No Priors Ep. 58 | The argument for humanoid robots with Brett Adcock from Figure

Sarah Guo 0:00 (instrumental music plays) Hi, listeners. Welcome to another episode of No Priors. Today, we're here with Brett Adcock, the founder and CEO of Figure AI, which is developing and delivering humanoid general purpose robots that can do unsafe and undesirable jobs. They recently announced a monster round of funding, 675 million, from Microsoft, OpenAI, Nvidia, Intel, and Jeff Bezos. Brett, thanks so much for doing this.

Brett Adcock 0:31 Yes, sir.

Sarah Guo 0:32 You have this wild company, uh, doing humanoid robots. You just raised, uh, almost $700 million. Can you talk a little bit about how you get from, uh, a farm to software to, uh, vertical takeoff and landing to ro- humanoids?

Brett Adcock 0:48 Pretty normal path. (laughs)

Sarah Guo 0:49 Yeah. That's what I did too.

Brett Adcock 0:50 Yeah. (laughs) Yeah. So my story started in, I grew up in Illinois on a third generation farm. And, um, it ended up basically at a pretty early age started coding and getting to software and building things. Um, and that basically has been now about 20 years of building companies. Um, a little over 10 in software and a little under 10 in hardware. Um, at one point I started a software company and sold it, and then I started a company called, um, Archer Aviation. We build, uh, electric vertical takeoff and landing aircraft. And then, uh, about 21 months ago, I started Figure.

Sarah Guo 1:29 Can we pause there for a second? Because most people aren't like, "Oh, I'll just start an aircraft company." Like, how do you go from a software business, feels less exotic to me, to that?

Brett Adcock 1:38 I grew up around a lot of hardware, and so I, um, I looked at hardware as like I really wanted for a long time to build hardware, build like, um, kind of E- areas of deep tech.

Sarah Guo 1:49 Mm-hmm.

Brett Adcock 1:49 Only way to really do that was like self fund your own venture and get it really moving. So after I sold, I sold Vettery in 2017, and right away I knew I wanted to build electric aircraft. And I actually went back down to-

Sarah Guo 2:02 Do you fly planes or something? And-

Brett Adcock 2:03 I'm super passionate about A- like A, fixing traffic problems. We, we have like, you know, half the world live in cities and traffic's just getting worse and worse. There's just been no... There's been no solution there. And two is, um, big believer in sus- sustainable transport. Uh, I think, you know, all transport besides rockets will move electric, uh, hopefully in our lifetime. So what we do at Archer is we build vertical takeoff and landing aircrafts. So aircraft are kinda like helicopters but fully electric. Uh, they can take off among helicopter landing pads, like inside of a city, and they can take you from here back to San Francisco in under 20 minutes.

Sarah Guo 2:35 I'm sold.

Brett Adcock 2:36 That'd be a dream (laughs) -

Sarah Guo 2:37 It is.

Brett Adcock 2:37 ... instead of driving for two hours. So, it was really hard business. I, I basically started the company out of University of Florida. Um, I did, I started, uh, in engineering at University of Florida and, um, basically built a lab there for the first two years and built aircraft. And then moved the company out to California about three or four years ago.

Sarah Guo 2:56 That's now a public company. It's on the clearance path. How do you go from that to humanoid robots?

Brett Adcock 3:02 Uh, it was a less of a leap from software into Archer than it was from Archer to Figure. (laughs)

Sarah Guo 3:06 Yeah. But what's the thesis for it?

Brett Adcock 3:08 The thesis here

Brett Adcock 3:09 is that if you assume the technologies are, are possible to build a humanoid robot, and just for listeners, humanoid means human form, so legs, arms, hands, um, and you can do basically human-like work with a humanoid robot, um, A, it's gonna be the biggest business in the world by probably order of magnitude. Uh, half of GDP is human labor. Like it order of magnitude bigger than like all of transportation market. It's just an enormous industry. Two, is we think we can have like significant economical value if we can basically have robots doing real work every single day. I think it'll be a age of abundance as it relates to goods and services prices. And three is I, I think we'll have a... Over enough time, I think we'll have an impact on the AGI timeline here. It distills down to perhaps the most important business I think I could be working on in my life. So I left 21 months ago to start Figure, and what we're trying to do here is commercialize humanoid robots. We're trying to get them in the market, uh, in a significant way, build a fleet of robots, and then build an AI data engine to train those robots on how to do useful, useful work.

Sarah Guo 4:09 Why does being humanoid matter? Right? I think there are plenty of people who work in robotics that say like, "Uh, like, bipedal is really unstable." There are lots of reasons that these humanoid, um, forms are not optimal for doing-

Brett Adcock 4:21 Mm-hmm.

Sarah Guo 4:21 ... lots of different work. We can have 10 arms and be stronger and whatever else.

Brett Adcock 4:25 I think there's like really two schools of thought of how to go about this problem. We can either rebuild like thousands or millions of special types of robots that do special use cases all over the world, or we can build a humanoid robot. And the reason we believe humanoid robots are the right solution is that the whole world was built for humans. It was built around humans, meaning, um, the way we look biologically had a significant impact on the way our environment looks. If we were 10 feet taller and we had like, you know, six arms, a lot stronger, the world would look a lot different. When people ask like, "Is the humanoid the right form factor?" it's the wrong question. Just the wrong question to be answered. Uh, the humans are, the humans like the ideal form, it's the wrong way to look at it. We're like a weird biological species. We're a weird form.

Sarah Guo 5:07 But that's the world that exists.

