No Priors: Artificial Intelligence | Technology | Startups - The argument for humanoid AI robots with Brett Adcock from Figure

Episode Date: April 4, 2024

Humans are always doing work that is dull or dangerous. Brett Adcock, the founder and CEO of Figure AI, wants to build a fleet of robots that can do everything from work in a factory or warehouse to folding your laundry in the home. Today on No Priors, Sarah got the chance to talk with Brett about how a company that is only 21 months old has already built humanoid robots that not only walk the walk by performing tasks like item retrieval and making a cup of coffee but they also talk the talk through speech to speech reasoning.  In this episode, Brett and Sarah discuss why right now is the correct time to build a fleet of AI robots and how implementation in industrial settings will be a stepping stone into AI robots coming into the home. They also get into how Brett built a team of world class engineers, commercial partnerships with BMW and OpenAI that are accelerating their growth, and the plan to achieve social acceptance for AI robots.  Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @adcock_brett Show Notes:  (0:00) Brett’s background (3:09) Figure AI Thesis (5:51) The argument for humanoid robots (7:36) Figure AI public demos (12:38) Mitigating risk factors (15:20) Designing the org chart and finding the team (16:38) Deployment timeline (20:41) Build vs buy and vertical integration (23:04) Product management at Figure (28:37) Corporate partnerships (31:58) Humans at home (33:38) Social acceptance  (35:41) AGI vs the robots

Hi listeners, welcome to another episode of No Pryors. 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. Yes, sir. You have this wild company doing humanoid robots.

You just raised almost $700 million. Can you talk a little bit about how you get from a farm to software to vertical takeoff and landing to rope humanoids? Pretty normal path. Yeah, that's what I do, too. Yeah, yeah. So my story started, and I grew up in Illinois on a third-generation farm. and it ended up, basically, at our pretty early age,

started coding and getting the software and building things. And that basically has been now about 20 years of building companies, a little over 10 in software and a little under 10 in hardware. At one point, I started a software company and sold it, and then I started a company called Archer Aviation. We build electric vertical takeoff and landing air. aircraft. And then about 21 months ago, I started Figure. 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? I grew up on a lot of hardware. And so I looked at hardware as like I really wanted for a long time to build hardware, build like kind of airs of deep tech. The only way to really do that was like self-fund your own venture and get it really moving. So after I sold, I sold Vetteri in 2017, and right away I knew I wanted to build electric aircraft. And I actually went back down to- Do you fly planes or something? I'm super passionate about, like, A, fixing traffic problems. We have like, you know, half the world living 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 the sustainable transport. I think, you know,

all transport besides rockets will move electric, uh, hopefully in our lifetime. So what we do at our sure is we build vertical takeoff landing aircraft. So aircraft that are kind of like helicopters, but fully electric. They can take off from a helicopter landing pads like inside of a city. And they can take you from here back to San Francisco in under 20 minutes. And sold. That would be a dream instead of driving for two hours. So that was a really hard business.

I basically started the company out of University of Florida. I started an engineering at University of Florida and 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. That's now a public company. It's on the clearance path. How do you go from that to humanoid robots? I would say less of a leap from software into Archer

than it was from Archer to figure. Yeah, but what's the thesis for it? The thesis here is that if you assume that technologies are possible to build a humanoid robot, and just for listeners, humanoid means human form, so legs, arms, hands. And you can do basically human-like work

with a humanoid robot. A, it's going to be the biggest business in the world by probably order of magnitude. Half of GDP is human labor, like an 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 will be a age of abundance as it relates to goods and services prices. And three is I think we'll have over enough time. I think we'll have an impact on an 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 into market in a significant way, build a fleet of robots, and then build an AID data engine and train those robots on how to do useful work. Why does being humanoid matter? I think there are plenty of people who work in robotics that say like bipedal is really unstable. There are lots of reasons that these humanoid forms are not. optimal for doing lots of different work, we can have 10 arms and be stronger and whatever else. 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 the way we look biologically had a significant impact in the way our environment looks. if we were 10 feet taller and we had like, you know, six arms

a lot stronger, the world looked a lot different. When people ask, like, is the humanoid the right form factor? It's the wrong question. It's just the wrong question to be answered. 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.

