Moonshots · 2026-02-11 · 105 min

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

Peter DiamandisDave BlundinBrett Adcock

Podcast ↗

TL;DR

Adcock’s core claim is that general-purpose autonomy—not manufacturing volume—unlocks a labor market the hosts size at roughly $50 trillion. Figure could finance and build 100,000 robots, he says, but that means little if they require teleoperation or replay fixed motions; “If you don’t solve that, none of this matters.” Conversely, he believes a truly general humanoid could attract demand for a billion units immediately.
Helix 2 is Figure’s claimed architectural break: the remaining 109,000 lines of C++ are gone, leaving an end-to-end neural stack for more than 40 degrees of freedom. Its learned System Zero controller coordinates full-body movement while System 1 integrates cameras, fingertip touch and palm cameras; onboard inference drives motor torques hundreds of times per second. The resulting room-scale kitchen behavior—including using a hip and foot—was something Adcock said, “You could never code.”
Figure’s prospective moat compounds through fleet data rather than task-specific software libraries. One neural network handles logistics, dishes and other work, with Adcock reporting positive transfer as diverse data is added: “Once one robot learns how to do a task, every robot in the fleet knows it.” Figure designed Helix 2 around the pre-training set, Figure 03 around Helix 2, and is bringing 3,000 B200s online for pre-training.
The strongest operating evidence offered was one error across 67 hours of continuous package work over multiple robots. The robots reportedly worked at human speed, found and positioned barcodes and even patted packages flat for the scanner; a six-month BMW deployment also ran every workday. Adcock’s real benchmark, however, is autonomous work lasting days in an unseen location—not karate, backflips or videos with “a guy in Tennessee driving it.”
Manufacturing is being scaled in parallel, with robots being targeted for Figure’s own production lines during 2026. The current facility can support four lines at roughly 12,000 units each, just under 50,000 annually, while a near-term target is one robot every 30 minutes. Longer term, Adcock discussed $10,000-$20,000 robots and hopes that within 24 months “all the robots will build all the robots,” although one billion units at $20,000 would still require $20 trillion of working capital.
The home roadmap is aggressive but explicitly staged rather than a mass-market promise. Adcock expects that by the end of 2026 Figure might place a robot in an unseen home for fairly long-horizon work, measure interventions per hour, day or week, and begin limited user deployments the following year; “I don’t want to ship slop.” He also said Figure is not yet ready for fully autonomous operation freely around his children or to hold his newborn, making that personal threshold the readiness test.
Adcock expects “far less than 10” global humanoid winners and views China collectively as Figure’s only serious competitive threat today. He nevertheless argues that closed-loop autonomy remains scarce worldwide and that every major technology company will enter because “you have no choice.” Figure intends to keep its model tied to its own vertically integrated hardware, declining to license it on safety grounds and describing safe deployment as a “fiduciary duty to our civilization.”

Digest · the substance, structured for research

1. Figure went from functional prototype to robot-filled campus in 18 months

Diamandis and Blundin toured roughly 300,000 square feet, with another 400,000 under development, and estimated they saw at least 100 complete Figure 03 robots plus many more hands, heads and partial assemblies. Diamandis disclosed that his venture fund invested in two earlier Figure rounds.
Figure 01 was deliberately inelegant: an approximately 130-to-140-pound CNC-aluminum machine designed and made to walk in under a year. Adcock’s priority was to “unlock the AI and controls team,” learn actuators, batteries, wiring, structures and sensors, and provide hardware for the first bimanual neural policy.
Figure 02 internalized the wiring, added cameras, compute and roughly twice the battery capacity; Figure 03 then dropped to about 135 pounds while retaining speed and torque, carrying approximately 20 kilograms. It added soft coverings, fingertip touch, palm cameras, a passive toe and fewer pinch points.

2. The Keurig task killed the coded-robot roadmap

The decisive experiment was not a backflip but Figure 01 making coffee. A bimanual neural network picked up a K-Cup, opened the machine, inserted the pod and ran a sequence lasting several minutes—the first time Figure saw neural control work across a meaningful humanoid task.
That demonstration answered two early questions together: could Figure build a capable, affordable electric humanoid, and could it avoid “coding your way out of this problem”? Adcock’s conclusion two years ago was categorical: “We have to just go all in on neural nets. The whole stack needs to be neural nets.”
Figure previously maintained several hundred thousand handwritten C++ lines—expensive to test, difficult to deploy reliably and incapable of representing every contact-rich behavior. Helix 1 removed most of them but retained a coded lower-body controller; Helix 2 removed the final 109,000 lines.
Blundin’s distinction was that learned behavior produces useful surprises as well as mistakes. The kitchen robot used its hip to close something and its foot to lift a dishwasher door, actions the team had not scripted; Adcock’s verdict was, “You could never code this.”

3. Helix 2 closes the loop from perception to motor torque

Helix 2’s System Zero, or S0, is a full-body reinforcement-learned controller. Learned locomotion exists elsewhere, Adcock conceded, including in staged martial-arts behavior, but he had not seen it integrated with learned perception and manipulation across an entire moving humanoid.
The accompanying System 1 work integrates head and rear cameras, downward-looking torso cameras, fingertip tactile signals and Figure 03’s palm cameras. Those extra viewpoints matter when the head camera is occluded—for example, while a hand reaches into a cabinet or removes pills from a cartridge.
Inference runs fully onboard, turns sensor observations into motor torques and updates control a few hundred times per second. The robot can coordinate eyes, hands, feet, legs, pelvis and torso without waiting for a remote server, then recover from errors and replan while still handling an object.
The architectural refactor took most of a year. Figure moved from strong stationary tabletop manipulation to room-scale autonomy: walking through a kitchen, opening storage, selecting objects and putting them away. Adcock’s next spatial milestone is graduating from “finish the whole room” to the full house.

4. Figure 03 was designed as a body for Helix

Adcock described a reversed design hierarchy: Figure 03 was built for Helix, Helix 2 was built for the pre-training data, and sensors, operating system, middleware, firmware, thermals and embedded compute were chosen accordingly. “How do we give Helix a body?” became the hardware program’s governing question.
The dimensionality explains why generic hardware and text models are insufficient. With more than 40 degrees of freedom and motors that can rotate through 360 degrees, Adcock characterized the pose space as 360 to the power of 40—“more states of the humanoid than atoms in the universe.”
Figure 03’s actuators reportedly retain three-to-five times the speed headroom shown in current demos, but Adcock resisted the assumption that faster is automatically better. More arms, extreme running or maximum actuator speed add cost, mass and danger without necessarily increasing conveyor throughput; Blundin noted that a plate becomes hazardous at 5X speed.

5. Language supplies semantics, but embodiment demands its own physics

Adcock corrected the simplified story that Figure merely left OpenAI. OpenAI and Microsoft co-led Figure’s Series B, and the parties explored next-generation humanoid models, but Adcock said Figure’s internal team “ran circles around them” for much of a year and eventually saw no reason to train outsiders on its embedded-model work.
Language and vision-language models still matter: their weights encode objects, semantics and common-sense relationships, helping a robot understand that an item is a water bottle or interpret a verbal request. Adcock called that grounding “super critical” inside Helix rather than dismissing LLMs outright.
Blundin’s challenge was that an LLM may seem to know how to play soccer or grasp a bottle while lacking physical competence. Adcock agreed: it does not know the required elbow angle, fingertip pressure, pelvis motion or contact dynamics. “This is not an LLM. The LLM knows none of this.”
A zero-shot experiment at Adcock’s new Hark lab made the gap concrete. A multimodal model received a digital joystick and was told to find an exit and leave the building; it chose roughly the right direction, then walked the robot into a clear glass wall.

6. Fleet data is the compounding moat

Figure has organized the stack around acquiring high-quality, diverse pre-training and post-training data, training a common model and deploying new weights to the fleet. Adcock’s current view is that extending known capabilities to tasks such as towel folding and dishes is primarily a data problem—difficult data, but not necessarily another robot redesign.
There is no separate dishes network, logistics network or downloadable library of motions. Figure reports positive transfer as tasks are combined: added experience improves generalization even when it comes from another segment, echoing Blundin’s analogy that learning piano might make someone “a slightly better soccer player.”
The economic asymmetry is fleet-wide learning. Human skills disappear or must be taught person by person; at Figure, “once one robot learns how to do a task, every robot in the fleet knows it.” That cumulative dataset is both the principal asset and Adcock’s reason for expecting only a few scaled providers.

7. Most humanoid spectacle fails Adcock’s autonomy test

Adcock’s sharpest industry criticism targeted teleoperation presented as autonomy. He compared it with selling a supposed self-driving car while “there was a guy in Tennessee driving it,” arguing that weak hardware can still produce compelling video when a hidden human supplies every decision.
Open-loop martial arts and backflips rank little higher in his hierarchy. A person can perform the motion in a capture suit, after which a small reinforcement-learned policy replays it blind; Adcock said such models may have only about a million parameters and can be trained with open-source code on one desktop GPU.
Diamandis pushed back that kung-fu footage remains fascinating and frightening regardless of its control method. Adcock’s answer was technical rather than aesthetic: replay does not perceive disturbances or reason about the scene, whereas useful robots need closed-loop responses around 200 hertz—“100,000 times” harder, in his rough comparison.
His minimum evidence standard is uncut neural control without a remote operator; he claimed almost no other humanoid footage exceeds one continuous minute. The ultimate benchmark is harder still: drop a robot into an unseen Airbnb and have it complete days of useful work. “We’re, like, so far from that.”

8. Package work supplies the episode’s hardest reliability evidence

The logistics policy runs entirely through neural networks and handles packages at what Adcock called human speed. It separates parcels, finds each barcode, rotates and places the package, and sometimes pats it flat so a scanner under the conveyor can read it.
Figure’s latest reported run produced “one error over 67 hours,” across multiple robots. Blundin emphasized that an operation occurs every second or two, making the run more informative than a short polished demonstration, although the episode did not define the error or total action count.
A six-month Figure 02 deployment at BMW provided a different test: robots operated every workday and proved the system could function commercially. Adcock’s candid retrospective was “80% of the things we got right and 20%…wrong”; the working architecture was too brute-force to replicate across 100,000 units, prompting the Helix 2 redesign.

9. Generalization must precede mass deployment

Adcock rejected manufacturing output as today’s primary scorecard: “What’s impressive today is not manufacturing.” In his estimate, general robotics might be solved with only 100 machines; producing 100,000 units that require operators or replay fixed trajectories merely scales an incomplete product.
The differentiating demonstration would be ten robots entering unfamiliar places and doing useful work, not a giant fleet inside one controlled installation. Long-horizon generalization must therefore be solved before volume has full economic value—“If you don’t solve that, none of this matters.”
Manufacturing still has to advance in parallel because high-rate assembly and robot design require repeated learning cycles. Figure is therefore building production capability while progressing through Adcock’s “level bosses”: short neural episodes, room-scale work, unseen environments, multi-day reliability and eventually broad general purpose.

10. Consolidation leaves only a few global winners

Diamandis compared today’s field—more than 150 Chinese robotics companies by one cited report and perhaps ten serious US players—to the hundreds of early car and tire makers. Asked for an endpoint, Adcock answered “far, far less than 10” humanoid groups globally.
Adcock praised China’s talent, entrepreneurial intensity and hardware output, yet said Figure has seen little closed-loop AI control from Chinese systems. Asked who genuinely threatens Figure, he answered “certainly China” collectively and said he does not currently see another comparable competitive threat.
That competitive assessment did not become a nationalist thesis. Adcock rejected the media framing of an inevitable US-China battle, saying visits to China feel collaborative, like “everybody’s team human” and “team humanity,” even as Figure moves most of its direct supply chain elsewhere.
Every major technology company will nevertheless enter, in his view, because human labor represents a little under half of GDP and the hosts framed the opportunity at $50 trillion. The catch is execution: Adcock put humanoids near rocket-level difficulty and harder than the electric aircraft he built at Archer.

11. Vertical integration is capability architecture, not just cost control

Figure tried to source motors, hands and other systems in its early days and concluded that the technology readiness was too low. A vendor failure in communications, power, sensing, thermals, firmware or reliability leaves the robot maker waiting “or you die,” so Figure now designs core components and performs final assembly itself.
The resulting iteration compressed cost as well as capability. Blundin described roughly a 90% manufacturing-cost reduction from Figure 02 to Figure 03; in a later exchange, Adcock said the Figure 01-to-03 reduction was “about the same” as that comparison and cited machine parts and tooling, without giving a separate exact percentage for Figure 01 to Figure 02.
Off-the-shelf robots lack the necessary sensors, thermal envelope, power and onboard compute. Adcock described teams compensating with giant backpacks, separate batteries, wires and overclocked processors—useful for hobby demonstrations but akin to buying a rocket and fastening a second stage onto its side.
Despite that integration, Adcock forecast that by summer Figure would have “almost none” of its supply chain remaining in China. He framed this as an operational transition, not endorsement of geopolitical hostility.

12. Figure is industrializing production before autonomy is finished

At the production operation called “Baku” and “Bacu” in different transcript passages, and identified during the tour as BotQ, Figure is trying to reach a near-term pace of one robot every 30 minutes. Adcock committed to placing Figure robots on those assembly lines during 2026, then gradually displacing human work through more humanoids and conventional high-volume automation.
The present facility can accommodate four lines, each rated around 12,000 units annually, for slightly under 50,000 at full utilization. Figure is currently building thousands, then intends to move through tens of thousands and hundreds of thousands before attempting millions.
Adcock expects the current site to look low-volume within five or ten years. Figure is already “spinning up resources” for future facilities capable of millions, but his sequencing remains explicit: learn each manufacturing scale rather than pretending today’s prototype line can jump directly to planetary demand.

13. Early customers are laboratories for a leased workforce

Figure retired Figure 02 at the end of the prior year and intends to deploy Figure 03 across multiple signed industrial and commercial customers during 2026. Adcock said the company knows the targeted geographies, tasks and schedules, while 50-to-100 customer discussions have generated enough demand for the next two or three years.
The preferred commercial structure is leasing—Adcock’s deliberately provocative line was, “Humans are leased, so we lease humanoids.” He remained open to sales because the larger objective is distribution: getting enough robots into daily work to improve products and operational competence.
The Grid, a newly opened test facility, will eventually contain roughly 250-to-300 robots operating around the clock across model homes and commercial settings. A second-story mission-control room receives each robot’s video and telemetry, allowing the team to observe both the fleet and the environment through the machines’ own sensors.
Real deployments expose work that demos omit: fleet operations, safety, maintenance, repair and facility integration. BMW taught Figure these disciplines; the Grid is intended to compress that learning before thousands of customers depend on 24/7 service.

14. Onboard inference makes connectivity and battery less constraining

Blundin contrasted ordinary training GPUs with dedicated inference hardware that is neither an H100 nor a GB300, inferring it might be 10-to-100 times cheaper and faster. Adcock confirmed the important point—not the numerical estimate—that fast policy inference runs onboard without consuming the robot’s entire power budget.
Figure 03 includes Wi-Fi, 5G through an eSIM and Bluetooth; users can even text the robot. Persistent connectivity is desirable for fleet functions, but autonomy cannot depend on it: latency or a dropped network must not brick the machine in the middle of physical work.
A full charge supports roughly four-to-five hours of operation from an approximately two-kilowatt-hour pack. Inductive charging through the feet runs near two kilowatts, yielding about an hour to recharge; thin charging mats can sit by a conveyor or kitchen, enabling opportunistic charging rather than an all-day battery.

15. Hark extends Adcock’s “synthetic human” thesis into digital work

Asked whether AGI requires embodiment, Adcock did not give a clean yes-or-no answer. His definition spans both domains: an intelligent system should reason, remember, communicate and “touch the world both digitally and physically,” rather than begin each chat as an “advanced Google search engine.”
Hark, his recently founded AI lab, is pursuing that digital half. In one example, a single prompt requested a CAD monster truck for his son; the model found and installed a CAD package, learned relevant parameters and produced a clean-sheet design in under an hour while operating tools like a human.
Adcock thinks frontier labs are chasing an overly abstract form of reasoning and copying one another instead of building persistent multimodal agents. The system the hosts called Claude Bot, later Malt Bot, illustrated the product overhang: simple Markdown instructions, tools, MCP and APIs around a model such as Opus could already produce “magical things.”
Figure’s physical training investment is similarly rising: 3,000 B200s were going live for pre-training, with a larger future allocation planned. Across Hark and Figure, Adcock predicted the next 12-to-18 months could deliver “the largest AI transformation we’ve ever seen.”

