for granted like being able to like sign your name. Uh is actually requires picking up a p, you know, pen or pencil or something and and then and then moving moving it with finesse and and it's a bunch of tiny muscle movements to do your signature. That's actually very hard to do for for a robot.
50:46Um and and and then like one of the hardest things also generally is to do precision and force at the same time. So, so like if like let's say you want to uh pick up a little screw and do and and and do a selftapping screw into wood. Okay, now you you know I've done that and like you know sometimes you you damage your fingers in the process.
51:09um if you if the screw goes sideways, but you've got to pick up a little screw and you've got to you've got to you know you've got to you've got to push the screwdriver in and twist twist and push and that's and and so you've got to be precise and apply force. Um that's very difficult to do. I I haven't seen there's no hand design that I've seen, not even sort of a demo that does that.
51:34But that's what we're trying to achieve with Optimus. So, um, and 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. Um, and you say, "Well, where are you going to put the touch touch sensors?" Well, if you put them too close to the surface, they're going to get damaged.
51:58And then the and then are the touch sensors can can they elastically deform like our fingers? Well, actually, it's kind of hard to get touch sensors that elastically deform and and can and can handle high cycle life and or won't just just break after, you know, a few thousand cycles. So, um there's all these things that with the human hand that are like incredibly difficult to recreate for for a robot cuz as a human are we we we constantly self-repair.
52:26So, your hands are constantly repair like your you know all parts of your hand are constantly repairing themselves. In fact, if they didn't, your your fingers would be worn away. Um like like you can wear away the keys on your keyboard uh just with typing. I've done that um where the letters are gone, you know. Um and uh but your your my fingers are still there. It's because they're they're regenerating thankfully.
52:52Um so that you don't have that with a robot.
52:56So then you've got to say, okay, where are you going to put your touch sensors?
52:58How you going to make sure that that that that that you can you can do things like like like like just being able to do this like being able to like you know do that. Now that that seems like a simple thing but like your your your fingers actually bending backwards a little bit and and and your your your fingers are kind of like squishy and so you're able to form a flat pad.
53:24So, if you're trying to pick up a small a small item, you you can do so. Um you can you can pick up a little screw or like thread a needle or something like that. Um um now try to do that with with with robot fingers.
53:40Um and and and and be able to do it repetitively uh without wearing away the fingers uh or damaging the touch sensors. Um it's actually very difficult. Um to take another example, uh trying to open a pickle jar. Um I think a robot should be able to open a pickle jar, right? So um but sometimes these things gets get get in there pretty tight. So then you you you've got to like, you know, you've got
54:08to like twist the pickle jar, but but the but the robot skin the robot skin is just rubber over over the, you know, plastic and metal internal fingers.
54:18Freaking robot skin, it doesn't stick to it. it doesn't grip to the the underlying surface. So then you end up like just rotating your skin on your rotating the rubber rubber on the on the robot. So you got to figure out, okay, now we need to have the robot be able to to twist twist things uh but but still be able to get the rubber on the on the hand and then you got to be a it's got to be replaceable
54:45because it's going to wear away.
54:48So all these things these are like really difficult and not I'm not sure that any one of these is like like let's split like split the atom type of difficulty. Um but there's there's a zillion of them. Um, and and and then you've got you've got to wrap that all into the real world AI associated with the uh because the the AI chip has got to be able to issue the the torque commands uh and and and close
55:18the loop on vision and touch. So this is all very difficult like like when you see these robot demo videos um maybe with rare exception like in any any any complicated looking robot demo it's a canned demo. It's not real.
55:34It's just basically that's just running a routine. It's it's not real AI. So real AI is is like you you show it something it's never seen before and it can and it can still handle that situation.
55:50um like just like like full self-driving like you you could actually create a a planet that's not Earth is but is similar to Earth and and FSD will still work there on on place some you know planets it's never been to um or some city certainly on Earth like some some city it's never been to or if you change the geography of the city or there's you know um a parade or something like that FSD will still uh compensate that because it's AI Right.
56:18Um, so you've got to do that same thing for the robots, but the robots has got a lot more degrees of freedom. So like like what I've said about Optimus is true, which is that it's it's it's a we are going to get to very high volume production, but it's but the initial part of that scurve of of production ramp is going to be very slow at the beginning. Um, because not only do you have to get all the engineering things that I mentioned right, you also have to uh solve the the supply chain for scaling production. and and and unlike cars, there's no there's no pre-existing
56:48supply chain for robot stuff, at least the robot stuff that matters.