But that's the world that exists. We built it so we can interact with it. If you go to Mars today, you're going to want to go grab coffee, you're going to want to walk around, you're going to want to live in a habitat. You're going to want to do things. and you're going to build a human world around it. And then so if we want to automate work, you want to build a general interface to that.

You want to build like the equivalent of like the keyboard and mouse to the internet and 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, the average like non-expert human.

So it's really clear to us here at the right form factor is the humanoid. You can build one hardware system that can do everything, 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 don't believe that, you're going to go out in the world and look at every single use gate, 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 skill sets that one single person can't do like in a full stack software role. It needs firmware and embedded software and operating system and rotor and stater 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're going to go build millions of those. How is that even tractable? How do you do that across the whole world and all these special use cases? And then you're going to make a role. Well, it is how the robotics industry works today.

Specialized robots, right? Yeah. I'm not saying that's efficient, but I think that's where the question comes from. I don't know if we should be reasoning by that analogy. Okay, one thing you and I have talked about before is like, Is this the right decade to build the company? Like, what made you say, like, we have it now. We can do it.

We need to prove it. And I think we've started to prove it on, like, an MVP level. Like, you can see the robot doing pretty useful stuff now. And it's been 21 months. Took the first year to hire enough employees to come here to do stuff. So we've 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, what we've been able to accomplish. Can I actually just describe the two public demos so far?

and like why they're important? Yeah, we've been doing kind of like two divergent set of demonstrations of the world. The first is we do plan as a business to start launching into more kind of industrial solutions, like more like the corporate labor market, you know, manufacturing, supply chain logistics, those type of areas. We think that allow us to build the AID data engine quicker because we're shipping robots faster and 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 is different. There's like a higher variance of failures where we're like, you know, if we drop like the number one dad cup at home or the number one mom cup, like, no, not great. We drop a bin in like a warehouse, like, who cares?

You know what I mean? It's a little bit different scenario. Also, safety is impacted. There's pricing compression as we move into the consumer world. There's just a bunch of stuff that happens. So we've done a few demos so far. First is we've done like been moving, very traditional industrial solutions roles where we're taking bins from palace into conveyor systems.

We're doing that fully autonomous end-to-end on our robots now, all bipedal. And the second is we're doing 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. It's able to do useful works, like go and grab things like an apple or a certain plates or make a coffee and a curic.

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 speech, it's taking in video feeds and the cameras. We're processing that on a model and then 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. It's like we go out and do this and like everything just slows. We hit this like, you know, this upper bound and we just can't push through. We don't even know where the upper bound is right now. And that's what's really exciting for us and why we're really.

excited about the next 24 months is 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. I don't think this was really possible five years ago. Looking back on the last, like, you know, even a few decades, the, like the power train or the system that we use is all electromechanical 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 significant last couple decades. We just, we didn't have the runtime 10, 20 years ago in the robot with like lithium

mile batteries to make this really work. We didn't have the power out of the actuators, much of your 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 water per kilogram has improved, you know, maybe a 7% kegaker since, you know, last two decades. That makes sense. If it can't carry more than 20 miles or it can't carry fast enough to go to the highway, it's just not a useful car.

Run like, yeah, if it was really heavy and it runs like, you know, 10 minutes and it can't carry anything and the speeds and the motors are not very great. Like, you just can't get anything useful. I think the second thing is in locomotion controllers, like 10 years ago, humanoid robots, bipedal was a huge risk. Like they were clumsy, they were slow, they were falling over. The DARPA Robotics Challenge is a good example of that, like, about, you know, about 10 years ago now. you couldn't look at it and envision that being really useful or at least in your home

that's completely changed here we actually started walking our humanoid from the time I filed the C corp in the business and we walked the robot it was under a year from when we started which was pretty impressive feat and I think we probably

we're probably doing some of the some of the best work in the world on bipedal locomotion here on it from a controller controller standpoint and the third is like basically AI systems like the computation the algorithms were just not

feasible to do, I would say, 10 years ago. I guess I'd say arguably the fourth which would be, you know, we've spent a lot of time now working with open AI

as we announced and we believe the default user interface to the robot is speech. You're going to want to talk to the robot. Even an industrial setting when you're unboxing the robot for the first time,

we think the initialization process is speech. There's no, there's no like, you know, open up your phone. Configuration, demonstration. Yeah, you're talking to it. And I think, I mean, humans, that's what we're doing today, right?