16. The home robot is being framed as a persistent social agent

Figure’s destination is “a human in a bodysuit” that accepts language, applies common-sense reasoning, remembers context and performs ordinary work. Although speech, memory, perception and physics can be described as components, Adcock believes they ultimately converge into one pre-trained omni model.
Personality and emotional awareness are becoming product requirements rather than cosmetic extras. Adcock wants a robot that notices when his children return from school sad, remembers their history and responds with enough EQ to talk with them—not merely an appliance waiting for the next instruction.
Elder care is personal for him: his parents have operated senior housing in the Midwest for about 15 years. He wants humanoids to help people “age in place” at home, combining household work, observation and companionship rather than forcing every care need into an assisted-living facility.
That breadth supports the general-purpose form factor. Humans perform billions or trillions of distinct activities, and one shared model can learn across them; bespoke pipe-cleaning, mining or surgical machines may remain, but Adcock expects humanoids to dominate the plurality while specialized robots stay niche and expensive.

17. Surgical dexterity may arrive before surgical intelligence

Adcock said he felt “pretty confident” that by the end of 2026 Figure’s hardware could perform most physical motions a surgeon performs. His claim was conditional and narrower than autonomous surgery: depending on the procedure, a teleoperator might use the system for real surgery, while the medical “brain” would still require far higher performance.
This is where he defended teleoperation after criticizing it as a marketed product. Teleop is an excellent hardware test and data source: if payload, range of motion or dexterity prevents a human operator from completing a motion, a learned policy cannot rescue it; “if you can teleop, you can learn it” once sufficient data exists.
Figure is already augmenting human-like sensing. Palm cameras improve blind reaches, tactile sensors measure fingertip contact, torso cameras watch occluded feet and rear cameras expand awareness; Adcock was also open to infrared, ultraviolet and other modalities that could eventually exceed human perception.

18. Home rollout is gated by interventions and a newborn-level safety bar

Figure can already perform pockets of dishes, laundry and kitchen work, but Adcock wants days or weeks of connected behavior in a home the robot has never seen. “I don’t want to ship slop” was his answer to demands for a precise consumer launch date.
His best estimate was that by the end of 2026 Figure could place a robot in an unseen home for fairly long-horizon work. The key metric then becomes human interventions: once an hour, once a day, once a week or once a month. Initial user-home shipments could follow the next year if that curve improves.
Scale would proceed iteratively: one successful home, then ten, 100, 1,000, 10,000, 100,000 and eventually ten million. Adcock acknowledged that competitors might advance faster and even said generality “could happen in a couple months,” but treated a staged deployment and feedback cycle as unavoidable.
Asked when he would trust Figure to hold his newborn, Adcock said, “We’re not there now.” The release bar is free, fully autonomous operation around his own children, backed by redundant real-time safety architecture and an accumulated safety record—not supervised visits where engineers “babysit it.”

19. Safety keeps Figure’s model and hardware inseparable

Adcock separated semantic safety from intrinsic safety. The model must understand why knocking over a candle or boiling pot is dangerous, while the machine itself must remain safe around people, pets and animals even when software or components fail.
Privacy and cybersecurity form another layer because household robots will continuously perceive intimate spaces. Figure has an in-house cybersecurity team spanning product, commercial and corporate systems; Adcock emphasized disclosure of what is collected, where it goes, encryption and keeping the information private.
Figure is also developing foundational behavioral rules for non-volatile memory “at the chip level.” Adcock said the company has its own variation on Asimov’s laws but declined to disclose it; safety, privacy, reliability, maintenance, fleet operations and financing all need to be packaged before mass deployment.
Blundin asked whether Figure might franchise Helix to other robot makers. Adcock’s answer was “No”: without ownership of the sensors, actuators and failure modes, Figure could not guarantee safety. “We have a fiduciary duty to our civilization to build really safe humanoid robots at scale.”

20. Tens of billions of robots imply a financing market as large as the product

Adcock expects manufacturing volume eventually to push humanoids toward $10,000-$20,000. Diamandis—not Figure—translated a $20,000 machine into a hypothetical lease near $300 monthly, $10 daily or roughly 40 cents hourly, arguing that this price would expand household demand far beyond one robot.
If all goes well, Adcock sees one humanoid per person plus roughly five-to-seven billion, perhaps ten billion, in the commercial workforce—“tens of billions” overall. At $20,000 each, however, one billion robots alone represent $20 trillion of working capital; he pointed to trillion-dollar vehicle-leasing and credit-receivables markets as financing precedents.
Two prerequisites dominate that scale equation: a neural model that generalizes and robots building robots. Adcock hopes that within 24 months “all the robots will build all the robots,” while manufacturing software and lines are being designed for humanoids to assemble successors and remove them from the line.
Diamandis asked whether individuals will own robots that earn income or whether hyperscalers capture the surplus. Adcock answered that Figure will sell robots at scale and users will be able to deploy them for whatever work they choose; his larger claim was abundance through ubiquitous goods and services. “It’s gonna feel like 2080 up in here.”

Verification Notes

The transcript alternates between “Baku” and “Bacu” for the production lines and separately identifies the toured operation as “BotQ”; the digest does not resolve those names.

Peter H. Diamandis

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

Brett Adcock

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

Dave Blundin

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

Brett Adcock

Yeah.

Dave Blundin

This incredible asset.

Brett Adcock

The one thing that's important here is that once one robot learns how to do a task—

Peter H. Diamandis

Yes. Everybody learns.

Brett Adcock

Every robot in the fleet knows it. Humans don't operate like this.

Peter H. Diamandis

Yeah. When do we start seeing robots building robots?

Brett Adcock

We will put robots on our Bakku lines this year. Listen, this is going to be the largest economy in the world. It's going to be a super impactful business. It'll lead to ubiquitous goods and services for anybody, an age of abundance, and it's going to be a super fun business, too. It's going to build a sci-fi future we all want.

What you're seeing is that every major group in the world will get into this space. You have to. You have no choice.

Peter H. Diamandis

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

Brett Adcock

My best guess is—

Dave Blundin

Now, that's a moonshot, ladies and gentlemen.

Peter H. Diamandis

So Dave and I are in San Jose at Figure headquarters.

Dave Blundin

Mm-hmm.

Peter H. Diamandis

We just did a podcast with our extraordinary friend Brett Adcock.

Dave Blundin

Over here.

Peter H. Diamandis

Yeah, Figure 01.

Dave Blundin

This is the original.

Peter H. Diamandis

Yeah.

Dave Blundin

Still somewhat functional.

Peter H. Diamandis

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

Dave Blundin

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 H. Diamandis

Here's Figure 02. Much more beautiful.

Dave Blundin

Mm-hmm.

Peter H. Diamandis

Much more functional, running neural nets across the board, dumping all the C++. Can you live long and prosper?

Dave Blundin

Yeah.

Peter H. Diamandis

Hi.

Dave Blundin

Hi.

Peter H. Diamandis

And here we go with Figure 03, the workhorse right now. We just did a tour. I mean, we probably saw 100 of these walking through the hallways, on test stands, cleaning dishes.

Dave Blundin

Brett, what's been fun is they added a flexible toe, too, so it can go down like this.

Peter H. Diamandis

Yeah.

Dave Blundin

Before, it had this clunky foot here.

Peter H. Diamandis

And Figure 03 has the palm camera.

Dave Blundin

Palm cam?

Peter H. Diamandis

Yeah.

Dave Blundin

They cut about 30 pounds off the weight and 90% of—

Peter H. Diamandis

Of the cost.

Dave Blundin

Manufacturing cost.

Peter H. Diamandis

Wow, crazy.

Dave Blundin

Yeah.

Peter H. Diamandis

Amazing. Yeah.

Dave Blundin

It's all front-loaded.

Peter H. Diamandis

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

Dave Blundin

Yeah.

1. Figure Rebuilds Its Robot Stack

Peter H. Diamandis

I'm here at Figure headquarters with Brett Adcock and DB2. Brett, it's been about 18 months since we did a podcast on Moonshots together. I am blown away by how far you've come.

Dave Blundin

18 months in AI time, that's like a decade.

Brett Adcock

Dude, welcome to Figure headquarters. What do you think?

Peter H. Diamandis

Yeah, it's extraordinary.

Dave Blundin

Holy crap.

Peter H. Diamandis

I mean, just—

Dave Blundin

Wow.

Peter H. Diamandis

Describe it. We just went on a tour. You've got about 300,000 square feet, with 400,000 square feet under development here. I mean, there are Figure 03 robots walking down the halls. There are fully autonomous robots, I guess, running Helix 2. You just released Helix 2 today.

Brett Adcock

Today.

Peter H. Diamandis

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

Dave Blundin

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

Peter H. Diamandis

Yeah.

Dave Blundin

Hundreds.

Peter H. Diamandis

At least a hundred or so.

Dave Blundin

Yeah. Well, there are a lot of partial robots out there, too, so it's hard to—

Peter H. Diamandis

Picking up Figure heads. That was fun.

Dave Blundin

How many hands do you think we saw? There were many more hands than there were robots—

Peter H. Diamandis

The hand line, the head line, the torso line.

Dave Blundin

Actually, picking up the head was the most surreal—

Peter H. Diamandis

This is where the pelvis is made.

Dave Blundin

Part.

Peter H. Diamandis

Yeah, for sure.

Dave Blundin

Yes.

Peter H. Diamandis

I still remember, during my first visit with you—full disclosure, my venture fund is invested in 2 of your earlier rounds. I'm super proud of the progress that you've made. I still remember your Figure 01 putting a Keurig cup in a coffee maker, and that was a big deal because it was done with neural nets and not C++.

Brett Adcock

That honestly was a big inflection point for us. I think a few things we needed to really run down were: Can you build an electric humanoid that's low-cost and capable like a human, just on 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 human-type representations—

Peter H. Diamandis

Yep.

Brett Adcock

—and then do tasks? When we were doing the Keurig task, it was basically a bimanual neural net running on the robot, which has now evolved into Helix. It was able to do the whole thing. It was a smaller task, but it was a few minutes long: picking up the Keurig cup, opening the coffee maker, putting it in, and running it.

It was the first time we saw a true instance of neural nets really working on a bimanual humanoid robot.

Peter H. Diamandis

Yeah.

Brett Adcock

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.” That started basically 2 years ago now.

Peter H. Diamandis

That's crazy.

Brett Adcock

And then you guys saw Helix 2 today—

Peter H. Diamandis

Yes.

Brett Adcock

—which is basically the best release we've ever had.

Peter H. Diamandis

So we'll run a clip of Helix 2 while we're describing it, because what we saw was Figure 03 running Helix 2—

Brett Adcock

Mm-hmm.

Peter H. Diamandis

—in full autonomy—

Brett Adcock

Mm-hmm.

Peter H. Diamandis

—going into the dishwasher, picking stuff up, and putting it away, not preprogrammed.

Brett Adcock

Yeah.

Peter H. Diamandis

And I loved the human elements of it, like using its hip to close something and its foot to raise the dishwasher.

Dave Blundin

That's the neural net difference, though. You get unexpected behavior, both good and bad—

Brett Adcock

Yeah.

Dave Blundin

—but things you could never code up.

Brett Adcock

You could never code this.

Dave Blundin

Your career went from software company to VTOL company. Now this has got to be the first neural net platform.

Brett Adcock

Yeah. The things that you can do with neural nets now completely blow my mind versus code. 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 only so far you can really push code heuristics. A humanoid robot using only code is just a dead end.

Dave Blundin

Yeah.

Brett Adcock

It's just not going to work.

Dave Blundin

Yeah.

Brett Adcock

Yeah.

Dave Blundin

Yeah, it's amazing to me how you accumulate data, and the data becomes this incredible barrier to entry—

Brett Adcock

Yeah.

Dave Blundin

—this incredible asset. If you were writing all this in C code, that C code would have millions, hundreds of millions of dollars invested in it—

Brett Adcock

Yeah.

Dave Blundin

—and you would not want to mess it up.

Brett Adcock

Yeah.

Dave Blundin

With the neural net, you can say, “Look. Hey, guys. Retrain it from scratch.”

Brett Adcock

Yeah.

Dave Blundin

Right off the bat. It's just a completely different approach. So that's why people are way underpredicting how important—

Brett Adcock

Yeah.

Dave Blundin

—or how quickly this is going to evolve, because it's a completely different paradigm.

Brett Adcock

Well, we've lived through it. Maybe a year or 2 ago, we had several hundred thousand lines of C++ code.

Dave Blundin

Several hundred thousand.

Brett Adcock

Handwritten code.

Dave Blundin

Probably $100 a line to write it.

Brett Adcock

Yeah. Very expensive, very hard to test, and very hard to get out reliably.

Dave Blundin

Yeah.

Brett Adcock

Also hard to model all the different behaviors that we would need to test—

Dave Blundin

Yeah.

Brett Adcock

—and then we removed a majority of all that in Helix 1, where we still had a lot of lower-body control being run in basically the control stack in C++.

Dave Blundin

Yeah.

Brett Adcock

And then today, we removed the remaining 109,000 lines of C++.

Dave Blundin

All neural nets.

Brett Adcock

All neural nets today. That's the full body. That took it from being able to do really good tabletop manipulation, like you saw with the Keurig coffee, to the work we do with logistics. All of that's being done in neural nets.

We've been showing amazing progress there. But getting the whole body out of there and being able to move dynamically through a scene while manipulating and planning is just a whole other challenge. We basically spent the greater part of a year refactoring the Helix architecture to enable this to work. You're talking about now moving through space like a human.

Dave Blundin

Yeah.

Brett Adcock

Having control of the full body, with all joints.

Dave Blundin

Eye, hand, foot, and leg coordination. Everything is—

Brett Adcock

Sensor data in—

Dave Blundin

Yeah.

Brett Adcock

Cameras, tactile. We have palm cameras.

Dave Blundin

Yeah.

Brett Adcock

Basically doing inference on board the robot, fully embedded, and then being able to output torques into the motors and do that at a few hundred hertz—

Dave Blundin

Yeah.

Brett Adcock

—in terms of planning and 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 errors and replanning—

Dave Blundin

Mm-hmm.

Brett Adcock

—and fixing this, all done with the neural net now, end to end, over a pretty long horizon. For us, it's kind of room-scale autonomy, so we can now finish the whole room—

Dave Blundin

Yeah.

Brett Adcock

—which is important. Next, we're going to graduate to basically the full house.

Dave Blundin

See, that's one of the things that's really obvious when you're walking around looking at what everybody's doing and working on. You visualize a robot company having lots of people working on microcode, actuators, batteries, or whatever, but there's just a huge number of people out there at workstations, and they must be working on the neural nets. It's just got to be such a dominant part of what makes the thing actually look and feel human. The motions are so smooth.

And everybody, when they think about the history of robotics, they kind of chart these line charts, but it's not like that. It's a disruptive change from dropping that last 100,000, 105,000 lines of C code to moving to an all self-organizing neural approach. Completely different future.

Brett Adcock

It does. We make these technology progress steps, and I think it's been very apparent here. Every year to year, the whole business looks completely different.

Dave Blundin

Yeah.

Brett Adcock

A large part of that is trying to get the hardware, hands, and all this stuff in a good spot.

Dave Blundin

Yeah.

Brett Adcock

And then be able to basically have more range of motion, speed, and torque like a human. We're all in on neural nets, so it's been, what is the right data set for that for pre-training and post-training? Do we have the right training cluster? Do we have the right models?

Dave Blundin

Yeah.

Brett Adcock

And then deploying those really well on the same humanoid hardware. That's a full loop.

Dave Blundin

Yeah.

Brett Adcock

We've actually designed Figure 03 to—if you say what the guiding principle of Figure 03 is more than anything else, it was just designing for Helix. It's like, how do we give Helix a body?

Dave Blundin

So counterintuitive.

Brett Adcock

Everything: the feet, hands, head.

Dave Blundin

So build around the neural net.

Brett Adcock

We really looked at the neural net and 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?" All of it is encapsulated in this view that we need to go all in on neural nets and do human-like work.

Dave Blundin

I'm 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.

2. Figure Builds Beyond OpenAI

Peter H. Diamandis

So in the beginning, you had partnered with OpenAI on software, and you made a departure from OpenAI. I mean, I guess, are any of—

Brett Adcock

It wasn't quite accurate, but—

Peter H. Diamandis

Okay. Well, you can—

Brett Adcock

It's a story.

Peter H. Diamandis

You can correct it.

Brett Adcock

I think I met Sam and the OpenAI team, and they were just really interested in getting into robotics. In their early master plan, it was to get into basically shipping home robots.

They really wanted to have a very intimate relationship. They ended up leading our Series B along with Microsoft, and we started working on a collaboration agreement to help work on next-generation models for humanoids. We were, and still are, really big on how we language-condition the whole stack.

An LLM, in a lot of ways, is just this world model. It really understands, in the weights, basically what things are and what it should do. It has a lot of good semantic understanding.

We're trying to figure out how to tap that into the humanoid. How do we learn from this at scale and use some of those representations? The partnership just didn't work. Our team just ran circles around them for basically the better part of a year, and it came to a point where we were doing all the work ourselves internally.

We had a whole team here, a lot from some of the best labs in the world, and we were putting out work after work. The Keurig coffee stuff was done by us.

