Brett Adcock: Humanoid Run on Neural Net, Autonomous Manufacturing, $50T Market #229

Summary

Moonshots with Peter Diamandis — Episode #229

Guest: Brett Adcock
Title: Humanoid Run on Neural Net, Autonomous Manufacturing, $50T Market
Date: February 11, 2026

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Episode Overview

In this expansive and forward-looking episode, Peter Diamandis visits Figure AI headquarters in San Jose to interview Brett Adcock, founder of Figure AI, and track the astonishing progress in humanoid robotics. The conversation dives deeply into the emergence of general-purpose humanoid robots powered exclusively by neural nets, the radical evolution of autonomous manufacturing, data-driven advances, the coming $50 trillion humanoid robot market, the path to robots building robots, and the societal changes ahead. The episode is peppered with insights on AI, robotics engineering, safety, and the transformative economic and cultural potential of ubiquitous intelligent machines.

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Key Themes & Discussion Points

1. Explosive Progress in Humanoid Robotics

• Rapid Advancement: The pace of development in AI and robotics has been exponential, with annual changes that feel like a decade in other sectors.
  • “18 months in AI time, that’s like a decade, dude.” (Dave, 02:36)
• Yearly Reinvention: Every year, the technology, hardware, and business models look radically different.
  • “Year to year, the whole business looks completely different.” (Brett, 00:03; 09:17)

2. Neural Nets: The Core of Robot Intelligence

• Full-Stack Neural Nets: Transition from hundreds of thousands of lines of hand-written code to complete neural network-based control for humanoids.
  • “We removed a majority of all [C code] in the Helix one... today we removed the remaining 109,000 lines of C. There’s all neural net, all neural nets today. That’s the full body.” (Brett, 06:47)
• Why Neural Nets? Coding could not scale to the complexity and adaptability required for humanlike manipulation and movement.
  • “You get unexpected behavior... Both good and bad. But things you could never code.” (Dave, 05:36)

3. Data as the Moat & the Engine

• Fleet Learning & Shared Experience: Data gathered from every individual robot is instantly shared across the fleet, creating a rapid, compounding advantage.
  • “Once one robot learns how to do a task, every robot in the fleet knows it. And humans don’t operate like this.” (Brett, 00:14; 29:29)
• Training Sets: A relentless obsession with acquiring and leveraging the right real-world and synthetic data is essential for developing robust, generalizable neural nets.
  • “If you’re in the neural net game, it’s a data play.” (Brett, 29:01)
• Why Only a Few Winners: The ability to continually accumulate, use, and protect unique robot experience data will make this a winner-take-most market.
  • “I think very few humanoid groups [will succeed]... once one robot learns how to do a task, every robot in the fleet knows it.” (Brett, 29:29)

4. Hardware–Software Co-Design & Vertical Integration

• Design for Neural Networks: Figure’s hardware is built from the ground up to suit the neural nets, not the other way around.
  • “We built really looking at the neural net and we said, how do we fit this into a humanoid robot and what are the best sensors?” (Brett, 10:05)
• Vertical Integration: Figure manufactures almost all vital components in-house—from actuators to hands—to control speed, reliability, cost, and innovation.
  • “It would be great if we could go off and buy motors...but it doesn’t work like that.” (Brett, 43:09)
• Iterative Hardware Development: Each Figure version (1, 2, 3) has quickly improved in beauty, cost, capability, and reliability.
  • “We reduced cost like crazy on Figure 3.” (Brett, 43:06)

5. Figure’s Technology Stack Evolution

• Helix 2 Release: Represents the transition to a 100% neural-architecture robot—including full-body reinforcement learning controllers and integrated tactile sensors.
  • “Today we removed the remaining 109,000 lines of C. There’s all neural net, all neural nets today.” (Brett, 06:47)
• Tactile and Visual Sensing: Robotic hands now have palm cameras and tactile sensors for fine manipulation and occluded tasks.
  • “We now have tactile sensors in every fingertip...as well as palm cameras to understand how we’re grasping items.” (Brett, 28:06)

6. Autonomous Manufacturing and Humans Out of the Loop

• Robots Building Robots: The move toward robots on manufacturing lines will enable scaling to millions of units and accelerate the learning/manufacturing feedback loop.
  • “We will put robots on our Baku lines this year.” (Brett, 30:31)
• End-to-End Automation is Key: True value comes not only from physical automation, but generalization to unseen environments and unbroken autonomy (“long-horizon work”).
  • “What’s impressive is like a full end-to-end robot that is generalizing to an unseen place.” (Brett, 20:09)

7. Market Trajectory, Adoption, and Competition

• Scale Is Everything: The market for general-purpose robots could be as large as half of global GDP—$50–$80 trillion—driven by replacement and augmentation of human labor.
  • “It’s like half a GDP...$50 trillion.” (Peter, 47:44)
• Few Strategic Winners: Despite huge numbers of competitors (especially in China), only a handful (likely under 10) will win due to the data and engineering moats.
  • “Far less than 10.” (Brett, 17:49)
• China vs USA: China is the principal competitive region, especially due to scale and energy in both hardware and entrepreneurship.
  • “We don’t see anybody else besides China as a real competitive threat today.” (Brett, 47:04)
• Role of Big Tech: Apple, Google, Nvidia, Meta—every large tech player is investigating or entering humanoid robots.
  • “Every major group in the world will get in this space. You have to, you have like no choice.” (Brett, 47:44; 48:13)

8. Barriers: Safety, Privacy, and Trust

• Safety as Job One: Physical and cybersecurity robustness, fail-safe architectures, and relentless real-world testing are top priorities before home deployment.
  • “Until I put me and my kids and family on board, it’s not safe enough.” (Brett, 87:44)
• Privacy & Data Security: Robots in the home mean careful management of personal data, with dedicated internal cybersecurity teams.
  • “All this is super important. We have an entire team on cybersecurity here.” (Brett, 76:33)
• Intrinsic & Semantic Safety: Must understand both overt and subtle risks (e.g., knocking over a candle, children’s unpredictable movements).

9. Roadmap & Timeline to the Home and Beyond

• Industrial → Home: Industrial/commercial deployments (with BMW, etc.) harden Figure’s robustness. Home use comes next, then healthcare, elder care, and more.
  • “The home is huge in the end.” (Dave, 67:47)
• Alpha Home Deployments: Figure aims to put robots into real homes for long-horizon testing in 2026, with scaling in following years.
  • “My best guess... by end of the year we will be able to put a robot into an unseen home and be able to do fairly long horizon work.” (Brett, 83:15)
• Millions Then Billions: Manufacturing ramps exponentially—facility for 50,000/year planned; billions of robots is a realistic long-term goal.
  • “Every human should have a humanoid to do all your work, then 5 to 10 billion in the commercial workforce...tens of billions over planet.” (Brett, 69:02)

10. AGI, Embodiment, and the Future of Multimodal Intelligence

• Physical Embodiment for True Intelligence: Embodied AGI must physically interact with the world for persistent memory, tool use, reasoning, and truly general intelligence (“Jarvis”).
  • “I want this thing to...reason, have perfect memory, touch the world both physically and digitally.” (Brett, 58:29)
• Unified Models: The goal is a single omnimodal backbone for perception, action, language, and memory.
  • “We believe this all comes down to one model at the end of the day.” (Brett, 33:47)

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Notable Quotes & Memorable Moments

On the Data Moat & Robot Learning

Brett Adcock:

"Once one robot learns how to do a task, every robot in the fleet knows it. And humans don’t operate like this.” (00:14, 29:29)

On the Pace of Change

Dave:

“18 months in AI time, that’s like a decade, dude.” (02:36)

On Replacing Code with Neural Nets

Brett Adcock:

"We removed the remaining 109,000 lines of C. There’s all neural net, all neural nets today. That’s the full body." (06:47)

On the Coming Market

Peter Diamandis:

“It’s like half a GDP...$50 trillion.” (47:44)
Brett Adcock:
"It’s going to feel like 2080 up in here." (47:44)

On Partnership with OpenAI

Brett Adcock:

"Our team just ran circles around them... All this stuff was done internally. And at some point it just didn’t make sense to train other folks on how we basically build AI models internally for embedded systems like a humanoid." (11:08)

On Autonomous Manufacturing

Brett Adcock:

"We will put robots on our Baku lines this year." (30:31)
"At some point, I hope in 24 months where all the robots will build all the robots." (69:24)

On Safety & Trust

Brett Adcock:

"Until I put me and my kids and family on board, it’s not safe enough... When I feel safe enough to have a robot in my home full autonomously, end to end, around all my kids—that’s the point I would trust it." (87:44–88:05)

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Key Segments & Timestamps

• Touring Figure Headquarters & the Robots (01:56–04:05, 95:58–102:34)
• Figure's Transition to 100% Neural Nets (06:10–10:22, 26:17–29:29)
• Helix 2 and Technical Breakthroughs (24:27–29:01, 26:10–33:47)
• On Partners, Competition & Industry Consolidation (16:33–20:36, 46:09–48:09)
• Manufacturing Scale & Robot-Made Robots (30:01–31:29, 69:24)
• Data, Fleet Learning & AI Moats (29:29–33:36, 72:01–72:56)
• Market Projection & Social Implications (47:44–52:44, 69:02–73:53)
• Safety, Cybersecurity, and Home Deployment (74:59–77:24, 87:28–89:36)
• Personal Insights — Adcock's Background in Senior Care (54:41–56:35)
• General Purpose Robotics & AGI (58:29–62:11, 72:56–73:53)
• User Q&A (80:16–89:36)
• Demonstrating Robot Hardware Generations (95:58–102:34)

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Memorable Lightning Round

• Robots on Stage at Deadmau5 Concert:
  "We had several Figure 2s on stage just jamming. We had them all synced so that synced to the music as it danced, which is really cool.” (Brett, 81:50)

• Personal Metric for Trust in Safety:
  "Until I put my, me and my kids and family on board, it's not safe enough to fly anybody." (Brett, 87:44)

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Conclusion: The Humanoid Age is Here

Brett, Peter, and Dave paint a vivid picture of an imminent world brimming with affordable, safe, general-purpose humanoid robots—a world-shaking innovation as profound as the birth of the PC or the mobile phone, but supersized. Figure’s obsessive technical strides, integrated approach, and commitment to neural net learning and safety place them at the vanguard. The social, economic, and ethical implications of this transition—across labor, elder care, safety, privacy, and abundance—form the substrata of a moonshot future being realized year by year.

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For More

• Peter’s Humanoids Metatrend report: [Substack link in episode description]
• Figure AI: [Company website]
• Follow Peter on X: https://x.com/PeterDiamandis
• Next episode teaser: Exploring abundance, autonomy, and the ethics of AI-driven societies.

Transcript

[Peter Diamandis]

I am blown away by how far you've come.

[Brett Adcock]

The things that you can do with neural nets now just like completely blow my mind. Every year to year, the whole business looks completely different.

[Dave]

It's amazing to me how you accumulate data and the data becomes this incredible barrier to entry, this incredible asset.

[Brett Adcock]

The one thing that's important here is that once one robot learns how to do a task, every robot in the fleet knows it. And humans don't operate like this.

[Peter Diamandis]

When do we start seeing robots, building robots?

[Brett Adcock]

We will put robots on our Baku lines this year. Listen, this is like going to be the largest economy in the world. It's going to be super impactful business. It'll lead to, like, ubiquitous goods and services for anybody in age of abundance. And it's going to be a super fun business, too. It's not going to build the sci fi future we all want. What you're seeing is every major group in the world will get in this space. You have to. You have like, no choice.

[Peter Diamandis]

When are we going to see the first figure in the customer's home?

[Brett Adcock]

My best guess is, I think.

[Peter Diamandis]

Now.

[Dave]

That'S a Moonshot, ladies and gentlemen.

[Peter Diamandis]

So Dave and I are in San Jose at Figure Headquarters. We just did a podcast with our friend Brett Adcock. Extraordinary. And check it out, check it out. So, yeah, figure one, this is the original.

[Brett Adcock]

Yeah.

[Dave]

Still somewhat functional.

[Peter Diamandis]

Yeah. It ran the first large language model, the first neural net.

[Dave]

They built it in under a year. Brett actually was screwing these things together himself. And it was all about gathering telemetric data so they could build this.

[Peter Diamandis]

Here's figure two. Much more beautiful, much more functional. Running neural nets across the board. Dumping all the c. Can it do, you know, can you live long and prosper? I buy it. And here we go with Figure three is the workhorse right now. We just did a tour. I mean, probably you saw 100 of these walking through the hallways on test stands, cleaning dishes.

[Brett Adcock]

Brett with good thumbs.

[Dave]

They added a flexible toe too, so it can go down like this. And before it had just this clunky, clunky foot here.

[Peter Diamandis]

And figure three has the palm camera.

[Dave]

Palm cam.

[Peter Diamandis]

Yeah.

[Dave]

They cut about 30 pounds off the weight and 90% of the cost actor cost.

[Peter Diamandis]

Wow. Crazy.

[Dave]

Yeah.

[Peter Diamandis]

Amazing.

[Brett Adcock]

Yeah. It's the perfect height between the two of us.

[Peter Diamandis]

Welcome to Moonshots, everybody. I'm here at Figure headquarters with Brett Adcock and DB2.

[Brett Adcock]

Brett.

[Peter Diamandis]

It'S been about 18 months since we did a podcast on Moonshots together, and I am blown away by how.

[Dave]

Far you've come 18 months in AI time, that's like a decade, dude.

[Brett Adcock]

Welcome to Figure Headquarters. What do you think?

[Peter Diamandis]

Yeah, it's extraordinary. I mean, just to describe, we just went on a tour, about 300,000 square feet, 400,000 square feet under development here. I mean, there are figure three robots walking down the halls. There's fully autonomous robots, I guess, running Helix 2. You just released Helix 2 today?

[Brett Adcock]

Today.

[Peter Diamandis]

I got it while I was flying up here. We have these robots doing everything from kitchen tasks to packages to manufacturing of different type of. I mean, how many robots do you think we saw? Seriously?

[Dave]

I wasn't counting as we. Hundreds, maybe not a thousand.

[Peter Diamandis]

Yeah, hundreds. At least. At least 100 or so.

[Dave]

Yeah, well, there's a lot of partial robots out there too, so it's hard.

[Peter Diamandis]

To picking up figure. Figureheads.

[Dave]

How many hands do you think we saw? There's more. Many more hands.