Brett Adcock 5:09 We built it so we can interact with it. If you go to Mars today, you're gonna wanna go grab coffee, you're gonna wanna walk around, you're gonna wanna like live in a habitat. You're gonna wanna do things. And you're gonna build a human world around it. And then so if you wanna automate work, you wanna build a general interface to that. You wanna build like the equivalent of like the keyboard and mouse to the internet in the physical world. That equivalent is a human form. You can do everything a human can. And the world was optimized specifically for us. It wasn't optimized for us to have two more arms or nine feet tall. It was optimized just for the average human.

Sarah Guo 5:44 Mm-hmm.

Brett Adcock 5:44 The average like non-expert human. So it's really clear to us here that the, the right

Brett Adcock 5:51 form factor is the humanoid. You can build one hardware system that can do everything.

Sarah Guo 5:55 Mm-hmm.

Brett Adcock 5:55 Meaning you can spend all this money and time on this robot that can be amortized over like, you know, millions of different tasks and applications. And then conversely, let's say you don't believe that.... you gotta go out in the world and look at every single use ca- like, every single task and build a special robot for it. That special robot needs a company, needs a brand. It needs a team, it needs a culture. It needs to go raise capital. It needs ... the team needs probably an order of, like, 15 different skillsets that one single person can't do, like in a full stack software role. It needs firmware, and embedded software, an operating system, and rotor, and stator, and electromagnetics, and battery systems, and BMS systems, and power distribution systems, and thermals, and ... the list goes on. Controls, and AI, and everything else. And so they need to raise a lot of capital to sustain it. And then you gotta go build millions of those. How is that even tractable? (laughs) How, how, uh, you know, how do you, how do you do that across the whole world in all these, like, special use case ... And then you're gonna make a ro-

Sarah Guo 6:53 Well, it is, it is how the, the robotics industry works today. Specialized robots, right?

Brett Adcock 6:59 Yeah.

Sarah Guo 6:59 I'm not saying that's efficient, but-

Brett Adcock 7:01 Yeah.

Sarah Guo 7:02 ... uh, I think that's where the question comes from.

Brett Adcock 7:04 I don't know if we should be reasoning by that analogy.

Sarah Guo 7:06 Okay.

Brett Adcock 7:07 I think that's the wrong way to look at it.

Sarah Guo 7:07 One, one thing you and I have talked about before is like-

Brett Adcock 7:09 (laughs)

Sarah Guo 7:09 ... is this the right decade to build the company? Like, what made you say, like, "We have it now, we can do it"?

Brett Adcock 7:15 We need to prove it, and I think we've started to prove it on like a m- MVP level. Like, you can see the robot doing pretty useful stuff now, and it's been 21 months. Took the first year to h- hire enough employees to come here (laughs) to do stuff, so we're kind of been around for kind of a year, maybe a year and a half of like real useful, like, run rate in terms of like, um, what we've been able to accomplish.

Sarah Guo 7:35 Can I ask

Sarah Guo 7:36 you to, um, just describe the two public demos so far and, like, why they're important?

Brett Adcock 7:41 Yeah. We've been doing, um, kind of like two divergent set of demonstrations to the world. The first is we, we do plan as a business to start launching into more kind of industrial solutions, like more like the corporate labor market, uh, you know, m- manufacturing, supply chain logistics, those type of areas. We think that'll allow us to, to build the AI data engine quicker because we're shipping robots faster and will ... it'll help us build manufacturing volumes quicker, which will help cost. Those are, like, the reasons why we're doing it. There's another market that we're extremely excited about, which is in the home. And the home is a really messy place. It's very unstructured. Everything's different. There's, like, a higher variance of failures where we're like, you know, if, if we drop like the number one dad cup at home or the number one mom cup, like, no gr- not great. If we drop a bin in, like, a warehouse, like, who cares? You know what I mean? It's a little, little bit different scenario. Also, safety is impacted. There's pricing compression as we move into the, uh, consumer world. There's just a bunch of stuff that happens. Um, so, so we, we've done a few demos so far. First is we've done, like, bin moving, very traditional industrial solutions roles where we're taking bins from pallets into conveyor systems. We're doing that fully autonomous end-to-end on our robots now, all bipedal. Um, and the second is we're doing, um, kind of full consumer level manipulation and, like, speech-to-speech reasoning. So we're able to talk with the robot. It's able to understand what we're saying. It's able to visualize over the scene. Uh, it's able to do useful works like go and grab things like an apple or certain plates or make a coffee in a Keurig. And it's able to do all of that end-to-end, not only autonomously, but only from neural nets. So it's taking in, uh, speech. It's taking in video feeds in the cameras. We're processing that on a, on a model and then in, doing inference, and then we're outputting trajectories across the robot. I would say, like, you know, as an entrepreneur, the one thing that we're always afraid of is hitting some, like, technical wall.

Sarah Guo 9:26 Mm-hmm.

Brett Adcock 9:26 (laughs) It's like, we go out and do this, and like, we, everything just slows. We hit this, like, you know, this upper bound, and we just can't push through. Um, we, we don't even know where the upper bound is right now, and, uh, that's what's really exciting for us and why we're really excited about the next tw- 24 months is we don't e- we, we don't even see it. We don't know where it's at, and we're still searching for that and moving as hard as we can to try to find it. Why is now the right time? Yeah, there's a few reasons. Uh, I don't think this was really possible five years ago. Looking back over the last, like, you know, even few decades, the, um, like, the power train or the system that we use is all electro-mechanical here, so it's batteries and motors. If you look at the amount of energy we have in the batteries or amount of, like, say, power torque density in the motors, those have improved significantly the last couple decades. We just wouldn't ... We didn't have the runtime 10, 20 years ago in the robot with, like, lithium ion batteries to make this really work.

Sarah Guo 10:17 Mm-hmm.

