A Humanoid Robot in Every Home? It's Closer Than You Think w/ Brett Adcock (at A360 2025) | EP #156

Summary

Moonshots with Peter Diamandis

Episode #156: A Humanoid Robot in Every Home? It's Closer Than You Think w/ Brett Adcock (at A360 2025)
Date: March 17, 2025

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Overview

In this forward-thinking episode of Moonshots, Peter Diamandis sits down with Brett Adcock, founder and CEO of Figure, to explore the rapid advancements in humanoid robotics and artificial intelligence. Adcock details his vision of integrating AGI (Artificial General Intelligence) with robotics, discusses the technological moonshots, and shares the breakthroughs and challenges in bringing humanoid robots into homes and the workforce. The episode dives deep into technical, ethical, and societal implications, combining engineering insights with real-world business impacts.

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Key Discussion Points & Insights

1. The Figure Vision: Humanoid Robots as AGI's "Deployment Vector"

[00:08–02:22]

• Brett Adcock shares his motivation for founding Figure after successful ventures like Archer Aviation. He envisions humanoid robots as the practical embodiment for AGI, solving the challenge of bridging digital intelligence and the physical world.

• Quote:

  "The humanoid robot is like the ultimate deployment vector for AGI. You need something that with no hardware changes can do everything a human can." – Brett Adcock [02:22]

• Adcock sees a risk in AGI existing only in servers, relying on humans for action, potentially leading to dystopian futures. A mechanical human, as a platform, allows AGI to act autonomously in the world.

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2. Blazing Speed in Hardware Iteration & Vertical Integration

[03:33–05:18]

• Figure achieved a working robot in under 12 months from its founding, and ships new hardware platforms every 12–18 months, driven by rapid iteration and lessons learned from prior hardware.

• Quote:

  "My rule of thumb is the first or second generation hardware is always going to suck. ...We are designing a new hardware platform every 12 to 18 months." – Brett Adcock [04:18]

• Complete vertical integration was a necessity due to the absence of a supply chain for humanoid robots—everything from actuators to operating systems is built in-house.

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3. Design for End-Use: Home and Workforce Applications

[05:48–09:30]

• Figure’s approach starts with clean-sheet design to maximize general-purpose capability, aiming to replace mundane household tasks—walking the dog, making coffee, laundry—and penetrate commercial labor markets, which represent half of global GDP.

• Robots are already operational with major customers like BMW, performing high-precision, repetitive tasks in manufacturing.

• Quote:

  "Our robots are doing that fully autonomously at the speeds we need to. ... No days off." – Brett Adcock [07:18–08:02]

• The target retail price is projected at $20,000–$30,000, making personal ownership feasible and attractive.

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4. Building the "Best Team in the World" & Startup Culture

[09:51–11:50]

• Brett attributes Figure’s velocity to assembling a world-class engineering team, cultivated through a culture of clarity, accountability, and strong work ethic.
• The team's unity is reinforced by a clear mission and the “dopamine hit” of shipping cutting-edge products.
• Quote:

  "In order to build one of the world's greatest products, you need one of the world's greatest teams. ... Everybody knows what they should be doing." – Brett Adcock [10:46–11:50]

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5. Tackling the Three "Impossible" Robotics Problems

[14:21–16:12]

• Recruiting technical talent hinged on Adcock’s upfront funding and the transparency that success was far from assured—three core challenges had to be solved in five years:

  • Humanoid hardware with human-like speed, range, and reliability
  • End-to-end neural network control rather than classic rule-based robots
  • True generalization: performing unseen tasks on command via natural language

• Quote:

  "You have to build incredible hardware… you need to solve this as a neural net problem, not a control problem ... and then you have to generalize: do something you’ve never seen before through speech." – Brett Adcock [14:21]

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6. Emergence of Helix: Proprietary AI Tailored for Robotics

[16:12–18:33]

• After experimenting with OpenAI models, Figure developed "Helix," an in-house vision-language-action model that allows robots to generalize and execute new tasks given only English commands.

• In an onstage demo, two robots who’d never seen the test groceries were able to interpret the instruction “put these groceries away,” inferring and executing all steps using Helix.

• Quote:

  "This is probably the most important AI update for robotics in human history." – Brett Adcock [18:21]

• Helix's emergent behaviors include robots looking at each other to coordinate object handoffs, paralleling critical nonverbal human communication.

