Brett Adcock, CEO of Figure

[00:00] The meta problem in robotics is to be able to solve a humanoid robot. If you can solve this, it'll build the biggest business in the world by a large factor. A little under half the world's GDP is human labor. You recently went viral for comments on your OpenAI partnership with Fingr. What happened there? So OpenAI led our series B a couple years ago. They brought in Satya and Microsoft. It got to a point where our team internally that was designing these models were running circles around OpenAI. We were just way better at this, so I fired them. I self-funded the whole company up front. You know, we got to a million a month of [00:30] - Making the robots to do the things we showed you today, is almost killed me. We had record production in March, and we're gonna try to 3X that by May. We wanna be able to get to a million units a year. - You've raised nearly $2 billion [00:40] publicly stated at $39 billion. Do you see capital as a risk or a constraint or the valuation as a risk? We'll just build like enormous of tens of trillions of revenue. We'll build something massive. I mean, what do most companies trade it? Tech companies trade it 10 or 20 times revenue. You're like $10 trillion or, you know, whatever, $100 billion to a trillion dollars in revs. Like this is going to be a huge business. [01:03] *outro music* [01:13] Brett, welcome to Sorcery. [01:15] Thanks for having me. [01:16] I don't know if this is going to be part one or part two, -but this is a series now. - I agree. I agree. We did the entire tour, and now we're going to do a sit-down interview. I figured we recently went viral for a lot of these hot takes at Hill and Valley, and so I--

[01:33] I think it would be great if we just start there. So what is your hottest take right now? [01:37] - In robotics? - Yeah. [01:41] Hot take. [01:43] I think one thing that we spend a lot of time on is doing things with like, [01:48] fully autonomous and end to end. And you asked us a few questions here when we walked in of like, is this like teleoperated? [01:55] we're not teleoperating this stuff. I think our hot take for robotics is this. It's like, it's kind of really, [02:00] difficult to see what's really happened in the space. [02:04] without coming on site and really seeing stuff. So I hope you had a good experience here today, seeing everything we're doing. I think the hottest take I have is like, [02:14] We just want humanoid robots to work, and they're working now. [02:18] It's pretty simple. We're seeing robots [02:21] to do everyday things like we saw clean up a living room, do commercial work. Like it's just it's cool to see it. Like it's cool to see that this is going to happen in the next few years. [02:29] Okay. [02:30] this is a very hotly contested space, and it's becoming more and more competitive. It was funny, because we did this interview with Skydio, and he was talking about-- Adam was talking about the drone cycles. There are drone cycles where there's hype cycles and that kind of thing. But humanoid-- [02:48] robots [02:50] are certainly at a different [02:52] scale and pace than before. So how does this feel with the competition? [02:58] Um, [03:00] Thank you. [03:02] I think like the...

[03:04] Listen, our internal goal is like, how do we get these things to do real stuff and get paid for it? And so we think about how to do autonomous, useful work. [03:13] That's our bar. And we need to do that with like AI models really well. And we need to do it on good hardware. It's like, you know, cost effective. We can make a lot of it. We make a lot of robots. You know, we think we're probably at this point a few years ahead of everybody, everybody globally doing this at this level, which is like, we're kind of early in the humanoid [03:34] you know, [03:34] book. And hopefully the next step is like, how do we get more robots out at scale? And we want to be first to be able to do that. [03:42] How do we get... [03:43] you know, hundreds and thousands and tens of thousands of robots in the world that run every day. [03:47] and the space is just early. There's a lot of, uh, we're just like, we're like in the first chapter of humanoids coming into society at scale. And, um, [03:57] Yeah, what we're pumped about here internally is it's working. And it's just chapter one. Chapter two is like get more out the door and get them working even like a bigger scale. And at some point, we want to really be able to generalize to do everything a human can. [04:09] How much do you want to produce a year? What's the goal? I will make [04:14] thousands of robots [04:17] over the, like, hear, like, [04:19] basically as fast as we can this year. [04:21] So we're ramping Bocu lines up as fast as we can. We had record production in March and we're going to try to 3x that by May. And we'll build thousands of robots. We already have the parts in house to do this and we're ramping up production. And then from there we want to build tens of thousands and hundreds of thousands. We want to be able to get to a million units a year.