[Peter Diamandis]

The hand line, the headline, the torso line.

[Dave]

Actually picking up the head was the most surreal.

[Peter Diamandis]

This is where the pelvis is made. Yeah, for sure. Yes. Pretty amazing. I still remember during my first visit with you, full disclosure, my venture fund is invested in two of your earlier rounds. Super proud of the progress that you've made. I still remember your figure one, putting a Keurig cup in a coffee maker. And that was a big deal because it was done with neural nets and.

[Brett Adcock]

Not C. I mean that honestly was like. I think it was a big inflection point for us. I feel like the. You know, I think a few things we need to really run down is can you build electric humanoid? It's like low cost, it's capable like a human. Like just the hardware side of things. The second thing is, can you figure out a way to not code your way out of this problem? How do we use a neural net to learn those like human type representations and then do tasks? And when we are doing the Keurig task, it was a basically bimanual neural net running on the robot, which is now like evolved into Helix. And I was able to basically do the whole kind of like, it was a smaller task. It was a few minutes long of picking up the Keurig cup, opening the coffee, putting it in, running it. And it was the first time we saw true instance of kind of like neural nets where they're working on a bimanual humanoid robot. And that was when we were like, okay, we have to just go all in on neural nets. The whole stack needs to be neural nets to make this work. And that started like. And that was basically two years ago now. And then you guys saw Helix 2 today, which is like the. Basically like the best release we've ever had.

[Peter Diamandis]

So we'll run a clip of Helix 2 while we're describing it because what we saw was figure three, running Helix 2 in full autonomy, going into the dishwasher, picking stuff up, putting it away, not pre programmed. And I loved the human elements of it, like using its hip to close close something and it's put to raise the dishwasher.

[Dave]

That's the neural net difference though. You get, you get unexpected behavior, you know, both good and bad. But, but things you could never code.

[Brett Adcock]

Up, you could never.

[Dave]

Your career went software company, vital company now. This has got to be the first neural net platform.

[Brett Adcock]

Yeah, the like we wouldn't like the things that you can do with neural nets now just like completely blow my mind versus code. Like we could never have done a quarter of the stuff that you saw today with the whole body with manipulation, with things that, you know, there's, there's only so far you can really push like coded heuristics into a human on robot. It's just a dead end. Yeah, it's just not going to work.

[Dave]

Yeah, yeah, yeah. It's amazing to me how you accumulate data and the data becomes this incredible barrier to entry, this incredible asset. If you were writing all this in C code, that C code would be. You'd have millions, hundreds of millions of dollars invested. You would not want to mess it up with the neural net. You can say look, hey guys, retrain it from scratch. Yeah, right off the road. It's just a completely different approach. And that's why people are way under predicting how important or how quickly this is going to evolve because it's a completely different paradig.

[Brett Adcock]

We've lived through it. I think maybe a year or two ago we had several hundred thousand lines of C code, several hundred thousand handwritten.

[Dave]

Code, probably 100 bucks a line to write it.

[Brett Adcock]

Yeah. Very expensive, very hard to test and get out reliably. And also hard to model all the different behaviors that we would need to test. And then we removed a majority of all that in the Helix one, where we still had a lot of lower body control being run in basically the control stack in C. And then today we removed the remaining 109,000 lines of C. There's all neural net, all neural nets today. That's the full body. And that took it from being able to do really good tabletop manipulation. You saw the curate coffee, the work we do with logistics, all that's to be done in neural nets we've been showing amazing progress there. But getting the whole body to get out of there and move dynamically through a scene while manipulating and planning is just a whole other. Like we basically spent like a greater part of a year refactoring the Helix architecture to be able to enable this to work. You're talking about now like moving through space like a human, having control of the full body.

[Peter Diamandis]

Eye, hand, foot, leg coordination.

[Brett Adcock]

Everything is sensor data. In cameras, tactile, we have camera, palm cameras, basically doing inference on board the robot, fully embedded and then be able to output torques into the motors and do that, you know, a few hundred hertz, you know, in terms of like that planning, control and do that reliably on very difficult tasks. These are bimanual tasks where it's grabbing and holding things, planning, moving the body, getting things out of the way, making like errors and replanning and fixing this. All done with the neural net now end to end over like a pretty long like for us it's like, you know, it's kind of like room scale autonomy. So we can like now like finish the whole room, which is important. And next we're going to graduate to like we see the full full house.

[Dave]

That's one of the things really obvious when you're walking around looking at what everybody's doing and working on you. You visualize a robot company having lots of people working on microcode or actuators or batteries or whatever. But there's just a huge number of people out there at workstations, they must be working on the neural nets because you know, it's, it's just gotta be such a dominant part of, of what makes the thing actually look and feel human. And you know, the motions are so smooth and you know, everybody, when they think about the history of robotics, they kind of chart these line charts. But it's not, it's not like that. It's a disruptive change from dropping that last hundred thousand, five thousand lines of C code to moving to an all self organizing neural approach. Yeah, completely different future.

[Brett Adcock]

It is like we make these like technology like progress steps. And I think it's been very apparent here like every year to year the whole business looks completely different. Yeah, in large part of trying to get the hardware, hands like all this stuff in a good spot and then you know, be able to basically have like more range of motion and speed and torques like a human and then be able to get like, you know, we're all in on neural net. So it's been like, you know, what is the right data set for that? For pre training and post training, do we have the right, you know, training cluster, do we have the right models? And then deploying those really well on the same humanoid hardware. That's like a full loop. Yeah, we've actually designed Figure 3 to. If you say like, what is the guiding principle of Figure 3 more than anything else? It was just designing for Helix. How do we design this to run like Helix on how do we give Helix a bot?

[Dave]

So counterintuitive. Everything just the build around the neural.

[Brett Adcock]

Net we built really looked at the neural net and we said, how do we fit this into a humanoid robot and what are the best sensors? How should it run? What does the operating system look like? Middleware, firmware, embedded software, like all of it is encapsulated in this view that we need to go all in on neural nets and do human like work.

[Peter Diamandis]

About to release the 2026 version of my Humanoids Metatrend report. It's a deep dive looking at 100 different robots in development right now. A deep dive into 10 of them including figure 150 pages. You can check it out at Substack for my paid subscribers. Anyway, super pumped. This is a field that's moving at exponential, hyper exponential speeds. So in the beginning you had partnered actually with OpenAI on software and you made a departure from OpenAI and I.

[Brett Adcock]

Mean I guess are any quite accurate.

[Peter Diamandis]

But like okay, well you can.

[Brett Adcock]

I mean I think I met Sam OpenAI team and they were just really interested in getting into robotics and it was like in their early master plan to get into basically shipping home robots and they really wanted to kind of like basically, basically work on a very intimate relationship. They ended up leading our co. Leading our series B along with Microsoft and we started working on basically a collaboration agreement to help work on next generation models for humanoids. And we were super big then and we still are on how do we language condition the whole stack, how do we use an LLM in a lot of ways is just this world model. It really understands in the weights basically what things are, what it should do has a lot of good semantic understanding. How do we tap that for the humanoid? How does it. How do we learn from this at scale and some of those representations? And it just like the partnership just didn't work. Our team just ran circles around them. Yeah for basically better part of a year. And it just came to a point where like it just made sense to just which we were, we were just doing all the work ourselves internally we had a whole team here, a lot from like some of the best like labs in the world and we were putting out like work after work. The Keurig coffee stuff was done by us. All this stuff was done internally. And at some point it just didn't make sense to train other folks on how we basically build AI models internally for embedded systems like a humanoid.

[Dave]

Did it turn out that LLM matters at all in physical? You could start with an open source LLM and like a voa, like a.

[Peter Diamandis]

Vision language action model.

[Brett Adcock]

Yeah, basically. I think the LLM is definitely a certain piece of this. We basically want to take the semantic grounding a vlm.

[Dave]

Yeah. Like the common sense.

[Brett Adcock]

Yeah. How do you understand from this, which we have in Helix today? Super critical. But getting to a point where we can understand physics in the robot and have it really be able to plan and reason at fast dynamic speeds was something that nobody in the world's ever really done before. And I think that's the work that we, I think have been excelling at and we love is just like how do we get it to understand physics.

[Dave]

I think most of our audience probably knows this, but just to rewind the tape, you know the LLMs GBG2 GPT3 built entirely on text data scraped right off the Internet.

[Brett Adcock]

Yeah.

[Dave]

And then they supplemented that with a ton of other data also in text form. And that creates this, this machine that has tremendous amounts of common sense. And if you ask it, hey, do you know how to play soccer? It says, yeah, of course I do. But then you try and install it in an actual physical moving machine and has no idea what it's actually doing.

[Brett Adcock]

Yeah. I mean like we seem to like touch everything in the world.

[Dave]

Yeah.

[Brett Adcock]

And we have this really high dimensional robot that has like, you know, 40 plus degrees of freedom. So like, and so like, you know, on the surface area, just the math around this is like the dimensionality space is really high. So you have like 40 motors. They all can spin 360 degrees.

[Dave]

Yeah.

[Brett Adcock]

So the amount of states the robot can be in like positions is like 360 to the power of 40. So there's more states of the humanoid than atoms in the universe.

[Dave]

That's a lot. You're not going to simulate those one by one.

[Brett Adcock]

Yeah, exactly. So like, so the question is like they don't need to like understand these false define contact dynamics of like I need to grab this water bottle, like where do I position my elbow, pelvis, like torso, head, like fingertips. How do I plan the, you know, to grab this you know, and then how do I put pressures on there and understand those representations really well, you know, from observations now into actions that I'm doing at test time. And this is not in. Ellen.

[Dave]

Yeah.

[Brett Adcock]

Knows none of this.

[Peter Diamandis]

Yeah.

[Brett Adcock]

No one knows this is a water bottle. They probably knows. Like, I need to grab it from the side. Um, and then. But like all this, like, like, you know, like all this implied physics that we need to do here, just. We have to go train models to go do that.

[Dave]

It's actually kind of weird because it thinks it knows how to do it too. You know, the. The LLMs feel like they can do things, you know, intuitively, and then they. They completely fail.

[Brett Adcock]

I mean, you can. We. We've done this. You can. I have done this. You can zero shot the LLMs inside a robot. We do it. We still do it.

[Dave]

Oh, really?

[Brett Adcock]

Actively. They just can't do anything. Just for fun.

[Dave]

Just to watch them fall.

[Brett Adcock]

Right?

[Peter Diamandis]

Yeah.

[Brett Adcock]

I mean, like, you know, I'm kind of interested. Like a project I've been doing is like, can we just. Like the other day I was like, can I zero shy? I'm working on this new AI lab that I founded recently called Hark, and we have this new AI model here that it's just completely.

[Dave]

Wait, wait, you founded a new AI lab? Well, rewind the tape here.

[Peter Diamandis]

What?

[Brett Adcock]

Yeah. Founded a new AI lab.

[Peter Diamandis]

I sent you this.

[Brett Adcock]

Did you?

[Peter Diamandis]

I did, yeah.

[Brett Adcock]

And we have like some new AI models and we actually put one of them into the figure robot like this month. And I was like, okay, let's just zero shot. Let's give the LLM, you know, let's give the model. This is like a multimodal model. Let's give this access to just like basic commands. Like basic, like xy basically. Can we give it like acceleration and XY coordinates for navigation? Like basically a joystick. Can we give it a digital joystick? And I asked it to like find the exit sign and just like go to get out of the building. And it just. And unfortunately it was going the right direction and ran into like a clear glass wal. Well, kids do that too.

[Peter Diamandis]

Exactly.

[Brett Adcock]

So we've stressed this. It just doesn't work. You're missing so much world understanding of what's really happening. How do we move my body? We're thinking it's pretty simple to grab an object, maybe with a stationary robot. But the robot we have for humanoids are moving the pelvis and head and torso and hands and arms. When you're reaching out to grab some over table your Pelvis is moving backwards. It's very difficult to command very high robotic physiology.

[Peter Diamandis]

You know, out of China this year, some of the government employees said, we've got a robot bubble. I don't know if you saw that article that came out. We have 150 plus robot companies in China. And I mean, there's a lot going on there in the U.S. i would say maybe there's 10 serious players. I mean, two or three who are extremely serious, including figure. But there's a lot of potential humanoid robot companies. I was just at CES and saw, I mean, it was a humanoid robot explosion and then as many or more hand companies, which is interesting. So I go back to sort of the early 1900s when there were like 250 car companies and like two or 300 tire companies. And then this massive consolidation occurs in GM and Chrysler and Ford. Sort of buy and consolidate. What. What do you think is going to happen with all the robot companies today?

[Brett Adcock]

I think it happens in every industry like this, especially in deep tech. This will all consolidate down to a few groups globally.

[Peter Diamandis]

Do you have a guest? Is it a triopoly? That's the right description. Is it, you know, more than 10? Less than 10.

[Brett Adcock]

Far less than 10.

[Peter Diamandis]

Far less than 10.

[Brett Adcock]

Yeah.

[Peter Diamandis]

Globally.

[Brett Adcock]

Globally.

[Dave]

It always seems in the US anyway to settle down to 2, 3 or 4. But the borders are not like with cars. Cars. A car is car. Right. And then actually you had cars and trucks and those were kind of separate for a while.

[Peter Diamandis]

Well, you also have different designs. Like, I want the plush interior.

[Brett Adcock]

Right.

[Peter Diamandis]

I want the Sportster. I mean, and that I think, I wonder, is it going to be. Are robots going to be differentiated by their vertical application, Their personal.

[Dave]

Exactly. There's so much more variety possible in robotics.

[Brett Adcock]

Yeah. I think everybody's just like taking for granted how difficult this is.

[Dave]

Yeah.

[Brett Adcock]

Like, this is like you have to go out and build like, basically like pretty novel, Very, very difficult hardware.

[Peter Diamandis]

Yeah.

[Brett Adcock]

Needs to be relatively cheap. Then you got to figure out how to make neural nets work on it, and then you got to make neural networks work on it at scale. And then you got to manufacture at scale. And then you're going to get these products out reliably that all work every day without any human intervention. Yeah. And you know, I think we talked a lot about this, like how we're doing like K cup coffee work. Like, I haven't seen any single humanoid in the world do that. Are able to do that today globally. And that's been two years.

[Peter Diamandis]

Yeah. I mean, by the way, A lot of the video we see is actually tele operations. I think, I wonder if people realize that a lot of the robot companies are tele operated versus fully autonomous. What we saw just walking around here was a four minute long fully autonomous operation on Helix 2. Right.