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7. Next-Generation Robots: Figure 3

[19:45–21:49]

• Figure 3’s design is described as a quantum leap over previous models: 90% cheaper, more compact, with superior sensing and hardware explicitly designed for AI learning. Rollout is scheduled for 2025, with mass production ambitions.
• Quote:

  "Figure 3 is just next level design. ... For me, like, the most proud moment I’ve had in engineering in my career." – Brett Adcock [21:21]

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8. Commercial & Home Deployment: Timeline and Scale

[21:49–26:06]

• Workforce robots will hit mass deployment first; demand is so high that major companies would absorb all available robots instantly. The home market is technically far tougher, as domestic environments are unstructured ("the Wild West").
• Still, Adcock predicts in-home alpha tests will begin this year (2025), with significant progress expected across the decade.
• Data—not algorithms—is now the last bottleneck; as datasets grow, home applications will rapidly approach viability.
• Quote:

  "I think you’re going to see it in the coming years being put into homes just through speech, be able to do very long horizon hours of work without any prompt." – Brett Adcock [26:06]

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Notable Quotes & Memorable Moments

• On AGI in the physical world:

  "You can’t solve this with anything else besides a human, like a mechanical human." – Brett Adcock [02:22]

• On culture and team:

  "We're in Silicon Valley, but almost like the anti Silicon Valley. You have to work every day in the office. ... We've assembled now a couple like hundreds of like the best engineers in AI robotics in the world." – Brett Adcock [11:19]

• On emergent AI behaviors:

  "Part of this was like emergent from training. So when the robots are doing handovers, they actually look at each other... as the clearing way signal for like we should be releasing the item into each other's hands, which is like really interesting." – Brett Adcock [18:42]

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Timestamps for Key Segments

• [00:08] Motivation for founding Figure; AGI in the real world
• [03:33] Fast iteration and hardware lessons
• [05:48] Design philosophies for product and market
• [07:18] Case study: BMW factory robots
• [08:19] Pricing, business model, home utility
• [09:51] Figure team and recruitment philosophy
• [14:21] The "three impossible" technical challenges
• [16:12] Building proprietary AI: Helix
• [18:33] Emergent robot-robot behaviors, communication
• [19:45] Figure 3 announcement and features
• [21:54] Mass production plans, workforce vs. home rollout
• [24:32] Home alpha tests in 2025
• [26:06] Long-term vision for home robots

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Takeaways

• Figure is executing a staggering pace of innovation, with humanoid robots transitioning from factory floors to homes possibly within the next few years.
• The current frontier is not just hardware or legacy AI, but emergent, fully end-to-end neural networks capable of generalizing across unstructured, unpredictable environments.
• Adcock’s candidness about the technical and cultural risks, as well as his leadership style and the “moonshot” mentality, offers a transparent look into the next decade of exponential technology.

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Transcript

[Peter Diamandis]

Archer was amazing. Then you jump into arguably what could be described as one of the most difficult businesses to get into. Why'd you start Figure?

[Brett Adcock]

The humanoid robot is like the ultimate deployment vector for AGI. It is truly my honor and pleasure to introduce to you Brett adcock, founder and CEO of Figure.

[Peter Diamandis]

You went from a cold start in 31 months to shipping your first robot.

[Brett Adcock]

We are designing a new hardware platform every 12 to 18 months. By the time I filed the C Corp, we had the robot walking in under 12 months. I think you're going to see it in the coming years being put into homes just through speech, be able to do like very long horizon hours of work without any problems. It was like an iPhone moment happening with Humanoids. Like, it's going to be. This is going to happen right now.

[Peter Diamandis]

Now that's a moonshot, ladies and gentlemen. I think most of you know that the news media is delivering negative news to us all the time because we paid 10 times more attention to negative news than positive news. For me, the only news worthwhile that's true and impacting humanity is the news of science and technology. That's what I pay attention to. And every week I put out two blogs. One on AI and exponential tech and one on longevity. If this is of interest to you and it's available totally for free, please join me. [e-mail] subscribe. That's-mandis.com Subscribe. All right, let's go back to the episode. Thank you for being here.

[Brett Adcock]

Yeah, thanks for having me.

[Peter Diamandis]

I know with three young kids and a robot factory in production and incredible team of engineers, you're really busy. And I don't take it for granted that you joined us here. My only request is next time I want a figure robot with you loud and clear. I begged him. And BMW has been taking the lion's share of them.