[04:45] And then we need a commercial progress to also to match that. [04:48] we have like so much commercial demand. It's like, it's hard to, [04:51] We have like this overwhelmingly amount of, we could, I think I could like put, [04:56] you know, [04:57] So I put so many robots into commercial customers today if they were all ready. So the big gap is here is like getting the robots ready to do at scale autonomous operations. So the bottleneck right now for commercializing them is? It's having enough of them and making them run to a level of human performance. [05:15] skill. What we don't want to do is like, [05:18] We don't want to put like a thousand robots out to market and have like a thousand problems every single hour. That's not good for any of us. So we had a small batch of robots that went out to B&W last year and did work every day. We ran for six months every single day. [05:32] It was phenomenal. We learned a ton. We refactored our whole approach to how to commercialize the software and AI systems after that. And that kind of led us to Helix 2, which is our second generation technology. [05:43] uh, AI model internally that we, like we launched a couple months ago. And, um, [05:48] Now it's like... [05:50] You know, how do we like... [05:51] put these robots into many different customers. We'll probably announce a lot of this in the next 90 days, and put them out into those groups at a decent scale this year. And assuming that goes well, we'll just keep compounding that. So we had robots there last year, that went well, we learned a lot, we'll have a much larger amount of robots going into many different customers this year. And then six and that goes well, we'll just keep scaling like crazy. Are you worried about Optimus?

[06:19] In my mind, this is not a manufacturing problem. [06:21] This is an intelligence problem. [06:24] You're almost four years old. How did you scale up this fast? What was the process like? [06:31] Yeah, I've been building companies for like about 20 years now, scaled a software company up. [06:37] pretty fast, sold it, scaled Archer up pretty fast, took it public. [06:42] And so [06:44] You know, every time [06:46] you know, I'm in this phase, I get to sit back and say, what did I learn from past experiences and how do I do it better? And, you know, at Figure, we took a very differentiated approach to basically vertically design everything. [07:00] I don't think there's any group in the world that [07:02] I wouldn't think on the robotic side that makes-- it designs more parts than we do on the robot. We design the motors, basically every part within there, the rotor, stator, everything. [07:12] the sensors, the structure, the kinematics, the joints, the batteries that you saw today, the battery packs, [07:21] That, I think, has really enabled us to control our destiny. [07:24] we get to build our own supply chain. And without that, you're left at the mercy of some vendor. And then if that has an issue, how are you going to go solve it? If it's got a code problem, do you understand it? Can you QA it? Can you fix it? Can you patch it? [07:38] So we understand the whole stack from top to bottom. There was enormous lift up front to get the right people here that could do that. And then we've now been iterating through, as you can see behind us, to a point where we have decently reliable systems now that run really well.

[07:52] But, you know, head on, I self-funded the whole company up front. We got to... [07:56] You know, we got to a million a month of burn in four months. It was like a no joke. We had the 40 person team in four or five months. They were like very good. And and then just here, you know, 100 hours a week, just trying to make it work with the team. And we, you know, we made a we've made some mistakes. We've learned a lot. We [08:12] done some things well and just recursively getting better. [08:15] Why'd you leave Archer? [08:17] The meta problem in robotics is being able to solve a humanoid robot. It's if you can solve this, it'll build the biggest business in the world by a large factor. A little under half the world's GDP is human labor. [08:28] I wanted to go work on building this, like this, [08:31] Holy grail of robotics. Um, and Archer, I, you know, I led design for every aircraft we have there. Uh, and, uh, [08:40] I feel... [08:43] I feel like this is now the decade we're going to bring humanoid robots to the masses. It's probably the most important, maybe one of the most important businesses of our lifetime. And so while I'm like a, you know, [08:56] Yeah, so I basically get to work now on what I think is probably one of the more important areas of my whole career. [09:05] In Archer, I built the whole team up, built like all the engineering design for all the aircraft and led the company through a public offering. Like, so we're now in a good spot to certify the aircraft and enter like federal airspace. And Enfigure is also in a good spot here to really scale up like physical intelligence to the world.