Peter H. Diamandis

Yeah.

Brett Adcock

All this stuff was done internally.

Peter H. Diamandis

Yeah.

Brett Adcock

At some point, it just didn't make sense to train other folks on how we built AI models internally for embedded systems like a humanoid.

Peter H. Diamandis

Did it turn out that the LLM matters at all in the physical world? You could start with an open-source LLM and tune it?

Dave Blundin

What is it, like a VLA—a vision-language-action model you're building?

Brett Adcock

Yeah. We basically want to take the semantic grounding, then a VLM—

Peter H. Diamandis

Yeah, like the common sense, you know.

Brett Adcock

—the vision that it has, yeah.

Peter H. Diamandis

Yeah.

Brett Adcock

What do we understand from this? Which we have in Helix today is 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 had ever really done before.

Peter H. Diamandis

Mm-hmm. Right.

Brett Adcock

I think that's the work that we've been excelling at and that we love: how do we get it to understand physics?

Peter H. Diamandis

I think most of our audience probably knows this, but just to rewind the tape, the LLMs—GPT-2 and GPT-3—were built entirely on text data scraped right off the internet. And then they supplemented that with a ton of other data, also in text form.

That creates this machine that has tremendous amounts of common sense. If you ask it, "Hey, do you know how to play soccer?" it says, "Yeah, of course I do." But then you try to install it in an actual physical moving machine, and it has no idea what it's actually doing.

Brett Adcock

Well, yeah. We've designed it to touch everything in the world. We have this really high-dimensional robot that has 40-plus degrees of freedom. On the surface, the math around this is—just the dimensionality is really high. You have 40 motors, and they can all spin 360 degrees.

Peter H. Diamandis

Yeah.

Brett Adcock

So the amount of states the robot can be in, like positions, is 360 to the power of 40. There are more states of the humanoid than atoms in the universe.

Peter H. Diamandis

That's a lot. Yeah.

Brett Adcock

Yeah, exactly.

Peter H. Diamandis

You're not going to simulate those one by one.

Brett Adcock

Yeah, exactly. So the question is, then, I need to understand these fine contact dynamics. I need to grab this water bottle. Where do I position my elbow, pelvis, torso, head...

Fingertips?

Dave Blundin

Yeah.

Brett Adcock

How do I plan to grab this? How do I put pressure on it?

Dave Blundin

Yeah.

Brett Adcock

How do I understand those representations really well, from observations into the actions I’m doing at test time? This is not an LLM.

Dave Blundin

Yeah.

Brett Adcock

The LLM knows none of this.

Dave Blundin

Yeah.

Peter H. Diamandis

No.

Brett Adcock

The LLM knows this is a water bottle. It probably knows that I need to grab it from the side. But all this implied physics that we need to deal with here—we just have to train models to do that.

Dave Blundin

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

Brett Adcock

Hmm.

Dave Blundin

But—

Brett Adcock

We’ve done this. You can zero-shot the LLMs inside a robot. We do it; we still do it even actively.

Dave Blundin

Oh, really?

Brett Adcock

They just can’t do anything.

Dave Blundin

Just for fun, just to watch them fall over.

Brett Adcock

Yeah. I’m interested in this. A project I’ve been doing is: can we just zero-shot it? I’m working on a new AI lab that I founded recently called Hark, and we have a new AI model that is completely incredible.

Dave Blundin

Wait, you founded a new AI lab? Rewind the tape here. What?

Brett Adcock

Yeah.

Dave Blundin

It’s called Hark?

Brett Adcock

I founded a new AI lab.

Dave Blundin

I sent you this.

Brett Adcock

Did you?

Dave Blundin

I did. I sent—

Brett Adcock

Hark?

Dave Blundin

Yeah.

Brett Adcock

Yeah. We have a new AI lab we’re working on.

Dave Blundin

Well, I’ll send it to you again.

Brett Adcock

We have some new AI models, and we actually put one of them into the Figure robot this month. I said, “Okay, let’s just zero-shot this. Let’s give the LLM—or rather, the model—access to basic commands.” This is a multimodal model. Can we give it acceleration and X, Y coordinates for navigation, basically like a joystick? Can we give it a digital joystick?

Dave Blundin

Yeah.

Brett Adcock

I asked it to find the exit sign and get out of the building.

Dave Blundin

Mm-hmm.

Brett Adcock

Unfortunately, it was going in the right direction and ran into a clear glass wall.

Peter H. Diamandis

Well, kids do that, too.

Brett Adcock

Yeah, exactly.

Peter H. Diamandis

I did that when I was a kid.

Brett Adcock

We’ve really stress-tested this. It just doesn’t work. You’re missing so much world understanding—what’s really happening and how to move my body. We think it’s pretty simple to grab an object with a stationary robot, but the robots we have for humanoids are moving.

Dave Blundin

Yeah.

Brett Adcock

The pelvis, head, torso, hands, and arms are all moving. When you’re reaching out to grab something off a table, your pelvis is moving backward. It’s very difficult to command a very high-dimensional robot.

Peter H. Diamandis

Robotic physiology.

Brett Adcock

Yeah.

3. The Humanoid Market Consolidates

Peter H. Diamandis

Over in China this year, some 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 there’s a lot going on there. In the US, I would say maybe there are 10 serious players.

Brett Adcock

Mm-hmm.

Peter H. Diamandis

I mean, 2 or 3 who are extremely serious, including Figure. But there are a lot of potential humanoid robot companies. I was just at CES and saw a humanoid robot explosion.

Brett Adcock

Yeah.

Peter H. Diamandis

And then as many or more hand companies, which is interesting.

Brett Adcock

Yeah.

Peter H. Diamandis

I go back to the early 1900s, when there were about 250 car companies and 200 or 300 tire companies. Then this massive consolidation occurs, and GM, Chrysler, and Ford buy and consolidate. 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. These will all consolidate down to a few groups globally.

Peter H. Diamandis

Do you have a guess? Is it a triopoly? That’s the right description. Is it more than 10, less than 10?

Dave Blundin

It always seems—

Brett Adcock

Far, far less than 10.

Peter H. Diamandis

Far less than 10.

Brett Adcock

Yeah.

Peter H. Diamandis

Globally.

Brett Adcock

Globally.

Peter H. Diamandis

Yeah.

Dave Blundin

It always seems, in the US anyway, to settle down to 2, 3, or 4, but the borders are not obvious. With cars, a car’s a car, right? Then you had cars and trucks, and those were kind of separate for a while. But—

Peter H. Diamandis

You also have different designs. “I want the plush interior—”

Dave Blundin

Right.

Peter H. Diamandis

“I want the sportster.” I mean, and that, I think—

Dave Blundin

Mm.

Peter H. Diamandis

I wonder, are robots going to be differentiated by their vertical application or their personalities?

Dave Blundin

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

Brett Adcock

Yeah. I think everybody is taking for granted how difficult this is. You have to go out and build pretty novel, very difficult hardware.

Dave Blundin

Yeah.

Brett Adcock

It needs to be relatively cheap. Then you have to figure out how to make neural networks work on it, make neural networks work on it at scale, and manufacture at scale. Then you have to get these products out reliably, with all of them working every day without any human intervention.

Dave Blundin

Yeah.

Brett Adcock

We talked a lot about how we’re doing K-Cup coffee work. I haven’t seen a single humanoid in the world do that, or be able to do that today, globally, and it’s been 2 years.

Dave Blundin

Yeah.

4. Teleoperation Is Not Autonomy

Peter H. Diamandis

By the way, a lot of the video we see is actually teleoperation. I wonder if people realize that. A lot of the robot companies are teleoperated rather than fully autonomous. What we saw just walking around here was a 4-minute-long, fully autonomous operation on Helix 2, right?

Brett Adcock

I’ve built a lot of businesses in my day, and I’ve never seen so many companies with a human in the background commanding the robot and putting out updates. I’ve just never seen it. When I first started Figure, stuff was coming out, but now it’s every week: somebody is teleoperating a robot and putting out a video.

It would be the equivalent of having a self-driving car company with a guy in Tennessee driving it, while marketing it as if there were no humans in it.

Dave Blundin

Yeah, yeah.

Brett Adcock

It’s self-driving. We’re putting out teasers. In a lot of cases now, companies are selling the service. If you want to do this right, you have to believe in neural networks all the way down the stack. You have to build for general purpose.

Peter H. Diamandis

So the parameters that are going to define the successful top 2, 3, or 4 are neural networks and manufacturing—

Brett Adcock

Okay, I would say what’s—

Peter H. Diamandis

Not surprising.

Brett Adcock

What’s impressive today is not manufacturing. You could probably solve general robotics with 100 robots. We’re pushing hard on manufacturing, but you can probably solve that. What’s impressive is a full end-to-end robot that is generalizing to an unseen place. You can drop it into an Airbnb—

Dave Blundin

Yeah.

Brett Adcock

—and it can do long-horizon work with neural networks. Any long-horizon work in unseen places.

Peter H. Diamandis

What do you define as long horizon? Hours, days?

Brett Adcock

I would like to see days of work, yeah.

Dave Blundin

Yeah.

Brett Adcock

Fully autonomous days of work, at the very least. We’re so far from that. You have robots out there doing karate and jumping, which are preprogrammed, open-loop behaviors.

Dave Blundin

Yeah.

Brett Adcock

They’re not impressive. We do that. We’ve done that stuff here—the open-loop behaviors. Any college kid in a dorm room can do this with a robot.

Dave Blundin

Yeah.

Brett Adcock

So I think that, plus teleoperation—teleoperation is not impressive. You could build shitty hardware and still teleoperate it and put out videos. That is not hard.

What’s hard is doing full end-to-end neural networks in unseen places, or generalizing to this. If you can solve that, then the next step is figuring out how to get it out at scale. But we are still in the “who can solve general robotics?” phase.

of the humanoid phase, and it's just not impressive if I can build 100,000 robots right now that need teleoperation or can only—

Dave Blundin

Yeah.

Brett Adcock

Or can only do open-loop replay. It's just not cool. Right? Your only job is to build 100,000 robots right now.

Dave Blundin

Yeah.

Brett Adcock

We have the capital to do it, and we can do it, but what we really want to solve is that I can give you 10 robots and they can go into unseen places and do real, useful work. That's what's going to differentiate everybody.

Peter H. 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 building high-rate manufacturing for humanoids is going to be super hard, and you're going to have to go through a lot of iterative design processes.

Dave Blundin

Yeah.

Brett Adcock

So that's what we're doing now. We're bringing up higher-volume manufacturing as we're learning how to build in true general-purpose-ness. My view is that, if you think about these level bosses that happen—things that will hurt, that you need to graduate to—you need to graduate to doing very short periods of neural networks, 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 continuously doing neural networks today in humanoid solutions.

Dave Blundin

Yeah.

Brett Adcock

Everything's cut. All the films are cut or teleoperated.

Dave Blundin

Yeah, yeah.

Brett Adcock

It's pretty crazy. You watch any video, and you want to see it uncut. You want to see it done with neural networks, not teleoperated. You also want to see the stuff that we showed you here in person today, which is running for hours and hours. We run these robots with neural networks.

Peter H. Diamandis

The kung fu videos, whether they're teleoperated or fully autonomous, are actually fascinating and scary when you see them doing that.

Brett Adcock

But the technology around that is not great. You're basically putting somebody in a mocap suit.

Dave Blundin

Yeah.

Brett Adcock

You're having some guy do karate chops or walk around, and then you're running that open loop. You're running it blind. You're just hitting a replay button.

Dave Blundin

Right.

Brett Adcock

You can do that with a very simple RL neural net.

Dave Blundin

Yeah.

Brett Adcock

You can basically do DeepMimic on this.

Dave Blundin

Yeah.

Brett Adcock

It's super simple.

Dave Blundin

Yeah.

Brett Adcock

There's open-source code for this that you can run with basically 1 GPU on your desktop, and you can do it with any robot. Every robot has a very tiny amount of computing power. These are single-million-parameter models. They're very small. You don't need a lot of memory, and they're very simple to execute.

Dave Blundin

Yeah.

Brett Adcock

What you really want is good closed-loop control, where it's reasoning at around 200 hertz, or 200 times a second.

Dave Blundin

Sure.

Brett Adcock

It's dynamically responding to the scene.

Dave Blundin

Yeah.

Brett Adcock

That is literally 100,000 times harder than doing open-loop replay.

Peter H. Diamandis

I wonder what the human cycle time is. How many hertz?

Dave Blundin

Oh, God.

Peter H. Diamandis

Probably less than that.

Brett Adcock

Much lower than that.

Peter H. Diamandis

I would imagine.

Brett Adcock

One thing we've seen in our robot is that we can balance on 1 leg better than a human. We just have much better, faster dynamics.

5. Figure Development Accelerates

Peter H. 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.

Dave Blundin

And it 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. We're about a year in now. I think that was highly significant. We basically figured out how to run neural networks on a robot for long periods of time. How do we get the data for it? How do we train models? How do we deploy at test time?

Dave Blundin

Mm-hmm.

Brett Adcock

So you watch package logistics. I think I saw it running—

Dave Blundin

Saw this.

Brett Adcock

It's been running for days now.

Dave Blundin

Yep.

Brett Adcock

It's a neural net all the way down the stack. It's learning how to grab packages, individualize them, find the barcode, and 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 with high accuracy and at high speed.

Dave Blundin

High speed, though. That's the part that jumps.

Brett Adcock

Human speed.

Dave Blundin

Well, because a lot of what you see in robotics—

Peter H. Diamandis

It's as fast as a human would do it.

Dave Blundin

Yeah.

Brett Adcock

Our last run—we had 1 error over 67 hours, over multiple robots.

Dave Blundin

Over 67 hours.

Brett Adcock

Crazy. You know what I'm saying?

Dave Blundin

Yeah. It's doing an operation every second or 2.

Brett Adcock

Yeah.

Dave Blundin

So 67 consecutive hours of that is a lot.

Brett Adcock

It's nuts.

I'd say Helix is a big one, and then Figure 03. Figure 03 is a huge step change for us in hardware.

Peter H. Diamandis

What do you see going into 2026 here? We've got the next 11 and a half months. What are you excited about seeing?

Brett Adcock

Yeah.

Peter H. Diamandis

What are you excited about?

Brett Adcock

Our entire roadmap is around Helix 2 now. Helix 2 can go from doing the logistics use case while stationary to walking and moving, and basically do long-horizon, full-body control. We've now integrated all the sensors—the tactile sensors, camera, and palm—into the stack, and we're seeing improvements overall in the policy layer. We're getting better and faster at taking data and running it onboard the robot now.

Dave Blundin

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

Brett Adcock

Yeah.

Dave Blundin

So what defines it?

Brett Adcock

We have a couple of big steps. One is that we've integrated a fully learned controller—what we call System Zero—into the robot. The robot has a full-body reinforcement-learned controller in it.

Dave Blundin

Okay.

Brett Adcock

Now there's literally no code running on that robot, so it can move its whole body itself using a full learned controller inside Helix. We call it S0.

Dave Blundin

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

Brett Adcock

There are reinforcement-learned controllers out there. A lot of the karate stuff you see and things like that use them, but nobody has totally integrated one into the whole body for learned manipulation and perception. Nobody's shown that actually working while moving around and doing the things we saw today. I don't even know if anybody has shown it stationary, standing, and doing learned policies. Actually, probably not in the world. So we're getting it integrated into a stack that we can actually use going forward.

I think one of the things we learned at BMW last year—we were there for 6 months, and we deployed our Figure 02 robots every single day—was that, with the stack we had, we got about 80% of the things right and 20% of the things wrong. The things that we got wrong, we didn't want to scale.

Dave Blundin

Yeah.

Brett Adcock

It was working. The robot ran every single workday, and it worked. But we learned that we didn't want to ship 100,000 robots with this architecture stack. It was just too hard to scale.

Dave Blundin

Yep. Mm-hmm.

Brett Adcock

It was too brute-force.

Dave Blundin

Yep.

Brett Adcock

So we've worked for almost a year now on what the ideal architecture is, where we can go out and accumulate large sets of pretraining data—

Put it in the robot, and it can just do this work, and we get generalization from this. That’s what you’re seeing today.

Dave Blundin

So Helix 1 still had the C++ code in it. What defines Helix 2 is—

Brett Adcock

Helix 1 had a lower-body controller—

Dave Blundin

Okay.

Brett Adcock

—that was still written in C++.

Dave Blundin

Yeah.

Brett Adcock

Everything else—the whole upper body—was full neural nets.

Dave Blundin

Okay.

Brett Adcock

And so we basically completed the full body now.

Dave Blundin

Okay.

Brett Adcock

In doing so, we also did some work at the system level, the System 1 level, where we integrated all the sensor modalities from the hands—

Dave Blundin

Mm-hmm.

Brett Adcock

—and the rest of the robot into the stack. For example, we now have tactile sensors in every fingertip that we’re using on Figure 03—

Dave Blundin

Oh, yeah.