[Brett Adcock]

Yep, we do have a lot. We actually have them running every. Every day now. So, like, they're there today running and in their largest plant.

[Peter Diamandis]

Why'd you start Figure? I mean, you had this incredible. You have a few incredible successes, and Archer was amazing. And then you jump into arguably what could be described as one of the most difficult businesses to get into.

[Brett Adcock]

Yeah, I think we really need to figure out a way to give, like, AGI a body here. I think it's like a really negative or, like, almost like dystopian future if we figure out how to solve AGI and it lives in a server somewhere and it's like, you know, more intelligent than all of human, like everybody. And ultimately, if it wants to do something in the physical world, it'll have to ask or boss a human to do it. And the humanoid robot is like the ultimate deployment vector for AGI. You can't solve this with anything else besides a human, like a mechanical human. You need something that is a single platform that with no hardware changes, can do everything a human can. And you need something that can also be good for the neural nets. Like the neural net here in a humanoid can basically learn, basically learn from transfer learning. It can basically multitask across a variety of different applications, which is really good for neural net. So we basically can build one single neural net foundation model that can power the whole robot to do everything end to end.

[Peter Diamandis]

I mean, massive congrats. You went from a cold start in 31 months to shipping your first robot, which is extraordinary. I mean, a lot of Companies get their PowerPoint decks ready and raise their first capital in that period of time. And we're going to be seeing some of the robots in back here. When I visited you up north, you showed me around, we did a podcast together and you showed me figure one, and here's figure two, and here's the designs for figure three. One of the things I truly find amazing is the speed of your iteration. Can you speak to that and how important rapid iteration in hardware is? Because hardware is hard.

[Brett Adcock]

Yeah, this is a hard problem. We have to figure out how to do something that's never been done before. And it's like a very complex system. Definitely more complex from an engineering perspective than Archer was like building an electric aircraft. So, yeah, my rule of thumb is the first or second generation hardware is always going to suck. You know, like the first iPhone was not great. Like the first, first time you make something like you're never going to get it right in hardware. You have to do that. Like you have to see like five years in the future. You have to know exactly what the product does and then you have to clean sheet design it for the exact thing, day one. And if you mess up any of those, you can't go back and fix it through the design process. You have like long lead time, supply chain, everything else. So we are designing a new hardware platform every 12 to 18 months.

[Peter Diamandis]

By the way, that's pretty amazing just to hear that, right? Every 12 to 18 months, a brand new iteration.

[Brett Adcock]

I mean, yeah, we had a figure one walking by the time I filed the C Corp, we had the robot walking in under 12 months.

[Peter Diamandis]

Another thing you've done is you've completely vertically integrated.

[Brett Adcock]

Yeah, that was out of necessity. Like, there's no supply chain for humanoid robots. Like, there's no like motor vendors, actuator vendors, sensors, battery systems, structures like kinematics, like all the software, which is like pretty vast. It's like firmware, embedded systems, operating systems, middleware controls.

[Peter Diamandis]

So walk me through your factory, your fact you walked me through it before. But like what are the different segments of what's going on there?

[Brett Adcock]

Yeah. In terms of like design for how we're.

[Peter Diamandis]

Yeah, I mean you've got, you've got component building, testing, integration, all those things.

[Brett Adcock]

So we, so we clean. Clean sheet design everything from basically the ground up. Like all the hardware is like clean sheet design. We look at like ultimately what does the product need to do? The product needs to. You basically want to talk to a robot and you want it to just do things without any human intervention. You just wanted to go out and do stuff in the world. So we're designing for a capable robot that can go out and do everything from putting robots in a home to walk your dog, make coffee, do the laundry, and then the commercial workforce, which is like roughly half of GDP is human labor. So it's like the largest market in the world.

[Peter Diamandis]

Yeah, 110, $120 trillion. The global GDP, your TAM, is like 50 to 60 trillion dollars. That's pretty good.

[Brett Adcock]

Yeah, it's like it's going to build the biggest business in the world by a long shot. Like this in our lifetime. Like the space. Yeah. So we have, basically we. So we're looking at like the end markets where the robot needs to go. We do all the hardware design, which is like kinematic design, joints, motors, battery systems, sensors. We do all the software, firmware, embedded systems, controls, all the AI work end to end. And then we do all the testing and manufacturing and integration and fleet operations and deliver those to the clients. So we have robots now. We have two commercial customers. The first is BMW. We have robots there that are operating every single day. They're in Spartanburg, South Carolina. They're helping to build cars.

