00:36:24 · Ty Morse, Bernt Øivind Børnich
[00:36:24] S0: on the Internet data, then you get a little bit of your your own data. But eventually, in order to really get the data that you need to train a super genius robot, you just have to have a whole bunch of these Mhmm. In operation. Like, if you think about if you have 50,000 next year, suddenly, and they're operating, let's say, sixteen hours a day, you're getting 16 times 50,000 every single day of new data, I assume. When you're thinking about, like, your bottlenecks for scaling your data, is, like, one of the biggest bottlenecks just we need more robots in the field? [00:36:55] S1: Yeah. And they need to be in very diverse environments and very diverse applications. In reality, you're almost never data bound. You're diversity bound. So when people say, like, oh, you need very large amounts of data. That's true, but you don't actually need just large amounts of data. You need an extreme diversity of different experiences and tasks and things that you haven't seen before. And if you have enough of that, that becomes a lot of data because you have so many different things. But lots of data of the same thing doesn't really help you. Right? [00:37:29] S0: Let's say you ship 50,000 robots next year. What is the breakdown of where those different robots are going to go? Because, like, maybe, you know, initially, I was thinking, like, 50,000 to homes. But maybe you don't want that. Maybe you want, like, 10,000 to homes and, you know, 2,000 that'll just go into the woods and build a cabin or something. I don't know. I think you just
00:37:48 · Bernt Øivind Børnich
[00:37:48] S1: just cross the stack. Like a couple years more out. But I... One of my dreams is to be able to leave my house and say, like, hey, NEO. We're having a part... Party this weekend, and I would like my garden to be Japanese. And then just, like... It'll, like, go and reconfigure the entire garden and, like, build a nice pavilion and, like, you come back, like, a week later and it's like, oh, it's ready. That's nice. But that's gonna be a couple of years out. So so so this year, there will be a couple of enterprise applications where we have very large customers so we can deploy a lot of robots into specific verticals. And then there's gonna be some to the home, and it's gonna be a lot to the platform. It's gonna be a good good mix of all of them. I think it's important to get, like, information from all of them and, like, get the lessons learned from all of them. But I also think it's important to have some applications where solving the task to actually be truly useful and, like, a return on investment for your customer point of view is very important because that's how you drive adoption. Right? And you need scale to get the cost down and to get the reliability up and everything else. So, a good good mix of these. We're not quite ready to announce yet what the specific enterprise applications and customers are, but that's in the works.
00:39:07 · Ty Morse, Bernt Øivind Børnich
[00:39:07] S0: Do you kind of think about it also in the same way that maybe you need a bunch of, like, folding data? And so one way to get that in an enterprise setting might be like, hey, old Navy. You know, here's 5,000 robots, and they just spend all day folding clothes, and then you get, you know, 5,000 times sixteen hours a day of [00:39:25] S1: folding. Application. We're not gonna do folding as our main application, so I can I can debunk that myth? Everyone else is doing folding, so that there's already a lot of folding data. [00:39:34] S0: Okay. So everyone else is doing folding. What is the one thing that no one is doing that seems fucking obvious to you? Is that... Could you say? [00:39:40] S1: No. Fuck. Someday, I'll reach out to you again. Someday being just a few months, and tell you, like, here's what I thought about. [00:39:46] S0: Okay. Next year, we can [00:39:47] S1: we can know. Okay. We we think we've figured this out. We we we think we have a couple of things we're gonna do that. It's gonna be, very material. [00:39:56] S0: I remember this one situation where one, like, oil company bought a fertilizer company, and then suddenly, like, all these other oil companies started buying fertilizer companies. Like, the first guy was just like, I just bought it, you know, for shits and gigs, and then other... A bunch of other people started doing it for no reason. And is it like full... Some guys started unfolding, and then everyone else is like, well, they're working on folding. Maybe we should too.
00:40:15 · Bernt Øivind Børnich
[00:40:15] S1: No. It's actually... Folding is kinda... We also did folding. Like, you can go on our YouTube channel. Like, you you can see us using Eve actually a few years ago to, like, fold shirts. So I guess we were early in folding. It's been this incredibly hard problem because it's it's soft goods. Right? Like, so it's it's... You never know how it's gonna look. Like, you crumb plate, it looks completely different. Like, a cup, the same cup looks as... Like the same cup every time. It might be slightly dirty, but it's the same shape. Clothing is a different shape every time you look at it. So it's incredibly hard to simulate because it's, like, soft and deformable. So it was just this large open problem in robotics. How do you do folding? Extremely hard to do in a classical sense. And it just happens to be one of those things that are very easy to do with AI. You leave very little data and it just works. I don't know. Maybe someone's working on, like, a very nice academic paper that can explain to you exactly why that is the case. I don't actually know. But it is one of the simplest things to do. But it's something that used to be very complex, and now it's very simple. So it's like the most overused demo because it looks like you're extremely capable because you can fold these clothes, but it's actually not that hard. It's actually quite simple. It's one of the simplest problems to
00:41:31 · Ty Morse, Bernt Øivind Børnich
[00:41:31] S0: I love this line. Starting a startup is like chewing glass and staring into the abyss, and being a founder is being someone that enjoys that process. When was the last time that you experienced utter disappointment? [00:41:43] S1: I mean, this happens every week. Mean, there's always things that don't go well. So what I've learned is you have to enjoy the journey. Like, that's the secret. Like, there's... Because there is no end. Right? Like, there's... It's not like, oh, if I just fix these things that I know is wrong, then everything's gonna be good. No. No. No. Then you get to play on the next level where more stuff is wrong. And if you fix that, you get to the next level where even more shit is wrong. And it's just worse every time. Right? Because things become bigger and more complex and like so. So you're never gonna get to the end of it. So if you don't think that's fun, then you're not gonna last. Like, you you have to really enjoy the journey. And I've always just been a fan of, like, I'd rather have the highs and then endure the lows than kinda, like, just be in the middle. So I I feel very fortunate that I get to have the highs and the lows every week. It's never boring. At least I can say it was never been boring. Sometimes, I'll admit, I've woken up and thought, why the heck didn't I just design an app? Like, why do decide to build a robot? Like, there's easier ways to make money. But [00:42:52] S0: But you weren't really in it for the money. [00:42:54] S1: Well, no. I wasn't. And that wouldn't have been as fun.