[Brett Adcock]

I've never like I've built a lot of businesses in my day. I've never seen so many companies with a human in the back commanding the robot and putting out updates in my life. I've just never seen it. I've like, I, you know, when I started first started thinking it was stuff was coming out. But now it's like every week is somebody just teleoperating a robot and putting out a video and it's just, it'd be the equivalent of like I have a self driving car company and there's a guy in Tennessee driving it and we're like marketing as like there's no humans in it. Self driving. We're putting out teasers. We where in a lot of cases now there's companies selling the service. Like so I think like, I mean if you want to do this right, you got to believe in neural nets all the way down the stack. You got to basically build for general purpose purposeness.

[Peter Diamandis]

So the parameters that make it's going to define this success, successful. Top two, three, four, the neural nets. Manufacturing.

[Brett Adcock]

Okay. I would say like what's impressive today is not manufacturing. You could probably solve, you know, we're pushing on manufacturing hard, but you can probably solve General Robotics with 100 robots. You. What's impressive is like a full end to end robot that is generalizing to an unseen place. Like you can drop it into an Airbnb and be able to do long horizon work with neural nets. I mean any long horizon work in unseen places.

[Peter Diamandis]

What do you define as long horizon?

[Brett Adcock]

Hours? Days. I would like to see days of work. Yeah, full autonomous days of work. And at least, at the very least, and we're like so far from that. You have robots out there doing karate and jumping, which is. These are pre programmed open loop behaviors. They're not impressive. We do that. We've done that stuff here. You know what I mean? We've done the open loop behaviors. There's just like any college kid in a dorm room can do this with a robot and yeah. So I think that plus teleoperation. Teleoperation is not impressive. You could build a shitty hardware and still teleoperate it and put out videos. That is not hard. Yeah. What's hard is to do full end to end neural network in unseen places are generalized to this. And then if you can solve that, then the next step is like, how do you get that out at scale? But we are still in the like who can solve general robotics phase of the humanoids phase. And it's just not impressive. If I can build 100,000 robots right now that need teleoperation or can just do open loop replay, it's not cool. If we like, Brett, your only job is to build a hundred thousand robots right now. Yeah, we have the capital to do it and we can do it, but like what we really want to solve is like I can give you 10 robots and they can go into insane places and do real useful work. Like that's what's going to differentiate.

[Peter Diamandis]

So iterate that until it's right. And then mass produce.

[Brett Adcock]

Yeah, you basically want to be bringing up mass production in parallel because like building like high rate manufacturing for humanoids is going to be super hard and you're going to like have to go through like a lot of iterative design process. So it's what we're doing now. We're bringing up higher volume manufacturing as we're like learning how to build true general purposeness. Uh, so, but my view is like, if you think about these like, these like level bosses that happen that will like, that will hurt like that you need to graduate to, you need to graduate to doing like, you know, first very short periods of like neural network, which we haven't seen a lot of in the world today. I don't think there's anything over a minute long in the world that's doing neural nets continuously today.

[Dave]

And humanoids, that's amazing.

[Brett Adcock]

Everything's cut, all the films are cut or teleoperated. It's pretty crazy.

[Dave]

So I'm really glad you're telling us that.

[Brett Adcock]

Yeah, no, I mean like that's like you watch any video you want to. You want to see it uncut, you want to see it with neural nets, like not teleoperated. Like um. And then you want to see stuff we showed you here in person today that are like, that are running for hours and hours. And just like we, we run these robots with, I mean the kung fu.

[Peter Diamandis]

Videos, whether they're totally operated or fully autonomous, are, are actually fascinating and scary when you see them.

[Brett Adcock]

But the technology around there is not great. I mean you're basically putting somebody in a mocap suit. You're having some guy like do karate chops or walking around, right? And then you're running that open loop. You mean you're running that blind, you're just hitting a replay button.

[Dave]

Right.

[Brett Adcock]

And you can do that with a very simple like RL neural net. Like you can basically do deep mimic on this. And it's, it's super simple like any. There's like open source code for this. You can do with like basically one GPU on your desktop and you can do with any robot. And every robot has a very tiny amount of computer. These are like, these are single million parameter models, are very small. You don't need a lot of memory, and they're very simple to execute. What you really want is good closed loop of control where it's reasoning like over like 200 hertz or 200 times a second.

[Peter Diamandis]

Sure.

[Brett Adcock]

And it's dynamically responding to the scene. And that is literally a million times, 100,000 times harder than doing open loop.

[Peter Diamandis]

What the human sort of cycle time is. I mean, it hurts.

[Dave]

Oh, God. Much lower than that.

[Peter Diamandis]

Yeah, I would imagine.

[Brett Adcock]

One thing we've seen about a robot is we can balance on one leg better than a human. We just have much better, faster dynamics.

[Peter Diamandis]

Can we talk about the speed of development here? So 2025. I'm just trying to imagine you put out this beautiful post every week on X about the progress in the robotics field and what's going on here at figure. Just constantly. Locomotion was a big step forward, excuse the pun for figure. Just seeing it walk and then run very naturally. What else Was significant in 2025 for you?

[Brett Adcock]

I mean, we launched Helix in 2025 about this time last year, about a year in now. I think that was like highly significant. Like, we basically figured out how to run like, basically like longer, like over long periods of time. Neural networks on a robot. How do we get the data for it, how do we train models, how do we deploy to test time, how do we get to do you use. You watch like package logistics. I think you guys saw it running. It's been running for days now. And it's just like, it's a neural net all the way down the stack. It's learning how to grab packages kind of like, you know, individualize them, find the barcode, position it down. It'll even pat the package down so the barcode reader below can see it and scan it. It's doing that at a very high, like, assume that at high, like accuracy and assume that at high speed.

[Dave]

So high speed, though, that's part of the. Yeah, no, it's visually crazy. Well, yeah, because a lot of what you see in robotics, it's as.

[Peter Diamandis]

It's as fast as a human would.

[Brett Adcock]

Yeah, we like our last, like, we see air now. Like, last one we did, we did like we had one air over 67 hours. It's continuous over 67 hours.

[Dave]

This thing is.

[Brett Adcock]

Is crazy.

[Dave]

Yeah. It's doing an operation every second or two. So 67 consecutive hours of that is a lot.

[Peter Diamandis]

So if you had to guess at.

[Brett Adcock]

So I would say, I would say Helix is a big one and then figure three. So figure three is a huge step change for us in hardware.

[Peter Diamandis]

If I could. What do you see then going in 2026 here? We got the, you know, next 11 and a half months.

[Brett Adcock]

Yeah.

[Peter Diamandis]

What are you excited about?

[Brett Adcock]

We will build like our entire roadmap around Helix 2. Now. We will basically. Now Helix 2 can like go from like doing the logistics, use case stationary to walking and moving and basically do like long horizon for full body control. So that means the rope. And then we basically have now integrated all the sensors, tactile camera, palm into the stack. And we're seeing like improvements overall in the policy layer. So we're getting better and faster about like, basically like taking data and basically running it on. On board the robot now.

[Dave]

So I wanted, I wanted to ask you, like, what defines Helix 2 because you're probably incrementally improving the neural net every day.

[Brett Adcock]

Yeah.

[Dave]

So what, what is the.

[Brett Adcock]

A couple big steps. One is we basically have an integrated, basically a fully learned, what we call System zero, which is our controller into the robot. So the robot has a full body reinforcement learn controller in it. So basically now we have literally no code written on that robot. So it can basically move the whole body itself using a full, basically learn controller inside of Helix. We call it S0.

[Dave]

Has anyone else ever done that before? That's got to be.

[Brett Adcock]

There are reinforcement learn controllers out there. Like a lot of the karate stuff you see and things like like that or that. But nobody's really integrated that in the whole body for learned manipulation and perception. And nobody's showed that actually working with like moving around and doing things that we shall today. Yeah, I actually don't even know if anybody showed it stationary standing and doing learn policies actually. Probably not in the world. So like getting it integrated into a stack now that we actually use going forward. I think one of the things we learned at like we were in BMW last year and we were there for like, we did six months, like redeployed our figure two robots every single day. Yeah. The biggest thing we learned there is like the stack. We had, I think about 80% of the things we got right and 20% of the things we got wrong. Meaning like the things that we got wrong on. We didn't want to scale. It was working. The robot ran every single, every single workday and we, it worked. But then we learned like okay, I don't want to ship a hundred thousand robots in this like architecture stack. It's just like too hard to scale.

[Dave]

Yep.

[Brett Adcock]

It'd be like too brute force.

[Dave]

Yep.

[Brett Adcock]

And so we basically worked on basically for almost basically a year now on like okay, what is the idea architecture where we can go out and accumulate large sets of pre training data, put it in the robot and it can just like do this, do this work and we, we emerge generalization from this and that's what you're seeing today.

[Dave]

So helix1 had the C code in it still. So we had, what defines Helix too.

[Brett Adcock]

Is helix1 had a lower body controller that was still written in C and everything else full upper body was full neural nets.

[Peter Diamandis]

Okay.

[Brett Adcock]

And so we basically completed now the full body.

[Dave]

Okay.

[Brett Adcock]

And then in doing so we also did some work on our system level, System one level where we integrated all the sensor modalities now from the and the rest of the robot into the stack. So like for example we now have tactile sensors in every fingertip that we're using on Figure 3 as well as palm cameras to understand how we're like we're sometimes occluded and sometimes we want to better basically better understand how we're grasping items. So we put a bunch of stuff about, about we're picking pills and stuff out of pill pill cartridges that you like literally occluded from, from the hand, your hands like literally in front of the head camera. But we still really want to understand where we're going. So I think so so now with figure with Helix 2 we basically have a full stack end to end with neural nets and we, we feel we can, we, we feel confident scaling the pre training data set into Helix even go as far as like we've designed Helix 2 for the pre training data set and then we've designed, and then we designed the robot for, for Helix 2. So we've like, we've designed everything around data.

[Dave]

Yeah.

[Brett Adcock]

And how do we get data at scale if you're, if you're in the neural net game? It's like a data, it's a data play.

[Dave]

Right.

[Brett Adcock]

It's like how, how like high quality and diverse.

[Peter Diamandis]

So it's experience, it's just gathered experience field in all kinds of circumstances.

[Brett Adcock]

Like where can we find.

[Dave]

I know everybody knows this already, but it's accumulating and it never goes away. It's, it's, it's incredible.

[Peter Diamandis]

Unique data progress.

[Dave]

Well, yeah, you teach somebody how to scuba dive or how to play piano and they have that knowledge, they live, then they die. Then you have to teach somebody else. This is completely accumulating.

[Brett Adcock]

The reason why I think there'll be like a very few humanoid groups is like the one thing that's important here is that once one robot learns how to do a task, every robot in the fleet knows it. And humans don't operate like this.

[Peter Diamandis]

Yeah, I wish we did.

[Brett Adcock]

I watch my kids like, kids like learn how to do stuff and they just don't listen. I wish we melt.

[Peter Diamandis]

So 2026 predictions. What's your, what's your boldest predictions for figure? What is your goals for this year? What do you imagine?

[Brett Adcock]

Yeah, I mean we basically, we're spinning up Baku like production enormously right now for figure three.

[Peter Diamandis]

So you said something like a, a robot every 30 minutes you expect.

[Brett Adcock]

We're trying to get there in the near term right now.

[Peter Diamandis]

Amazing.

[Brett Adcock]

Which you guys saw. We walked through Baku today. What'd you guys think of.

[Peter Diamandis]

Yeah, a lot of humans.

[Dave]

I wish everyone could see it. I guess it's all secret. You can't, you can't. Camera through there.

[Brett Adcock]

We have it. There's a lot of IP there because like you see exposed boards and actuators and stuff people with. It's cool, right?

[Peter Diamandis]

Maybe we can, we can mix it in here. But. So whenever there's a lot of humans. When do we start seeing robots, building robots.

[Brett Adcock]

We will put robots on our Baku lines this year.

[Peter Diamandis]

Okay.

[Brett Adcock]

And then phasing like, basically like phasing humans out of there will be a combination of getting more robots there and doing more high volume, like automation over in Baku.

[Peter Diamandis]

Okay, so that's the first 2026 objective.

[Brett Adcock]

We want to scale up robots. Baki for sure. The second thing is we want to scale out robots in the industrial commercial workforce. So we have like multiple clients now. We've signed up. They are like buying or leasing robots from us. And we are going to get those out at scale in 2026. We know exactly where we're going geography wise, what use cases are going to be, deployment schedules. We want those to be figure three. So we've just retired end of last year, figure twos. And now we're basically building the arsenal of figure threes out as we're scaling up manufacturing to get them out to the World and run every day. We like the commercial workforce because it really helps harden our ability to run robots every day. Like, what we're here to do is like, we're here to build robots and run them in the, in the world. And they run 24 7.

[Peter Diamandis]

Your ideal customer is who I know a lot of people who would love.

[Brett Adcock]

We have, to be frank, like, so much demand for customers. We have like, we've talked to 50, 100 customers or so in the last like six to 12 months. We really want to be like kind of all in with a smaller group of customers and really spend time with them, integrate well into their facilities and, you know, do well. We're still at this, like, we're still early, right. We don't have like thousands of robots right at these places. We want to as fast as we possibly can. But like, once we get to certain, like, I mean, we could probably ship like, I think, I think we could ship an enormous amount of robots into the current customer base we have now. Like, so we see like, you know, we're kind of good now for the next like two or three years in terms of like, we have so much demand. Like, we like, they're kind of waiting for us to like ship at scale.

[Peter Diamandis]

Leasing versus sale.

[Brett Adcock]

Yeah, we have like service. We have like a, you know, we really like the leasing model. Humans are leased. So, you know, thought of it that way. Yeah, they used to be a bot.

[Peter Diamandis]

At least these days.

[Brett Adcock]

Yeah. You lease humans, so we like, we lease humanoids today. You know, we won't be like, we're not opposed. I think what really matters is trying to figure out how to find the right distribution to get robots out of scale. Like, it'll really help us get really good at what we do. Like, it's one thing to like show a demo or whatever else, but like, you know, when we had robots at, you know, in our commercial customer last year at BMW, like, it was just taught us a ton about like running every day fleet operations, safety, like repair and maintenance. Like, there's a lot of other things that need to come, like, come, come through on the ecosystem that we need to get right. So I say second thing is like getting robots out of scale commercial customers. And then the last thing, which is arguably the most important for us is we want to solve general robotics. Yeah. Uh, we want to basically like the analogy is like we want to build a human in a body suit that you can just talk to that has like common sense reasoning, you can communicate with, that has like, like basically almost like you Know like almost perfect memory, what's really happening or what's going on in your life, um, that can maybe talk to you, almost be your companion, I mean and then go off and do things that you would like, like you, an everyday human would want to do. And I would expect them to get up to speed on those tasks at or faster than human can.