[Peter Diamandis]

We've got some video, I think from the BMW plant. If we can roll in background or repeat that video.

[Brett Adcock]

Yeah, we'll show that. And we have a second customer we just signed. And then within 30 days of starting the work, we were doing the work all end to end with neural nets. And this is like one of the largest logistics companies in the world. And then we're also pushing really hard on the home. So. Yeah, here's a quick update for BMW. So we have just robots here that are basically doing like, basically putting sheet metal on fixtures. This is a job that every major manufacturing company in the world does. Our robots are doing that fully autonomously at the speeds we need to. Basically, hip, high performance with no human intervention.

[Peter Diamandis]

No faults, no failures and no drug testing. No sick days.

[Brett Adcock]

No. No days off.

[Peter Diamandis]

No days off.

[Brett Adcock]

No days off.

[Peter Diamandis]

Yeah, 24, seven.

[Brett Adcock]

Totally.

[Peter Diamandis]

I mean, it's a. It's an interesting thing, Right? Think about this. Let me jump into one thing in volume. In the future, I believe I heard you say you'll see these at a price point of 20 to 30 thousand dollars. You still hold that?

[Brett Adcock]

Yeah, we, like, done a lot of work on the bill materials. Like, if you start breaking this down, like to the bare, like, you got to basically look at it line item by light item, and what it really looks like. And what. Basically what it looks like in, like, highway manufacturing. There's really nothing in the system right now that would show that this product should be very, like, very extremely, like, expensive.

[Peter Diamandis]

The calculation I do is if I. If I was going to lease a $30,000 car, it's about 300 bucks a month, which is, by the way, $10 a day and 40 cents an hour. So here's my question. How many of these humanoid robots would you own at 300 bucks a month operating? 24 7. No complaints, no fights with their girlfriends or boyfriends. I mean, the number could well be multiple per human.

[Brett Adcock]

Yeah, you're going to want one. They're going to see, like, I woke up. Like, I wake up every morning and help unload the dishwasher and pick up kids toys. Like, I never want to do any of that ever again. Like, you know, it's just like, not like something I needed to be doing when I get home or I'm at the house. We really haven't had a lot of innovation in the home for, like, almost 50, 70 years. We have, like, same appliances, same stuff. Like, we need old.

[Peter Diamandis]

We had old robots. We call them dishwashers now.

[Brett Adcock]

Yeah, they're just like, been around for a long time. Yeah. And us humans are having to, like, work with it. Right. Like, we have to work with that machine every day. And it's just like, not something you'll do anymore in the future. You'll just talk to the robot and have it do it. It'll be on a schedule any moment. You can just call it, text it, talk to it, and it's asking me to do stuff and it'll go do it. It'll know you better than it'll know you, just like yourself.

[Peter Diamandis]

I remember a couple years ago, I'm very proud, bold as an early investor in figure. And I brought Timor to meet you. And I said, listen, the thing, first of all, Brett's an incredible operator. Multiple successes. What's one of the best predictors of the future? It's what a person's done in their past. Right? It is very much one of the best predictors. But what I found amazing, that sold me instantly beyond your charm is the team you pull together. Can you talk about that? Because I think a lot of people in the audience here are focused on their moonshots. This very much is a moonshot.

[Brett Adcock]

Yeah.

[Peter Diamandis]

You, you exit Archer. How did you capitalize? What did you start? How do you pull your team together? Can you describe that early moment?

[Brett Adcock]

Yeah, like, you know, having found a lot of companies in my lifetime, I get to like go back every time and like, what did I mess up on? What did I get right? Try to make things better. Fundamentally, the things that I spend a lot of my time on is just like building. Basically, in order to build one of the world's greatest products, you need one of the world's greatest teams. And then you need to align that team with what the shared vision is. And everybody needs to be accountable for that and understand it. And then you got to figure out how to hit the gas pedal, like really hard. So the entire culture at figure, even at Archer when I built initial team was very deliberate. And even at figure, if you go to the website now we have the culture deck, we have the master plan. We have like things laid out that are like really unique. We're in Silicon Valley, but almost like the anti Silicon Valley. You have to work every day in the office. We work five to seven days a week. We work really hard and not a lot of people want to do that. And that's fine. There's just not the right people for us. We've assembled now a couple like hundreds of like the best engineers in AI robotics in the world. There's just like, no. Nobody even close to what we've done.