Brett Adcock 10:17 Uh, we didn't have the power out of the actuators, electric motors to make this happen. So like, a best analogy is like, you know, 10 years ago a Tesla went 100 miles, and now my Tesla goes 300 plus miles. It's because specific energy in the battery cell on a watt hour per kilogram has improved, you know, maybe a seven percent CAGR since, you know, last two decades.

Sarah Guo 10:36 That makes sense. Like, if it, you know, can't carry more than 20 miles or it can't carry-

Brett Adcock 10:40 It-

Sarah Guo 10:41 ... fast enough to go to the highway, it's just not a useful car.

Brett Adcock 10:42 Run like ... Yeah.

Sarah Guo 10:43 Yeah.

Brett Adcock 10:43 If it was really heavy and it runs like, you know, 10 minutes and it can't carry anything and the speeds of the motors are not very great, like, you just can't get anything useful.

Sarah Guo 10:51 Mm-hmm.

Brett Adcock 10:52 I think a second thing is locomotion controllers. Like, 10 years ago, humanoid robots, bipedal was a huge risk. Like, they were clumsy. They were slow. They were falling over. Um, the DARPA Robotics Challenge is a good example of that. Like, about, you know, about 10 years ago now, uh, you couldn't, like, look at it and envision that being, like, really useful, or at least in your home. Uh, that've completely changed here. We actually started walking our humanoid r- uh, from the t- ... From the time I filed the C-corp in the business and we, uh, walked the robot, it was under a year from when we started, which was a pretty impressive feat. Um, and I, I, I think we, we probably ... Or we're probably doing some of the, like, some of the best work in the world on bipedal locomotion here on a, from a controlling, controller standpoint. And the third is, like, basically AI systems, like the, the computation, the algorithms, um, were just not feasible to do, I would say, 10 years ago. Um, and I can say arguably the fourth would, which would be, uh, you know, we, we spent a lot of time networking with OpenAI as we, we announced. And, um, we believe the default user interface to the robot is speech. Um, you, you're gonna want to talk to the robot. We've ... Even in an industrial setting-

Sarah Guo 11:58 (laughs)

Brett Adcock 11:58 ... when you're unboxing the robot for the first time, we think the, the initialization process is, is speech.

Sarah Guo 12:03 Hmm.

Brett Adcock 12:03 There's no, um ... There's no, like, you know, opening up your phone-

Sarah Guo 12:05 Configuration, demonstration.

Brett Adcock 12:07 Yeah, you're talking to it.

Sarah Guo 12:08 Yeah. Hmm.

Brett Adcock 12:09 And I think, um-... and, I mean, humans, that's what we're doing today, right? We, we, we do gestures when we talk, um, either through, you know, written text or we're- we're speaking. Um, whether it's transcribed or not, we're- we're- we're- s- we're- s- we're, like, using language, and we think that's primarily the main user interface, we think, uh, by default, the robot's gonna be using. Uh, and, you know, five years ago, that wasn't possible, either.

Sarah Guo 12:33 You said you- you- the team wasn't sure where the bounds were. Like,

Sarah Guo 12:38 where are the walls you can imagine hitting, and what were you afraid of when you started?

Brett Adcock 12:42 I mean, this is just a hard problem. There's just, uh, if you're afraid of walls, this is, like, the nightmare scenario for anybody, um-

Sarah Guo 12:49 It's just a, it's just a fun house of walls.

Brett Adcock 12:51 Yeah. We're- we have this, like, one of our five corporate values is, like, aggressively optimistic. (laughs)

Sarah Guo 12:56 We looked at some slides together, um, where you were looking at one of the trades and some risk on a component, and you had these mitigating factors-

Brett Adcock 13:05 Yeah.

Sarah Guo 13:05 ... and the second mitigating factor was sprint harder. Like, we will figure it out.

Brett Adcock 13:09 Yes. Like, work harder.

Sarah Guo 13:10 Yeah.

Brett Adcock 13:10 Uh, and that's-

Sarah Guo 13:11 Love that.

Brett Adcock 13:11 ... that's the only solution to the- to the- to the risk at this point. Um, the- the- the risks are profound. Like, um, nobody's been able to, um, deliver a commercial humanoid into a market in human history. Um, we have to e- n- not only, like, like, the threshold is we have to do a certain amount of an equivalent of humanlike work performance, which is extremely hard. Humans are very productive. Uh, and we have to do that reliably and continuously o- over the course of months and years.

Sarah Guo 13:45 Mm-hmm.

Brett Adcock 13:46 Um, and then to add onto all that, our robot has, like, you know, over 30 degrees of freedom, like, you know, joints that can move on the robot, and, you know, the amount of a- like, the amount of the action space or the amount of orientations the robot could possibly be in is- is extremely high. Either you have to code your way of saying this is w- you know, I have to write C++, or I have to write a script for everything the robot should do.

Sarah Guo 14:11 It's not gonna happen. Yeah.

Brett Adcock 14:12 Everywhere in the world, you have to- you have to solve that through robot learning, um, is the short answer. Uh, so and we haven't seen that work extremely well in human history, either. I mean, we're- we're watching that unfold right now with self-driving cars. We have to take all of those challenges on hardware never having been done before, uh, reliability and safety and performance, and then we have to learn everything. So yeah. To get back to your question, it's like they're all there. They're all, like- like, they're all, like, in the shadows, all these problems. There's all these o- other shadow of, like, unknown unknowns that you hit at every single, you know, at every single time. The reason why we don't feel like we see an upper bound is 'cause now we kind of see the roadmap for the next 12 to 24 months, and we're kind of just optimizing. We're just making the robot, like, more reliable and faster. We're not trying to get the robot to do, like, an end-to-end application for the first time. We've already done, we've done that, and we've done that for our first clients' use cases. It just has to be more robust.