Brett Adcock

—as well as palm cameras.

Peter H. Diamandis

Mm-hmm.

Dave Blundin

Yeah.

Brett Adcock

That’s to understand when we’re occluded and to have a better understanding of how we’re grasping items. We put in a bunch of stuff about picking pills and things out of pill cartridges, where the hand literally occludes them. Your hand is literally in front of the head camera—

Dave Blundin

Yeah.

Brett Adcock

—but we still really want to understand where we’re going.

Dave Blundin

Yeah.

Brett Adcock

So now, with Helix 2, we basically have a full stack, end to end, with neural nets. We feel confident scaling the pre-training data set into Helix. In fact, I’ll even go as far as to say we designed Helix 2 for the pre-training data set.

Dave Blundin

Yeah.

Brett Adcock

And then we designed the robot for Helix 2. We’ve designed everything around data.

Dave Blundin

Yeah.

Brett Adcock

How do we get data at scale? If you’re in the neural net game, it’s a data play. It’s about how high-quality and diverse—

Peter H. Diamandis

So it’s experience. It’s just gathered experience—

Dave Blundin

Yeah, quantified.

Peter H. Diamandis

—in the field, in all kinds of circumstances.

Brett Adcock

Where can we find—

Dave Blundin

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

Peter H. Diamandis

Unique data.

Dave Blundin

—forward progress. You teach somebody how to scuba dive or how to play piano, and they have that knowledge. They live, then they die, and then you have to teach somebody else. This is completely accumulating information.

Brett Adcock

The reason why I think there will be very few humanoid groups is that the one thing that’s important here is that once one robot learns how to do a task—

Peter H. Diamandis

Yes. They’re ready to learn.

Brett Adcock

—every robot in the fleet knows it. Humans don’t operate like this.

Dave Blundin

Yeah. No, it’s—

Peter H. Diamandis

I wish we did.

Brett Adcock

I watch my kids. I let kids learn how to do stuff—

Peter H. Diamandis

Some people—

Brett Adcock

—and they just don’t listen, right?

Dave Blundin

Vulcans do. Vulcans can do it.

Brett Adcock

I wish we—

Peter H. Diamandis

Melding.

Brett Adcock

I wish we did.

6. The 2026 Production Push

Peter H. Diamandis

So, 2026 predictions: what’s your boldest prediction for Figure? What are your goals for this year? What do you imagine?

Brett Adcock

We’re spinning up Baku production in a big way right now for Figure 03.

Peter H. Diamandis

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

Brett Adcock

We’re trying to get there in the near term.

Peter H. Diamandis

Amazing.

Brett Adcock

You guys saw it—we walked through Baku today. What did you guys think of BotQ?

Peter H. Diamandis

Yeah, it’s—

Dave Blundin

That’s wild.

Peter H. Diamandis

—a lot of humans there.

Dave Blundin

I wish everyone could see that. I guess it’s all secret. You can’t bring a camera through there.

Brett Adcock

We haven’t. There’s a lot of IP there, because you see exposed boards—

Dave Blundin

Yeah. That’s so sad.

Brett Adcock

—and actuators and stuff.

Peter H. Diamandis

Well, if we can get people with—

Brett Adcock

It’s cool, right?

Peter H. Diamandis

If we get some footage—

Dave Blundin

That was so cool.

Peter H. Diamandis

—in video, maybe we can mix it in here. There are a lot of humans—

Dave Blundin

When do we start seeing robots building robots?

Brett Adcock

We will put robots on our Bacu lines this year.

Dave Blundin

Okay.

Brett Adcock

Phasing humans out of there will be a combination of getting more robots there and doing more high-volume automation over at Bacu.

Dave Blundin

Okay, so that’s—

Brett Adcock

Mm.

Dave Blundin

—the first 2026 objective. Hit that—

Brett Adcock

We want to scale up robots at Bacu for sure. The second thing is that we want to scale out robots in the industrial commercial workforce.

Dave Blundin

Yep.

Brett Adcock

We have multiple clients that we’ve signed. They’re buying or leasing robots from us, and we’re going to get those out at scale in 2026. We know exactly where we’re going geographically, what the use cases are going to be, and the deployment schedules. We want those to be Figure 03s, so we just retired Figure 02s at the end of last year.

Dave Blundin

Mm-hmm.

Brett Adcock

Now we’re basically building the arsenal of Figure 03s as we scale up that manufacturing, to get them out into the world and running every day. We like the commercial workforce because it really helps harden our ability to run robots every day.

Dave Blundin

Yeah.

Brett Adcock

What we’re here to do is build robots and run them in the world, and they need to run 24/7.

Dave Blundin

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

Brett Adcock

To be frank, we have so much demand—

Dave Blundin

You do.

Brett Adcock

—for customers. We’ve talked to 50 to 100 customers or so in the last 6 to 12 months. We really want to be all in with a smaller group of customers and spend time with them, integrate well into their facilities, and do well. We’re still early, right? We don’t have thousands of robots running at these places. We want to get there as fast as we possibly can.

I think we could probably ship an enormous number of robots to the current customers we have now. We see that we’re kind of good for the next 2 or 3 years in terms of demand. We have so much demand. They’re kind of waiting for us to ship at scale.

Dave Blundin

Leasing versus sale?

Brett Adcock

We really like the leasing model.

Dave Blundin

Yeah.

Brett Adcock

Humans are leased.

Dave Blundin

Yeah.

We can model it that way. At least these days. They used to be bought way back.

Brett Adcock

At least these days. You lease humans, so we lease humanoids today. We’re not opposed to selling them. I think what really matters is trying to figure out how to find the right distribution to get robots out at scale.

Dave Blundin

Yeah.

Brett Adcock

It’ll really help us get good at what we do. It’s one thing to show a demo or whatever else, but when we had robots at a commercial customer last year, at BMW, it taught us a ton about running them every day, fleet operations, safety, repair, and maintenance. There are a lot of other things that need to come through in the ecosystem that we need to get right.

I’d say the second thing is getting robots out at scale to commercial customers. The last thing—which is arguably the most important for us—is that we want to solve general robotics.

Dave Blundin

Hmm. Yeah.

Brett Adcock

We want to solve general robotics. The analogy is that we want to build a human in a bodysuit that you can just talk to, that has common-sense reasoning, that you can communicate with, and that has almost perfect memory of what’s really happening or what’s going on in your life. It can maybe talk to you and almost be your companion.

Dave Blundin

I mean—

Brett Adcock

And then go off and do things that an everyday human would want to do. I would expect them to get up to speed on those tasks as fast as or faster than a human can.

Dave Blundin

Are there 2 different models driving it, then? The VLA model for the body, the physics, and the embodiment, versus an LLM for conversation and memory?

Are you doing both?

Brett Adcock

We believe this all comes down to one model at the end of the day—one omni model that is trained early in pre-training and helps fuse all this together. You could think of it like we need to have speech, we need to have language-conditioned policies, we need to understand physics really well, and we need to remember things and be able to recall them 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 Blundin

Yeah.

Brett Adcock

We've been really focused on getting the core building blocks built, but over the next year or two, I think you'll see us... I think I just want a robot in my home I can talk to—

Dave Blundin

Sure.

Brett Adcock

—that can remember things, especially my kids. My kids come home sad from school or something, and I want the robot to understand that.

Dave Blundin

Yeah.

Brett Adcock

I want it to have the EQ—the self-awareness to see that and talk to them. I think all this is something we want to spend more time on. We're spending more time on it now internally.

Dave Blundin

Is there already a big MoE model where, depending on the task you're doing, it'll run different parts of the neural net, or does it need everything?

Brett Adcock

We have one neural net now that's basically—

Dave Blundin

One module.

Brett Adcock

There are no libraries of neural nets that we pull down.

Dave Blundin

That's interesting.

Brett Adcock

So there's no dishwashing neural net or logistics neural net, as you saw here.

Dave Blundin

Yeah, because at scale, if you teach the thing every physical motion, there's a massive number of combinations. The storage is actually dirt cheap.

Brett Adcock

Yeah.

Dave Blundin

But the processing is very expensive.

Brett Adcock

Yeah. Even better—

Dave Blundin

So—

Brett Adcock

We've seen positive transfer now with all this data coming in. The robot can generalize better with more information, even if it's the same segmentation.

Dave Blundin

Yeah.

Brett Adcock

Yeah.

Dave Blundin

It's weird. More knowledge is better. It does cross-transfer. Playing piano—

Brett Adcock

Yeah.

Dave Blundin

—makes you a slightly better soccer player.

Brett Adcock

Yeah.

Dave Blundin

But 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 there to—

Brett Adcock

Yeah, you don't want to nuke it. Yeah, for sure.

Dave Blundin

Yeah.

Brett Adcock

That's where we try to build the best-in-the-world models here and build a great team that can ultimately deploy robots that are useful. I think showing this type of usefulness—whether it's a lot of the stuff you saw today or a diversity of that—is super important for a humanoid robot. It needs to be able to do everything a human can, which is—

Dave Blundin

Yeah.

Brett Adcock

And on the distribution curve, we probably do billions or trillions of unique, very unique things in the world that we talk about.

Dave Blundin

One of the things you said on our tour that totally tells me we're on the right track is that you're using normal GPUs for training, like everybody, but inference-time compute is on super-fast, dedicated, non-H100, non-GB300 hardware—

Brett Adcock

Yeah.

Dave Blundin

—which has got to be at least a factor of 10 or 100 cheaper and faster, which means—

Brett Adcock

Yeah, it's also running fully onboard.

Dave Blundin

And it's running fully onboard. Yeah.

Brett Adcock

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

Dave Blundin

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

Brett Adcock

You also have an issue where we've run models offboard the robot, but if we lose communications or have some network latency—

Peter H. Diamandis

So I wanted to go there—

Brett Adcock

Yeah. You know what I mean? If you lose internet, it's hard to do work, and it's—

Peter H. Diamandis

Yeah.

Brett Adcock

For most humans, it's—

Peter H. Diamandis

We hit on supply chain, batteries, and comms. On the comms side, do you imagine you're going to be running a 6G network on there besides Wi-Fi? What's going on in batteries these days?

Brett Adcock

Yeah.

Peter H. Diamandis

Yeah.

Brett Adcock

From a network perspective, or communications back to the robot, we have Wi-Fi onboard. We have 5G and a SIM card—an eSIM—onboard, so we can... The robot, you can text the robot. You know, we can—

Peter H. Diamandis

You can have James[?].

Brett Adcock

Yeah.

Dave Blundin

Yes.

Brett Adcock

We can have a network outside of a Wi-Fi connection, and then we also have Bluetooth onboard, so it's almost like a walking phone or something like that.

Peter H. Diamandis

Mm-hmm.

Dave Blundin

Yeah.

Brett Adcock

We want connection at all times, but ideally you also want the robot to be able to perform work without a connection. So you really want a lot of onboard intelligence, in case you lose internet, so the robot's not bricked. Humans, for the most part, can do work without their cell phone. Not teenagers.

Dave Blundin

Yeah, that's going to be like that.

Brett Adcock

Not teenagers. Okay.

Peter H. Diamandis

So, batteries—

Dave Blundin

Yeah.

Peter H. Diamandis

They've been improving. What's the 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 Blundin

Yeah, no connector. You just stand.

Brett Adcock

Yeah.

Peter H. Diamandis

Yeah, which is kind of like—

Brett Adcock

You can stand there.

Dave Blundin

Inductive charging.

Brett Adcock

It's really cool.

Peter H. Diamandis

What kind of battery life are you getting? What do you expect in 2 or 3 years for battery life? So today, it's what?

Brett Adcock

We run basically around 4 to 5 hours per full charge in the battery, if we're starting at full battery life.

Peter H. Diamandis

Sure.

Brett Adcock

And then through full depth of discharge, we can charge wirelessly at about 2 kilowatts through the feet, inductively. We have about a 2-kilowatt-hour battery pack, so it's about an hour or so for a full charge—

Dave Blundin

Mm-hmm.

Peter H. Diamandis

Hmm.

Brett Adcock

—on the robot. So we can do 4 or 5 hours on, an hour off.

Peter H. Diamandis

That's great.

Brett Adcock

Yeah, it's great. I think folks are over-indexing too much on how long the robot can run on a single charge.

Peter H. Diamandis

Mm-hmm. Yeah, I don't expect—

Brett Adcock

The only thing that's really—

Peter H. Diamandis

I don't expect there are that many tasks you need a robot—

Brett Adcock

Humans take a few hours in. You go take a little break and do stuff. So I think there's ample time to do opportunistic charging, maybe send another robot in. We can put this little thin mat anywhere in the world. It could be on a conveyor system or wherever else. It could be at home in front of the kitchen, and you can just charge there while doing work.

Peter H. Diamandis

Mm-hmm.

Dave Blundin

Yeah.

Peter H. Diamandis

By induction.

Brett Adcock

Which is really cool.

Dave Blundin

Yeah. You don't have to have any wires or things like that you're pulling from the wall.

Dave Blundin

Yeah.

Well, I think one of the greatest value-adds you're doing right now is that 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 saying, "Oh, my God. Can you believe it can sprint now? Oh, my God, it can do a backflip now. Oh, my God, it can do..." And you're like, "Well, it depends on whether you programmed that in C, teleoperated it, or did it actually learn this from all—"

Brett Adcock

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

Dave Blundin

Yeah, exactly. It's so hard. So when people say, "How long does it run on one charge of the battery?" you're relating it to your cell phone.

Peter H. Diamandis

Yeah.

Dave Blundin

But it's not relevant in the—

Brett Adcock

I think—

Dave Blundin

—the inflection point we're going to.

Brett Adcock

Yeah. The summary here is: I need to see real closed-loop control of a robot moving around, touching and moving things like a human would.

Dave Blundin

Yeah.

Brett Adcock

That's where the hardest problems all sit, and that's where we've seen this huge wave of humanoid explosion, like you said, out of China and things like this.

Dave Blundin

Yeah.

Brett Adcock

But we've seen a very steep drop-off in getting to the next point, which is: show me a minute of the robot doing Keurig or something like that.

Dave Blundin

Yeah.

Brett Adcock

Uncut, closed-loop.

Dave Blundin

Yeah, real-time.

Brett Adcock

Yeah. You just haven't seen that. And I think you will, and I think there's a lot more levels to go from there. And that took us 2 years to go from a few minutes of tabletop manipulation with neural nets—

To a point where we can do kitchen work—room-scale autonomy. That was 2 years of working 7 days a week. We were here a lot of nights getting there, so it gives you a little sense that you're not going to do that in 6 months from there.

I think there's a lot of hardware, low-level C software, firmware, embedded systems, sensors, neural nets, and data. All of that came together to build this. We couldn't do the same work today on a robot that we could buy off the shelf today.

7. Figure Builds Its Own Supply Chain

Peter H. Diamandis

You vertically integrated and made the choice to vertically integrate, but how much of the supply chain ties back to China?

Dave Blundin

Hmm.

Brett Adcock

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

Peter H. Diamandis

Amazing.

Brett Adcock

Anymore.

Peter H. Diamandis

Do you buy into the U.S. versus China AI and robot competition? How do you think about that?

Brett Adcock

I don't. I spend a decent amount of time in China, and I love it.

Peter H. Diamandis

Yeah.

Brett Adcock

China is great. I go there and, you know, you're watching TV here in the U.S. and it's just this massive conflict and battle and everything. Then you go to China and everybody's just trying to help and win, trying to work and collaborate, and it feels like a startup incubator.

Peter H. Diamandis

It's just like a one-way—a one-way competition.

Brett Adcock

It just feels like everybody's Team Human.

Peter H. Diamandis

Yeah.

Brett Adcock

Team humanity.

Peter H. Diamandis

Yeah.

Brett Adcock

Go win. It's so great. Then you come back here and you're poisoned with all this stuff online, articles, and television, and it's just not like that when you're boots on the ground and going to do this. It's like, let's go as one and go win.

I love that spirit of trying to progress this technology as a giant lever arm for humanity, to bring abundance for everybody and make it a sci-fi future we all want to live in, which is really exciting.

Peter H. Diamandis

Yeah. Oh, my God. It is. We want to speed-run Star Trek, is what we talk about.

Brett Adcock

Exactly.

Peter H. Diamandis

I see a figure on the Moon, a figure in orbit—

Dave Blundin

A figure on the ocean floor.

Brett Adcock

100%.

Dave Blundin

The equivalent is that you guys make your own actuators and motors here, and part of that is because you want the exponential-growth effect, but part of that also is that the supply chain just doesn't exist to give you the parts here.

Brett Adcock

Well, yeah, the improvements—

Dave Blundin

In China—

Brett Adcock

I mean, you and I were just talking about the improvements made between Figure 02 and Figure 03—

Dave Blundin

Yeah.

Brett Adcock

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

Dave Blundin

Yeah.