[Peter Diamandis]

Seriously. Like, incredible.

[Brett Adcock]

Yeah, like, it's unbelievable. My whole business team has been with me at a Veteri Archer now figure out they're just. I mean We've spent 15 years together. There's unbelievable operators. They give me the ability to like spend basically all my time on product engineering, to basically build the best product possible. And they help scale the business, which is great. Hiring, just recruiting hr, like legal, just like Finance, across the board, they're great. So, yeah, the team's insane. But what's even better is, like, the culture is just absolutely, like, dialed in. Like, everybody knows what they should be doing. I don't do one on ones things like that. We have a shared vision of what to do, and we work really hard to go get there. And the dopamine that we all get is the same. Like, we. We want to ship product. That's what we're aligned to. Like, that's what everybody, like, basically, yeah, gets their dopamine, which is really great. So it's like this shared fuel that we have to ship product. And this is such a hard thing. Like, this humanoid stuff is like. It's like maybe one of the most complex things I could have worked on. And you just. You have to have that fundamentally, or there's literally zero shot this is going to work.

[Peter Diamandis]

You know, we're going to hear from Travis Klanick tomorrow, who's going to say very much the same thing that you're. You're what we call your massive transformative purpose. That. That clear mission vision, and then aligning your team and culture around that which starts with you. So you made a commitment of your own capital to get it going, and then you start calling people at other companies. And what was your pitch?

[Brett Adcock]

To raise capital.

[Peter Diamandis]

What's that?

[Brett Adcock]

To raise capital or recruit?

[Peter Diamandis]

No, no, no. To get those employees on board.

[Brett Adcock]

Oh. The pitch in 2022 was, I'm going to fund this whole thing for many years. You know, it was expensive. Like, we got to a million a month of burn in six months. So it wasn't like. But I was like, full pedal to the metal from day one. I just, like, knew exactly what to do. I mean, Archer is kind of like a flying robot in a lot of ways. So I knew how to build teams. I know how to, like, we knew the product, what to do. I knew the technical understanding of, like, the powertrain and control systems and embedded software and sensors. So it was like, you know, we just, like, went really quickly out of there. The pitch was like, hey, I'm going to fund it. So there's like, no funding risk, at least in the near term. Like, next couple of years, there's a good chance for us to build, like, the next. It was like an iPhone moment happening with Humanoids. Like, it's going to be. This is going to happen right now.

[Peter Diamandis]

And what did you tell them?

[Brett Adcock]

The probability of success was pretty low. Like, the thing. The thing that we had to do was, like, we had to do, we needed to prove, like, three things that have never been done before that you had to go get all three of those right in the next, like, sub, like, you know, sub five years or you fail for sure. You have to build like incredible hardware for humanoids that's like extremely complex. It can never fail. It's always got to work. And it's got to work at human speeds with human range of motion. Nobody's ever done that before. Like most robots that walk around can't even walk right. Like they fall over the time. It's very complex. Like maybe like rocket turbofan level complexity in terms of hardware systems. The second is you need to be. This is a neural net problem, not a control problem. You can't write code your way out of this. You can't hire a PhD with a robot and solve every problem. You have to basically ingest, like, human like data in the robot through a neural net. And it's got to be able to then imitate what the humans do. So you have to solve that, which has never been solved on a humanoid system of like, you know, it's like a high dimensionality system, not like a robot arm on a table, which most of none of those have AI. And then the third thing you have to do is you have to figure out how to generalize. You have to do something that's the holy grail of robotics. You have to figure out how to look at something you've never seen before through speech, tell robot how to do it, and then be able to execute that task fully end to end, just with one neural net. And I wrote about this in my master plan in 2022. It's like, we need to solve those. If you can solve those, you're in the right decade. You're going to go build the iPhone moment for this whole space. And we're in full liftoff. But those looked pretty dire at the time. In 2022, there was just nothing out there. I mean, you had Boston Dynamics that was like leaping around and doing backflips and parkour and stuff, but nowhere near the level of manipulation and dexterity you needed for humanoid robots to enter the home. So I think we can confidently say now we have solved or we're making substantial progress on all of those. Amazing, which is great. So I think y.

[Peter Diamandis]

There was a pivotal moment late last year where you said OpenAI was a large investor and you were baselining OpenAI's AI systems and you made a critical decision, say, nope, we have to build our own AI internally. Helix can you speak to that moment? And I'd like to show the video of figure at home along that lines.