We do gestures and we talk, either through, you know, written text or we're speaking. Whether it's transcribed or not, we're like using language. And we think that's primarily the main user interface. We think by default, the robot's going to be using. And, you know, five years ago, that wasn't possible either.

You said the team wasn't sure where the bounds were, like, where are the walls you can imagine hitting or what were you afraid of when you started? I mean, this is just a hard problem. There's just, if you're afraid of walls, this is like the nightmare scenario for anybody. It's just a fun house of walls. Yeah, we have this like one of our five corporate values is like aggressively optimistic. We looked at some slides together where you were looking at one of the trades and some risk on a component. and you had these mitigating factors and the second mitigating factor

was sprint harder like we will figure it out yes like work harder and that's the only solution to the risk at this point the risks are profound like nobody's been able to deliver a commercial humanoid

into a market in human history we have to not only like the third threshold is we have to do a certain amount of an equivalent of human-like work performance, which is extremely hard. Humans are very productive. And we have to do that reliably and continuously over the course of months and years. And then to add on to 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 action space or the amount of orientations, the

robot could possibly be in is extremely high. Either you have to code your way of saying this is, you know, I have to write C++ plus or I have to write a script for everything the robot should do. Everywhere in the world, you have to solve that through robot learning is the short answer. So, and we haven't seen that work extremely well in human history either. I mean, we're watching that unfold right now with cell driving cars. We have to take all of those challenges on hardware, never having been done before, 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 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 because now we kind of see the roadmap for the next 12 to 24 months and we're kind of just optimizing. team. 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 client's use cases. It just has to be more robust. Zero to 100 or zero to,

you know, walking bipedal robot in a year since you incorporate. How do you assemble a team that's so multi-domain so quickly? So has been my first rodeo having built a like, you know, built all, like, all My team's previously a better E and Archer. When I started to figure, there was a few things that no matter like what company it was, I would do and I would do again. 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 lives 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. Or aggressively optimistic. 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 basically the first year like hyper focus 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 top and we basically built out the teams with in detail of like who this all these groups should look like, whether it's controls, AI, 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 rotor, stator transmission, sensors, thermals, motor controls. That all makes sense as like a picture of it. But then like the reality of somebody who spends a lot of time recruiting for early stage

companies is like, I can't just go like pick up that guy up from Boston Dynamics because I decided he's the right. Yeah. So I then went out and found everybody online that I thought was the best in the world. And then I did 300 phone calls over six months. And I called emailed all of them. So Jerry picks up and he's like, sure, Brett. They'll say sure in the first call, I wish they did that. So a few phone calls later, yeah, a few meetings later they do. Yes. Or a certain percentage will. Yes. This is no different than when I did at Vetteri. I built, you know, 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 the 30, 6090s. I brought them 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, 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 people ship. Entrepreneurs, you heard it here. Just follow the plan. It was just a lot of hard work,

but we have, 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 to keep using it over time. Like, that's the secret. And so everything starts axiomatically with like, where's that team and how do you go get them? So it was a brute force effort. Great. My favorite. My favorite type of strategy.

Aggressively optimistic. How far away are we from like, you know, companies buying robots on mass deploying them? So companies are, you know, paying us now for robots 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 and real work sales getting paid for it. And I feel decently confident. For our audience, what's a work sell? Just, you know, like, I'm at BMW and I'm supposed to be, you know, moving a bin or a box.

This is your work sell or removing bins over here to conveyor system. That would define a work cell. Like, I need to be in this location. I need to be doing this job. It's basically a job or a task. So I feel pretty confident of the next 12 to 18 months. We'll start doing those. And then we've got to make it reliable.

extremely reliable, extremely safe as we like, you know, branch out to hundreds and then thousands on, you know, inside of the say a factory floor. And then we got to manage a fleet of it. And then we have to do AI training at scale. And then updates that to that fleet at scale. And then we have to manufacture at scale. No problem.