[Peter Diamandis]

Is there two different models then driving it? The, the VLM model for the body and the physics and the embodiment versus an LLM for conversation and memory.

[Brett Adcock]

We believe this all comes down to one model at the end of the day that is one omni model that is trained early in pre training that helps fuse all this together. But yeah, you could think of it like we need to have speech, we need to have language condition policies, we need to understand physics really well. We need to remember things and be able to recall that easily. We need to have some sort of personality on the robot. I think one thing that you're going to see more and more is we really want to make this robot something you can spend time with.

[Dave]

Yeah.

[Brett Adcock]

And we've been really focused on getting the core building blocks built. But like over the next year or two I think you'll see us. I think I just want a robot at my home. I can talk to like remember things, talk to my kids. My kids come home like SAT from school or something. I want the robot to understand that, have the eq, like self awareness to see that, talk to them. Like I think all this is like something we want to spend more, we're spending more time on now internally.

[Dave]

Is it, is it already a big moe model where it'll have different like depending on the task you're doing, it'll run different parts of the neural net. Or does it.

[Brett Adcock]

We have like one neural net now that's like, that's basically. There is no like libraries of neural net that we pull down. That's interesting. So there's no like dishes neural net or like, or like logistics neural net that you saw here?

[Dave]

Yeah, because, because you know, at scale like if you teach the thing, every physical motion has massive number of combinations. The storage is actually dirt cheap.

[Brett Adcock]

Yeah.

[Dave]

But the processing is very expensive.

[Brett Adcock]

Yeah, even better. We've basically seen that. We've seen positive transfer now with all this data.

[Dave]

Yeah.

[Brett Adcock]

Like coming in. The robot can journalize better with more information. Even if that's weird.

[Dave]

Like more knowledge is better. It does, it does cross like it paint playing piano. It makes you a slightly better soccer player. But also you don't want to Run the whole parameter set for piano playing when you're playing soccer. It's an interesting little hybrid problem.

[Brett Adcock]

Yeah. You don't want to, you don't want to nuke it. Yeah, for sure. Yeah. I mean, that's where we try to build best in the world models here and build a great team that can ultimately deploy robots that are useful. I think showing like, you know, like this type of usefulness, like either it's like a lot of stuff you saw today and a diversity that is super important for a humanoid robot needs to be able to do everything a human can, which is like the distribution curve. It's like, like, you know, we probably do like billions or trillions of unique, very unique things in the world.

[Dave]

Yeah. One of the things I said on our tour that totally tells me we're on the right track is that you're using normal GPUs for the training like everybody. But the inference time compute is on super, super fast, dedicated non H100 non, you know, GB300 hardware, which has got to be, you know, at least a factor of 10 or 100 cheaper and faster.

[Brett Adcock]

It's also running fully on board.

[Dave]

And it's running fully on board.

[Brett Adcock]

So we can basically do like very fast inference and policy deployment. Yeah.

[Dave]

And it's also not sucking down the entire power of the robot.

[Brett Adcock]

Yeah, yeah. I mean, you also have an issue where like, you know, we've also run models off board the robot, but if we lose communications or have some.

[Peter Diamandis]

So I wanted to go there, you.

[Brett Adcock]

Know what I mean? Like, if you like lose Internet, it's like hard to do work. And it's like humans, we hit on.

[Peter Diamandis]

Supply chain batteries and comms. So on the comm side, do you imagine we're going to be. You're going to be running like a 6G network on there. Besides WI fi, what's going on in batteries these days?

[Brett Adcock]

Yeah, we have. So from a network perspective or you know, communication back to the robot, we have WI FI on board. We have a 5G and SIM card ESIM on board. So we can. The robot, you can text the robot, James. They can have a network outside of like a WI FI condition. And then we also have Bluetooth on board. So almost like a walking phone or something like that you would think of. So we want, I mean, you really want like connection at all times, but you also want, I mean, ideally you want a connection all the time, but you also want the robot to be able to perform work without a connection.

[Dave]

Yeah.

[Brett Adcock]

So you really want like a lot of Onboard intelligence, you know, that we basically. In case you lose Internet, the robot's, like, not bricked. I mean, humans, for the most part, can do work without their cell phone. Not teenagers.

[Dave]

Yeah, that's going away. Yeah.

[Peter Diamandis]

Okay, so batteries, I mean, they've been improving. What's battery life right now? I love the charging mechanism, by the way. For those who don't know. You're charging basically through your feet.

[Brett Adcock]

Through your feet?

[Dave]

Yeah. No connector. You just stand.

[Peter Diamandis]

Yeah.

[Brett Adcock]

It'S really cool.

[Peter Diamandis]

What kind of battery life are you getting? What do you expect in two, three years to get for battery life? So today it's what?

[Brett Adcock]

Yeah, we run basically around, like, four to five hours per, like, full. Full charge in the battery. And if you're, you know, we're starting at full battery life and then. And then through full depth of discharge, and then we can, like, charge wirelessly about 2kW through the feet inductively. So it's about. And we have about a 2 kilowatt hour battery pack. So it's about an hour or so for a full charge on the robot. So we can do, like, you know, four or five hours on, an hour off. That's great. And, yeah, it's great. I think, like, I think folks are over indexing too much on how long the robot can run on a single charge. Yeah, I don't.

[Peter Diamandis]

I don't expect that many tasks.

[Brett Adcock]

Humans take like, a few. Like, a few hours in. You're not like, you know, you go take a little break, like, do this stuff. So it's like, I think there's, you know, ample time to do opportunistic charging. Maybe send another robot in. We also can charge it. We basically can put, like, this little thin mat anywhere in the world. Like, it could be, like a conveyor system or wherever else could be at home and from the kitchen, you can just charge there while doing work, which is really cool. So you don't have to have any wires or things like that you're pulling from the.

[Dave]

Well. I think one of the greatest value adds you're doing right now is people are over indexing on all kinds of weird things because they're physical beings and they're watching the robot do physical things, and they're saying, oh, my God, can you believe it can sprint now? Oh, my God, I can do a backflip now. Oh, my God, I can do. And you're like, well, it depends whether you program that in C or you tell or operated it or did it actually learn this?

[Brett Adcock]

Yeah, I think most of Those are open loop. They're just like replay buttons.

[Dave]

Yeah, exactly. And it's just so hard. So when people say, well, how long does it run with one charge on the battery? You're kind of relating it to your cell phone. Yeah, but it's not, it's not relevant in the inflection we're going.

[Brett Adcock]

Yeah, I think you just got to like, the summary here is just like, I need to see like real open, like I say, real closed loop control of a robot moving around, touching and moving things like a human would. And that's where the hardest problems all sit. And that's where we've seen, we've seen this huge wave of humanoid explosion, you said, out of China. It's like this. But we've seen this very steep drop off from getting to that point next, which is even like, show me a minute of the robot doing Keurig or something like that. Uncut closed loop in real time. Yeah, like, and I just, like you just haven't seen that. And I think, I think you will. And I think there's like, there's. Then there's like a lot more levels to go from there and that, you know, that took us two years to go from like a few minutes of tabletop manipulation with neural nets to a point where we can do like kitchen work, like, you know, like room, like room scale autonomy. And that was two years of working seven days a week. We're here a lot of nights getting there. So it just gives you a little sense of you're not going to do that in six months from there. So I think there was a lot of both hardware, low level software, firmware, embedded system sensor and then neural net and then data. All of that came together to build this. We couldn't have done this work on. We couldn't do the same work today on a robot that we could go buy today.

[Peter Diamandis]

You vertically integrated? I made the choice to vertically integrate. But supply chain, how much supply chain ties back to China?

[Brett Adcock]

I think in like the next, like, I think by summer we'll have almost none of our supply chain in China anymore.

[Peter Diamandis]

And do you buy into the US versus China sort of AI and robot competition? How do you think about that?

[Brett Adcock]

I don't, like, I just like, I spend a decent amount of time in China. I love it. China. It's great. Like I go there and it's. I know you're like, you're watching TV here in the US and it's just like this massive conflict and battle and everything. And then you go to China and everybody's just like trying to help and win and trying to work and collaborate and feels like a startup incubator and.

[Peter Diamandis]

It'S just like a one way fun, one way competition.

[Brett Adcock]

It just feels like everybody's on team human, team humanity to go in and it's so great. Then you come back here, you're like poisoned with all this like stuff online and like articles and television and it's just like, it's not like that when you're like on boots on the ground and going to do this. It's like let's go as like, as one and go win. And I just like love that spirit of like trying to like just progress this technology as a giant lever arm for humanity to bring like you know, to bring abundance basically for everybody and just make it, make it like a sci fi future we all want to live in.

[Peter Diamandis]

Which is like, oh my God, it is. That is we want to speedrun Star Trek is what we talk about.

[Brett Adcock]

Exactly.

[Peter Diamandis]

It's like yeah, see figure on the moon, figure in orbit, figure on the ocean floor, 100%.

[Dave]

So the equivalent like you guys make your own actuators, motors here. And part of that is because you want the exponential growth effect. But part of that also is the supply chain just doesn't exist to give you the parts here in China.

[Peter Diamandis]

I mean you're talking about the, you just. And I were talking about this, the improvements made between figure two and figure three.

[Dave]

Yeah.

[Peter Diamandis]

Because you have all of the ability to iterate in terms of speed and cost. I mean the, the figure, the numbers that you shared on cost, it was like 90 reduction.

[Brett Adcock]

Yeah, we reduced costs like crazy on figure three.

[Peter Diamandis]

It's crazy.

[Brett Adcock]

I think we, listen we vertical clinic, we have to. It would be great if we can go off and buy like motors and we can plop them in the robot. It doesn't work like that. It'd be great if we can go by hands and just like, like you know, screw them on to the end. It just literally doesn't work. Yeah, if you go through the engineering work to basically understand how we do comms and power and sensors and failure cases and thermals and you know, low level firmware and embedded software, like it just like there's like one of the cost, something breaks or reliability, something breaks in that equation and you're like left with like, hopefully the vendor fixes it or you die. It doesn't work. None of this stuff like the technology readiness of these things are really low. We would love to have gone out and like bottle of Stuff in the early days we tried and we basically just failed at all of it. So we had like, okay, we need to go design ourselves. And then now we manufacture. Like we do all final assembly and everything here.

[Dave]

Yeah.

[Brett Adcock]

And we all, we do that, you know, in some cases because, like, nobody knows how to do that. Well, we do that a little bit for ip. Like, we really want to control that here and understand, like, what we have, like people have access to. And then we also want to get good at making a lot of robots. Like, like what we need to get good at long term is like, probably a few things getting data at scale that can run neural nets and then basically doing Helix really well and then making a lot of robots and then getting those things out at the world at scale. Pretty simple equation.

[Dave]

In the day, it just feels like the journey of getting figure up and running must have been so much harder than it would have been in China. But then once you have everything built in house, all the actuators, training, the neural net and everything in house, then you have a massive advantage versus anything going on in China. Because if you'd been locked into a supply chain, it's, it only has certain models.

[Brett Adcock]

It's like, it's like, even if we use like an existing supply chain for all this stuff, the robot wouldn't be able to do what you saw today.

[Dave]

Yeah.

[Brett Adcock]

Just can't do it. If you go out and buy like a robot, humanoid robot, the shelf today, we can't get it to do this. Yeah, we've, like, we've, we've, we've bought robots off the shelf. We've looked at them like, you just, like, you can't get them to do this work. They don't have the right sensors, they don't have compute thermals. They don't have the right. The hardware, the hands, the head. Like the, all of these are, are built around our neural net stack.

[Dave]

Yeah.

[Brett Adcock]

And.

[Dave]

Well, that's a new thing too. The neural net is, is incredibly integrated with this specific hardware.

[Brett Adcock]

If you watch folks that are trying to buy these robots off the shelf, like say from China. Yeah. They'll, they'll come, they'll end up retrofitting them themselves with these giant backpacks. They'll have like, power. They'll have compute there. They'll have thermals and have a wire hanging out. They got to hook that into the back. He probably has his own local battery. They'll hook it into the back of the robot. Like, they have to like, take it and they have to like, overclock it and it's just like, it's just like a, it's just the wrong way of doing this. Yeah. It's like a hard thing. It's like a, it's like buying a. It's like doing rockets and like buying a rocket and you know, here's. We'll put stage two on the side or something like that. It just doesn't really work at scale. It works for like in the early days like hobby grade like demonstrations and things like this. But if you really want to do robotics at scale, you're going to have to go design yourself.

[Peter Diamandis]

Yeah. Looking at the companies coming out of China, Unitree engine, AI and so forth. Do you have any which are the ones that you're most interested in? Excited as friendly competition, if you would.

[Brett Adcock]

Yeah. I think one thing that's great about China is we're just seeing as you mentioned earlier, this explosion of really great talent and robots coming out the door.

[Peter Diamandis]

And great entrepreneurial work ethic there. Right.

[Brett Adcock]

It's awesome. It's great and I think it's just good for humanity and this needs to happen. I think the thing that I think we've not seen is we've not seen any closed loop like AI like control from these systems at all. Yeah, we've seen a huge lack thereof of that stuff. I mean unit's like here's the, here's the robots. We'll sell them and they're doing a ton of like basically open loop like looking.

[Peter Diamandis]

They're hand, hand controllers.

[Brett Adcock]

Yeah. So I think like, but like doing that is very, it's almost, it's orthogonal work from designing the like the system the right way for a full autonomy. But there's I think you know, if we can think about like who figure really competes with as our main competition. It's certainly China. So like as a whole and you.

[Peter Diamandis]

Know for manufacturing, I mean for human low cost labor, I think just for.

[Brett Adcock]

Humanoids like we really don't see anybody else besides China as a real competitive threat today. Fascinating.

[Peter Diamandis]

Do you rumors about Apple getting into the business. They cut down their car, their car project and the rumors are that they're heading towards humanoids. Have you heard that?

[Brett Adcock]

We've heard this. We've had every major, I've been in conversation with every major tech company in the world last 12 months and then.