[09:25] I recently had Michelle Del Buono from A16C Perennial on its Mark and Ben's multifamily office. And we were talking through liquidity events and what you do as a founder for your first liquidity event, or second or third. Why did you decide after the IPO to start another company? Yeah, and fund it? Yeah. [09:49] I started a few other companies actually since then as well. [09:53] I think the short story is I feel that [09:58] I've been watching the humanoid space for like [10:00] couple of decades. We've been seeing humanoids for a long time. They've just been like the wrong, we're on the wrong vector. Like we were building the wrong stuff or we're doing it in a hobbyist grade. [10:09] you know, um, [10:11] the engineering decisions were like, I think we're not [10:13] maybe correct. And I just, I felt there was a need to, [10:17] advance the space much more rapidly. [10:19] And I'm doing the same with Hark and Cover. I have a couple other companies I'm doing where I also feel a similar situation there where I feel like if [10:26] uh [10:27] left to the world to go do it, I'm unclear if we would head to the right direction. [10:32] And I think for humanoids, like, you know, the best we had four years ago was like a, [10:36] Boston Dynamics had a hydraulic humanoid called Atlas. It dripped oils everywhere. It lasted 20 minutes. It was very big. Very unsafe. You could never put it on exahumans. Classical controls methodology there. [10:48] And it was just like, man, you need to like, it's like a, you know, Boston Amics is really a large heritage around research. [10:54] not commercialization.

[10:56] or not as much commercialization. So I just felt that there was a need for [11:01] a group to come in to really like send this thing to the masses. [11:05] And I felt without my intervention in the space, I don't know if we would have gotten there. [11:10] or maybe we will. We'll find out. But I think figure has really shown that we've been able to kind of push the timelines left now. [11:17] in this chapter book to get to get to get into the real world and um [11:21] And like I said, I think it's a significant business. [11:24] Sorcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies, including one in three venture-backed startups in the U.S. Nearly 40% of startups fail because they run out of cash. Brex is literally built to help founders avoid that. Unlike traditional banks that let your money sit idle, chipping away at it with fees, Brex is designed to help you spend smarter and move faster. [11:54] powerful account. You can send and receive money globally at lightning speeds, get 20 times the standard FDIC coverage through their partner banks, and even high yield from day one. With same day and even same hour liquidity, access your funds anytime. Companies like Scale AI, DoorDash, Service Titan, HIMSS, Anthropic, Flexport, Robinhood, and Plaid trust and use Brex. [12:24] Turing is training the next generation of AI with tasks that require real expertise and real world judgment. That's why companies like NVIDIA, Anthropic, Salesforce, and Gemini partner with Turing. Turing builds realistic reinforcement learning environments and data systems based on real operational traces, the kind of infrastructure Frontier Labs need to train superintelligence. Visit Turing.com slash S-O-U-R-C-E-R-Y.

[12:54] Thank you. [12:54] How do you have time to also work on Harkin Cover? The trick is just not sleep. [12:58] Do you have an eight sleep or you just don't sleep? You just don't sleep. [13:04] Okay, listen, I like... [13:07] Also explain what those companies do for people. Okay, so Cover is basically designing detection systems for K-12 schools. [13:16] So school shootings in the US have gone up 10x the last 10 years. [13:21] And just like this is like actual like a weapon's been fired. And it's just like basically got fully out of hand. My view is that it's a perception problem. We need to see if people have guns, like kids or students have guns on them when they're entering schools. If they do, like it's going to get them off the students. If they don't let those students go in. There's a technology that was designed at NASA Jet Propulsion Lab about a decade ago. [13:44] that it can detect weapons underneath clothes and in backpacks and bags from like, uh, 10, 20, you know, 5, 10, 20 meters away. [13:53] And they built it for the Iraq-Afghanistan War to find bomb vests and other explosives and stuff on folks [13:59] from a standoff distance. So think about this like a, like a technology similar to like the L3, like scanning systems at an airport, but you can only do that from a few feet away. But if you do that 10x further away, you can just basically scan everybody as they're coming in schools. And it's not just a school thing, you can use it every public venue in the world. And, um, [14:17] It's been all my passion. I've been following the space in here. Like this is like a... the nerdy topic here is called terahertz imaging radar.