Yeah. I think like, I think if we can solve the robot humanoid doing these full stuff, the other things are extremely doable. It's extremely doable to take a robot that is reliant. liable and that can do the performance and make a lot of them. A lot of these other problems have been solved before. There's very hard versus new problem. 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 just seems like, you know, we should be able to manufacture millions. I certainly don't. I hope we don't hit that word block. And then, yeah, I feel like in a mass way, I think, because, you know, like the manufacturing volumes will, like, The dependency here is getting the robot to do fully reliable, useful, continuous work. And as soon as that's happening, we're parallel pathing volume manufacturing.

Figure operates as a really unique company, I think, or I've just not encountered many orgs that work this way. 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? The trade, I'll, like, build versus buy? Like, should we use ourselves or should we go buy them is something we do at every single, like, component level. So it's not a philosophy of, like, we're going to build everything from scratch to begin with.

No, no, I think our philosophy is we would rather, um, our, yeah, our philosophy is that we would rather not do, do it, and we'd rather buy. But you ended up building a huge amount. Yeah. We started buying by, like, we bought a, yeah, the mix of. Basically everything but the GPU. It feels like that. Yeah. Everything, maybe besides the battery cell and the GPU and CPU at this point,

it feels like we've done or what we're doing. 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. We have to make sure their performance is good. We have to push products.

We have to check it. We have to integrate it in. It has to not break anything. We have to maintain it. When it gets broken, they have a supply chain to go manage, like, it's a mess. So eyes wide open, like, how do you end up? There's no mature supply chain for what we're doing, and there is no other option. We have this philosophy that we have at the, we do like a 9 o'clock stand up every day on the, like every morning, like rain or shine.

And during like bringing up processes where we're bringing up like new robots and stuff, it's a mess. Like the robots like never really work well in 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 available that we could go buy that would satisfy our needs. So we've been forced to go build. And like design and then, you know, in a lot of cases we even manufacture. Yeah, I saw the machining.

We did buy a decent amount of things. We found that we're like an incredible dependency for us last year. We had to go build teams around them to go, like to go fix it 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. 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?

From like a thesis perspective, I strongly believe in like an iterative design approach. We really don't believe on spending a lot of time, like just doing research and analyzing, we spend a lot of time on just testing, building the testing here. And that helps us really shake out all the problems. It helps us learn, helps us recursively add it into a continuum of product that's coming down, coming out. And so first, that's our strategy. We want to be continuously updating the hardware and software forever.

I don't think it will ever be good enough for us. So we have a whole process built around building a robot from a basically hardware and software design that we run here. We first set out with understanding who are the customers, like what does a robot need to do? From there, we basically set requirements, like, okay, we need the robot to lift this much pounds,

it needs to run this long, and needs to charge here, the safety requirements so that it can't, battery can't burn down the building. There's a bunch of stuff we have to, the environment on IP rating has to be done on the actuators. There's just a bunch of requirements that come from there. From there, we look at those requirements and we do engineering design. And we have basically like three big phases.

We have a conceptual and preliminary and critical design review that we do here throughout the year. The whole company is involved. So we have these like design gates that we work through. Similar practice that I instituted exactly similar. Well, similar practice. I instituted Archer from an engineering design perspective or philosophy. And yeah, we work through in a very methodical way, like all the way through that, serially. And how does integration and testing work in a way that's different from a software company since you've also done that?