[Peter Diamandis]

Nvidia and Google and even Sam. I mean everybody's making noises about meta, Amazon.

[Brett Adcock]

Yeah, yeah. Listen, this is like going to be the largest economy in the world. It's like half a GDP, roughly a little under half a GDP is human labor.

[Peter Diamandis]

$50 trillion.

[Brett Adcock]

Yeah. This is like the next great place to be and I think it's going to be super impactful business. It'll lead to like ubiquitous goods and services for anybody. An age of abundance. And it's going to be a super fun business too. It's like going to build a sci fi future we all want. It's going to feel like, it's going to feel like, it's going to feel like 2080 up in here. So what you're seeing is every major group in the world will be, will get in this space. You have to, you have like no choice.

[Dave]

You have to have a major group being Apple, Microsoft, Google, every.

[Brett Adcock]

I think every major player that wants to do this. I think the thing that I think it's gonna be hard is like we're doing like rocket type difficulty and design here. So it's like if, you know, if Meta is doing like you're building rockets, you'd be like, that'd be crazy.

[Dave]

Yeah.

[Brett Adcock]

And there's, I would think maybe the humanoid is probably up there with like rocket design. It's certainly harder from an engineering perspective than when I built Archer, you know, building like electric aircraft. And that was hard. Yeah, that was a very. These are a 6,000 pound aircraft, 12 motors, 6 independent battery systems. We built our own control stack and embedded systems. Like, like did all the structural design ourselves, things like this. So I think it's probably up there with like some of the hardest hardware on the planet and you, you just have gotta be all it.

[Dave]

Well let me ask you about that because we were talking backstage at Abundance360 last year and you had a, the basic tech stack had six layers of competency. You could probably rattle them off the top of your head actually.

[Brett Adcock]

But yeah, this is for Archer figures.

[Dave]

This is for robotics prior to neural nets I guess. So it applied to Archer and figure.

[Brett Adcock]

Sure.

[Dave]

But what, what were they again?

[Brett Adcock]

It was, I mean like Archer was basically like a flying aircraft. So you know, I basically build electric vertical takeoff and landing aircraft.

[Dave]

Right.

[Brett Adcock]

That is basically like a. Sorry. It's like a flying robot is what it meant. Yeah. That has, you know, basically has battery systems on board as electric motors. Electric motors, just basically a stator rotor gearbox. Yeah, pretty simple. We have a little bit more sensors in our actuators than that. But like for the most part like in there's like, you know, there's encoders and stuff in there, things like that. You have basically a control software. How do we control this thing and make it move around. And in the case of Archer and figure it's very overactuated system. So Archer has 24 degrees of freedom. We have propellers end up tilting. We have pitch on the blades, you have flaps on both the tail in the wing. And then figure we have over 40 or so on the system. You have embedded software on board and so sensors. So how do you get the compute? Sensors and embedded software all talk to each other. Then you have like structures. Okay. So those are like kind of the core ingredients of like a robot or something. Like physically moving through the world.

[Dave]

Yeah. So my question is, you know, traditionally the employee base would be like experts in 1, 2, 3, 4, 5 and 6. And like they'd be really, really good.

[Brett Adcock]

Yeah.

[Dave]

So then you come in and overlay this with Helix and you've got this massive neural network thing. Is that, is that a seventh grade competency or is that something that permeates the other like, or did you take all of your microcontroller experts and start training them on neural network?

[Brett Adcock]

The next thing Archer is like, how are you going to plan? And you do it through a pilot. My aircraft, Midnight is a piloted four passenger aircraft. So like who's doing the planning? And basically higher level, like basically higher level behaviors in the stack. That's a lower level control encode what to do. And here at figure it's been changing over time, but now it's entirely neural nets. With Helix too. Yeah. So it's like who's going to, I mean what, what is the highest level behavior telling the rest of the stack what to go do? Yeah, where's that? It can come from a human, it can come from a joystick, it can come from an open loop behavior which we see like we talked about before, or it can come from like a neural net that's like doing the planning and reasoning. So like the, you know the kitchen demonstration you guys saw today and released, like the. What's telling the robot what to go do next and what to pull the rack out of the dishes and to go grab the cups and not to, not the coffee cups, but the water cups. That's a neural net making that planning. In the case of my aircraft at Archer, it's a pilot determining when to take off, when to hover and when to transition into full flight and then how to sort of descent.

[Peter Diamandis]

What's a different type of neural net?

[Brett Adcock]

It's a human biological neural net.

[Peter Diamandis]

Yeah.

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[Peter Diamandis]

Let's talk into application layers. So we're seeing your movement into the home besides the industrial base and such. And healthcare is going to be a big part of this elder care, helping people stay healthy at home. By the way, you just came through Fountain, through Fountain Life. How was the experience for you?

[Brett Adcock]

Thanks for referring me. Yeah, it was great. I went down to a clinic a couple weeks ago which went into Orlando.

[Peter Diamandis]

Orlando, yeah, yeah, headquarters.

[Brett Adcock]

I didn't know what to expect. You know I've done like, I've done like full body MRIs and the CT scans and blood work before but like got. There was basically a full stack. I mean you know this but like it's a full stack.

[Peter Diamandis]

Everything measurable about you.

[Brett Adcock]

Yeah, exactly.

[Peter Diamandis]

Like 200 gigabytes of data.

[Brett Adcock]

Exactly. And spent like five hours there. Left got the download like last week. It was just, it was unbelievable. Like what was great about it was that I could get basic comprehensive understanding of like, like my body, what's happening. But also somebody there reporting it out and talked to me through how, how I understand it, what to do next and a plan and basically build a plan from there. It was, it was great. Like I actually purchased it from like I purchased it as well for my, my parents and things like this. I think it's just a great gift.

[Peter Diamandis]

Yeah, Dave, we need to get you there too.

[Dave]

Why Orlando and why not somewhere else?

[Brett Adcock]

Like wait, I was on the east coast so I popped down to Orlando and so it was just easy for me.

[Peter Diamandis]

Yeah, we got New York, Orlando, Naples, Dallas, Houston's opening, Miami and la. Anyway, back to the conversation here. I can imagine this is going to up the value of health in the home a lot. So one of my visions of the future is you're constantly being monitored for your blood biochemistries, what your Protein levels, your vitamin levels and so forth. And that's being uploaded to figure in the kitchen cooking your meals, ideally suited for what you need in that moment. And then the whole elder care side, how do you think about. About that?

[Brett Adcock]

Yeah, so my, like, growing up. I grew up. I grew up on a farm Midwest, and then my parents got into like, independent assisted living like, 15 years ago. So I kind of, kind of grew up around like, senior care a little bit in my life.

[Peter Diamandis]

You got into that business?

[Brett Adcock]

Yeah, my, like, my. My parents own and operate senior housing, like, senior housing facilities in the Midwest.

[Dave]

So wait, they're still in Illinois?

[Brett Adcock]

Yeah, still. Still Midwest. Yeah.

[Dave]

Wikipedia says Your hometown has 2,000 people in it.

[Brett Adcock]

I grew up in like, like Muequa, Illinois, man. I think it was like 1800 people when I grew up. Yeah. Like middle of nowhere. We had like, no, like, no traffic lights, like, no fast food. It's a dry town. It was just like a whole different world.

[Dave]

Oh, man.

[Brett Adcock]

Yeah.

[Dave]

Do that parades for you when you go back home.

[Brett Adcock]

And man, it's just like parades going through this.

[Dave]

Can you imagine that?

[Brett Adcock]

Yeah.

[Peter Diamandis]

So you understand the, the value of, of a fully autonomous human robot.

[Brett Adcock]

Yeah, we got to put like. I'm, like, really passionate about figuring out how to let, like, be able to ship robots into, into senior care and letting people age, place, age in place at home.

[Peter Diamandis]

Yes, yes.

[Brett Adcock]

Like, even, like, you know, it's like, it's, you know, hard. It's hard to get people to move into living facilities.

[Dave]

Well, how does that work? So you're sold out, you know, three years into the future, you can't make them fast enough to keep up with the demand. And then you've got BMW, you've got a bunch of industrial use cases, but then you've got this in home and you've got, like.

[Brett Adcock]

I'll, like, give you my, like, you know, level with you on how I think about things. We've been spending the last, like three and a half, we're about three and a half years old, trying to figure out how, like, what the right recipe is in the first instance of, like, what a general purpose, like, architecture would look like for humanoids. We believe we found it internally and we understand what that is and we believe we know how to make robots now and put them out. And we're going to run them really hard this year. We're going to run them.

[Peter Diamandis]

You showed us. What do you call it? The Grid.

[Brett Adcock]

Yeah, Grid.

[Peter Diamandis]

Can you describe what we saw?

[Brett Adcock]

The Grid's like my favorite place here. It's like it's one of, we have like four buildings on campus. It's one of our buildings here. And we have the facility outfitted that we're going to expand like hundreds of robots into that will run 24 7. And it has like a little mission command post, like that's like second story, like kind of like a 007 like situation room. And you can see every robot there. And it's going to be doing both home and commercial workforce spinning up like right now. The facility just got open like this week. You guys saw. It's like squeaky clean. And we'll start shipping figure threes into it like this month.

[Peter Diamandis]

So model homes, model factories, model operations.

[Dave]

Well, so within, within mission control, you think of like watching the robots, but the robots also have their own vision which transmits back. So it's more like, you know, in the combat movies. We're back at the home base, they're watching the invasion or whatever you're seeing through the eyes of the soldiers. You've got all that data coming back into mission control too. So, so if, if the robot, you know is 200 and how many in there at any given time? A couple hundred.

[Brett Adcock]

250, 300.

[Peter Diamandis]

250.

[Dave]

300 robots building a house or doing and all that video and telemetry comes back into mission control as they do it.

[Peter Diamandis]

Do you believe that AGI requires embodiment? There's a lot of conversation that's been put forward on that note.

[Brett Adcock]

I think my, my definition like, I think like, so I'm getting the chance right now to spend a lot of time on both like the physical AI and also digital AI at Hark. So they kind of both, you know, like both a bit. And I think when I talk to AI today or use it, I just feel like it's so dumb. It just feels like you're starting like a new chat. You're like basically asking it for like knowledge retrieval. It's like an advanced Google search engine. You know what I view is like, I kind of like think about like we want to build like the future. We want to be like Jarvis or we want to build like Justin's. I, I, I'm, I want this thing to, I want to talk to, I.

[Peter Diamandis]

Want Jarvis so bad.

[Brett Adcock]

Yeah, I wanted to talk to me. I want to reason. I want to have like perfect memory. I want it to be able to touch the world both digitally and physically. I want to be able to be general purpose, be able to do things like for me, think about reasoning through things. We have we have Hark now designing CAD from scratch. It's going out and finding. You ask it to go build a cat thing. I asked it to build basically a monster truck for my son and CAD and it's going out. It's like finding a CAD package. It's installing it, it's opening up. It's like learning how to basically build CAD in the parameters it needs to look at for building monster trucks. And it was often does it and we can do that in under an hour now fully end to end and just clean sheet, clean sheet from a single problem. And it's using tools and computers like a human can. And we're going to give it all the same tools. Like we're going to give it all the tools that figure uses for like, for cad, for fea, all this different stuff and it's going to learn all these.

[Dave]

And was that the inspiration for Hark? The fact that, you know, there's a lot of LLMs out there doing a lot of things but none of them are really connected to cad and you have so much experience, you know from your.

[Brett Adcock]

My inspiration for Hark is I feel like we're like chasing like all the big frontier labs are chasing this like very abstract version of like, like reasoning.

[Dave]

Well specifically anthropic wants to dominate coding and code self improvement and then OpenAI wants to dominate.

[Brett Adcock]

I want to dominate like a sci fi AI future.

[Dave]

I want like Jarvis. Everyone knows Jarvis.

[Brett Adcock]

Jarvis. Like I want like the smartest person in the world with everybody.

[Dave]

Yeah. We have like the anointment like the idea that these things go out into the solar system and then ultimately out in the galaxy and start making themselves out of raw materials doing this.

[Brett Adcock]

Everybody's like copying the other frontier lab that's copying their frontier lab. Like nobody's building true multimodal systems that really can reason and understand and have persistent memory. Yeah. And like that can go out and touch the world and do things. That's my version. AGI is like I can do what humans can do and humans are just not sitting there giving me Google search engines.

[Peter Diamandis]

Right.

[Brett Adcock]

Which is what we have now. It's terrible. And in one aspect it's great because like this new alien technology like dropped on the planet in 2022 and we're like trying to figure out what to do with it and but the other aspect is like there's so much the models can do now. There's such an overhang in the product capabilities and you know, we're, we're understanding that better now at Hark we're seeing that better now at figure and I think we're just, we're like abstractly getting to a place where we're building like synthetic humans at scale. And these humans can be both digitally, like work on the computer, use tools, they can physically be there, but they'll be able to like, reason with you, talk, have memory, understand you, and they'll be able to go off and do anything a human can.

[Peter Diamandis]

Have you been tracking claudebot now? Multiple?

[Brett Adcock]

Yeah, I've been tracking cloudbot. It's really cool.

[Peter Diamandis]

Yeah, they, they renamed it to Multipot.

[Brett Adcock]

I think it just like shows you how complacent a lot of the frontier labs have been.

[Peter Diamandis]

Yeah.

[Brett Adcock]

Where you have like such incredible capabilities. Capabilities that can be with. With very simple harness and very simple like markdown, Markdown files and very simple tools. You can give it on, on the back of Opus or whatever, whatever you're going to use can do like magical things for the world. And we've had that for like, for a long time now. Like, not like it just, it wasn't like they went out and built a new AI model for this. They basically just put us some harnessing and some, you know, MCP and APIs around this and it like basically went out and can like basically be your executive assistant. It's really awesome. And there's, there's a huge area here to give that to every person in the world and make it easy.

[Dave]

Yeah.

[Brett Adcock]

And we're doing some model development now at heart, that is like I, I think truly state of the art and I'm excited about that. And we're also doing some of that now in the physical world where a figure. So we have this like digital versus like physical thing that I'm seeing on both. And I'm just like so excited about this future. Even the next, like 12 to 18 months. The next 12 to 18 months, I think will be like the largest transformation we've ever seen.

[Peter Diamandis]

Yeah.

[Brett Adcock]

And getting back to your point about like, what do we do with health care and robots? We're going to make a shit ton of robots. Like we're spending up resources right now both at Baku, you're seeing now, and future Baku to basically be able to make like millions of robots.