[14:26] And that's what we do here at Cover. [14:28] And so I have a team, most of which are from NASA Jet Propulsion Lab. So one is I own the IP from NASA Jet Propulsion Lab. I spun it out two years ago, and I'm funding this project. -Oh. -Yeah. -How did you get it? -I bought it. Oh. [14:39] they'll sell it to you. [14:41] Yeah. [14:41] So they sold it to me. 100%, you own it 100%? I own it, yeah. Okay. And then Caltech, which is like NASA basically, JPL, has a small, very small minority interest in the business. [14:53] Yeah. And then a lot of the core team that did that is on my team now on cover. [14:58] And it's awesome. The images we get, it's like a hardware and AI problem. It's like a vision problem that you have to solve with AI. And we have prototypes now that are running now. And we will deploy, hopefully, to our first schools in beta by end of year. [15:12] and we wanted to deploy, and there's 130,000 K-12 schools. There's like a huge amount we have to manufacture and get out the door. So I've been self-funding that company for two years. [15:21] We're making incredible progress. And I have a team in Pasadena, right next to Jet Propulsion Lab in LA, and we're funding that. So, and then I have a separate company called Hark. [15:31] It's an AI lab I started about seven, eight months ago. It's trying to design really highly personalized intelligence. [15:37] So we're designing next generation AI models and also the next generation of AI devices. [15:43] to interact with AI. Like right now we interact with AI through like 20 year old computers, like phones and [15:48] MacBooks and stuff like that. And they're just, they're not the ideal [15:51] like intermediary and interfaced AI. [15:54] So we just came out of stealth like two weeks ago.

[15:58] and we have a team of 50, and we're building really awesome, magical AI models, and we're building really magical hardware. [16:06] You recently went viral for comments on your OpenAI partnership with Figure. What happened there? Yeah. So OpenAI led my series B a couple of years ago. [16:20] And as a part of that, we did a collaboration agreement to work on next-generation AI models together. [16:26] And it was great. I got to know the team really well over there. [16:31] Sam and the rest of the group, and they let her around. They brought in Satya and Microsoft. They co-led it. And... [16:39] And then we spent basically a year collaborating together on how do we get AI models to work on a humanoid? Or how do we get language models to work on a humanoid? And they were very interested in robotics, and we were very interested in understanding better about how do we get language models on robots. What part do they play or do they play a part in robotics? And so we spent a year working with them. [17:03] But nice folks, like, [17:05] You know, I was in the I was [17:08] like working with them for, you know, [17:10] almost every day, every week. And it got to a point where we were just like our team internally that was designing these models were running circles around OpenAI. [17:18] We're just way better at this. [17:20] And, um, [17:23] We were better about testing on the robots, training the models, all of it. My team had come from robot learning backgrounds for over a decade. And I think there was also some interest as OpenAIM was watching us get into robotics.

[17:37] And so I fired him. [17:39] Why did you let them invest in the first place? [17:42] I got to know Sam well and the team, and I thought there could be a lot of potential strategic interests from both of us, like how to develop some of these systems and learn from each other. And I think that's a great idea. [17:57] It turned out I was kind of wrong on that. [17:59] Yeah. Is that when or even before that, when you started to become more secure here? Because even coming in, my phone is covered. You know, there's restricted areas. It's very close on IP. Like what? Yeah. Well, we've always been pretty secure. I think what we're doing is like a very high IP risk. [18:18] So we really think carefully about our engineering CAD and software, making sure it's very secure from a cybersecurity perspective and internal security perspective. And then our office is really open. You can see a lot of stuff when you're coming here. Yeah. So we used to have people come in and just snap photos randomly. Like, "Whoa, that's not okay." I'm like, you know, I like hardware or something in front of the person. We're also in the Bay Area and there's a lot of honeypots and spines. [18:48] drone sitting there looking in the office, right over here. - Really? - Main area. Yeah. We're like, "Oh my gosh, we got to change some things here." Did you find out who that was? We didn't find out who it was, but we tinted all the glass. We have a really strict security, both physical and digital now. [19:04] I mean, we're designing some crazy stuff, so we want to protect it at all costs here. But no, we've kind of always been like this.