Sarah Guo 15:11 Zero to 100 or zero to, uh, you know, walking bipedal robot in a year since you incorporate, how do you assemble a team that's so multi-domain

Sarah Guo 15:20 so quickly?

Brett Adcock 15:20 So hasn't been my first rodeo. Uh, having built the, like, you know, built the all- like, all my teams previously at Bettery and Archer. Um, when I started Figure, there was a few things that no matter, like, um, what company it was, I- I would do and I would do again. Uh, the first is we set the mission, vision, values. I then wrote a master plan, which is basically like a 10-year journey over what we're trying to do. I wrote a culture document, which also is online, about, like, what we stand for here. Like, we like to move fast. We, you know, we do this. We don't do this.

Sarah Guo 15:53 We're aggressively optimistic.

Brett Adcock 15:55 Yeah, exactly. We, like, we work hard, things like that, we have to say. Um, we work in the office every day, five to seven days a week. I spent a l- I spent basically the first year, like, hyper focused on building the team. How do we build, like, the world's best engineering team, and who are those people? And I built an org chart. You know, I'm at the- I'm at the top, and we basically built out the teams with- in detail of, like, who this- all these groups should look like, whether it's, you know, controls, AI, um, actuation, battery systems, kinematics, integration and tests, industrial design, all of this, and then the skill sets underneath there. So it could be motor. We have like a, you know, rotor, stator, transmission,

Brett Adcock 16:38 sensors, thermals, motor controls.

Sarah Guo 16:41 That all makes sense as, like, a picture of it-

Brett Adcock 16:43 Yeah.

Sarah Guo 16:44 ... but then, like, the reality of somebody who spends a lot of time recruiting for-

Brett Adcock 16:47 Okay.

Sarah Guo 16:47 ... early stage companies is like-

Brett Adcock 16:48 Yeah.

Sarah Guo 16:48 ... I can't just go, like, pick that guy-

Brett Adcock 16:49 Okay.

Sarah Guo 16:49 ... up from Boston dynamics 'cause I- I-

Brett Adcock 16:51 Okay.

Sarah Guo 16:52 ... decided he's the right guy.

Brett Adcock 16:53 Yeah. So I then went out and found everybody online that w- I thought was the best in the world, and then I did 300 phone calls over six months, and I cold emailed all of 'em.

Sarah Guo 17:05 So Jerry picks up, and he's like, "Sure, Brett."

Brett Adcock 17:08 They don't say sure on the first call. I wish they did that.

Sarah Guo 17:10 (laughs)

Brett Adcock 17:10 Um, so a few ph- calls later, yeah, or a few meetings later, they do. Yes. Uh, or a certain percentage will. Yes. This is no different than what I did at Bettery. I built, you know, the f- the first few hundred like this at Archer. Um, and yeah. The first, you know, 30 to 50 over the first year, I, um, I identified the role in the org chart, what skills were necessary. I went out and found the right ideal, uh, person. I cold emailed. I did phone calls. I closed them. I gave them offer letters. Um, I wrote their 30-60-90s. I brought 'em in. I worked on them with a shared vision of what to do, and I worked with them day to day next door to them. I literally s- I literally sit right there on the floor with everybody else, and I attend every engineering meeting. And I work with them on designing the product and making those trades locally on speed and timeline and what to do and help them sh- help people ship.

Sarah Guo 18:03 Entrepreneurs, you heard it here. Just, just follow the plan. (laughs)

Brett Adcock 18:06 Yeah, just follow the plan. It was just a lot of hard work, but we have, um... we have, uh, e- you know, you really want to build an incredible team. Then you want to build a really great product, and then you want to get that product to market that's really great, and you want people to really like it.... and then you want them to keep using it over time. Like, that's the secret. And so everything starts axiomatically with, like, where is that team and how do you go get them? So, um, it was a brute force effort-

Sarah Guo 18:29 Great.

Brett Adcock 18:29 ... to say the least.

Sarah Guo 18:30 My favorite, my favorite type of strategy.

Brett Adcock 18:32 Yeah (laughs) .

Sarah Guo 18:32 Aggressively optimistic, how far away are we from, like, you know, companies buying robots en masse, deploying them?

Brett Adcock 18:40 So, companies are, you know, paying us now for robots, um, as we're, like, delivering them this year. We hope over the next 12 to 18 months, our robots are in our clients' test, like, real facilities, doing real work in real work cells, getting paid for it. And I feel decently confident-

Sarah Guo 18:57 For our audience, what's a work cell?

Brett Adcock 18:59 Um, just, you know, like, I, I'm at BMW and I'm supposed to be, you know, moving a bin or a box. This is your work cell, and we're moving bins over here to a conveyor system. That's, that would, uh, define a work cell. Like, I need to be in this location. I need to be doing this job. Uh, it's basically a job or a task. So I feel pretty confident in the next 12 to 18 months we'll start doing those. Um, and then, then we gotta make it reliable, uh, extremely reliable, extremely safe as we, like, you know, branch out to hundreds and then thousands on, you know... in, inside of, say, a factory floor. Um, and then we gotta manage a fleet of it, and then we have to do AI training at scale. Um, and then updates that to, to, to that fleet at scale. Um, and then we have to manufacture at scale.

Sarah Guo 19:39 No problem.

Brett Adcock 19:40 Yeah. I think, like... I think if we can solve the robot humanoid doing useful stuff, the other things are ex- extremely doable. It's extremely doable to take a robot that is reliable and that can do the performance and make a lot of them.

Sarah Guo 19:53 A lot of these other problems have been solved before.