[Peter Diamandis]

How long before these robots are your physician, your surgeon able to actually support all, you know, the complexity of a medical procedure?

[Brett Adcock]

I think from a hardware perspective, in 2026 we'll be able to do like from a hardware work, what surgeons can do.

[Peter Diamandis]

Yeah.

[Brett Adcock]

And I, I think I see. No, you know, given where we're at with our roadmap and things like that, with, with figure, I see no reason.

[Peter Diamandis]

It's pretty fast.

[Brett Adcock]

Yeah, it's pretty fast. I feel pretty confident by the end of this year you'll have a hardware system that, you know, you can basically, if you could like teleoperate or something like that, you could like basically be able to do like real surgery, depends what type. But I think like, like most things.

[Peter Diamandis]

And then the AI system is just layering on top of that.

[Brett Adcock]

Yeah. Then you got to get the brain to work really well at these things and like, you know, this has got to work at the highest level of like performance. Let me ask you.

[Peter Diamandis]

But federated learning gives you an incredible amount of.

[Brett Adcock]

I think we're like knowledge. I think we're very close to this work. I think we're, we, we've already shown if we can get the right data and the hardware, if the hardware can do it. Like if the, you know, the, the, the simple like hack is if you can tell you have the robot to do it, we can learn it.

[Peter Diamandis]

Yeah, I mean that's, it's a really important point. People need to understand if you can tell you operate the robot, if the mechanical systems, the motors, the, you know, the fidelity can be done.

[Brett Adcock]

Yeah, we're just like. And then we're like dumping on teleop. But like teleops got one good. A couple good things where like it's a really good testing tool. It proves out, and it proves out the hardware. And if, if the, if you can't teleoperate it, you're not gonna be able to learn it. Meaning if there's restrictions in the range of motion or payloads, you pick up something heavy, the robot can't do it during operation. It's not gonna be able to do it in a learn policy. So, so I think if you can teleop, you can learn it from a hardware perspective. I think we'll be able, we'll be there in terms of like more dexterous type things we talked about here. And then I think, I think what we've already shown is if we can get the right data for it, we can get the hardware to basically do anything that's capable of.

[Peter Diamandis]

And then you can add infrared, ultraviolet, you can add all kinds of additional sensors into the system.

[Brett Adcock]

Yeah, for sure. I mean we have it now, we have it with, tucked up like, with like the palm camera is a good example. Like humans on palm cameras. And we've been, we've been now seeing, we've been now seeing a boost in performance maybe. I do a lot of cool things. We're reaching in a cabinet now we can use.

[Dave]

As soon as the on the tour. As soon as I heard it it's like duh. Totally makes sense.

[Brett Adcock]

Sense, yeah.

[Dave]

I mean how many times a day are you like reaching.

[Peter Diamandis]

Your phone down there to get the camera to look at it? Yeah.

[Dave]

I'm sure we would have evolved an eye right here if it were physically possible.

[Peter Diamandis]

It is interesting question for you. You've got the cameras in the head again mirroring, mirroring a human. And the hands. Why aren't there cameras rear facing or 360° maybe?

[Dave]

There are.

[Peter Diamandis]

I just bought, I just bought a amazing drone. The anti gravity drone. Have you seen it? It's the VR headset. It's got 360 above, 360 below, backwards, forwards and it's extraordinary.

[Brett Adcock]

So how do you think we do we have it on the robot? You do they all have backward facing cameras?

[Peter Diamandis]

Okay, I have to ask this question for our.

[Brett Adcock]

If you just like go over and look behind them, they have cameras. Okay. Do they?

[Peter Diamandis]

I'm seeing it rotate here on here. So one of our moonshot mates, Celine Bismill, you might know me as they might of co founders with Ray at Singularity University. He's like why in the world are there only two hands? Why don't we see robots with like four hands or six hands?

[Brett Adcock]

So to put that to bed personally. Yeah, we get asked this a lot. It's like why not like, you know, why not like superhuman and all these different things. Which is a lot of the questions.

[Peter Diamandis]

I think.

[Brett Adcock]

My summary to this is like our goal is to be able to do what humans can and then you want to do it the cheapest and like lightest possible way you can. Like the lighter the better for safety. The cheapest is obviously very important. All of those will affect manufacturability and scale. When you start building things that are better than human in a lot of ways, like if it can, you know, run a three minute mile or if you can do backflip, if it's got like, like you know, bunch of arms, it's gonna make the robot really heavy, it's make it really costly. It's gonna be really hard to manufacture. And and then your question is like okay, when I look at like the logistics use case, I don't think you actually have four arms or six arms and move any faster. The line is like relatively. It's like you know, maybe a meter or so in Depth, uh, you gotta kind of get a package. The package needs to be roughly in the center of the conveyor system, so the scanner below it can scan it and put a label on. Um, so, you know, in, in that case, we basically have another 3-5x in terms of speed. And the actuators that we could run. The software's not enabling, so it doesn't know how to do it yet. So we can run like three to five times faster than what you saw today.

[Peter Diamandis]

Wow.

[Brett Adcock]

Because we had the whole body to run.

[Peter Diamandis]

Yeah.

[Brett Adcock]

We can run the robots that, like, when we look at it like, in terms of like radians a second maybe traditionally look at RPMs. Yeah, we look at Radiance second. Here we have another three to five times headroom in the actuators that you're seeing now.

[Peter Diamandis]

I would love to see a robot.

[Dave]

The thing is the cost of a mistake. Like, you know, when you're on loading the dishwasher at the current rate of speed, the cost of mistake is relatively low. You start running 3 to 5x faster. That thing glitches, that plate is moving fast.

[Brett Adcock]

I just don't know if it's really needed. Like you're gonna get a really expensive robot and it's gonna be like less safe. It's be harder to manufacture and they're gonna have like a, you know, over time you're gonna get the robots down to 10, $20,000. You're gonna have a 10 to $20,000 robot there and you're like a really expensive robot. Let's call it $50,000. Yeah, that, that and like in cost is really a function of manufacturing volumes. So you really want to build like the car.

[Dave]

Well, that's why going after the industrial use case is such a.

[Brett Adcock]

Or just like the home, it needs like every. Like this for this.

[Dave]

Well, the home is. The home is huge in the end, but if you're running three to five times faster than what we're seeing right now in the home and you, you know, you kick the cat or something like that, that's not great. In the industrial use case, everything is kind of taped off, you know, and it's, it's.

[Peter Diamandis]

I remember I was interviewing you for my next book, which comes out in April. Here it is. Where is guys? Or. We've talked about this, but I'm super excited about. And of course you and figure are prominent in the book because this is godlike. I mean, it's extraordinary. We're giving life to new systems. I was interviewing you about how many and what the price point is. Yeah, and I want to just double down on that because the numbers are pretty staggering and they make sense. So if you're actually getting the price down to $20,000 a robot, I haven't heard 10,000 a robot, but 20,000 a robot, you're leasing a robot for like 300 bucks a month, $10 a day, 40 cents an hour. And then you ask the question, okay, if it's really 10 bucks a day, how many would you own or would you have. You end up with a lot of robots. So what's your estimate on the number of robots on planet Earth? 2035, 2040? Where do you think that's going?

[Brett Adcock]

I mean, I think it's relatively straightforward to think that every human should have a humanoid to do all your work and then we should have maybe an order of like 5 to 7, maybe 10 billion in the commercial workforce. So I think, I think, like, I think if all goes well, I think you could basically build tens of billions of humanoids over planet. Okay. Yeah. I mean, you're basically building like a replica of a human. That's really cheap, that works 24, 7.

[Peter Diamandis]

Yeah.

[Brett Adcock]

And so, like, there is really no. And then, you know, we will be at a point, I hope in 24 months where the, all the robots will build all the robots.

[Dave]

Well, that's where I wanted to ask about scale because you said, you know, we're going to ramp up to millions a year. Like, well, one per person on the planet is 8 billion, so millions per year really isn't that much.

[Brett Adcock]

Yeah.

[Dave]

So then you're like, okay, the self improvement loop is going to be incredible here.

[Brett Adcock]

Yeah. You also need like, like, it's funny, like we talked about this, but you also need like tons of working capital. If you put a billion robots on the planet, even if they're, let's call it $20,000 a piece, you're talking $20 trillion of working capital.

[Peter Diamandis]

I mean, they're not that there's a billion cars on the planet right now. There's not like more than that.

[Dave]

But if you tried to, if you tried to build them in five years, it took, it took 80 years to accumulate those cars. Some of Those cars are 30, 40.

[Brett Adcock]

Years old cars on the planet. But we have like, we make a billion or more cell phones a year. So like. Yeah, and I think this is more cell phone, like where it's going personal. Like, you're like, I don't. We even go back and forth on like, if your robot breaks, do you want like a brand new Refurbished robot or do you want the old robot you used to have because you've known it and you understand it, it's got a personality. It's like, I think it's going to be with you, it's going to know everything about you.

[Peter Diamandis]

Why would you just have a personality transfer?

[Brett Adcock]

You could, but I think there's like some inner workings of like, I like, it's got like all the, you know, scratches on it that you know it. It's just like it's your thing and it's got like a little bit of a feeling. But yeah, they're like, for sure, like, I think that'll be fine.

[Dave]

But let me ask the geeky finance question though, just before we lose the topic here.

[Brett Adcock]

Sure.

[Dave]

So if you have an all neural network based system, it can learn at an incredible rate. The technology is advancing remarkably. You look 24 months in the future. The demand is on the order of billions, not millions like you said. To build that out in one iteration. You use the cell phone as an analogy. But Apple had 15 years to profitably ramp up production to a billion units a year.

[Peter Diamandis]

Yeah.

[Dave]

And so the demand is there to do it in one year, but you would need a trillion dollars, some. Some insane amount of capital.

[Peter Diamandis]

But that's no longer insane amount of capital. I mean we're seeing.

[Brett Adcock]

I think you can so.

[Dave]

Well, what do you do? You leave the world starved asking for the robot for five years? Or do you raise the trillion dollars.

[Brett Adcock]

If you look at like credit card receivables or car leasing? These are trillion, trillion dollar markets a year in terms of financing. So I think the financing market's there for this. What do you do? I think like one, one is you got to solve the neural net game. You have to be able to scale with neural nets and you have to solve pre training and you have to solve generalization. So you have to solve for a general purpose robot that is like, that is like table. You have to, you have to solve this. That's why we're so obsessed with like trying to solve it here figure if you don't solve that, none of this matters.

[Dave]

Yeah.

[Brett Adcock]

The second step is you have to have robots in the loop like building other robots. So those two things have to be solved and you have to design the robot in order to make sure it can hopefully design itself at the end of the day. So there's a bunch of stuff we're putting in place in terms of manufacturing, execution, software, the lines, all the design of it so we can at scale have humanoids go in building other humanoids and get them off the line. So I think, I think this is. And it took us a while to kind of. I think these adoption curves are shortening and shortening. And I do think if we could solve a general purpose humanoid robot today that could do everything you wanted, I think we could ship a billion of them today.

[Peter Diamandis]

Yeah. Say again?

[Brett Adcock]

I think we should have a billion today.

[Dave]

Yeah, I totally agree.

[Brett Adcock]

So basically it comes down to like, can you get the neural nets to work at scale? Can you get the models good enough to generalize to this at scale? Real general purpose, Call it a general purpose robot, like a human ensuite. And then can you get robots and then we build another robot?

[Dave]

Well, the other thing is that's really compelling is like the neural net is the only IP you need to protect. So as long as you have the federated learning coming back to the mothership and all the training is happening centrally, like, you know the Star Trek Genesis project, right? You get a little capsule, it has basically the germ of like DNA. You could ship literally a box to Kenya. That's like, here's the figure box. It opens up and it starts making a figure manufacturing plant right out of thin air in the middle of Kenya. And if there's capital there to bring the resources to it, then that's how you get infinite.

[Peter Diamandis]

The innermost loop is energy and AI.

[Dave]

And intelligence and local mining for the materials or whatever. But it's completely self contained. But the key is that you just unlocked that capital that wanted to build something productive while all of the IP is still flowing back 100x in the GDP to train the neural net centrally. 100x the GDP of that jurisdiction. There's latent capital all over the world.

[Peter Diamandis]

So we talk about there's a lot of fear out there in the world about losing jobs to AI and to robots. And the reality is the conversation has shifted now to, well, no, this is going to create massive abundance and universal high income. And that happens if, in fact, rather than the company hiring a robot to replace me, if I hire a robot to go out and do my work for me, and in fact, it's able to get triple my salary because it's working three shifts and it's doing that for me and then earns enough to get a second robot working for me. And so the question becomes, where is that capital captured? And is it inside the hyperscalers? Is it inside of the individual? So that's going to be the interesting conversation coming up. How do you think about that, Brett?

[Brett Adcock]

I mean, we're going to sell robots at scale. You're going to be able to deploy as many robots as you want to. Whatever you want to do. Yeah, it'll just do whatever you want. Like, no, no instruction manual. What do you want it to do? It'll learn it. It'll research the Internet. It'll use digital tools if it needs to. It'll talk to you. It'll reason future is going to be safety and privacy.

[Peter Diamandis]

Let's talk about safety in the home and privacy in the home. You know, there were lawsuits over the last years with, with Google and Amazon of it's listening to you in your bedroom and so forth. How do you address safety and privacy? Or is it going to happen? It's just too early because we're not.

[Brett Adcock]

I think they're just like really hard questions to answer, like in one one go. Because, like, there's a bunch of different safety implications here that are like, just safety is like probably the number one thing to tackle to get robots into the helmet scale. Yeah, um, there's like a semantic understanding of safety. Like if there's a, you know, a candle lit and I knock it over by accident, or if there's like a, you know, boiling pot of water if I hit it. Like, just understanding how to be safe in an environment where humans are at, and there's actually the intrinsic safety of, like, can the robot be with humans and animals and pets and be safe? Yeah, like those, those, like that has to be solved. We can talk like at length about, like, how we're going to solve those problems. And then you have the whole privacy, cybersecurity, other aspects of this that need to be with good intention. How do we solve those problems? We are working on all of those now. They are very difficult things to go get. Right. I do see a path where we can build intrinsically really safe robots around people and pets. We have a plan for how we're going to do that.

[Peter Diamandis]

I mean, they could be safer than humans by a large margin. Just like autonomous cars are safer than humans, end of the day.