[19:12] A little paranoid, which always helps. [19:14] - Typical founder trait. - Yeah, exactly. - Speaking of that, so one of our sponsors is Brex, and they're about performance, spending smarter, moving faster. [19:24] I'm curious from your standpoint, [19:26] As a leader, how do you maintain your performance, whether it's like mental or like team leadership wise? You asked before, like, how do I have time to do all this stuff? Yeah. [19:40] You know, I think I always have these three pockets of time in my life. I feel like I have my family, I have work, and you have things you do with friends and people you know. Whatever, annual trip with friends or golf or whatever else it is from college. [19:54] About like, you know, when I was at Archer, I got to a point where I was like, man, I don't really have time to do all three anymore. So I decided like five years ago to like stop. [20:02] doing like the annual golf trip. [20:04] and stop doing like this person's in town I haven't seen in 10 years. We need to go out to dinner. I just don't do that anymore. I spend all my time with family or my companies. That's all I do. And it's kind of nice. It's like the stuff I really care about. And I get to kind of go all in on it and do a really good job. So here I'm like, I was at the office last night till like midnight, but like I come home every night to have dinner with my kids. So I'm at home by six and I do dinner and do bedtime and then come back in if I need to, or stay home, depending on what's going on.

[20:34] I get the, like, make sure I unblock the most pernicious problems of these companies and help, help scale. [20:40] So anyway, I think from a time perspective, yeah, I kind of have to be very thoughtful about, about what I do when I'm even in the office now. [20:50] I had to be really thoughtful about spending my time on. So what I've learned over time, you asked when you came here, it's like, do you have like the corner office? Or do you have like a, you're in the, you know. - The bullpen. - The bullpen. [21:01] I mean, I have like a, uh, so I, [21:04] One day after I took Archer public, I woke up one day, [21:08] And I was like, I feel like I was in this Groundhog movie where I was like in this conference room with all my C-suite and was in there like every day. [21:15] And, [21:16] You know, usually I'm like on the floor with product engineering, helping to build, build aircraft design. And I was just like stuck. [21:23] I was stuck in like two day quarter board meetings and analyst callbacks and talking about, you know, my CHRO or GC and all these different things. And I was like, this is just something's wrong here. I mean, it's like a lead office over here, like looking down at everybody, like not doing real work on the ground. [21:42] And... [21:43] I made a decision around that time to just basically remove everything on my plate to spend time on product engineering. I do a few things. You're the first person that's actually seen the whole office like this, which is cool. We don't do a lot of these, and I love your show, so it's great to have you here and give a sneak peek to the world on what we're doing. Thank you. Yeah, and then the rest of my time is on how do I work on product engineering? How do I advance the humanoid to do better things? Or how do I make cover system out to the world when you start testing it and getting feedback?

[22:13] and how do we get Hark models and devices out to the world at scale? Like, those are the things I think really matter. Matters less about, like, doing traditional PR and, you know, like, going to, like, trade shows and, you know, um... [22:25] sitting on these panels and stuff. None of this matters. It's not real. So I try to spend my time in the bullpen and then I try to maximize my time both when I'm in or out of the office on things that are important. [22:38] VCX by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View the portfolio at GetVCX.com. That's GetVCX.com. [22:52] Some of you may not have heard this yet, but our sponsor Public just launched something called Generated Assets, and it brings AI into investing in a way I've honestly never seen before. Here's how it works. You type in an idea like AI-powered supply chain companies with positive free cash flow or defense tech companies growing revenue over 25% year over year. Public's AI then dispatches a swarm of agents that scan every single U.S. stock, evaluates them, and instantly builds a custom index around your thesis. [23:22] why each stock is included. And before you invest, you can even backtest your idea against the S&P 500, so you're making decisions with real context, not just guessing. And beyond generated assets, Public lets you invest in stocks, bonds, options, crypto, all in one place. They'll even give you an uncapped 1% match when you transfer your investments over from another platform. If you want to build a portfolio that actually reflects your thesis, visit public.com slash sorcery,