Brett Adcock

Is that right?

Dave Blundin

We reduced costs like crazy on Figure 03.

Brett Adcock

That's crazy.

Dave Blundin

It would be great if we could go out and buy motors and plop them in the robot. It doesn't work like that. It would be great if we could go buy hands and just screw them onto the end. It literally doesn't work.

Brett Adcock

If you go through the engineering work to understand how we do comms, power, sensors, failure cases, thermals, low-level firmware, and embedded software, there's a cost. If something breaks in that equation, or there's a reliability issue, you're left with, hopefully, the vendor fixes it—or you die. It just doesn't work.

Dave Blundin

Yeah.

Brett Adcock

None of this stuff—the technology readiness of these things is really low. We would have loved to have gone out and bought all this stuff in the early days. We tried.

Dave Blundin

Yeah.

Brett Adcock

We basically failed at all of it. So we thought, “Okay, we need to design it ourselves,” and now we manufacture it. We do all final assembly and everything here.

We do that in some cases because nobody knows how to do it well. We do that a little bit for IP—we really want to control that here and understand what people have access to. We also want to get good at making a lot of robots.

What we need to get good at long term is probably a few things: getting data at scale that can run neural nets, basically doing Helix really well, making a lot of robots, and then getting those things out in the world at scale. A pretty simple equation.

Dave Blundin

It just feels like—

Brett Adcock

Yeah.

Dave Blundin

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—you have a massive advantage versus anything going on in China. If you'd been locked into a supply chain—

Brett Adcock

It's—

Dave Blundin

—that only has certain models and makes—

Brett Adcock

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

Dave Blundin

Yeah.

Brett Adcock

It just can't do it. If you go out and buy a humanoid robot off the shelf today, we can't get it to do this.

Dave Blundin

Yeah.

Brett Adcock

We've bought robots off the shelf. We've looked at them. You just can't get them to do this work.

Dave Blundin

Yeah.

Brett Adcock

They don't have the right sensors. They don't have compute. They don't have thermals. They don't have the right hardware—the hands, the head. All of these are built around our neural net stack. All the bots—

Dave Blundin

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

Brett Adcock

If you watch folks who are trying to buy these robots off the shelf, say, from China, they'll end up retrofitting them with these giant backpacks.

Dave Blundin

Yeah.

Brett Adcock

They'll have power, compute, and thermals there. There'll be a wire hanging out. They'll hook that into the back of the robot. It probably has its own local battery. They have to take it and overclock it, and it's just the wrong way of doing this.

Dave Blundin

Yeah.

Brett Adcock

It's a hard thing. It's like buying a rocket and saying, “We're going to put stage 2 on the side,” or something like that. It doesn't really work at scale.

Dave Blundin

Yeah.

Brett Adcock

It works in the early days for hobby-grade demonstrations and things like that.

Dave Blundin

Yeah.

Brett Adcock

But if you really want to do robotics at scale, then you're going to have to go design it yourself.

Dave Blundin

Looking at the companies coming out of China—Unitary, Engine AI, and so forth—which ones are you most interested in or excited about as friendly competition, if you would?

Brett Adcock

One thing that's great about China is that there just seems to be an explosion of really great talent and robots coming out the door.

Dave Blundin

And great entrepreneurial work ethic there, right?

Brett Adcock

It's awesome. It's great.

Dave Blundin

Yeah.

Brett Adcock

Things are just good for humanity, and this needs to happen. I think the thing that we've not seen is any closed-loop AI control from these systems at all.

Dave Blundin

Yeah.

Brett Adcock

We've seen a huge lack of that stuff. Usually it's like, “Here are the robots. We'll sell them,” and they're doing a ton of basically open-loop work, looking at—

Dave Blundin

Yeah, they're hand controllers.

Brett Adcock

Yeah. Doing that is almost orthogonal work from designing the system the right way for full autonomy.

But if we were to think about who Figure really competes with as our main competition, it's certainly China as a whole.

Dave Blundin

For manufacturing, I mean—for human, low-cost labor.

Brett Adcock

I think just for humanoids, we really don't see anybody else besides China as a real competitive threat today.

Dave Blundin

Fascinating.

Peter H. Diamandis

Rumors about Apple getting into the business? They cut down their car project, and the rumors are that they're heading toward humanoids.

Dave Blundin

Have you heard that?

Brett Adcock

We’ve heard this. I’ve been in conversations with every major tech company in the world over the last 12 months.

Dave Blundin

And then NVIDIA, Google, and even Sam—

Brett Adcock

Yeah.

Dave Blundin

Everybody’s making noises about heading into the space.

Brett Adcock

Meta, Amazon.

Dave Blundin

Yeah.

Brett Adcock

Yeah.

Dave Blundin

Yeah.

Brett Adcock

Listen, this is going to be the largest economy in the world. Roughly a little under half of GDP is human labor.

Dave Blundin

Right, $50 trillion.

Brett Adcock

Yeah. This is the next great place to be, and I think it’s going to be a super impactful business. It’ll lead to ubiquitous goods and services for anybody, an age of abundance, and it’s going to be a super fun business too.

Dave Blundin

Yeah.

Brett Adcock

It’s going to build the sci-fi future we all want.

Dave Blundin

Yeah.

Brett Adcock

It’s going to feel like—

Dave Blundin

Yep, it is.

Brett Adcock

It’s going to feel like 2080 up in here.

Dave Blundin

Yep.

Brett Adcock

So what you’re seeing is that every major group in the world will get into this space. You have to. You have no choice.

Dave Blundin

The major groups being Apple and Microsoft—

Brett Adcock

Yeah.

Dave Blundin

Google.

Brett Adcock

I think every major player that wants to do this.

Dave Blundin

Yeah.

Brett Adcock

I think what’s going to be hard is that we’re doing rocket-type difficulty and design here.

Dave Blundin

Yeah. Mm-hmm.

Brett Adcock

If Meta were building rockets, you’d be like, “That’d be crazy.”

Dave Blundin

Yeah.

Brett Adcock

I would think humanoid robotics is probably up there with rocket design. It’s certainly harder from an engineering perspective than when I built Archer, building electric aircraft, and that was hard.

Dave Blundin

Yeah.

Brett Adcock

These were 6,000-pound aircraft with 12 motors and 6 independent battery systems. We built our own control stack and embedded systems. We did all the structural design ourselves, things like this. So I think it’s probably up there with some of the hardest hardware on the planet, and you just have to be all in.

Dave Blundin

Well, let me ask you about that, because we were talking backstage at Abundance 360 last year, and you had—the basic tech stack had 6 layers of competency. You could probably rattle them off the top of your head, actually.

Brett Adcock

Is this for Archer or Figure?

Dave Blundin

This was for robotics prior to neural nets, I guess. So it applied to Archer and Figure.

Brett Adcock

Sure.

Dave Blundin

What were they again? It was—

Brett Adcock

Archer was basically a flying aircraft.

Dave Blundin

Yeah.

Brett Adcock

So I basically build electric vertical takeoff and landing aircraft.

Dave Blundin

Right.

Brett Adcock

It’s a flying robot, is what I meant. It has battery systems on board.

Dave Blundin

Yep.

Brett Adcock

It has electric motors.

Dave Blundin

Yep.

Brett Adcock

Electric motors are basically a stator-rotor gearbox. We have a little bit more sensors in our actuators than that, but for the most part, there are encoders and things like that in there.

Dave Blundin

Okay.

Brett Adcock

You have basically control software: How do we control this thing and make it move around?

Dave Blundin

Yep.

Brett Adcock

In the case of Archer and Figure, it’s a very over-actuated system. Archer has 24 degrees of freedom. We have tilting propellers, and we have pitch on the blades. We have flaps on both the tail and the wing. Figure has over 40 or so degrees of freedom in the system.

Dave Blundin

Yeah.

Brett Adcock

You have embedded software on board and sensors.

Dave Blundin

Okay.

Brett Adcock

So how do you get the compute, sensors, and embedded software all to talk to each other?

Dave Blundin

Yeah.

Brett Adcock

Then you have structures.

Dave Blundin

Okay.

Brett Adcock

Those are the core ingredients of a robot or something physically moving through the world.

Dave Blundin

So then my question is, traditionally, the employee base would be experts in 1, 2, 3, 4, 5, and 6.

Brett Adcock

Yeah.

Dave Blundin

They’d be really, really good.

Brett Adcock

Yeah.

Dave Blundin

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

Brett Adcock

The next thing in Archer is: How are you going to plan? You do it through a pilot. My aircraft, Midnight, is a piloted 4-passenger aircraft. So who’s doing the planning?

Dave Blundin

Yeah.

Brett Adcock

You have higher-level behaviors in the stack that tell the lower-level control and code what to do.

Dave Blundin

Yeah.

Brett Adcock

Here at Figure, it’s been changing over time, but now it’s entirely neural nets—with Helix too.

Dave Blundin

Hmm.

Brett Adcock

What is the highest-level behavior telling the rest of the stack what to go do?

Dave Blundin

Yeah.

Brett Adcock

Where does that come from? 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 a neural net that’s doing the planning and reasoning.

The kitchen demonstration you guys saw today that we released—what’s telling the robot what to go do next? What’s telling it to pull the rack out of the dishwasher and go grab the cups—not the coffee cups, but the water cups? That’s a neural net making that plan.

Dave Blundin

Yeah.

Brett Adcock

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.

Dave Blundin

Yeah.

Brett Adcock

And then how to descend.

Dave Blundin

And it’s a different type of neural net.

Brett Adcock

It’s a human biological neural net.

Dave Blundin

I guess.

Brett Adcock

Yeah.

8. Robots Move Into The Home

Peter H. Diamandis

Let’s talk about application layers. 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. That includes elder care and helping people stay healthy at home.

By the way, you just came through Fountain? You got through Fountain Life?

Brett Adcock

Yeah, I did.

Peter H. Diamandis

How was the experience for you?

Brett Adcock

Thanks for referring me. It was great. I went down to a clinic a couple of weeks ago.

Peter H. Diamandis

Which one? In Orlando?

Brett Adcock

Orlando.

Peter H. Diamandis

Yeah, headquarters.

Brett Adcock

I didn’t know what to expect. I’ve done full-body MRIs, CT scans, and blood work before, but when I got there, it was basically a full stack. You know this, but it’s a full stack.

Peter H. Diamandis

Everything measurable about you. 200 gigabytes of data.

Brett Adcock

Exactly. I spent about 5 hours there, left, and got the download last week. It was unbelievable. What was great about it was that you get a comprehensive understanding of my body and what’s happening, but also somebody there reporting it out and talking me through how we understand it and what to do next.

Peter H. Diamandis

And a plan.

Brett Adcock

And basically build a plan from there.

Peter H. Diamandis

Yeah. Yeah.

Brett Adcock

It was great. I actually purchased it as well for my parents. I think it’s just a great gift.

Peter H. Diamandis

Oh, hell yeah.

Brett Adcock

Yeah.

Peter H. Diamandis

Dave, we need to get you there too.

Dave Blundin

Why Orlando, and why not somewhere else, like Wyoming or something?

Brett Adcock

I was on the East Coast, so I popped down to Orlando. It was just easy for me.

Peter H. Diamandis

Yeah, so we’ve got New York, Orlando, Naples, Dallas, Houston opening, Miami, and Los Angeles. Anyway, back to the conversation here.

Brett Adcock

Yeah.

Peter H. Diamandis

I can imagine this is going to increase the value of health in the home a lot, right? One of my visions of the future is that you’re constantly being monitored for your blood biochemistries—your protein levels, your vitamin levels, and so forth—and that information is being uploaded to Figure in the kitchen, ideally cooking your meals suited to what you need in that moment.

Brett Adcock

Yeah.

Peter H. Diamandis

And then there’s the whole elder-care side.

Brett Adcock

Yeah.

Peter H. Diamandis

How do you think about that?

Dave Blundin

About that.

Brett Adcock

Growing up, I grew up on a farm in the Midwest, and then my parents got into independent and assisted living about 15 years ago. I kind of grew up around senior care a little bit in my life.

Dave Blundin

You got into that business?

Brett Adcock

Yeah. My parents own and operate senior-housing facilities in the Midwest.

Dave Blundin

Nice.

So wait, they’re still in Illinois?

Brett Adcock

Yeah, still in the Midwest.

Dave Blundin

Wikipedia says your hometown has 2,000 people in it. Is that right?

Brett Adcock

I grew up in Maquoketa, Illinois. I think it was about 1,800 people when I was growing up.

Dave Blundin

Farm boy.

Peter H. Diamandis

Small.

Brett Adcock

Yeah, in the middle of nowhere. We had no traffic lights and no fast food. It was a dry town. It was just a whole different world.

Dave Blundin

Oh, man.

Brett Adcock

Yeah.

Dave Blundin

Do they have parades for you when you go back home?

Brett Adcock

Man, it’s just—

Dave Blundin

They have robot parades going through the streets.

Brett Adcock

Yeah.

Peter H. Diamandis

Can you imagine that?

Brett Adcock

Yeah.

Dave Blundin

So do you understand the value of a fully autonomous humanoid robot?

Brett Adcock

We’ve got to figure out how to ship robots into senior care and let people age in place at home. I’m really passionate about that.

Dave Blundin

Yes. Yes.

Brett Adcock

It’s hard—

Dave Blundin

Age in place.

Brett Adcock

It’s hard to get people to move into assisted-living and care facilities.

Dave Blundin

Well, how does that work? You’ve sold out 3 years into the future. You can’t make them fast enough to keep up with the demand, and then you’ve got BMW and a bunch of industrial use cases.

Brett Adcock

Yeah.

Dave Blundin

But then you’ve got this in-home—

Brett Adcock

Yeah.

Dave Blundin

And you’ve got—

Brett Adcock

Really? Okay. Maybe I’ll level with you on how I think about things. We’ve been spending the last 3½ years—we’re about 3½ years old—trying to figure out what the right recipe is, in the first instance, for what a general-purpose architecture would look like for humanoids. We believe we’ve found it internally, and we understand what that is.

Dave Blundin

Yeah.

Brett Adcock

We believe we know how to make robots now and put them out, and we’re going to run them really hard this year.

Dave Blundin

You showed us—what do you call it? The Grid?

Brett Adcock

Yeah, the Grid.

Yeah.

The Grid is my favorite place here. We have 4 buildings on campus, and it’s one of them. We have the facility outfitted to expand to hundreds of robots that will run 24/7. We have this little mission command post on the second story, kind of like a 007 situation room, and you can see every robot there.

It’s going to be doing both home and commercial workforce. We’re spinning up right now. The facility just opened this week.

Dave Blundin

Mm-hmm.

Brett Adcock

You guys saw it.

Dave Blundin

Yeah.

Brett Adcock

It’s squeaky clean, and we’ll start shipping Figure 03s into it this month.

Dave Blundin

Yeah.

Peter H. Diamandis

So: model homes, model factories, model operations.

Brett Adcock

Yeah.

Dave Blundin

Within mission control, you think of watching the robots, but the robots also have their own vision, which transmits back. It’s more like those combat movies where, back at the home base, they’re watching the invasion or whatever. You’re seeing through the eyes of the soldiers.

Brett Adcock

Yeah.

Peter H. Diamandis

Yeah.

Dave Blundin

You’ve got all that data coming back into mission control, too. So, if there are 200—how many are in there at any given time? A couple hundred?

Brett Adcock

250, 300.

Dave Blundin

250 or 300 robots—

Brett Adcock

Yeah.

Dave Blundin

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

Brett Adcock

Yeah.

9. Embodiment Defines AGI

Peter H. 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’m getting the chance right now to spend a lot of time on both physical AI and digital AI at Hark, so kind of both. I think when I talk to AI today or use it, it just feels like it’s so dumb.

Dave Blundin

Mm-hmm.

Brett Adcock

It just feels like you’re starting a new chat. You’re basically asking it for knowledge retrieval. It’s an advanced Google search engine.

What I envision is that we want to build the future. We want to build Jarvis—

Dave Blundin

Yes.

Brett Adcock

—or we want to build the Jetsons. I want to talk to it.

Dave Blundin

I want Jarvis so bad.

Brett Adcock

I want it to talk to me. I want it to reason. I want it to have perfect memory. I want it to be able to touch the world, both digitally and physically. I want to build it to be general-purpose, able to do things for me and reason through things.

Dave Blundin

Yeah.

Brett Adcock

We have Hark now designing CAD from scratch. You ask it to build a CAD thing. I asked it to build a monster truck for my son in CAD, and it’s going out, finding a CAD package, installing it, opening it up, and learning how to build CAD and the parameters it needs to look at for building monster trucks. It goes off and does it, and we can do that in under an hour now, fully end to end.

Dave Blundin

Clean sheet.

Brett Adcock

Clean sheet from a single prompt, and it’s using tools and computers like a human can. We’re going to give it all the same tools. We’re going to give it all the tools that Figure uses for CAD and FEA, all this different stuff, and it’s going to learn all this.