[Brett Adcock]

Yeah, these have like, superhuman perception. We can see basically all around us at all times. We're always on, we're always computing, like, what to go do. We're, you know, so I think, you know, assuming nobody's trying to be like, like, you know, mean to the robots or things like that, I think we should be extremely safe around everything we're doing. And then as we're on privacy, like, you know, these are going to be in your home. So Being upfront about what data we're collecting and where that data is going and how we're keeping that data private and encrypting that data. All this is super important. We have an entire team on cybersecurity here in house on both of the product and commercial side, corporate side that are working through. Like, how do we think about this at scale right now? They're great from the big, the big companies have been doing this for a long time. And we think about this the corporate side as well as the product side, on the robot side as well. Yeah.

[Peter Diamandis]

Your facility here, which is your sort of prototype manufacturing facility. 50,000 robots a year.

[Brett Adcock]

You imagine that facility can support about four lines. Each line can do about 12,000 units a year. So a little under 50,000 units a year, which is the next step up, you think? I mean, we're building like thousands of robots right now. Um, so like that's a big push we're doing, right? I mean, you just, you saw it today. Like, that's the, the figure C stuff we're doing off the lines today, you know, and then there we want to go to tens of thousands and then hundreds of thousands and millions. I think we need to take those, like, steps as a company to go do that. This facility will top out 50,000. A little under 50,000 units a year at full, full capacity. So kind of think about a long term, like our. You probably be low volume when we look back in five or 10 years and be like, do you think you.

[Dave]

Might franchise out the neural net and the circuitry around it? You know, because all these other people are saying, oh, I'm building a robot that cleans industrial pipes. I'm building a robot. You know, all these different form factors.

[Brett Adcock]

No, no, just I think it's super unsafe. I think we see these robots out there like this. I think like they're around humans. We don't have, like, we don't own the hardware. We don't know what they're doing. It's like our neural net in it. Like, I think it's interesting. Yeah, I think it's like a. It's similar to Archer, when we were doing Archer, like building Archer out. Like, I think it's like a safety critical system. Especially like Archer, since they're licensing out to their folks and stuff like that is very problematic. I think here it's like the same thing. Humans even are, right? We have a fiduciary duty to our civilization to build really safe humanoid robots at scale. And just giving this AI system or even Hardware to anybody that would want. This is not something we will entertain.

[Dave]

So then when do you branch out into other form factors like you know, things that work underwater things.

[Brett Adcock]

I don't think, I think the amount of. I think in the future everything that'll move will be a robot besides humans. And within that I think humanoids will dominate the plurality of all robots. It'll just be so big a percentage of them. Like the other robots will be like niche and expensive and done like super duty trucks that you have like out mining. They'll just be like made for specific areas. Maybe underwater as you said or some.

[Dave]

Parts like heart surgery, you know you've got or, or brain surgery. You've got these very, very fine tuned. It's like a robot controlling a robot.

[Brett Adcock]

I think you're left with like very expensive equipment that's very siloed. Like you really want to build a general purpose machine that can, that can learn across a variety of different tasks and have that transfer learning. I think that's extremely important here and that needs a very high variety of rich data. This is only going to help the robot system get smarter and better. So my view is I think just it'll be like humanoid robots on humanoid robots everywhere on the planet and there will be other robots there, but it'll just be like a niche businesses.

[Peter Diamandis]

When I was following up here, I posted your video that you released on Helix 2 today. And then we asked the community for questions and this blew up with a whole bunch of amazing questions. So one of the questions is do you have a blooper reel and can, can folks see it? And then what's the weirdest task someone on your team has tried to teach it to do and it absolutely did not work. And that's from Ben Casper here.

[Brett Adcock]

Ben Casper, nice. The weirdest task and it did not work well. Like weirdest task. Listen, every.

[Peter Diamandis]

The jogging was interesting.

[Brett Adcock]

Well okay, jogging was fun. Jogging was cool because we like really had a steerable jogger and a lot of this work in like running has been like again open loop. But we had a steerable RL controller. We could do another one which I actually have a gift for you. It kind of goes in. I have a 2, 2 figure deadmau5 hats.

[Peter Diamandis]

What's that mean?

[Brett Adcock]

We basically we opened at Red Rock late last year at a deadmau5 concert and had robots on stage. So we generally don't venture out into weird of stuff. There you go. Nice. And we actually we had Deadmau five last two holiday parties. I figured which Is like fun. And we generally are pretty much like how do we design something really useful? But then we've had some pockets of time to, to do. To do fun stuff like this a bit. So I think like having robots on stage at Deadmau 5 and Red Rock was just oh, that's fantastic. I flew in for. It was just, it was unbelievable.

[Peter Diamandis]

And you had them on stage?

[Brett Adcock]

We had them on stage. We had a several figure twos on stage just jamming. We had them all synced so that synced to the music as it danced, which is really cool. So what it heard it was like moving towards which is on stage last.

[Peter Diamandis]

Year at the Abundance Summit. But figure wasn't with you. So need to get you back there with figure in the loop.

[Brett Adcock]

Totally.

[Peter Diamandis]

Yeah, for sure. So when are we going to see the first figure in a customer's home? Is the next question.

[Brett Adcock]

Yeah, we want to, we want to. I want to ship robots when they're really ready. I don't want to ship slop.

[Peter Diamandis]

Best guess young earliest, latest window.

[Brett Adcock]

We, we. We pro. I think last year I said in, you know, in this year, in 20. 2025, we. In 2026, we launch. We launch a robot to do like end to end homework, like an alpha test scene like in my home to.

[Dave]

Do like full like mopping, cleaning, full.

[Brett Adcock]

Scale, like you know, long horizon work.

[Peter Diamandis]

Figure you and your daughter.

[Brett Adcock]

Yeah.

[Peter Diamandis]

Putting stuff into.

[Brett Adcock]

We've done like pockets of work really well. Like we've done like dishes and laundry and all this. And we can like you're seeing some of this getting tied together now, but like I want to do it across like days and weeks of work and I want to be able to drop it into somebody's home it's ever seen and also make that really work well. And I want to be able to talk to it and I want it to be able to understand me and be able to remember things and we'll show us stuff. I'll be able to walk through a room and show it like almost like a visitor you have at your house for a week and like understand like what to go do.

[Peter Diamandis]

27, 28, 29.

[Brett Adcock]

My, my best guess is I think, you know, I think. Well, I'll tell you what. We're. We're working until midnight every night to solve this problem. It's like, it's like, it's like we are here every weekend, every night to try to figure out how to solve this. This is question we're in Charles General Robotics. This is kind of where we want to head. I think by end of the year we will be able to put a robot into an unseen home and be able to do fairly long horizon work. And then you want to measure how many human interventions you have. Is it every, it's once an hour, Is it once a day, once a week, once a month? And I think we'll do that. I think that would be a huge accomplishment for us. I think we'd be on the path to solving general robotics and then I think next year you'd be on a path where you could ship them into users homes and start making sure they work well. So I think anybody that tells you like hey, we're going to ship them or teleop them in the home or we're going to ship them in at scale in a year, like there's you, you've got to ship in small quantity and they got to work well and then you got to work out the problems and you got to then ship again. You have to have an iterative design roadmap which we have here we need to learn. So it's going to work well at one, this is going to work well at 10 homes it's going to work well. 100, it's going to work well. A thousand, it's going to be 10,000, it's going to be 100,000, it'll be a 10 million. So I think it's going to be like super exponential growth curve. Exponential growth curve. So I think is there anything to.

[Dave]

Worry about there in terms of time to market is because you know the industrial use, like I said, you're sold out for years to come anyway.

[Peter Diamandis]

This competition going to come in grab market before.

[Dave]

Yeah, we, we feel with your kids.

[Brett Adcock]

Or something the work we show today and the work we showed two years ago has never been done in our mind whether any other human company in history.

[Peter Diamandis]

Yeah.

[Brett Adcock]

And so that if that's the marker, it's whenever somebody can do the Keurig test for a couple minutes with uncut film and I can like watch it closed loop do it with Bimin. Even not even just standing, that's your two years away from where we're at. So I think we'll see like we're trying to push and continue to pull ahead but I think hopefully by next year, by next year we can basically really show like real general purpose inside the robot. Maybe even soon as this year. Like I mean listen, it could happen in a couple months. We are, we are, we, we have the right stack now. We are, we are, we are building Data sets at scale like so quickly. We are spending so much time and money on this internally. We just launched our new B200 cluster within like Nvidia helped, Jensen helped. That went live like, like this year.

[Dave]

How many, how many GPUs in your.

[Brett Adcock]

We, we just, we are going live. We have, we have 3,000 B2 hundreds that like that are going live and we have another set of much larger GPUs that we plan to put out here and training or we just use it for pre training.

[Dave]

Yeah, you do it here physically.

[Brett Adcock]

Here we. No, we do not use physically.

[Dave]

A lot of power.

[Brett Adcock]

Yeah, a lot of power.

[Peter Diamandis]

So J Crate asks a question to the science fiction geeks amongst us. So what's beyond the three ASMoV laws for you? Have you thought about that? Have you thought about sort of fundamental laws to program into your robots?

[Brett Adcock]

I think you really want to put these rules down into the kind of non volatile memory on board the robot.

[Dave]

Oh yeah.

[Brett Adcock]

At the chip level, substrate level.

[Peter Diamandis]

Yeah, I mean you must have thought about that.

[Brett Adcock]

We've been thinking about this quite a lot like and you know it's in one hand we still want to solve like general purposeness. In the other hand we don't. We also want to figure out like once, once we're like close there, how do we also get all the supporting things ready to go. And the is one of those, it's like, it's like safety needs to be there, like privacy needs to be there, fleet operations, like the reliability of the robot, the like maintenance plan for like how we're going to service this and everything in the business model, all of it in financing, all of it need to be packaged, ready to go. So we're working through all these now. I don't know, it's funny, it's like, it's like you know, ASMOV got a lot, a lot of things right and I feel like a lot of the three like you know, like these foundational rules for how do we treat human is like, you know, we have our own spin on this that we won't like publicly tell today. But like that but like you know the goal is like to do good work and it is something everyone learns.

[Dave]

Internally in corporate training and memorizes and all that.

[Brett Adcock]

It's something that we want to put, we put and we're going to continue to put on all the robots.

[Peter Diamandis]

So you have a new world. You have a newborn child.

[Brett Adcock]

Oh yeah.

[Peter Diamandis]

So the question here from KK says when would you trust figure to hold your newborn?

[Brett Adcock]

Yeah. That's an interesting.

[Dave]

So the new, the figure 3 is soft. It looks like it's designed for the home, but it's still about this, the same.

[Brett Adcock]

I like this question a lot because at Archer I always say, like, until I put my, me and my kids and family on the board, it's not safe enough to fly anybody.

[Dave]

Yeah.

[Brett Adcock]

And like, I wouldn't do that today at Archer, and I hope soon I could do that. Um, I figure here, I think it's the same question is like, when I feel safe enough to have a robot in my home.

[Peter Diamandis]

Well, you've had it in your home.

[Brett Adcock]

But, but like, you know, I've been there, we've had folks there and you know, we monitor it. Yeah, I think we're like truly safe and we're not there now. And I think that's a good bar for us to hit. It's like when I can put a robot in my home full autonomously, end to end around all my kids. I think that's the point where I would trust it. I think that's the point. I would say, like, this is ready for everybody and it's a good, it's a good like heuristic for us to really try to hit. And that's our goal here, is to be able to put it like, you know, like free reign in my home to go do. And now we like, you know, we're there with it, we babysit it and like, we watch it and it works good. I've showed videos of the robot. I'd be kids like with the robot, like there. But like, you know, I think we're doing it in a safe way. The robots have been totally safe, which is great. But like the is one is we need to build like a system safety architecture that's really, really fault tolerant and redundant in real time. And we're, we've done that and we're doing a better job of that in the future. And two is you have, you just have to build a safety track record for this. Like, there's, there's nothing better than like actually proving this thing is gonna be safe.

[Dave]

Well, it's a nice barrier to entry too, if you, you know, kind of take the Apple road to. It's gotta be a great out of the box experience. Well, that means not stepping on the cat, certainly not dropping the baby and then the cyber security side of it too. Not transmitting everything back and having it posted on the Internet. Yeah, but if you get that reputation, which it sounds like of all companies I've met, you're perfectly positioned to get that reputation. Don't make a mistake along the way. And then everybody just says, you know what? I'm going to choose a figure robot. Because I just feel it's the same way people feel about the Apple brand with cyber security.

[Brett Adcock]

Yeah. So I think, I hope people walk away from this knowing that, like, general purpose robots are coming. It feels very close. And then there's a lot of other things around there like, like that you have to get right to build this scale.

[Peter Diamandis]

Your main message you want to get across here at everybody watching.

[Brett Adcock]

I think the main message we feel every day if people are excited about, like, AI and robotics, is that this is going to happen really soon. Yeah. And it's happening. I mean, people don't have.

[Peter Diamandis]

I don't think people have a clue of how fast this transition time is going.

[Brett Adcock]

I mean, just go to our YouTube and watch our videos. Last two years, they like and like, watch them side by side. That's dramatic, the change every single year. I mean, you saw it today in person. And our robots now are, you know, have been in like, you know, customer sites and things. Like, it's been out and we're going to continue to show it more. But, like, it is hard to feel because you don't see it every day. But at some point you're going to walk out probably in San Francisco. It'll be the first. And you'll see more humanoids than humans.

[Peter Diamandis]

Yeah.

[Brett Adcock]

And I think that'll be an amazing day.

[Peter Diamandis]

Right now I'm driving in Santa Monica.

[Brett Adcock]

I was just.

[Peter Diamandis]

By the way, we just did a podcast earlier this morning. Cathy Wood, who sends her best.

[Brett Adcock]

Oh, cool.

[Peter Diamandis]

She's a huge fan of yours.

[Brett Adcock]

Kathy invested in me at both Archer and Figure and she's great.

[Dave]

Yeah, she feels the same way.

[Peter Diamandis]

She does very proud to be an investor in Figure. And I was telling her, you know, when I'm out with my kids right now in Santa Monica, we do something like counting the number of waymos that we see, we'll see like 10 Waymos. And then the coco robots, the little ground robots, like starship bots and such. I mean, they're all over the place.

[Brett Adcock]

Crazy.

[Peter Diamandis]

And it's interesting, right, because you first time you see it, you're playing out your phone, you're taking a photo. It's really cool. And then you take it for granted and then it's in your way.