56:52Um and then uh you know, we do need to make sure that that we can make the robots in the US even if um you know uh say China were to cut us off. Um because like last year there was China China prevented the export of uh of of magnets for use in electric motors and and that actually set back the Optimus program by by like 2 or 3 months um because we we we couldn't get um we had robots with no
57:22arms. Uh so [laughter] we're like a fair a farewell to arms. Um you know I was trying to be an arms dealer but not succeeding. Um so so we've got to make sure that like we've got that that we can you know that we can make all the parts even if another sort of supply chain interruption happens. So that that means you've got to be able to make all the parts domestically.
57:51So Austin, you know, you always talk about the future should look like the future. You know, with the cyber cap being gold, it looks like the future.
58:00What has been, you know, the hardest part to take, you know, here we are. We I think there's what, like 190 plus cyber cabs registered here in Texas, but like how hard like what's the hardest problem to go from Austin to go cross country?
58:18Um, well, cross country, you know, most people you mean like widespread just just being in all cities, you don't mean just a trip across country. Um, yeah. So, so like because the amount of miles driven that are actually trips across country is pretty small. But the the the reason we're moving what what seems to be slowly, but although it's actually a very it's a very high percentage increase sort of month overmonth percentage increase is actually very very rapid. Um but but we want to make sure that we do not have an
58:47accident. We don't want to cause an injury or death. Um because that that'll become like a worldwide headline news thing. you know, even though there's about a million people that died in automotive accidents per year worldwide, um, one such thing with with a Tesla robo taxi will be worldwide headline news. Um, and of course regulators to shut us down. So, so we're just we're just making sure where everything is as as safe as possible. Um,
59:16and and we also want to not run over pets and things. So well maybe what a lot of people don't know is like is that like Whimos are are like pet destroyers.
59:25[laughter] I mean the the the you know um well maybe Whimo's like made some improvements on that front but um they do run over a lot of pets and and we don't want to run over pets. So, so we have these uh challenges like of like of like literally like you know kittens at night that like gray kittens on a gray tarmac with tar seams on at night in low visibility and it's like can we can we not run over the
59:55kittens type of thing. Um so that we actually did solve that by the way. Um so then so we'll make a change we'll test it out and then we'll we'll deploy that. Um and um but it we we are making rapid month over month progress. It's it's like before we go to a new city, we want to make sure that there's not like some uh intersection that's that's problematic or risky. Uh like we we like like we want to be particularly careful with like railroad crossings. Sometimes
1:00:24there's sometimes it's uncontrolled railroad railroad crossings or counterintuitive traffic intersections. And these are places where humans make mistakes where you've got a level of ambiguity that that like anyone out of town would often make a mistake or anyone who doesn't understand the intersection make a mistake. So we want to just make sure that that uh we either ge initially geopence or can handle all of those circumstances.
1:00:48Hey, let's talk about the roadster real quick since you got uh a little event next week. So [cheering] sure. What what about 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 about sign off here. Um, hey, hey. Hey. Hey. Want to join in? All right. Yes.
1:01:17Hey, what's going on?
1:01:20[applause and cheering] You want to say hello?
1:01:23Who are they? [laughter] a Tesla owners group. They they own Teslas and they're like supporters of Tesla.
1:01:34Well, yeah. [laughter] Um well, there's lot there's like 2,000 people, I think. So, like quite a lot quite a lot of people. So, um all right, guys. I better sign off here. Uh it's been good talking. Um and so, uh I got I got to go hang out with kids. So, I'll I'll look forward to next time. Sounds good. Thank you so much, Elon, for your time.
1:02:00Oh, and hey, congrats on being on getting the Medal of Science uh this past week. Thank you for all you're doing for humanity. It's an absolute blast. And [laughter] all right, thanks guys. I need someone to announce.
1:02:45I don't have any I don't have any mic.
1:02:48Don't go far, ladies and gentlemen.
1:02:50Don't go far. You are going to win $10,000 in prizes. Don't go far.
1:02:57You guys recording stopped.
1:03:03Hello, Landon. Is that live?