Brett Adcock 19:55 Yeah.

Sarah Guo 19:55 They're just very hard.

Brett Adcock 19:56 There's-

Sarah Guo 19:56 Versus new problem.

Brett Adcock 19:58 Yeah. Like-

Sarah Guo 19:59 Hm.

Brett Adcock 19:59 Yeah, I don't want to wake up in like 10 years and say, "We had the humanoid really work well, we just can't make enough of them." That, that just seems like a... you know. We should be able to manufacture millions. Um, so I, I, I, I certainly don't... I hope we don't hit that roadblock. Um, and then... yeah, I feel like, um, in a mass way, I think as... you know, like, the, the manufacturing volumes will, like, um... the, the dependency here is getting the robot to do fully reliable, useful, continuous work. And once that... as soon as that's happening, we're parallel pathing, uh, volume manufacturing.

Sarah Guo 20:33 Figure operates as a really unique company, I think, or I- I've just not encountered, um, many orgs that work this way. Uh,

Sarah Guo 20:41 you are as vertically integrated as I've ever seen. You have your own actuators, you wrote your own operating system. Like, why do that when the project itself is so huge to begin with?

Brett Adcock 20:52 The trade of, like, build versus buy, like, should we do this ourselves or should we, um, go buy them, is, um... it's something we do at every single, like, like, component level.

Sarah Guo 21:02 So it's not a philosophy of, like, we're gonna build everything-

Brett Adcock 21:04 Oh, no.

Sarah Guo 21:04 ... from scratch to begin with?

Brett Adcock 21:05 No, no. I think our philosophy is we would rather, um... or yeah, our philosophy is that we would rather not do-

Sarah Guo 21:10 Mm-hmm.

Brett Adcock 21:10 ... do it, and we'd rather buy. But you ended up building- Yeah, we ended up building-

Sarah Guo 21:13 ... a huge amount.

Brett Adcock 21:13 ... everything.

Sarah Guo 21:14 Yeah (laughs) .

Brett Adcock 21:14 Yeah (laughs) . Uh, we started buying by... like, we bought a... yeah, the mix of-

Sarah Guo 21:17 Basically everything but the GPU.

Brett Adcock 21:19 It's... it feels like that, yeah.

Sarah Guo 21:20 Yeah.

Brett Adcock 21:20 Everything maybe besides, like, the battery cell and the GPU and the CPU, at this point-

Sarah Guo 21:24 Mm-hmm.

Brett Adcock 21:24 ... it feels like we've done-

Sarah Guo 21:26 Mm-hmm.

Brett Adcock 21:26 ... or we're doing. And to be clear, our default is to buy. Like, building is extremely difficult. Any part that we have to go build, we have to have, like... we have to put job listings out, we have to go hire people, we have to manage humans and make sure they're happy.

Sarah Guo 21:37 Mm-hmm.

Brett Adcock 21:38 We have to make sure their performance is good. We have to push, you know, push product. We have to check it. Uh, we have to integrate it in. H- it has to not break anything. We have to, uh, maintain it when it gets broken. Uh, we have a supply chain to go manage, like... um, it's like... it's a mess (laughs) .

Sarah Guo 21:53 So eyes wide open-

Brett Adcock 21:54 Um-

Sarah Guo 21:54 ... like, how do you end up?

Brett Adcock 21:55 There is no mature supply chain for what we're doing, and there is no other option.

Sarah Guo 21:59 Mm-hmm.

Brett Adcock 22:00 We have this philosophy that we have at the... we do, like, a nine o'clock standup every day on the, um... like, every morning, like rain or shine. And during, like, bring-up processes where we're bringing up, like, new robots and stuff, it's a mess. Like, the robots, like, never really work well on the first time. Everything breaks because we're, like, getting all the systems to start working together. There are things on software and hardware that haven't communicated before. There's just nothing, um, available that we could go buy that would satisfy our needs.

Sarah Guo 22:25 Mm-hmm.

Brett Adcock 22:26 So we've been forced to go build. And, like, design and then, you know, in a lot of cases, we even manufacture.

Sarah Guo 22:33 Yeah, I saw the machining (laughs) .

Brett Adcock 22:34 Uh, we did buy a decent amount of things. We found that we're, like, in, uh, incredible dependency for us last year. We had to go build teams around them to go buil- like, to go fix it-

Sarah Guo 22:42 Mm-hmm.

Brett Adcock 22:42 ... um, on both the sensor side and software side. Yeah, I don't think you can build a humanoid robots company without kind of going all in on all of it.

Sarah Guo 22:49 Big question, but can you describe, like... if you want to run a hardware project, a hardware and software project like this, with this complexity, at velocity, like, how do you manage product development?

Brett Adcock 23:00 From, like, a thesis perspective, I, I strongly believe in,

Brett Adcock 23:04 like, an iterative design approach. We really don't believe on l- spending a lot of time, like, just, just doing research and analyzing. We spend a lot of time on just testing, bui- building and testing-

Sarah Guo 23:13 Mm-hmm.

Brett Adcock 23:14 ... uh, here. And, um, that helps us really shake out all the problems. It helps us learn. It helps us recursively add it into a continuum of product that's coming down, uh, coming out. And, um... so first, that's our strategy. We, um... we want to be continuously updating the hardware and software forever. It'll... I don't think it'll ever be good enough for us. Um, so we have a whole process built around building a robot from a des- like a basically hardware and software design that we run here. We first set out with understanding who are the customers, like, what does the robot need to do? From there, we, uh, we basically set requirements, like, "Okay, we need the robot to lift this much pounds. It needs to run this long. It needs to charge here." The safety requirements are that it can't... battery can't burn down the building. There's a bunch of stuff we have to... um, the environment on IP rating has to be done on all the actuators. There's just a bunch of requirements that come from there. From there, we look at those requirements and we, we do engineering design. And we have basically-... like, three big phases. Uh, we have a conceptual and preliminary and critical design review that we do here throughout the year. The whole company's involved. So we, um, have these, like, design gates that we work through. Similar practice that I instituted, exactly similar, well, similar practice I instituted at Archer from a, from an engineering design perspective, or philosophy. And, um, yeah. We work, we work through in a very methodical way, like, uh, all the way through that serially.