[23:48] Paid for by Public Investing. Full disclosures in the description. [23:52] Enterprise AI runs on Merge, the AI infra platform for integrations, agent tooling, and model orchestration, so your teams ship product, not plumbing. Mistral, Dropbox, and Drada already trust Merge in production. Start building at Merge.dev. [24:08] Founders scale faster on Deel. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit Deel.com slash sorcery. That's D-E-E-L.com slash sorcery. [24:23] You mentioned a bit of inspiration on ideas. [24:28] around coming from sci-fi, but on the leadership standpoint, like who are people that you admire or have looked up to that like help carry you forward and keep you motivated? Yeah, I was, I mean, kind of like a generation behind like Steve Jobs and that whole, you know, crew, like Jeff Bezos, which is a big investor for us at Figured. [24:47] you know, I get to basically have access to and talk with. And, um, [24:52] You know, for me, like, I want to play the game, like, [24:54] 11 out of 10 [24:56] I want to go really hard at this. So we're really serious. I'm a pretty competitive person. So if I'm going to do this and sacrifice my life, [25:03] to do this in my time, I just want to nail it. I want to win and I want to do everything to the most hardcore level possible. And so I think the folks I looked up to of the world are the folks that have done that, have been successful, but I have like really devoted their time to being kind of best athlete in those situations.

[25:21] Um, [25:23] So, yeah, and I'm looking at myself here, like, how do I make these companies work? [25:27] There's just nothing worse than like 10, 15, 20 years from now, these things aren't working. [25:32] Like, I missed all my golf trips. You know what I mean? I missed like time with the family. It's just like, I just missed all this stuff in my life. And I... [25:40] At this point, I have plenty of money. So it's like, I'm doing this because I love it. [25:43] and I better be getting better at it. [25:46] And, you know, [25:47] recursively improving. And I think I look up to the folks like that, that have done that through their time. If you look at like Steve and [25:54] Jeff and some of these groups that are kind of a generation above me, they've been good at many things in their life across many different areas. And they've also, too, devoted their life to doing this and have been a good source of inspiration. And it's been hard and they've done it over decades. [26:10] This stuff is like not... [26:12] The stuff that doesn't happen overnight. I mean, I've been doing 20 years now doing all this stuff and I feel like I'm just starting. [26:18] What are the biggest risks to the business? [26:21] The humanoid thing is just so hard. [26:24] Um, [26:25] I can't even explain it very well. It's... [26:29] Getting the robots to do the things we showed you today, it was been like... [26:32] has almost killed me. [26:34] And we have such an uphill battle to go do that. [26:38] Same with Archer. We have to fly aircrafts. [26:40] every day above cities and [26:42] make it really safe. And that's like one of the safest for those transportations you have. [26:46] So the stuff I'm working through is just like, [26:49] um, [26:51] Like if you look at the odds of these things, you know, working, they're like super low.

[26:57] And I think that's probably pretty accurate. [26:59] So I basically had like a, [27:01] I basically have a funnel of the hardest, most pernicious problems I have to go solve every day. [27:07] And they're like, they're really, really tough. I, um... [27:11] So like my biggest risks, there's like a list of risks, lists, like, [27:16] A very long list of risks. [27:18] and things that could hurt, like things that could like make us not make it. [27:22] The figure, the most important thing is to be able to do end-to-end useful work over a long time horizon. [27:29] I want to be able to put a robot into a home and be able to do like seven to ten hours of work successfully without failures. [27:37] with no human intervention and do that every day forever. [27:41] It's just like a hard problem. Nobody's ever shown that. The robot's very complicated. It's like a turbofan or a rocket. [27:49] Like an aircraft from scratch. [27:51] it's just like a very difficult thing that we've also designed this entire supply chain from scratch. So it's like, [27:56] It's got a ton of problems. [27:57] you know, it doesn't work the first time. [27:59] We're working those problems out. We used to run that robot here, figure one. And man, that robot could run for like an hour. [28:07] And it would like fault. [28:08] We know all the reasons, or it fall. [28:10] or would lose power. [28:13] And, you know, we moved to figure two. I think we probably, like, [28:16] Maybe we saw that once a day. [28:18] And now figure three, like I think, you know, everywhere here, they're all running all day. [28:23] And we'll see faults every week.