[Brett Adcock]

Yeah.

[Peter Diamandis]

Right.

[Brett Adcock]

So my wife and I, anytime we go, like we had like last, last weekend with Date Night and took away mo downtown and it was Just it was, it just so unbelievable. And the experience feels like it's you, you, you just as a, you know, as an engineer like working on these like hard projects, I feel like the, like the like the amount of engineering work they had to go do to put it together safely. Google did essentially you control the controlling.

[Dave]

Of the music and the lights and the environment. Like if you take a New York City cab and you get in the back and it's like this smoky hell and then you get into a Waymo and you use the app and you, you turn it into your little paradise.

[Peter Diamandis]

It's like such a night job job taking the product. Right. I mean Larry Page saw the product win the DARPA grand challenge back in 2005.

[Brett Adcock]

Yeah.

[Peter Diamandis]

And committed to it and, and brought the team.

[Brett Adcock]

I mean I think it's been like 16, 17 years.

[Peter Diamandis]

Yeah. And just they stuck with it. You know, an astro teller at X basically built it out and then way most amazing, amazing product.

[Brett Adcock]

They've been like undeterred for like 16, 17 years. Like don't worry about it. We're was going to make it and they did it and it's unbelievable.

[Peter Diamandis]

Yeah, no, amazing.

[Brett Adcock]

It's very inspirational.

[Peter Diamandis]

Kudos to them.

[Dave]

Can I ask you my geeky sci fi meets geopolitics question du jour? So I just got back from Davos on Friday. Today's Tuesday, so nine, nine time zones away. And the big topic at Davos of course is Greenland. And all the Europeans are saying Greenland could never possibly be mined. It's impossible to extract minerals from this frozen cold tundra.

[Brett Adcock]

Yeah.

[Dave]

And we have some family mining operations in Minnesota where it's not nearly as cold, but still pretty damn cold.

[Peter Diamandis]

You don't have, you know, mild thick ice sheets.

[Dave]

We do not have mild thick ice sheets. But I think if you're talking about a billion and then 8 billion robots and you need the materials and that's the only constraint and you have robots that can operate, buddy. Seriously, you think we're going to be doing asteroids before Greenland?

[Peter Diamandis]

No, we'll do Greenland first.

[Dave]

But you think Greenland is viable? Like I'm not talking about 20 years from now too. I'm talking like if you want to build a billion robots and say six.

[Peter Diamandis]

Years from today, 50 trillion dollar marketplace.

[Brett Adcock]

Yeah.

[Peter Diamandis]

So that demand that, that truck, don't.

[Dave]

You think you'd find a way to get through the ice, you know, given, you know, a million robots working on it?

[Brett Adcock]

I'd hope so. Yeah. I think we'd find like, maybe better, like maybe even better physics but definitely better engineering solutions for this. And then we would be able to put unlimited amount of capacity of humans at it.

[Dave]

Yeah.

[Brett Adcock]

Through humanoids.

[Dave]

Yeah, yeah, that's what I'm thinking too.

[Brett Adcock]

Yeah.

[Dave]

Because the machinery, the machinery that I see is like, it's massively automated. It's still driven by people, it's still operated by people. It doesn't need to be.

[Brett Adcock]

Yeah, it's just like, it's crazy. That works, right? Yeah. Like the humanoid, like just like the neural nets, it's like, it's just, it's, it look, it's just the thing is.

[Dave]

When you, when you make it work on unloading the dishwasher, people don't realize how close that is to working on every other task.

[Brett Adcock]

The dishwasher in like folding laundry. These things that we're already doing are like so hard. Yeah, they're like such hard tasks. Like you have like these compliant materials that are all changing with you dynamically. Everything's not in the right same place. It's like very different than being in a conveyor system or manufacturing something like that. And they already can do it today. Yeah, we can do it. And now it's a matter of like doing it better. Yeah. And doing it like, you know, higher reliability across more diverse, you know, across the distribution of what humans do every day. Like that's a data play.

[Dave]

The thing is, if you, if you achieve that goal by hacking together 100,000 lines of C and tell operating it, it would look the same, but it would be nowhere near as conquering every other problem. But if you did it purely, it's nothing but a neural net and it's purely trained. That means you're within a millimeter of every task we could possibly define.

[Brett Adcock]

You feel like the millimeter here is just data. Like the only difference of why I can do the logistics and now I can learn like, you know, towel folding or while I can learn like dishes or whatever we end up showing in manufacturing. Literally, it's just data. Yeah, it's just data goes in the neural net. Now I can do this work because the robot hardware doesn't even need updates. It just use a new neural net weights on board. You know, I think like we're just bound by data now and I think that's like the, it's like, you know, it's like not a trivial thing to do to get the right pre training set for this at scale. But like we, we have a bet that I think will work and we've been deploying that at scale for the last Three or four months. Yeah. And I think. Well, stay tuned. I mean we're, we're working through it so like. And I hope this will lead to really. I think you'll see a lot of positive transfermers from the robot that's able to generalize to a lot of things.

[Peter Diamandis]

Amazing. One last thing before we wrap up. I would love. Can we pull the camera in close and maybe give us a tour of figure three?

[Brett Adcock]

Yeah, let's do it.

[Peter Diamandis]

Thanks for the close up and intimate tour. So figure one.

[Brett Adcock]

Figure one. So we basically. One cool thing about figure one is we designed most of the system in house. We didn't care about looks. We cared about unblocking the AI and controls team like something they could use from a software perspective. So we designed and walked this robot in under one year. Since I incorporated the company, we think it's probably one of the fastest times in history.

[Dave]

That's a lot of parts.

[Peter Diamandis]

Did you draw, did you draw this by hand.

[Brett Adcock]

David? Basically our design lead designed this not as prettiest robot, but I think it's. I think it has like it had what we needed, which is like a functional robot. We can get up off the ground and start using for like all the a policy deployment. We did the Keurig K cup with this robot and I moved the hand. You can definitely move it. Yeah. Are you sure?

[Dave]

This going to be a collector's item someday. You break this thing, you bought it.

[Peter Diamandis]

So it's heavy.

[Brett Adcock]

Yeah, it's about maybe 130, 140 pounds.

[Dave]

Not that different from.

[Brett Adcock]

Yeah, not bad.

[Dave]

That's all aluminum.

[Brett Adcock]

It's all aluminum.

[Peter Diamandis]

All cnc.

[Brett Adcock]

We CNC aluminum most of the structures.

[Peter Diamandis]

Yeah. And then what else should we know.

[Brett Adcock]

About this before we move to figure two? We basically, we wanted to care about speed, so we didn't really care about wiring some electronics. Like a lot of the design it was mostly just like get a functional humanoid robot Alice we can do development on.

[Peter Diamandis]

Right.

[Brett Adcock]

So we did that. We built a few of them. We did a lot of like, we did our first neural network on this robot which is like I think was phenomenal. We did so much development with it really quick. We also learned how to build actuators, battery systems, wiring structures, kinematics, joints. Like all this is like stuff we learned. Yeah. Different sensors. And then we used all this and we integrated now into figure two.

[Dave]

So you got the cost down, I know, from 2 to 3 by 90%. What was the cost from here to there? Probably another 90.

[Brett Adcock]

About the same to be frank.

[Peter Diamandis]

Wow.

[Brett Adcock]

Yeah. A lot of it was machine parts and we moved out the tool parts, the three.

[Peter Diamandis]

So you've got two cameras here.

[Brett Adcock]

Two cameras here. We have a back camera unit. Yep. We also have the cameras right here in the torso pointing down so we can see where the feet are at. In case you have a box included.

[Peter Diamandis]

Come take a look at the. At the camera in the back of the robot here. One second.

[Brett Adcock]

Where's the camera pointing down? It's right there in the public.

[Peter Diamandis]

So back here you've got what's going on here. So there's camera ports here.

[Brett Adcock]

Yep. We obviously have a camera backward facing camera. We have different ports for like debugging if we need to hook up like a, you know, cable to it. We can also turn the robot on and off from here.

[Peter Diamandis]

Amazing. Yeah.

[Brett Adcock]

And then basically we moved all the wires internally to this robot. We all the structures is exoskeleton. So all the exterior loads, like almost like my aircraft Archer.

[Peter Diamandis]

Sure.

[Brett Adcock]

The skin, the outside housing to loads. We do the same thing here. So all the outer shell took all the loads. We have our second generation actuators. We had our third generation hands that are on this robot. We have like more cameras on board. We have about, I think double or triple the amount of computer shoots and about double the batteries. About our battery capacity on board. Yes.

[Peter Diamandis]

And the degree of beauty went up.

[Brett Adcock]

Yeah. Like.

[Peter Diamandis]

Yeah.

[Brett Adcock]

Yes. David did a good job making this like much, much like more presentable.

[Dave]

So funny. It is venting heat out the armpits just like so.

[Brett Adcock]

Yeah. It actually sucks anywhere in here. Push it out through the torso and the bottom.

[Dave]

Okay. What's going on in the back of the.

[Brett Adcock]

Yep, those are like. We basically have these different paddings on the knees and some parts of the arms to basically make it so that if you basically got your finger stuck here. Oh, safety. Maybe it would maybe would hurt it, but wouldn't like cut it off.

[Peter Diamandis]

Yeah.

[Brett Adcock]

You know what I mean? So like so similar to maybe what you see like a car door.

[Peter Diamandis]

Sure.

[Brett Adcock]

Today.

[Peter Diamandis]

And here's the workhorse.

[Brett Adcock]

This is our. Yeah, this is our figure three. Yeah. So we basically. A couple things. We made the robot like much skinnier and lower mass, but kept all the speeds and torques the same. So it's just as powerful and just as fast, but also like kind of skinnier. Less weights. This is about 135 pounds.

[Peter Diamandis]

35.

[Brett Adcock]

This is about 150. A little over 150 pounds.

[Peter Diamandis]

Carrying weight.

[Dave]

How much weight can you carry A different hand.

[Brett Adcock]

About about 20 kilos, 20 kilos.

[Dave]

Yeah.

[Brett Adcock]

Completely different hand. The hands have a glove, tactile sensors, compliant material on it for better grass. And also a camera. All the parts basically, or most of the robot is soft wrapped. You can see it kind of being up here, like a squishiness to the chest and different parts of the robot. We have no more, very few pinch points in the robot. What else? We reduce the cost massively. We have a better thermal system, compute system. We increase also compute on this robot as well from the last generation. We have new feet that have a toe. You might think of the toe is like.

[Peter Diamandis]

Yeah, no, it's a major part of the toe.

[Brett Adcock]

It's helpful for. It's a passive toe on the foot. But you might think of this like it helps to walk better. But it's not just that, but when we get down on our, you know, get down here, we're on our toe box. Really helps basically get the range of motion. Without that, you might need more joints. Another thing on the robot, talk about.

[Peter Diamandis]

The face, because this is a big question of, you know, do you develop, do you show facial features or not? And you went, what do you think?

[Brett Adcock]

What do you had Westworld or you had Irobot?

[Dave]

Wow.

[Peter Diamandis]

I mean, I mean it comes across, it's beautiful, right? High degree of beauty and it comes across sleek. But it could have like a negative little dystopian feel with a black face.

[Brett Adcock]

So we have three screens on the robot. This is powered off, we have a main screen, we have two screens on the side and obviously a bunch of cameras and sensors in the head. So on the screens we basically do anything. You could watch a Netflix, Netflix movie.

[Dave]

By the way, the brain.

[Peter Diamandis]

Look into my eyes.

[Brett Adcock]

Yeah, whatever you want. Like kids get bored. It's like, let's throw some up there. Yeah.

[Dave]

So the brain is right in here, which makes a ton of sense to me.

[Brett Adcock]

Yeah.

[Dave]

And it's where the Romans, ancient Romans.

[Brett Adcock]

Thought you basically need a lot of onboard computation. There is nowhere else to put it right now.

[Dave]

Yeah, exactly. And also it's easier to vent the heat from here too. And then you just put all the sensors up here and it just totally makes sense.

[Peter Diamandis]

I guess I could put a latex face over the head if I wanted.

[Brett Adcock]

Yeah, you. You can like basically put a silicon face and put hair on it. We also have other outfits. This is one of our logistics bots. Same robot, basically, but we're able to outfit it with different types of soft goods. And we have another robot here that we basically have also put the work that's wearing a jacket. This is like cut resistant. So they all have different, different traits. Some of these gloves are also better for grafting different materials that might be, say, dusty or maybe it's like a piece of sheet metal or it's slick.

[Dave]

Do you think it would operate in zero g? You just need a better training set.

[Brett Adcock]

And I think so.

[Peter Diamandis]

Yeah.

[Brett Adcock]

I think we really love to run. I've got a scale in space.

[Dave]

You're going to populate the. I've got my zero G airplane.

[Peter Diamandis]

We could, we should, we should take it upside.

[Brett Adcock]

Yeah, let's get these things on there.

[Dave]

Yeah, that'd be a great test.

[Brett Adcock]

Yeah.

[Dave]

Well, look, we're going to build data centers in space very soon. Someone who needs to assemble them, like zero G is the operating.

[Brett Adcock]

And then we'll get other planets too. It'll be super important.

[Peter Diamandis]

Yes, yes.

[Dave]

Materials.

[Peter Diamandis]

And then we'll disassemble the moon and the asteroid belt and we'll use it for materials.

[Dave]

Alex, I love you said that.

[Brett Adcock]

Let's do it.

[Peter Diamandis]

If you made it to the end of this episode, which you obviously did, I consider you a moonshot mate. Every week, my moonshot mates and I spend a lot of energy and time to really deliver you the news that matters. If you're a subscriber. Thank you. If you're not a subscriber yet, please consider subscribing so you get the news. And as it comes out, I also want to invite you to join me on my weekly newsletter called Metatrends. I have a research team. You may not know this, but we spend the entire week looking at the Metatrends that are impacting your family, your company, your industry, your nation. And I put this into a two minute read every week. If you'd like to get access to the Metatrends newsletter every week, go to diamandis.com metatrends that's diamandis.com/metatrends. Thank you again for joining us today. It's a blast for us to put this together every week. Ready to relax in your dream bath retreat without the stress of figuring out every detail yourself. At the Home Depot, your bath upgrade is covered shop fully designed rooms and curated bath collections to go from inspiration to transformation. Fast savings of up to 40% will make it easier on your budget and find everything you need from tubs to toilets and haul the tile in between to bring your vision to life. The Home Depot dream Baths built.