Sarah Guo 24:42 And how, how does, um, integration and testing work in a way that's different from a software company, since you've also done that?

Brett Adcock 24:48 We tr-

Sarah Guo 24:48 I'd imagine really differently.

Brett Adcock 24:49 Yeah. We try to, we try to test and... We try to prototype and test as fast as we can to see if we were right.

Sarah Guo 24:55 Mm-hmm.

Brett Adcock 24:55 Same with software. It just happens on a longer timeline.

Sarah Guo 24:57 Okay.

Brett Adcock 24:58 Well, software, you'll come in one day, um, and you'll, and I'll say, "Okay, um, we talked to the client, we believe the cli- we've, we talked to the client, we believe we have all these things on the product backlog list we wanna do." You'll somehow have some heuristics where you'll score those, and you'll basically comb the backlog and you'll say, "I'm gonna go... We're, we're gonna add these, like, six things to the sprint." You'll do story points, and you'll basically, you'll, you'll assign those out and you'll basically manage that whole process. And then you'll launch it-

Sarah Guo 25:22 Mm-hmm.

Brett Adcock 25:22 ... and then you'll get feedback, right? You'll try to either A/B test things or you'll watch the analytics, and you'll say, "Did that work? Did that work?"

Sarah Guo 25:28 Mm-hmm.

Brett Adcock 25:28 Uh, you, you really wanna do that. You wanna have a, kind of a scientific method around it, say like, "Okay. Was that... Did that actually help, you know, fix this problem?" Uh, same here. We have the client, we have requirements that we set, like they need to do this. We are designing things, say, like, we are har- designing hardware from scratch, like, and, um... So we take our... If we're designing an actuator, we're gonna take our CAD system and we're gonna, from scratch, design it. We're gonna make, uh, assumptions on, and trade studies on, like, what the different trade-offs are of how we could do it up front, so we don't spend a lot of time designing something that just didn't work.

Sarah Guo 26:01 Mm-hmm.

Brett Adcock 26:01 Uh, so we're gonna be pretty methodical about it, like, much more methodical than you are a software, because the timelines are, you know, order of magnitude plus longer.

Sarah Guo 26:07 Yeah.

Brett Adcock 26:08 And then-

Sarah Guo 26:08 I think, to me, that's the key. Because there's a lot of, like, so- you know, you have design choices and you're like, "Uh." (laughs)

Brett Adcock 26:13 Yeah. No, yeah, you have to... Yeah, exactly. You, uh-

Sarah Guo 26:16 "I like this framework." (laughs)

Brett Adcock 26:17 Yeah. No, you can't... Yeah. It's like, um... You have to, you have to be very objective about those decisions. You have to say, "Okay, from actually a design perspective, there's, like, there's, like..." You know, at a very high level, "Do we wanna have hydraulic systems?" Um, you know, like, is it pneumatic? Is it electro-mechanical? Like, all these different ways of, like, say, powering the joints. And then from there, we can go to, "Okay, we want it to be like a, like a electric motor driven, or electro-mechanical driven actuator." Um, "Do you want it to have a, like a linear drive or rotary drive?" Um, we have a lot of rotary drives on our, um, on our, on our, um, on our, um, our robot. And then from there, "Okay, how is..." Like, if you look at the cross-section of the actuator, if you cut it in half, "How are we actually gonna design it?" Um, and, "What are the requirements? Like what does it need to actually do when we actually make it?"

Sarah Guo 27:04 Mm-hmm.

Brett Adcock 27:04 So, so all of those are, like, top-down, uh, driven. And, uh, we do that through trade studies and requirement setting, and, and then iterative design is the process of picking those choices, building it as fast as you can, and then going back and saying, "D- did it actually accomplish what I wanted it to do?"

Sarah Guo 27:22 Mm-hmm.

Brett Adcock 27:22 That timeline is, like, 10 times longer than software.

Sarah Guo 27:25 Right.

Brett Adcock 27:25 Maybe 100 times in some cases.

Sarah Guo 27:27 Yes.

Brett Adcock 27:27 So the trick here is, like, how do you compress that? If you look at the best hardware engineers in the world, or, like, that have been around, they do, um, they do the iterative design process, and they do it at a speed, uh, faster than anybody else.

Sarah Guo 27:40 Friend of mine runs a company called Zipline, which is these delivery drones. And, uh, I thought it was striking that, like, the number one thing that they were looking for in their interview process, besides, like, overall technical ability, was just, like, someone's intuition for rapid prototyping.

Brett Adcock 27:56 Yup. Speed is, like, one of our five company values. We hire for it here. It's extremely important. Because if you got to that point faster and went back, it's like... And you made a mistake-

Sarah Guo 28:08 Mm-hmm.

Brett Adcock 28:08 ... great, you have, like, time to go fix it, right? 'Cause it took you three months instead of a year. But if it took you, like, a year to get there, or even two years to get there, and you were wrong, you die. (laughs)

Sarah Guo 28:17 What you described just doesn't feel a lot like software development to me.

Brett Adcock 28:20 Yeah. (laughs)

Sarah Guo 28:20 But I, I understand, I understand the principles. Let's, um, let's zoom out and talk a little bit about the business and just, like, implications. You know-

Brett Adcock 28:26 Right.