Dave Blundin

Was that the inspiration for Hark? The fact that there are 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 from your—

Brett Adcock

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

Dave Blundin

Well, specifically, Anthropic wants to dominate coding and code self-improvement, and then OpenAI wants to dominate—

Brett Adcock

I want to dominate a sci-fi AI future.

Dave Blundin

Yeah.

Well, Jarvis. Everyone knows Jarvis.

Brett Adcock

I want to build Jarvis. I want the smartest person in the world with everybody.

Dave Blundin

Yeah.

There’s also the von Neumann probe. It’s the idea that these things go out into the solar system and then ultimately out into the galaxy and start making themselves out of raw materials.

Brett Adcock

Nobody’s doing this.

Dave Blundin

Yeah.

Brett Adcock

Everybody’s copying the other frontier lab that’s copying their frontier lab. Nobody’s building true multimodal systems that really can reason and understand, have persistent memory, and go out and touch the world and do things. That’s my version of AGI: I can do what humans can do, and humans are not sitting there giving me Google search answers.

Dave Blundin

Right. Right.

Brett Adcock

Which is what we have now.

Dave Blundin

Right.

Brett Adcock

It’s terrible. In one aspect, it’s great because this new alien technology dropped on the planet in 2022, and we’re trying to figure out what to do with it. But the other aspect is that there’s so much the models can do now.

There's such an overhang in the product capabilities.

Dave Blundin

Mm-hmm.

Brett Adcock

We're understanding that better now at Hark. We're understanding that better now at Figure. I think we're abstractly getting to a place where we're building synthetic humans at scale.

Dave Blundin

Mm.

Brett Adcock

These humans can be both digital—they can work on the computer and use tools—and physical. They'll be able to reason with you, talk, have memory, understand you, and go off and do anything a human can.

Dave Blundin

Yeah.

Peter H. Diamandis

Have you been tracking Claude Bot now, Malt Bot?

Brett Adcock

Yeah, I've been tracking Claude Bot.

Peter H. Diamandis

Yeah.

Brett Adcock

It's really cool.

Peter H. Diamandis

Yeah, they renamed it to Malt Bot.

Brett Adcock

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

Peter H. Diamandis

Yeah.

Brett Adcock

Where you have such incredible—

Peter H. Diamandis

They're—

Brett Adcock

—capabilities—

Peter H. Diamandis

Capabilities—

Brett Adcock

—that can be used with a very simple harness and very simple Markdown files, and very simple tools you can give it, on the back of Opus or whatever you're going to use.

Peter H. Diamandis

Yeah.

Brett Adcock

It can do magical things for the world.

Peter H. Diamandis

Yeah.

Brett Adcock

And we've had that for a long time now. It wasn't like they went out and built a new AI model for this. They basically just put some harnessing, MCP, and APIs around this, and it basically went out and can basically be your executive assistant.

Peter H. Diamandis

Mm.

Brett Adcock

It's really awesome.

Peter H. Diamandis

Yeah.

Brett Adcock

And there's a huge area here to give that to every person in the world and make it easy.

Peter H. Diamandis

Yeah.

Brett Adcock

We're doing some model development now at Hark that is truly state-of-the-art, I think, and I'm excited about that. We're also doing some of that now in the physical world at Figure, so we have this digital-versus-physical thing that I'm seeing on both. I'm just so excited about this future, even over the next 12 to 18 months. The next 12 to 18 months, I think, will be the largest AI transformation we've ever seen.

Peter H. Diamandis

Yeah.

Brett Adcock

And getting back to your point about what we do with healthcare and robots, we're going to make a shit ton of robots. We're spinning up resources right now, both at Bachy you're seeing now and future Bachy, to basically be able to make millions of robots.

Peter H. Diamandis

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

Brett Adcock

I think from a hardware perspective, in 2026 we'll be able to do—

Peter H. Diamandis

Mm-hmm.

Brett Adcock

—what surgeons can do.

Peter H. Diamandis

Yeah.

Brett Adcock

I see no reason we can't do that, given where we're at with our roadmap and things like that with Figure.

Peter H. Diamandis

That's pretty—

Peter H. Diamandis

That's pretty fast.

Brett Adcock

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

Peter H. Diamandis

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

Brett Adcock

Yeah. Then you have to get the brain to work really well—

Peter H. Diamandis

Yeah.

Brett Adcock

—at these things, and this has got to work at the highest level of performance.

Peter H. Diamandis

Let me ask you—

Brett Adcock

But—

Peter H. Diamandis

—federated learning gives you an incredible amount of—

Brett Adcock

I think we're—

Peter H. Diamandis

—knowledge.

Brett Adcock

I think we're very close to this working. I think we've already shown that if we can get the right data and the hardware—if the hardware can do it—the simple hack is that if you can teleoperate the robot to do it, we can learn it.

Peter H. Diamandis

Yeah, I mean, that's a really—

Brett Adcock

And—

Peter H. Diamandis

—important point people need to understand. If you can teleoperate the robot, if the mechanical—

Brett Adcock

It just—

Peter H. Diamandis

—systems, the motors, and the fidelity can be done.

Brett Adcock

Yeah. I think we're dumping on teleop, but teleop has a couple of good things. It's a really good testing tool.

Peter H. Diamandis

It proves out the hard—

Brett Adcock

And it proves out the hardware. If you can't teleoperate it, you're not going to be able to learn it, meaning if there are restrictions in the range of motion or payloads—if you pick up something heavy and the robot can't do it during teleoperation—it's not going to be able to do it in a learned policy.

Peter H. Diamandis

Yeah.

Brett Adcock

So I think if you can teleop, you can learn it from a hardware perspective. We'll be there in terms of more dexterous-type things we talked about here. What we've already shown is that if we can get the right data for it, we can get the hardware to basically do anything it's capable of.

Peter H. Diamandis

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

Brett Adcock

Yeah, for sure. We have it now with tactile sensors—the palm camera is a good example. Humans have palm cameras.

Peter H. Diamandis

I would Google that.

Brett Adcock

We've been seeing a boost in performance.

Peter H. Diamandis

Maybe I do.

Brett Adcock

A lot of cool things. We're reaching into a cabinet now, and we can use the palm cameras.

Peter H. Diamandis

It totally makes sense. As soon as I heard it on the tour, I was like, "Duh." It totally makes sense. I mean, how many times a day are you reaching? You can't—

Brett Adcock

We're kind of blind reaching in.

Peter H. Diamandis

—just putting your phone down there to get the camera to look at it.

Brett Adcock

Yeah.

Peter H. Diamandis

I'm sure we would have evolved an eye right here if it were—

Brett Adcock

Yeah.

Peter H. Diamandis

—physically possible.

Brett Adcock

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

Peter H. Diamandis

Maybe there are.

Brett Adcock

We do.

Peter H. Diamandis

I just bought an amazing drone, the Antigravity drone. Have you seen it? It's the VR headset.

Brett Adcock

Mm.

Peter H. Diamandis

It's got 360 above, 360 below, backward, and forward.

Brett Adcock

Yeah.

Peter H. Diamandis

And it's extraordinary. So how do you think about cameras?

Brett Adcock

We do. We have them on the robot.

Peter H. Diamandis

You do?

Dave Blundin

Yeah.

Brett Adcock

They are rear-facing cameras.

Peter H. Diamandis

Okay. I have to ask this question for our Moonshot Mates—

Brett Adcock

Yeah, if you just—

Peter H. Diamandis

—for some—

Brett Adcock

—go over and look behind them, they have cameras in the back.

Peter H. Diamandis

Okay.

Brett Adcock

Yeah.

Dave Blundin

Do they?

Peter H. Diamandis

I mean, I'm seeing it rotate here—

Brett Adcock

Yeah.

Peter H. Diamandis

—on here.

Brett Adcock

Yeah.

Peter H. Diamandis

One of our Moonshot Mates, Salib Alsmal—you might know him. He's one of my co-founders with Ray at Singularity University. He's like—

Speaker 4

Nope.

Peter H. Diamandis

"Why in the world are there only 2 hands? Why don't we see robots with 4 hands or 6 hands?"

Brett Adcock

Yeah.

Peter H. Diamandis

To put that to bed once and for all, personally.

Brett Adcock

Yeah. We get asked this a lot. It's like, "Why not superhuman?" and all these different things, which is a lot of the questions. My summary to this is that our goal is to be able to do what humans can, and then you want to do it the cheapest and lightest possible way you can. 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 humans in a lot of ways—if it can run a 3-minute mile, if it can do a backflip, if it's got a bunch of arms—it's going to make the robot really heavy.

Peter H. Diamandis

Yeah.

Brett Adcock

It's going to make it really costly. It's going to be really hard to manufacture.

Peter H. Diamandis

It's optimization.

Brett Adcock

And then your question is, okay, when I look at the logistics use case, I don't think you can actually have 4 arms or 6 arms and move any faster. The line is relatively small—it's maybe a meter or so in depth. You have to 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 it.

Peter H. Diamandis

Mm-hmm.

Brett Adcock

In that case, we basically have another 3 to 5× in terms of speed that we can run the actuators at. The software isn't enabling—

Peter H. Diamandis

Mm-hmm.

Brett Adcock

—because it doesn't know how to do it yet. So we can run 3 to 5 times faster than what you saw today.

Peter H. Diamandis

Wow.

Brett Adcock

Because we need the whole body to run.

Peter H. Diamandis

That’s amazing.

Brett Adcock

Yeah. When we look at it in terms of radians per second, we traditionally look at RPMs.

Peter H. Diamandis

Yeah.

Brett Adcock

We look at radians per second here. We have another 3 to 5X headroom in the actuators that you’re seeing now.

Peter H. Diamandis

I would love to see a robot do that.

Dave Blundin

I think the cost of a mistake when you’re unloading the dishwasher—

Brett Adcock

Yeah.

Dave Blundin

At the current rate of speed, the cost of a mistake is relatively low. You start running 3 to 5X faster and you—

Brett Adcock

It’s just like—

Dave Blundin

If that thing glitches—

Brett Adcock

I just don’t know if it’s really needed.

Dave Blundin

Yeah.

Brett Adcock

You’re going to get a really expensive robot—

Dave Blundin

Yeah.

Brett Adcock

—and it’s going to be less safe, it’s going to be harder to manufacture, and then you’re going to have a $10,000 to $20,000 robot there—

Dave Blundin

Right.

Brett Adcock

—and you’re going to have a really expensive robot. Let’s call it $50,000. Cost is really a function of manufacturing volumes.

Dave Blundin

Mm-hmm.

Brett Adcock

So you really want to build like the car.

Dave Blundin

Well, that’s why going after the industrial use case is such a no-brainer—

Brett Adcock

Or even just a home.

Dave Blundin

Because—

Brett Adcock

The home needs every—

Dave Blundin

Well, the home is huge in the end, but if you’re running 3 to 5 times faster than what we’re seeing right now in the home and you kick the cat or something like that, that’s not great. In the industrial use case, everything is taped off.

10. The Billion Robot Economy

Peter H. Diamandis

I remember I was interviewing you for my next book, which comes out in April. Here it is, Where Is God?

Brett Adcock

Oh, wow.

Peter H. Diamandis

We’ve talked about this, but I’m super excited about it. Of course, you and Figure are prominent in the book.

Dave Blundin

Hmm.

Peter H. Diamandis

Because this is godlike. I mean, it’s extraordinary. We’re giving life to new systems. I was interviewing you about how many robots there will be and what the price point is.

Brett Adcock

Yeah.

Peter H. Diamandis

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 $300 a month, $10 a day, $0.40 an hour. And then the question, when you ask the question, is: If it’s really $10 a day, how many would you own or would you have?

Brett Adcock

Yep.

Peter H. Diamandis

You end up with a lot of robots. So what’s your estimate on the number of robots on planet Earth in 2035 or 2040? Where do you think that’s going?

Brett Adcock

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 on the order of 5 to 7, maybe 10 billion in the commercial workforce. I think if all goes well, you could basically build tens of billions of humanoids on the planet.

Dave Blundin

Mm-hmm.

Brett Adcock

You’re basically building a replica of a human that’s really cheap and works 24/7.

Dave Blundin

Yeah.

Brett Adcock

We will be at a point, I hope, in 24 months, where all the robots will build all the robots.

Dave Blundin

Well, that’s what I wanted to ask about scale. You said we’re going to ramp up to millions a year. One per person on the planet is 8 billion, so millions per year really isn’t that much.

Brett Adcock

It’s nothing, yeah.

Dave Blundin

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

Brett Adcock

You also need tons of working capital. If you put a billion robots on the planet, even if they’re, let’s call it, $20,000 apiece, you’re talking $20 trillion of working capital.

Peter H. Diamandis

I mean, you’re not that much—

Dave Blundin

So there’s some land there.

Peter H. Diamandis

There’s a billion cars on the planet right now. There’s not more than that.

Dave Blundin

But if you tried to build them in 5 years—

Peter H. Diamandis

There’s a—

Dave Blundin

It took 80 years to accumulate those cars. Some of those cars are 30 or 40 years old and still running.

Brett Adcock

We have a couple billion cars on the planet, but we make a billion or more cell phones a year. So—

Dave Blundin

Yeah.

Brett Adcock

I think it’s just more cell-phone-like, where it’s going to be personal. We even go back and forth on whether, if your robot breaks, you want a brand-new refurbished robot, or whether you want the old robot you used to have because you’ve known it, you understand it, and it has a personality.

Dave Blundin

Yeah.

Brett Adcock

I think it’s going to be with you. It’s going to know everything about you. You’re going to talk to it every day.

Peter H. Diamandis

Yeah. Well, why wouldn’t you just have a personality transfer?

Brett Adcock

You could, but I think there are some inner workings. It’s got all the scratches on it that you know. It’s your thing, and it’s got a little bit of a feeling. But yeah, for sure, I think that’ll be fine. But at some point—

Dave Blundin

Wait, wait. Let me ask the geeky finance question, just before we lose the topic here.

Brett Adcock

Yeah, sure.

Dave Blundin

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, and the demand is on the order of billions, not millions. Like you said, to build that out in 1 iteration—you used the cell phone as an analogy, but Apple had 15 years to profitably ramp up production to 1 billion units a year.

Brett Adcock

It will be faster. Yeah.

Dave Blundin

And so the demand is there to do it in 1 year.

Brett Adcock

Yeah.

Dave Blundin

But you would need $1 trillion—some insane amount of capital.

Brett Adcock

Yeah, you’d need a lot of capital.

Peter H. Diamandis

But that’s no longer an insane amount of capital. I mean, we’re seeing—

Brett Adcock

I think you can—

Dave Blundin

So what do you do?

Brett Adcock

I think you can—

Dave Blundin

Do you leave the world starved, asking for the robot for 5 years—

Peter H. Diamandis

Yeah.

Dave Blundin

—or do you raise $1 trillion?

Brett Adcock

If you look at credit card receivables or car leasing, these are trillion-dollar markets per year in terms of financing. So I think the financing market is there for this. What do you do? I think one is, you have to solve the neural-net game. You have to be able to scale with neural nets, and you have to solve pre-training and generalization.

Dave Blundin

Yeah.

Brett Adcock

So you have to solve for a general-purpose robot. That is table stakes. You have to solve this. That’s why we’re so obsessed with trying to solve it here at Figure. If you don’t solve that—

Dave Blundin

Right.

Brett Adcock

—none of this matters.

Dave Blundin

Yeah.

Brett Adcock

The second step is you have to have robots in the loop building other robots.

Dave Blundin

Mm-hmm.

Brett Adcock

Those 2 things have to be solved, and you have to design the robot to make sure it can hopefully design itself at the end of the day. There’s a bunch of stuff we’re putting in place in terms of manufacturing execution software, the lines, and all the design of it, so we can, at scale, have humanoids go in, build other humanoids, and get them off the line.

Dave Blundin

Yeah.

Brett Adcock

I think these adoption curves are shortening and shortening.

Dave Blundin

Mm-hmm.

Brett Adcock

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 1 billion of them today.

Dave Blundin

Yeah.

Peter H. Diamandis

What did you say again?

Brett Adcock

I think we ship 1 billion today.

Dave Blundin

Yeah, I totally agree.

Brett Adcock

So basically, it comes down to—

Dave Blundin

Totally agree.

Brett Adcock

Can you get the neural nets to work at scale? Can you get the models good enough to generalize to this scale? Build a general-purpose robot—call it a general-purpose robot, like a human in a suit. And then can you get robots in the loop building other robots?

Dave Blundin

Well, the other thing that’s really compelling is that 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—

Peter H. Diamandis

Yeah.

Dave Blundin

Do you know the Star Trek Genesis project?

Peter H. Diamandis

Yeah.

Dave Blundin

You’ve got the little capsule. It has basically the germ of—

Peter H. Diamandis

The DNA.