[Brett Adcock]

Yeah, that'd be great. Okay, so what you're seeing is Helix. This is our large scale AI internally. It's basically a large scale vision, language, action model. This is public. It's on our YouTube. The prompt here that Corey gave, he leads the Helix team, was putting groceries on the table and the prompt was just put the groceries away. Not telling you where they go, not telling you where they are. Just put them away. The trick here, the. The tricky part for the robot is they never have seen any of the groceries before. In training, we purposely withheld all of these items. So it's like the first time the robot has ever seen these in its life with its own cameras and sensors. You basically have to solve the generalization problem in a home. Every home is different. We all have different toaster ovens, we have different appliances, we have different spatulas and silverware. It's located differently and things are changing throughout the day. So you really have to solve this. I call it semantic intelligence. But it's like this semantic grounding that's needed from a human world to robot world. Helix, we can talk about why I was able to do that. Is able to communicate on a single neural net on each robot and collectively together able to put these all away with just a single English plum. I think this was the first signs of life. I think I will go even more of a bolder claim. I think this is probably the most important AI update for robotics in human history. Everything in the future that moves will be a robot and it will be powered by AI agents like this. This was trained on also very little Data. It's like 500 hours of data trained in this.

[Peter Diamandis]

I love the way they're looking at each other to confirm, like, yes, I get it. Like, oh, where are you putting that thing? Yeah, I think that's a good. A good idea to put it up there.

[Brett Adcock]

Yeah. Actually it's.

[Peter Diamandis]

I mean, is that. Is that a created. You know, like they're about to look at each other here as he passes it over.

[Brett Adcock]

Like, I get like, listen, a part of this was like. It's funny. Part of this was like emergent from training. So when the robots are doing handovers, they actually look at each other. There's actually a very split second where like one robot needs to release the package or the item, other robot needs to grab it so it doesn't lose, like, basically like hold of the item and doesn't fall Down. So what happened emerging from training is the robots actually look at each other as the clearing way signal for like we should be releasing the item into each other's hands, which is like really interesting. The other stuff of like robots looking at each other and moving around, like, I think it's just overall important. There's like a certain level of communication that needs to happen from a robot in terms of like interaction design with humans. So you don't want like, you know, you don't want to walk in a room and have a robot just like not move and not look at you. Humans look and do nods and gestures. All of this is extremely important to learn. We need to learn these expressions of humans, just like we need to learn how to grab items. It's going to be super important as we at scale integrate robots into the entire world that this happens.

[Peter Diamandis]

I have a thousand questions for you. Let me hit a few rapid style here.

[Brett Adcock]

Yeah, let's do it.

[Peter Diamandis]

So figure three, when do I get to see. I saw the designs. When does figure three get shown?

[Brett Adcock]

Yeah, you keep asking this. You like this one? I do, yeah.

[Peter Diamandis]

It was a beautiful, it was a, I mean, you know, degree of beauty was increasing.

[Brett Adcock]

Yeah. I don't think people understand this. How like incredible. Well, they don't because we haven't showed it. But we, so we like, we're on. This is like the ones robots you saw here on the videos on stage were. Figure 2. It's our second generation robot. You can like kind of, I guess figure one's like online a little bit, but it's like, it's a little bit more gnarly. It's like got wires outside of it and it's a little bit more fast and it was a much more quicker design cycle to get this to our engineers to start doing real use case work. But figure two was like a feature complete robot that was supposed to be is able to do almost anything a human can or the vast majority of it. We haven't talked about this publicly a lot, but we're done now with figure three design. I think we'll probably show an update next week. Just a quick minor update. Not anything material as it relates to what we're going about for that process. Figure three is like you look at figure one to figure two and it's like a huge step up. You're like, wow. So from a college dorm room project to a real pretty decent robot. And the magnitude of the step up was pretty material. That same magnitude happened again on figure three. So if you were to See it, it's just unbelievable. We spent like 18 months designing it from scratch. The high level, it's just like 90% cheaper. It's smaller, it's less mass, it's got better sensors. Its hands, head and feet were designed for neural nets. It's a completely, I would say like, you know, figure two is probably the best humanoid on the market. Maybe, you know, probably not by a ton, but like, I think it's the best. 10%, 20%. Figure 3 is just like next level design. Like we've spent. It's definitely like the most like for me, like the most proud moment I've had in engineering in my career, like looking at that robot. And so we're going into production manufacturing with that this year. We'll have some more updates on that soon. That's the robot that we want to send everywhere into the world. We want to make it low cost, very high rate. It's even better just on so many dimensions.