I'd imagine really differently. Yeah, we try to test and we try to prototype and test as fast as we can to see if we were right. Same with software. It just happens on a longer timeline. Okay. Well, software, you'll come in one day and I'll say, okay, we talk to the client, we believe the client, we believe we have all these things on the product backlog list we want to do. You'll somehow have some heuristics where you'll score those and you'll basically comb the back. backlog and you'll say, I'm going to go, we're going to add these like six things to the

sprint. They'll do story points and you'll basically, you'll sign those out and you'll basically manage that whole process. And then you'll launch it and you'll get feedback, right? You'll try to either A, B, test things, you'll watch the analytics and you'll say, did that work? Did that work? You really want to do that. And you want to have that kind of a scientific method around it, say, okay, was that, did that actually help, you know, fix this problem? Same here. We have the client. We have requirements that we set, like they need to do this we are designing things like we are designing hardware from scratch like in so we take our we're designing an actuator we're going to take our CAD system and we're going to from scratch

design it we're going to make assumptions on and trade studies on like what the different tradeoffs are of how we can do it at front so we don't spend a lot of time designing something that just didn't work so we're being pretty methodical about it like much more methodical than you are software because the timelines are you know order of magnitude plus longer yeah I think to me that's the key because there's a lot of like you know you have design choice you know I'm like, yeah, you have to, yeah, exactly, you, uh, I like this framework. 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 actual your design perspective, there's like,

there's like, you know, at a very high level, do we want to have hydraulic systems, um, you know, like, is it pneumatic? Is it electromechanical? Like, all these different ways of, like, say, powering the joints. And then from there, we can go to, okay, we want to be like like an electric motor driven or electromechanical driven actuator. Do you want to have like a linear drive or rotary drive? We have a lot of rotary drives on our, on our, on our, on our, on our, a robot. And then from there, okay, how is, like, if you look at the cross section of actuated, if you cut it in half, how are we actually going to design it?

And what are the requirements? Like, what does it need to actually do when I actually make it? Uh-huh. So, so all of those are like top down, uh, driven and we do. that through trade studies and requirement setting and then iterative design is the process of picking those choices building it as fast as you can and then going back and saying did it actually accomplish what I wanted to do that timeline is like 10 times longer software right maybe 100 times in some cases yes 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 they do the iterative design process and they do it at a speed faster than anybody else. A friend of mine runs a company called Zipline, which is these delivery drones. And 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. 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, great. You have like time to go fix it, right? Because 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. What you describe just doesn't feel a lot like software development to me, but I understand the principles. Let's zoom out and talk a little bit about the business and just like implications, you know, if you can make figure work.

So you describe, you know, large public partnerships with BMW, with Open AI. Tell us about those. Yeah. So we announced BMW a few months ago. They're our first announced commercial customer. They are buying robots from us to ship into their manufacturing facilities. So the first facility we're going to launch into is Spartanburg, South Carolina. It's their largest facility in terms of vehicle production globally, which is great.

On order of what? They make like 1,400 cars a day. Okay. And then that plan is higher. in terms of car productivity than any other plant globally. Happens to be in the U.S., which is great. We can hop on a quick plane, go over there. And we hope over the next 12 to 18 months

where we have robots in that facility doing real work. We've already picked out the first five use cases. We've already also then chronologically ranked them in terms of what we're going to start on first. And we're already doing the first one. We're like actively working on doing that fully end-to-end reliably right now. What can you say about the Open AI partnership? Obviously, it makes sense that if you want to communicate with robots with speech

and you want them to have a world model and reasoning. Yeah, we're super excited to be working with Open AI. They've been really great. They started out in robotics and some of the team that's working on the project with us or from that period of time, which is really cool. The highest level, we're working with open air and building new AI models out for our robots to ship into commercial use cases. And what Open AI brings is 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 to do language reasoning on the robot. So think of this like the, you know, two parts of the brain or just the brain robot. We have a brain, like a centralized brain that you can talk to you. And you can say, I need you to go fold the laundry. 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.

We then build a path to go, you know, go find the hamper. We go and we go to grab it. Like, you know, like open-hand doesn't know how to grab it, doesn't know how to command the robot. 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 able to do that only with neural nets. So we're kind of like combining forces of like kind of the,

you know, we're doing like a low-level reasoning and they're doing like the language reasoning at the highest level. Yep. And we're combining those two together. And the more we spend time in this area, the more we feel it's needed to be able to do this at real scale. I don't think there's really a way to do this about a really intelligent BLM sitting at the top. We have now kicked off a process to be able to 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. I could talk back. I could ask what's in the scene. It could, like, 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, and that was like, you know, 13 days after or so after the announcement. So, you know, over the next six or 12 months, we're really excited to be working on these custom models with them. Speaking of robots with memory that you can talk to, what do you expect people to do with robots in the home?