Dave Blundin

You could ship literally a box to Kenya that’s like, “Here, here’s the Figure box.” It opens up, and it starts making a Figure manufacturing plant—

Peter H. Diamandis

Sure.

Dave Blundin

right out of thin air—in the middle of Kenya.

Peter H. Diamandis

Yeah.

Dave Blundin

And if there's capital there to bring the resources to it, then that's how you get infinite scale.

Peter H. Diamandis

Well, we talk about that. The innermost loop is energy and AI intelligence.

Dave Blundin

And local mining for the materials or whatever, but it's completely self-contained. But the key—

Peter H. Diamandis

Mm.

Dave Blundin

—is that you just unlocked that capital that wanted to build something productive—

Peter H. Diamandis

Yeah.

Dave Blundin

—while all the IP is still flowing back—

Peter H. Diamandis

And 100X-ing the GDP of Kenya.

Dave Blundin

—to train the neural net centrally.

Peter H. Diamandis

And just drop it.

Dave Blundin

Yeah, 100X the GDP of that jurisdiction. There's latent capital all over the world.

Peter H. Diamandis

So we talk about how there's a lot of fear out there in the world about losing jobs to AI and robots. 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, 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 3 shifts, and then earns enough to get a second robot working for me.

Dave Blundin

Mm-hmm.

Peter H. Diamandis

And so the question becomes: Where is that capital captured? Is it inside the hyperscalers? Is it inside the individual? That's going to be the interesting conversation coming up. How do you think about that, Brett?

Dave Blundin

Mm.

Brett Adcock

We're going to sell robots at scale. You're going to be able to deploy as many robots as you want and do whatever you want to do. You can always do whatever you want. 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. The future's going to be really fun.

11. Safety Unlocks Home Robots

Peter H. Diamandis

Safety and privacy. Let's talk about safety in the home and privacy in the home. There were lawsuits over the last years with Google and Amazon: It's listening to you in your bedroom and so forth.

How do you address safety and privacy? Or is it just going to happen? Is it just too early because we're not there yet?

Brett Adcock

I think those are some really hard questions to answer in one go, because there's a bunch of different safety implications here that are just—safety's probably the number 1 thing to tackle to get robots into the home at scale.

Dave Blundin

Yeah.

Brett Adcock

There's a semantic understanding of safety. If there's a candle lit and I knock it over by accident, or if there's a boiling pot of water and I hit it, just understanding how to be safe in an environment where humans are.

Dave Blundin

Yeah.

Brett Adcock

And there's actually the intrinsic safety of: Can the robot be with humans and animals and pets and be safe? That has to be solved. We can talk at length about how we're going to solve those problems.

And then you have the whole privacy, cybersecurity, and other aspects of this that need to be approached with good intentions: How do we solve those problems? We are working on all of those now. They are very difficult things to get right.

Dave Blundin

Mm.

Brett Adcock

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 H. Diamandis

They could be safer than humans by a large margin, just like autonomous cars are safer than humans at the end of the day.

Brett Adcock

Yeah. These have superhuman perception. We can see basically all around us at all times. We're always on. We're always computing what to go do.

Dave Blundin

Mm.

Brett Adcock

Assuming nobody's trying to be mean to the robots or things like that, I think we should be extremely safe around everything we're doing.

And around privacy, these are going to be in your home, so being upfront about what data we're collecting, where that data's going, and how we're keeping that data private and encrypting that data—all of this is super important.

Dave Blundin

Yeah.

Brett Adcock

We have an entire team on cybersecurity here in-house, on both the product and commercial side and the corporate side, working through how we think about this at scale.

Dave Blundin

Yeah.

Brett Adcock

Right now, they're great. They're from the big companies that have been doing this for a long time. We think about it as the corporate side as well as the product side, on the robot side as well.

Dave Blundin

Yeah.

Peter H. Diamandis

Your facility here, which is your sort of prototype manufacturing facility—50,000 robots a year, you imagine?

Brett Adcock

That facility can support about 4 lines. Each line can do about 12,000 units a year, so a little under 50,000 units a year at full run.

Peter H. Diamandis

What's your next step up, do you think?

Brett Adcock

We're building thousands of robots right now. That's the big push we're doing right now. You just saw it today.

Dave Blundin

Yeah.

Brett Adcock

That's the Figure 02 stuff we're doing off the lines today. Then we want to go to tens of thousands, and then hundreds of thousands and millions. I think we need to take those steps as a company to go do that.

This facility will top out at 50,000 a year, a little under 50,000 units a year at full capacity. Think about the long term: It'd probably be low volume when we look back in 5 or 10 years.

Dave Blundin

Do you think you might franchise out the neural net and the circuitry around it? All these other people are saying, “I'm building a robot that cleans industrial pipes,” and all these different form factors.

Brett Adcock

No.

Dave Blundin

No? Just keeping—

Brett Adcock

I think it's super unsafe.

Dave Blundin

Yeah.

Brett Adcock

I think we see these robots out there like this. They're around humans, and we don't own the hardware. We don't know what they're doing. It's our neural net in it. I think it's—

Dave Blundin

Oh, interesting.

Brett Adcock

Yeah. I think it's similar to Archer. When we were building Archer out, I think it's a safety-critical system.

Dave Blundin

Yeah.

Brett Adcock

Especially Archer—since they're licensing it out to their folks and stuff like that, that's very problematic.

Dave Blundin

Yeah.

Brett Adcock

I think here it's the same thing. Even with humanoids done right, we have a fiduciary duty to our civilization to build really safe humanoid robots at scale.

Dave Blundin

Yeah.

Brett Adcock

Giving this AI system or even hardware to anybody that would want it is not something we will entertain.

Dave Blundin

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

Brett Adcock

I think in the future, everything that'll move will be a robot.

Peter H. Diamandis

Mm-hmm.

Dave Blundin

Mm.

Brett Adcock

Besides humans.

Dave Blundin

Yeah.

Brett Adcock

Within that, I think humanoids will dominate the plurality of all robots. It'll just be such a big percentage of them that the other robots will be niche and expensive, like super-duty trucks that you have out mining. They'll just be made for specific areas, maybe underwater, as you said, or something else.

Dave Blundin

Or heart surgery or brain surgery. You've got these very, very fine-tuned systems. It's like a robot controlling a robot.

Brett Adcock

I think you're left with very expensive equipment that's very siloed. You really want to build a general-purpose machine that can learn across a variety of different tasks and have that transfer learning.

Dave Blundin

Mm-hmm.

Brett Adcock

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.

Dave Blundin

Yeah.

Brett Adcock

So my view is, I think it'll just be humanoid robots everywhere on the planet. There will be other robots there, but they'll just be niche businesses.

Dave Blundin

Mm-hmm.

Peter H. Diamandis

When I was flying up here, I posted your video that you released on Helix Studio today, and we asked the community for questions. It just blew up with a whole bunch of amazing questions.

One of the questions is: “Do you have a blooper reel, and can folks see it?”

Brett Adcock

No.

Peter H. Diamandis

“And then what's the weirdest task someone on your team has tried to teach it to do, and it absolutely did not work?” That's from a listener.

Brett Adcock

Ben Casper, nice. The weirdest task that did not work? Well, the weirdest task…

Peter H. Diamandis

Yeah.

Brett Adcock

Jogging was fun. Jogging was cool because we really had a steerable jogger, and a lot of this work in running has, again, been open-loop.

Peter H. Diamandis

Mm-hmm.

Brett Adcock

Actually, I have a gift for you. It goes there.

Peter H. Diamandis

Okay.

Brett Adcock

I have 2 Figure-deadmau5 hats.

Peter H. Diamandis

Okay. What does that mean?

Brett Adcock

We basically had robots on stage at a deadmau5 concert at Red Rocks late last year. We generally don’t venture out into weirder stuff. There you go. Nice.

Peter H. Diamandis

Yeah.

Brett Adcock

We actually had deadmau5 at our last 2 holiday parties at Figure, which was fun. We generally focus on how to design something really useful, but we’ve had some pockets of time to do fun stuff like this.

Peter H. Diamandis

Oh, that’s good.

Brett Adcock

I think having robots on stage at deadmau5 at Red Rocks was just—I flew in for it. It was unbelievable.

Peter H. Diamandis

Yeah, and you had them on stage for that.

Brett Adcock

We had them on stage.

Peter H. Diamandis

Oh, that’s great.

Brett Adcock

It was great. We had several Figure 02s on stage just jamming. We had them all synced, so they synced to the music as they danced, which was really cool.

Peter H. Diamandis

Yeah. I would love that.

Brett Adcock

They were moving toward it, which was actually wild.

Peter H. Diamandis

I had you on stage last year at the Abundance Summit, but Figure wasn’t with you, so I need to get you back there with Figure in the loop.

Brett Adcock

Totally.

Peter H. Diamandis

Yeah, for sure. When are we going to see the first Figure in a customer’s home? That’s the next question.

Brett Adcock

We want to ship robots when they’re really ready. I don’t want to ship slop.

Peter H. Diamandis

Best guess.

Brett Adcock

I think last year I said 2025. In 2026, we launch a robot to do end-to-end housework in alpha testing, in my home.

Peter H. Diamandis

Mm.

Brett Adcock

To do full—

Peter H. Diamandis

Like mopping, cleaning, dishwashing—

Brett Adcock

We’ve done pockets of work really well. We’ve done dishes and laundry and all this. You’re seeing something that’s getting tied together now, but I want to do it across days and weeks of work. I want to be able to drop it into somebody’s home that it’s never seen and also make that work really well.

Peter H. Diamandis

Yeah.

Brett Adcock

I want to be able to talk to it, and I want it to—

Peter H. Diamandis

Yeah.

Brett Adcock

—be able to understand me, remember things, show us things, and be able to walk through a room and show it around, almost like a visitor you have at your house for a week, and understand what to go do.

Peter H. Diamandis

2027, 2028, 2029? Your best guess—earliest or latest window?

Brett Adcock

I’ll tell you what we’re doing: We’re working until midnight every night to solve this problem.

Peter H. Diamandis

Yeah. You were here too.

Brett Adcock

We are here every weekend, every night, to try to figure out how to solve this.

Peter H. Diamandis

That’s a given. That’s—

Brett Adcock

This is the question. We call it general robotics. This is kind of where we want to head. I think by the end of the year, we will be able to put a robot into an unseen home and have it do fairly long-horizon work. Then you want to measure how many human interventions you have.

Peter H. Diamandis

Yeah.

Brett Adcock

Is it once an hour, once a day, once a week, once a month? 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.

I think anybody who tells you, “Hey, we’re going to ship them or teleoperate them in the home, or we’re going to ship them in at scale in a year,”—you’ve got to ship in a small quantity, and they’ve got to work well. Then you’ve got to work out the problems, and you’ve got to ship again. You have to have an iterative design roadmap, which we have here, and we need to learn.

Peter H. Diamandis

You can learn.

Brett Adcock

And learn. It’s going to work well at 1. Then it’s going to work well at 10 homes. Then it’s going to work well at 100. It’s going to work well at 1,000. Then it’s going to be 10,000. Then it’s going to be 100,000. Then it’ll be at 10 million. I think it’s going to be super steep.

Peter H. Diamandis

Exponential growth curve.

Brett Adcock

Exponential growth curve. So I think it’s going to be super steep.

Dave Blundin

Is there anything to worry about there in terms of time to market? Because the industrial use—you’re sold out for years to come anyway.

Peter H. Diamandis

The question is, is competition going to come in and grab the market before?

Dave Blundin

Yeah. Or bond with your kids or something.

Brett Adcock

We feel the work we show today and the work we showed 2 years ago has never been done, in my mind, by any other humanoid company in history.

Dave Blundin

Yeah.

Brett Adcock

If that’s the marker, the marker is whenever somebody can do the Keurig test for a couple of minutes with uncut film, and I can watch it do it closed-loop with bimanual manipulation—not even just standing. You’re 2 years away from where we’re at.

Dave Blundin

Mm-hmm.

Brett Adcock

So I think we’ll see. We’re trying to push and continue to pull ahead, but hopefully by next year—

Dave Blundin

By next year—

Brett Adcock

—we can really show real general purpose inside the robot. Maybe even as soon as this year. I mean, it could happen in a couple of months. We have the right stack now. We’re building datasets at scale so quickly. We’re spending so much time and money on this internally.

We just launched our new B200 cluster. NVIDIA helped; Jensen helped. That went live this year.

Dave Blundin

How many GPUs are in your—

Brett Adcock

We have 3,000 B200s that are going live, and we have another set of much larger GPUs that we plan to put out here.

Peter H. Diamandis

Is it for pre-training or—

Brett Adcock

We just use it for pre-training.

Peter H. Diamandis

—or for inference?

Brett Adcock

Pre-training.

Dave Blundin

You do it physically here?

Brett Adcock

No, we do not use it physically here.

Dave Blundin

That’s a lot of power.

Brett Adcock

Yeah, a lot of power.

Peter H. Diamandis

Jay Crate asks a question to the science-fiction geeks among us: What’s beyond Asimov’s 3 laws for you? Have you thought about that? Have you thought about fundamental laws to program into—

Brett Adcock

Yeah.

Peter H. Diamandis

—your robots?

Brett Adcock

I think you really want to put these rules down into the nonvolatile memory onboard the robot, at the chip level.

Dave Blundin

Oh, yeah.

Brett Adcock

At the chip level.

Dave Blundin

R2-D2.

Peter H. Diamandis

Mm.

Brett Adcock

Yeah. We’ve been thinking about this quite a lot. On the one hand, we still want to solve general-purpose-ness. On the other hand, we also want to figure out, once we’re close there, how we get all the supporting things ready to go.

Peter H. Diamandis

Yeah.

Brett Adcock

Safety needs to be there. Privacy needs to be there. Fleet operations, the reliability of the robot, the maintenance plan for how we’re going to service this, and everything in the business model—all of it, including financing—needs to be packaged and ready to go. So we’re working through all of these now.

Asimov got a lot of things right, and I feel like a lot of the 3 laws—these foundational rules for how we treat humans—we have our own spin on this that I won’t publicly tell today. The goal is to do good work.

Dave Blundin

Is this something everyone learns internally in corporate training and memorizes and all that?

Brett Adcock

It’s something that we put, and are going to continue to put, on all the robots.

Peter H. Diamandis

So you have a newborn relative.

Brett Adcock

A new what?

Peter H. Diamandis

You have a newborn child.

Brett Adcock

Oh, yeah.

Peter H. Diamandis

Right?

Brett Adcock

Yeah.

Peter H. Diamandis

So the question here from KK says, “When would you trust Figure to hold your newborn?”

Brett Adcock

Yeah.

Dave Blundin

The new Figure 03 is soft. It looks like it’s designed for the home.

Brett Adcock

That’s this.

Dave Blundin

But it’s still about 100 pounds, right?

Brett Adcock

I think it’s the same. I like this question a lot because at Archer I always say, “Until I put myself, my kids, and my family on board, it’s not safe enough to fly anybody.”

Dave Blundin

Yeah.

Brett Adcock

I wouldn’t do that today at Archer, and I hope soon I can do that. At Figure, I think it’s the same question: when do I feel safe enough to have a robot in my home?

Peter H. Diamandis

Well, you had it in your home, but—

Brett Adcock

But I’ve been there. We’ve had folks there, and we monitor it.

Peter H. Diamandis

Yeah.

Brett Adcock

I think we’re truly safe, but we’re not there now, and I think that’s a great bar for us to hit. When I can put a robot in my home fully 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 where I would say, “This is ready for everybody.”

It’s a good heuristic for us to really try to hit. That’s our goal here: to be able to put it in my home with free rein to go do what it needs to do.

Peter H. Diamandis

Yeah.

Brett Adcock

We’re there with it now. We babysit it, watch it, and it works well. I’ve shown videos of the robot being with kids, but I think we’re doing it in a safe way.

Peter H. Diamandis

Yeah.

Brett Adcock

The robots have been totally safe, which is great. Number 1, we need to build a system safety architecture that’s really, really fault-tolerant and redundant in real time.

Peter H. Diamandis

Mm-hmm.

Brett Adcock

We’ve done that, and we’re doing a better job of that in the future. Number 2, you have to build a safety track record for this. There’s nothing better than actually proving this thing is going to be safe.

Dave Blundin

It’s a nice barrier to entry, too, if you take the Apple road: it’s got to be a great out-of-the-box experience. That means not stepping on the cat, certainly not dropping the baby, and getting the cybersecurity side of it right, too—not transmitting everything back and having it posted on the internet.

Brett Adcock

Yeah.

Dave Blundin

But if you get that reputation—which it sounds like, of all the companies I’ve met, you’re perfectly positioned to get that reputation—don’t make a mistake along the way. Then everybody just says, “You know what? I’m going to choose a Figure robot because I just feel…”

Brett Adcock

Yeah.

Dave Blundin

It’s the same way people feel about the Apple brand with cybersecurity.

Brett Adcock

Yeah.