Sarah Guo 28:26 ... if you can make figure work. Um, so you described, you know, large public partnerships with BMW, with OpenAI. Um, like, tell us about those.

Brett Adcock 28:36 Yeah.

Brett Adcock 28:37 So we, um, we announced BMW a few months ago. They're, um, our first announced commercial customer. They are buying robots from us to ship into their manufacturing facilities. So the first facility we're, we're gonna launch into is Spartanburg, South Carolina. It's their largest facility in terms of f- like, vehicle production globally-

Sarah Guo 28:56 Mm-hmm.

Brett Adcock 28:57 ... um, which is great.

Sarah Guo 28:57 On order of, what, magnitude?

Brett Adcock 28:59 They make, like, uh, like, 1,400 cars a day.

Sarah Guo 29:02 Okay.

Brett Adcock 29:02 Um, and then that, that plant is higher, like, in terms of car productivity than any other plant globally. Um, happens to be in the US, which is great. We can hop on a quick plane, go over there. And we hope over the next 12 to 18 months, we're, we have robots in that facility doing real work.

Sarah Guo 29:18 Mm-hmm.

Brett Adcock 29:18 We've already picked out the first five use cases. We've already also then, um, chronologically ranked them in terms of what we're gonna start on first. Um, and we're already doing the first one. We're, like, actively working on, um, doing that fully and end to end reliably right now.

Sarah Guo 29:33 What can you, what can you say about the OpenAI partnership? It obviously makes sense that if you wanna communicate with robots with, uh, speech and you want them to have world model and reasoning-

Brett Adcock 29:43 Yeah. Yeah, we're super excited to be working with OpenAI. They've been really great. They started out in robotics, and some of the team that's working on the project with us were, were from that period of time, which is really cool. The highest level, we're working with OpenAI on building new model- AI models now for our robots to ship into commercial use cases. And what OpenAI brings is, they, they, I mean, they have the best vision language model in the world. And they have the best team in the world to work on the implementation of that. And we're working with them on trying it to, to do language reasoning on the robot. So think of this, like, the-... you know, like two, two parts of the brain. We're just a brain robot. We have a brain, like a centralized brain that you can talk to. And you can say, "I need you to go fold the laundry." Uh, that brain will then build tasks. It'll say, like, "I need to go do this, this, and this. I need to go grab the hamper." Um, we then build a path to go, you know, go find the hamper. We go, uh, we go to grab it, like, you know, like, OpenAI doesn't know how to grab it. It doesn't know how to command the robot.

Sarah Guo 30:45 Mm-hmm.

Brett Adcock 30:46 So we come in where we've designed AI systems here at Figure that can command the motors and the hands and the rest of the system to be ab- be able to do that only with neural nets.

Sarah Guo 30:57 Mm-hmm.

Brett Adcock 30:57 So we're kind of like combining forces of, like, kind of the, you know, we're doing like a low-level reasoning, and then we're doing, like, the language reasoning at the highest level.

Sarah Guo 31:04 Yep.

Brett Adcock 31:04 And we're combining those two together, and it's the more we spend time in this area, the more we feel it's, it's needed to be able to do this at real scale. I don't think there's really a way to, to do this without a, a really intelligent VLM sitting at the top. We have now kicked off a process to be able to, um, start integrating those and building new models from scratch. We put out a video a few weeks ago of working with them where we were basically doing speech to speech reasoning. We could talk to the robot. It could talk back. It could ask what's in the scene. It could, like, um, it could understand, you know, through memory, like, what happened in the past and implications it was making, you know, going forward. It was just, it was, um ... And that was like, you know, 13 days after, so after the announcement. Uh, so, you know, over the next six to 12 months, we're really excited to be working on, uh, these custom models with them.

Sarah Guo 31:49 Speaking of robots with memory that you can talk to, uh, what do you expect people will do with robots in the home?

Brett Adcock 31:55 Well, I think-

Sarah Guo 31:55 What are you gonna do? (laughs)

Brett Adcock 31:56 (laughs) What am I gonna do? Um-

Sarah Guo 31:58 Yeah.

Brett Adcock 31:58 Well, what we want our humanoids to do is to do, like, physical work, and we want them to be a generalizer- generalizable replacement for human labor. So what I'm gonna have them do is I'm gonna have them, over time, do my laundry, um, cook me dinner. I have, like, this ... Every day I get home, there's kids' toys-

Sarah Guo 32:19 Yeah.

Brett Adcock 32:19 ... everywhere. I need them to clean the kids' toys up every day. (laughs)

Sarah Guo 32:22 (laughs)

Brett Adcock 32:22 That, that'll be on the docket for task planning for the r- my robot every single day. I think over a long enough period of time, everybody will own a humanoid just to do work for them.

Sarah Guo 32:31 Mm-hmm.

Brett Adcock 32:32 And you will choose, like, like-

Sarah Guo 32:34 Gotta make them cheap?

Brett Adcock 32:36 L- labor will be optional, and you'll choose to do work or not. Um, yeah, we'll make them cheap. There's a lot of, um, preconceived notions that these will be really expensive. I, I do not think they'll be expensive. I think, um, you know, we're working on cost reduction and stuff now. Like, um, the robots we have now we've showed are, are expensive. Uh, they're not cheap. The work that we're doing now and into the future is working on trying to reduce cost, and cost is gonna come down to really affordable levels. It'll take time. A part of that, um, cost reduction curve happens whenever you get manufacturing volumes up to certain levels. When there's an experience curve, manufacturing volumes and costs follow. So there's a certain amount of cost reduction we can do from designing for manufacturing, and there's a certain amount of cost reduction we'll get with real scale.