Well, I think, what are you going to do? What am I going to do? Well, we'll be one. want our humanoid is 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 going to have him do is I'm going to have him over time do my laundry, cook me dinner. I have like this every day I get home. There's kids toys everywhere. I need him clean the kids toys up every day. That'll be on the docket for task planning for 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 and you will choose like to make them cheap labor will be optional and you'll choose to do work or not yeah we'll make them cheap there's a lot of precinct notions

that these will be really expensive I do not think they'll be expensive I think we're working on cost reduction and stuff now like the robots we have now we've showed are expensive they're not cheap the work that we're doing

doing now into the future is working on trying to reduce costs and cost is going to come down to really affordable levels. It'll take time. A part of that cost reduction curve happens whenever you get manufacturing volumes up to certain levels. There's an experience curve, manufacturing volumes and cost 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. Ford Model T school of robotics. Yeah, exactly. Yeah. So we just need to get robot shipped and shipped in a big way. And if we can ship millions into industrial solutions. We can use that as our pathway for the data collection process and volumes on

the manufacturing side. How do you think about social acceptance of robots? What are you already seeing? I need you're going to ask that. Yeah. I mean, I'm doing my best robot impression. I'm really excited. Yeah. Yeah, we also had this at Archer where it's like, you know, are people going to be okay with like things in the air over the city and things like aircraft taken off from your backyard? We didn't think really long and hard about how we were going to do that and integrating that solution. I think this notion of social acceptance that relates to safety and just robots in the world and everything else needs to be proven, like it needs to be shown and proven. I don't think somebody's going to wake up one day and say, okay, I think I'm okay. It's a Friday. I think I'm okay with human rights today. I think you're going to start gradually seeing robots in BMW in these different industrial places. They're going to be doing real work.

They're going to be building a safety record. We're obviously not going to be like having robots at BMW unsafe and then try to launch them in your home. It's just going to take time to build that out and to build that trust and build the brand like. So I think it'll be gradual and I think that trust and that social acceptance has to be earned. So we think about it. Everything we do, every time that everybody sees a robot, every time we put out a video, every time as we're launching into our first commercial customers, like we have to think about that from the very beginning. I think, you know, it's even more interesting because like every sci-fi movie we've ever seen has, like, ended poorly in this area for humanoid robots, you know?

It's just like, it's never, like, never great. So there's a little bit of that stigma, which is almost like fanciful. Yeah. There's always some contingent that's on the robot side. Just, you know, for the record, if they win, I'm on that side. Yeah, great. Yeah. Yeah.

I really wanted to work out well. And I think if it works out well, it'd be like, it'd be really cool. It'll feel like 50 years of the future got pulled forward today. It'll be just magical. So given that, I have to ask you, like, AGI, does this make it come faster? I think there are a bunch of people who think of the lack of, like, actuation for increasingly capable models as, like, the big actual safety barrier. But you're like, oh, here's the actuator. Yeah.

Yeah, it's funny. It's like, who's going to get the market first? The humanoids are AGI. 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 and whether we need the humanoid to get there like as it relates to the timeline for aGI or not 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 going to like it's going to ask you or force you to go do it whether they force you through money

or just going to you're going to have to go do the real work for it and if you walk into like a warehouse. Like we are the actuators for the, for the model. If you walk into a warehouse today or manufacturing facility, everybody's getting told what to do from a computer to a, you know, to a barcode scanner or a phone. They're literally getting, they're scanning something. They're getting told what to do next from a warehouse management system. They're literally a cyborg. This little dark, yeah. Yeah. And if we have like super artificial, like super intelligence, what do you think that's going to 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 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 kind of believe that over the next five or ten years, we really hope to make a significant impact on the AGI timeline here at Figure. We think that information is extremely, like coming off the robot is extremely important to solving this last big piece into the AGI timeline. Jerry's still out. We will see. But we're hopeful that we can, yeah, help here. Awesome.

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