Dave Blundin

So—

Brett Adcock

I hope people walk away from this knowing that general-purpose robots are coming. It feels very close. There are a lot of other things around that you have to get right to build this at scale really well.

Peter H. Diamandis

Is that your main message you want to get across here to everybody watching?

Brett Adcock

I think the main message we feel every day, if people are excited about AI and robotics, is that this is going to happen really soon.

Peter H. Diamandis

Yeah.

Brett Adcock

And it’s happening. I mean, you saw it today—

Peter H. Diamandis

But I don’t think people have a clue how fast this transition is going to be.

Brett Adcock

Just go to our YouTube and watch our videos from the last 2 years. Watch them side by side. It’s dramatic, the change every single year.

Peter H. Diamandis

Yeah.

Brett Adcock

You saw it today in person. Our robots have now been in customer sites and things. They’ve been out, and we’re going to continue to show more, but it’s hard to feel that because you don’t see it every day.

At some point, probably in San Francisco first, you’ll see more humanoids than humans. I think that’ll be an amazing day.

Peter H. Diamandis

Yeah.

Dave Blundin

Right now I’m driving in Santa Monica. I was just—

Peter H. Diamandis

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

Brett Adcock

Oh, cool.

Peter H. Diamandis

She’s a—

Brett Adcock

I know Cathie.

Peter H. Diamandis

She’s a huge, huge fan of yours.

Brett Adcock

Cathie invested in me at both Archer and Figure. She’s great.

Peter H. Diamandis

Wow.

Dave Blundin

Yeah. She feels the same way.

Brett Adcock

Yeah, sure.

Peter H. Diamandis

She does. She’s very proud to be an investor in Figure. I was telling her that when I’m out with my kids in Santa Monica, we count the number of Waymos that we see.

Brett Adcock

Isn’t it crazy?

Peter H. Diamandis

We’ll see 10 Waymos.

Brett Adcock

Yeah.

Peter H. Diamandis

And then the Coco robots, the little ground robots—

Brett Adcock

Yeah.

Dave Blundin

Mm-hmm.

Peter H. Diamandis

The Starship bots and such—I mean, they’re all over the place.

Brett Adcock

It’s crazy.

Peter H. Diamandis

The first time you see one, you pull out your phone and take a photo. It’s really cool, and then you take it for granted, and then it’s in your way.

Brett Adcock

Yeah.

Peter H. Diamandis

Right?

Brett Adcock

My wife and I took a Waymo downtown last weekend for date night, and it was just unbelievable.

Peter H. Diamandis

Yeah.

Brett Adcock

As an engineer working on these hard projects, I feel like the amount of engineering work they had to do to put it together safely is—I mean, you know what I mean?

Peter H. Diamandis

Google did this, actually.

Dave Blundin

You control the music and the lights and the environment. If you take a New York City cab and get in the back, it’s like this smoky hell. Then you get into a Waymo, use the app, and turn it into your little paradise. It’s like night and day.

Peter H. Diamandis

Google did such a beautiful job taking the product. Larry Page saw the product win the DARPA Grand Challenge back in 2005, committed to it, and brought the team on.

Brett Adcock

I think it’s been 16 or 17 years.

Peter H. Diamandis

Yeah.

Dave Blundin

Yeah.

Peter H. Diamandis

They just stuck with it. Astro Teller at X basically built it out, and then Waymo became an amazing product. It works.

Brett Adcock

They’ve been undeterred for 16 or 17 years. “Don’t worry about it. We’re just going to make it.” And they did it. It’s unbelievable.

Peter H. Diamandis

Yeah. Amazing.

Brett Adcock

It’s very inspirational.

Peter H. Diamandis

Kudos to them.

Dave Blundin

Can I ask you my geeky, sci-fi-meets-geopolitics question du jour? I just got back from Davos on Friday. Today’s Tuesday, so I’m 9 time zones away.

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.

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

Peter H. Diamandis

You don’t have mile-thick ice sheets.

Dave Blundin

We do not have mile-thick ice sheets, but I think if you’re talking about 1 billion and then 8 billion robots, and you need the materials, and that’s the only constraint—

Brett Adcock

Yeah.

Dave Blundin

And you have robots that can operate machinery—

Peter H. Diamandis

We can mine asteroids, buddy. Seriously.

Dave Blundin

You think we’re going to be doing asteroids before Greenland?

Peter H. Diamandis

No. We’ll do Greenland first.

Dave Blundin

You think we’ll do—

Peter H. Diamandis

Then we’ll do asteroids.

Dave Blundin

But you think Greenland is viable? I’m not talking about 20 years from now, either. I’m talking about if you want to build 1 billion robots in, say, 6 years from today. That’s realistic.

Peter H. Diamandis

It’s a $50 trillion market for this.

Dave Blundin

Yeah.

Peter H. Diamandis

That demand drives—

Dave Blundin

Don’t you think you’d find a way to get through the ice, given 1 million robots working on it?

Brett Adcock

I’d hope so. Yeah. I think we’d find maybe better physics, but definitely better engineering solutions for this.

Dave Blundin

Mm-hmm.

Brett Adcock

Then we’d be able to put an unlimited amount of human capacity at it.

Dave Blundin

Yeah.

Brett Adcock

Through humanoids.

Dave Blundin

Yeah.

Brett Adcock

Yeah.

Dave Blundin

That's what I'm thinking, too.

Brett Adcock

Yeah.

Dave Blundin

Because the machinery I see is massively automated. It's still driven by people. It's still operated by people.

Brett Adcock

Yeah.

Dave Blundin

It doesn't need to be.

Brett Adcock

It's crazy this shit works, right?

Dave Blundin

Yeah.

Brett Adcock

The humanoid neural nets are just—

Dave Blundin

It's—

Brett Adcock

It looks—it's just crazy.

Dave Blundin

The thing is, when you make it work on unloading the dishwasher—

Brett Adcock

Yeah.

Dave Blundin

People don't realize how close that is to working on every other task. If you do it—

Brett Adcock

The dishwasher and folding laundry—these things that we're already doing—are so hard. You have compliant materials that are all changing with you dynamically. Everything isn't in the same place.

Dave Blundin

Yeah.

Brett Adcock

It's very different than being on a conveyor system or manufacturing—

Dave Blundin

Yeah.

Brett Adcock

Something like that. They already can do it today.

Dave Blundin

Yeah.

Brett Adcock

We can do it, and now it's a matter of doing it better—

Dave Blundin

Yeah.

Brett Adcock

—doing it with higher reliability across a more diverse distribution of what humans do every day. That's a data play.

Dave Blundin

The thing is, if you achieve that goal by hacking together 100,000 lines of C++ and teleoperating it, it would look the same, but it would be nowhere near—

Brett Adcock

As—

Dave Blundin

—conquering every other problem—

Brett Adcock

—as flexible, yeah.

Dave Blundin

But if you did it purely—it's nothing but a neural net—

Brett Adcock

Yeah.

Dave Blundin

—and it's purely trained, that means you're within a millimeter—

Brett Adcock

Yeah.

Dave Blundin

—of every task—

Brett Adcock

We are—

Dave Blundin

—we could possibly define.

Brett Adcock

We feel like the limiter here is just data. The only difference between it doing logistics and learning towel folding, dishes, or whatever we end up showing in manufacturing is literally just data.

Dave Blundin

Yeah.

Brett Adcock

Data goes into the neural net, and now it can do this work.

Dave Blundin

Yeah.

Brett Adcock

It's just new neural net weights onboard.

Dave Blundin

Yep.

Brett Adcock

I think we're just bound by data now. I think it's not a trivial thing to do to get the right pretraining set for this at scale, but we have a bet that I think will work. We've been deploying that at scale for the last 3 or 4 months.

Dave Blundin

Yeah.

Brett Adcock

Stay tuned. We're working through it. I hope this will lead to—I think you'll see a lot of positive transfer emerge from a robot that's able to generalize to a lot of things.

Dave Blundin

Yeah.

Peter H. Diamandis

Yeah. Amazing. One last thing before we wrap up. Can we pull the camera in close and maybe give us a tour of Figure 03?

Brett Adcock

Yeah, let's do it.

Peter H. Diamandis

Thanks for the close-up and intimate tour. So, Figure 01.

Brett Adcock

Figure 01. One cool thing about Figure 01 is that we designed most of the system in-house. We didn't care about looks. We cared about unlocking the AI and controls team. It was something they could use from a software perspective.

Peter H. Diamandis

Yeah.

Brett Adcock

We designed and walked this robot in under 1 year, so I incorporated the company. We think it's probably one of the fastest times in history.

Dave Blundin

That's a lot of parts, man.

Peter H. Diamandis

Did you draw this by hand early on?

Brett Adcock

These are internally made, actually. David, our design lead, designed this. It's not his prettiest robot, but I think it had what we needed, which is a functional robot we could get off the ground and start using for all the AI policy development. We did the Keurig K-Cup with this robot.

Peter H. Diamandis

No way.

Dave Blundin

Can I move the hand, too?

Brett Adcock

You can definitely move it, yeah.

Peter H. Diamandis

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

Brett Adcock

Yeah. It's heavy. It's about 130 or 140 pounds.

Dave Blundin

That's not that different from—

Brett Adcock

Yeah, not bad.

Dave Blundin

That's all aluminum?

Brett Adcock

It's all aluminum.

Peter H. Diamandis

All CMC.

Brett Adcock

We CNC aluminum. Most of the structure is done.

Peter H. Diamandis

Yeah.

Dave Blundin

Mm-hmm.

Brett Adcock

Yeah.

Peter H. Diamandis

What else should we know about this before we move to Figure 02?

Brett Adcock

We cared about speed, so we didn't really care about the wiring and some electronics. A lot of the design was mostly just to get a functional humanoid robot out so we could do development on it.

Peter H. Diamandis

Right.

Brett Adcock

We did that. We built a few of them. We did our first neural network on this robot, which I think was phenomenal. We did so much development with it really quickly.

Peter H. Diamandis

Yeah.

Brett Adcock

We also learned how to build actuators, battery systems, wiring, structures, kinematics, joints, and different sensors. All of this is stuff we learned. Then we used it all and integrated it into Figure 02.

Dave Blundin

But you got the cost down from Figure 02 to Figure 03 by 90%. What was the cost from here to there? Probably another 90%.

Brett Adcock

About the same, to be frank.

Dave Blundin

Wow.

Brett Adcock

A lot of it was machine parts, and we moved a lot of the tooling parts to Figure 03.

Peter H. Diamandis

You've got 2 cameras here.

Brett Adcock

2 cameras here. We have a back camera—

Dave Blundin

Oh, on the unit?

Brett Adcock

You see? Yep. We also have cameras right here in the torso pointing down.

Dave Blundin

Amazing.

Brett Adcock

So we can see where the feet are in case you have a box occluding them.

Peter H. Diamandis

Come take a look at the camera in the back of the robot here for 1 second.

Dave Blundin

Where's the camera pointing down?

Brett Adcock

It's right there in the pelvis. See, right here.

Peter H. Diamandis

Back here, you've got—what's going on here? There are camera ports here?

Brett Adcock

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

Dave Blundin

Amazing.

Brett Adcock

Basically, we moved all the wires internally into this robot. All the structure is an exoskeleton, so all the exterior loads—almost like my aircraft at Archer—the skin, the outside housing, handles the loads.

Peter H. Diamandis

Sure.

Brett Adcock

We have the same thing here.

Peter H. Diamandis

Right.

Brett Adcock

All the outer shell took all the loads. We have our second-generation actuators and third-generation hands on this robot. We have more cameras onboard, about double or triple the amount of compute, and about double the battery capacity onboard.

Peter H. Diamandis

Yes. The degree of beauty went up.

Brett Adcock

Yeah.

Peter H. Diamandis

Significantly.

Brett Adcock

Yes. Our design lead did a good job making this much more presentable.

Dave Blundin

It's funny that it's venting heat out the armpits, just like a real—

Brett Adcock

Yeah. It actually sucks air in here.

Dave Blundin

Okay.

Brett Adcock

We push it out through the torso at the bottom.

Dave Blundin

Okay.

Brett Adcock

Yeah.

Dave Blundin

What's going on in the back?

Brett Adcock

We basically have different padding on the knees and some parts of the arms to make it so that if you got your finger stuck here—

Dave Blundin

Oh.

Brett Adcock

—

Dave Blundin

Safety.

Brett Adcock

Yeah. Maybe it would hurt your finger, but it wouldn't cut it off.

Dave Blundin

Yeah.

Brett Adcock

You know what I mean? It's similar to what you see on a car door today.

Dave Blundin

Sure.

Peter H. Diamandis

And here's the workhorse.

Brett Adcock

This is our Figure 03.

Dave Blundin

Nice.

Brett Adcock

We made the robot much skinnier and lower mass, but kept all the speeds and torques the same. It's just as powerful and just as fast, but also skinnier—

Dave Blundin

What's the mass now?

Brett Adcock

—and takes up less space. This is about 135 pounds.

Dave Blundin

135.

Brett Adcock

This is about 150, a little over 150 pounds.

Dave Blundin

Yep.

Peter H. Diamandis

Carrying weight—how much weight can it carry?

Dave Blundin

It's a very different hand.

Brett Adcock

About 20 kilos.

Peter H. Diamandis

20 kilos.

Brett Adcock

Yeah. It's a completely different hand. The hands have a glove, tactile sensors, compliant material on it for better grasp, and a camera. All the parts—or most of the robot—are soft-wrapped. You can see it even up here, with a squishiness to the chest and different parts of the robot. We have no, or very few, pinch points in the robot. What else? We reduced the cost massively. We have a better thermal system and compute system. We also increased compute on this robot from the last generation. We have new feet that have a toe.

Peter H. Diamandis

Sure.

Brett Adcock

You might think the toe is—

Peter H. Diamandis

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

Brett Adcock

It's helpful as a passive toe on the foot, but you might think this helps it walk better. It's not just that. When we get down here, we're on our toe box. It really helps us get the range of motion.

Dave Blundin

Oh, it's nice.

Brett Adcock

Without that, you might need more joints or something else on the robot.

Dave Blundin

This is just a totally flat foot over here.

Peter H. Diamandis

Brett, talk about the face, because this is a big question: Do you develop or show facial features or not? And you went—

Brett Adcock

Yeah. What do you think? What do you have—Westworld, or I, Robot?

Dave Blundin

Wow. I mean, it comes across as beautiful, right? A high degree of beauty. It comes across as sleek, but it could have a negative, a little dystopian feel with the black face.

Brett Adcock

Yeah. We have 3 screens on the robot. This is powered off. We have a main screen and 2 screens on the side. Then, obviously, we have a bunch of cameras and sensors in the head. On the screens, we can basically do anything. You could watch a Netflix movie.

Dave Blundin

Oh, with a brain—

Brett Adcock

Look into my eyes.

Dave Blundin

The brain is the screen.

Brett Adcock

Yeah, exactly. Whatever you want. If kids get bored, it's like, “Let's throw some up there.”

Dave Blundin

So the brain is right in here, which makes a ton of sense to me. And it's where the ancient Romans thought the brain dominated the heart.

Brett Adcock

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

Dave Blundin

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

Peter H. Diamandis

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

Brett Adcock

Yeah, you can basically put a silicone face on it and put hair on it. We could go—

Peter H. Diamandis

I could get to your hair.

Brett Adcock

Yeah, yeah. We also have other outfits. This is one of our logistics bots. It's basically the same robot, but we're able to outfit it with different types of soft goods. We have another robot here that we've also put to work; it's wearing a jacket. This is cut-resistant. So they all have different—

Dave Blundin

Oh.

Brett Adcock

—different traits. Some of these gloves are also better for grasping different materials.

Peter H. Diamandis

Mm-hmm.

Brett Adcock

Those might be, say, dusty, or maybe a piece of sheet metal, or slick.

Dave Blundin

Do you think it would operate in zero-G? You just need a better training set and—

Brett Adcock

I think so. We'd really love to run—

Peter H. Diamandis

I've got a zero-G airplane.

Brett Adcock

—robots at scale in space.

Dave Blundin

You're gonna populate the universe.

Peter H. Diamandis

I've got my zero-G airplane. We could—we should—

Brett Adcock

We should test it.

Peter H. Diamandis

—take it up.

Brett Adcock

Yeah, let's get these things on there.

Peter H. Diamandis

Yeah.

Dave Blundin

Oh, that'd be a great test.

Brett Adcock

Yeah.

Peter H. Diamandis

Yeah.

Dave Blundin

Well, look, we're going to build data centers in space very soon. Somebody needs to assemble them. Zero-G is the operating—

Brett Adcock

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

Peter H. Diamandis

Yes. Yes. That's—

Dave Blundin

A lot of materials.

Peter H. Diamandis

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

Brett Adcock

100%.

Peter H. Diamandis

Oh.

Dave Blundin

Alex will love that you said that.

Brett Adcock

Let's do it.