[28:27] Not on every single robot, but we'll see faults. [28:29] And we have a list of those and we're working those down. [28:32] But it's really hard. [28:34] And as we're growing the fleet, the absolute number of faults are rising and we got to go figure out how to solve those. [28:38] Because like you said before, the amount of states the robot can be in is so high. [28:42] So you can't really reasonably predict what the robot's going to look like at every single... [28:45] timestamp everywhere in the world. [28:48] You kind of can for a car. It's like, kind of can just drive on roads. [28:52] It's a hard problem too. So like, um, [28:55] Anyway, I think what I'm trying to say is we have a fun house of problems, and it's like, [28:59] never ending problem city for the company. Um, [29:03] We have to be able to manufacture unprecedented rates. We have to get humanoid robots to work autonomously without human intervention. Nobody's ever shown that. We have to make it work with like AI policies. The hardware can't fail. It's going to be really affordable. We got to make a lot of them. [29:16] We got to get consumers to want them. [29:19] That's a lot. You've raised nearly $2 billion. I think most recently publicly stated at $39 billion. Do you see capital as a risk or a constraint or the valuation as a risk or? [29:35] This will build like the biggest business in the world. Like a little under half of GDP is human labor in the commercial market. Like they pay wages for humans. We do human work. [29:44] So you're looking at [29:45] we will have the ability to ship [29:47] If the robots work well, billions of robots in the commercial workforce. [29:51] you'll produce [29:52] you know, there's like 30 trillion... [29:55] 40 trillion of wages paid every year to that.

[29:59] to like to folks that be doing work. We'll be able to expand that work. [30:05] automate more like a lot of it and continue to scale it up i think like that plus the stuff we're seeing in the home [30:12] will just build like enormous of tens of trillions of revenue. [30:17] you'll build something massive. I mean, what do most companies trade it? Tech companies trade at 10 or 20 times revenue. [30:23] You're in like $10 trillion or... [30:26] you know, whatever, $100 billion to a trillion dollars of revs, like this is going to be a huge business. [30:31] Do you have plans for the moon or Mars? [30:34] We love to send robots into space. [30:37] Yeah. [30:39] Let's get them out there. [30:40] - Okay. - I have a gift for you. - You do? - I do. - Is it a mini robot? - I have some figure swag. So yeah, some hats, shirts. [30:50] - So you can rep the figure brand here. - Oh, this is a lot of merch. - A lot of merch. - Wow, I'm gonna have to get a new closet. [30:57] This is great. It was funny. I don't think they caught this when we were recording the tour, but I asked you how you made the logo and you said it was the robot steps. Yeah, basically the robot takes footsteps like this. Even in simulation, they look like little squares. So you have like a little like kind of a walk, a walk. And then you also have a kind of an F. [31:19] - Abstract act? - Not really. [31:23] Okay, we'll keep the walk. Okay. Well, as we wrap up, what are you most looking forward to for this next year? For this year, I want to ship robots at scale out to the world. And the second thing I want to solve is I want to solve, there's like, we have like an extreme focus here to solve like what I call general robotics.

[31:42] Like a robot that can do everything a human can. I think about almost like a feeling of like a human in a bodysuit. [31:47] You can talk to, can look at you, reason, visual understanding, and you can drop into any place and kind of just look around, reason and understand. We want to solve that problem. [31:57] And that's a Helix problem for us. And so we have a huge focus to get robots out the door. We have a huge focus to solve general robotics here. [32:07] I want this to be the first place where we see... [32:10] to see AGI in the physical world. [32:12] And we think we have the recipe. [32:15] and we think we have the right... [32:19] training processes in place to do this. We, uh, [32:23] And this will be an important, this year or next, will be important to see if we can crack this. [32:26] Exciting. Well, thank you so much for the full day here, the entire tour of the whole campus, and the thoughtful discussion. Thank you. It was great to have you. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, Sorcery.VC, where we deliver a once a week top deals and tech headlines email, and also go deeper on our podcast interviews. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up. [32:56] Thank you.