[Peter Diamandis]

Talk to me about production rates over the next three, four years and when I'm going to see it in the home.

[Brett Adcock]

Yeah. So we have two tracks. We have workforce track, which is like. And then we have the home track. What most people don't get is the workforce is the big business. It's half of gdp. We can charge meaningfully more per robot than the house. And it's also easier. The things that the robot does is just like the same things almost on repeat. The home is like the wild west. It's like extremely hard. We have a huge safety area of not falling on any human or hurting people. There's a semantic in safety of not knocking over the candle and burning the house down. The home is just vastly harder. Maybe in self driving. It's like driving on the highway is workforce for us. And driving into the city is like the home. It's just like unbelievably difficult. Between our two first commercial customers, which are very large businesses, we have demand. Like if we had 100,000 robots today that all worked, they would take 100,000 robots today. And then we have like 50 customers I could sign by the weekend that are all Fortune 100 companies that we've literally visited. We know them. We just like, we can't. I've done a bunch of meetings today. At lunch, everybody's like, what do you think about helping out here in healthcare? All sound great. Like we're just like bombarded with the amount of demand here. You're thinking about the workforce. You have a certain number of supply of humans. It's literally going down demographically, the baby boomers are retiring, so you have less humans in the workforce. There's labor pains everywhere and there's a lot of job shortages. So anyway, we see just unbounded demand. I think we could ship a million robots this month if we had them all working and they're ready to go. And I think one thing that we're going to maybe add before you go, sorry, I knew you wanted rocket fire, but you guys saw BMW and you saw our second commercial customer. It took us a year to do BMW fully end to end at high speeds. Last summer, if you look at figure one, it was four minutes. Now we had down like 40 seconds and just a lot of great engineering work into it. We started working on Helix. It was just completely transformative. Completely. And then we said, okay, well what if we use Helix for this next use case for the second customer? And we did that whole thing end to end in under 30 days from scratch, had nothing. And I think if we had to do it all over again, we could maybe do it in less than 48 hours. And so the robots are going to learn how to do something in like the matter of hours here, not like 10 years from now, like this year. And I think that has pushed our timeline left multiple years for the home. Like the hardest thing, like the long pole in the tent for the home is like, is like semantic intelligence. Like, can I understand what the hell is going on anywhere it goes?

[Peter Diamandis]

So under over on the home is.

[Brett Adcock]

What we'll start alpha testing in the home this year, which means like our we'll be doing internal work on the home, like my home or like, like engineers homes.

[Peter Diamandis]

You want to get rid of that dishwashing duty, huh?

[Brett Adcock]

Dude, I can't do it anymore. Just like, what am I doing? It's just like not something I want to do. Like, I want to spend time with my family and kids and wife. You know, it's like just no bueno. So yeah, we got to fix that. I feel, I mean at this point we just feel data bound in the home. We think if we just increase the data set that we trained Helix with by a couple orders of magnitude, it would probably. Right now Helix. We put a little note on the website about Helix and one of the things we put in is you just drop small household objects in front of it. It can pick up almost every object we put in front of it. We put up this weird cactus toy from one of the kids rooms and it was seen and we're like, pick up the desert item and it's got to relate a cactus to a desert plant. And it was a toy and it was singing, it was moving and it picked it up. So all of that is in the weights. And it has a very large LLM backbone to it. So it really understands the world. Semantic grounding. So we think just like we just need more data now, basically data bound for it. So I guess there's a lot of confidence that you're seeing a sign of life now that you haven't seen in history, that a robot, intelligent robot in the world can be built. And the question is, we just got to keep extrapolating that on the curve far enough to where it's entering. And I think it's like this decade. I think you're going to see it in the coming years being put into homes just through speech, be able to do very long horizon hours of work without any prompt with any fix everybody.

[Peter Diamandis]

Thanks for listening to Moonshots. This is the content I love sharing with the world. Every week I put out two blogs, a lot of it from the content here. But these are my personal journals. The things that I'm learning, the conversations I'm having about AI, about longevity, about the important technology transforming all of our worlds. If you're interested again, please join me and [e-mail] subscribe that's diamandness.com scribe. See you next week on Moonshots.

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