Sarah Guo 33:17 Ford Model T school-

Brett Adcock 33:19 Yeah, exactly. (laughs)

Sarah Guo 33:20 ... of robotics. Yeah. (laughs)

Brett Adcock 33:20 Yeah, yeah. So, uh-

Sarah Guo 33:22 Right.

Brett Adcock 33:22 ... we just need to get robots shipped.

Sarah Guo 33:23 Okay.

Brett Adcock 33:23 And shipped in a big way. And if we can ship them ... We ... If we can ship millions into industrial solutions, uh, we can use that as our pathway for, uh, the data collection process and volumes on, uh, the manufacturing side.

Sarah Guo 33:36 How do you think about social acceptance

Sarah Guo 33:38 of robots? What are you already seeing-

Brett Adcock 33:40 I knew-

Sarah Guo 33:40 You show people.

Brett Adcock 33:41 ... I knew you were gonna ask that. (laughs)

Sarah Guo 33:42 Yeah. I mean, I'm doing my best robot impression. I'm really excited, but ...

Brett Adcock 33:44 Yeah. Yeah, we also had this at Archer where it was like, you know, are people going to be okay with, like, things in the air over the city and things like aircraft taking off in your backyard? We had to think really long and hard about how we were going to do that and integrating that solution. I think this notion of social acceptance as it relates to safety and just robots in the world and everything else needs to be proven. Like, it needs to be shown and proven. Uh, I don't think somebody's gonna wake up one day and say, "Okay, I think I'm okay. It's a Friday, I think I'm okay with humanoids today." I think it's, I think you're gonna start gradually seeing robots in BMW, in these different industrial places. They're gonna be doing real work. They're gonna be building a safety record. Um, we're obviously not gonna be, like, having robots at BMW unsafe and then try to launch them in your home. It's just gonna take time to build that out, and, and to build that trust and to build the brand like. Um, so I, I, I think it'll be gradual, and I think that trust and that social acceptance has to be earned.

Sarah Guo 34:39 Mm-hmm.

Brett Adcock 34:40 So we think about it, everything we do, every time that everybody sees a robot, every time we put out a video, every time we're ... as we're launching into our com- commercial customers, like, we have to think about that from the very beginning. I think, you know, it's, it's even interesting, more interesting because, like, every sci-fi movie we've ever seen has, like, ended poorly in this area for humanoid robots. (laughs) You know? And so yeah, it's just like, it's never like, um ... Ne- never great. So, uh, there's a little bit of that stigma, which is almost like fantas- fanciful. Um-

Sarah Guo 35:06 Yeah.

Brett Adcock 35:07 Yeah.

Sarah Guo 35:07 There's always some contingent that's on the robot side. Uh, just, you know, for, for the record, if they win, I'm, I'm on that side.

Brett Adcock 35:14 Yeah, great.

Sarah Guo 35:15 Yeah. (laughs) Okay.

Brett Adcock 35:15 Yeah. I really want it to work out well.

Sarah Guo 35:17 Yeah.

Brett Adcock 35:17 And I think if it works out well, it'll be like ... It'll be really cool. It'll feel like, um, 50 years of the future got pulled forward today. It'll be just magical.

Sarah Guo 35:26 So given that, I have to ask you, like, AGI, does this make it come faster? Um, I think there are a, a bunch of people who think of the, the lack of, like, actuation for increasingly capable models as like the big

Sarah Guo 35:41 actual safety barrier. But you're like, "Oh, here's the actuator," right? (laughs)

Brett Adcock 35:44 Yeah. Yeah, it's funny. It's like, who's gonna get to market first, the humanoids or AGI? Um, or are, you know, do we need humanoids to get there? My view here is that we need to get the humanoid robot figured out pre-AGI.

Sarah Guo 35:57 Mm-hmm.

Brett Adcock 35:58 And, uh, whe- whether we need the humanoid to get there, like, as it relates to the timeline for AGI or not, wh- what I think is a really quite dystopian future is if we have ... AGI's here, and AGI wants to do something. And it's gonna, like, com- it's gonna ask you or force you to go do it, whether it force you through money or it's just gonna, you're gonna have to go do the real work for it. And if you walk into, like, a warehouse today-

Sarah Guo 36:22 Like, we are the actuators for the, for the model.

Brett Adcock 36:26 If you walk into a warehouse today, or a manufacturing facility, everybody's getting told what to do from a computer to a, you know, to a barcode scanner or a phone.

Sarah Guo 36:34 Mm-hmm.

Brett Adcock 36:35 They're literally getting ... They're, they're scanning something. They're getting told what to do next from a warehouse management system. They're literally a cyborg.

Sarah Guo 36:40 Hmm, that's a little dark, yeah.

Brett Adcock 36:42 Yeah. And if we have, like, super artificial i- s- like super intelligence, um, what do you think that's gonna be like? My hope is that we can figure out the humanoid thing prior to that, and we can have humanoid robots doing all that work. It's, it's becoming more clear, I think at least to our team, that at least these large language models are having a really difficult time, like, reasoning around the physical world. Planning, actions, everything across the board. We, we kind of believe that, that over the next five or 10 years, we really hope to make a significant impact on the AGI timeline here at Figure. We think that information is extrem- like, coming out with a robot is extremely important to solving this last big piece into the AGI timeline. Jury's still out. (laughs) We will see. But we're, um, we're hopeful that we can, yeah, help here.

Sarah Guo 37:36 Awesome. No small plans. Small trades.

Brett Adcock 37:40 Yeah.

Sarah Guo 37:42 Find us on Twitter @nopriorspod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way, you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.