Hey everybody, it's your boy Jay Cal.

I'm here in Paris, [music] France at a

conference called Machina. It's

basically AI in the real world. [music]

[music]

Pardon my robot.

Thanks for tuning in and uh let's get

started. I'm going all in.

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>> All right, everybody. Our interviews

with the number one companies in

robotics today continue here in Paris.

Really excited to have Dr. Peter

Funkhouser on the program. You're the

co-founder and CEO of Anyotics. Uh you

make the ENM mo. Get it? A lot of you

have puns. Uh but you've been working in

this uh space for close to 20 years. The

company's been around for 10. First five

years kind of a research lab. Last 5

years your what do you call these dog

based robots?

>> Well, it's an inspection solution,

right? It's about data collection and

understanding in critical

infrastructure.

>> But the form factor is

>> a four-legged robot or a dog as you We

like to call it a dog Obot, but

>> why did that dog format become the

standard? You're not the only person

making it. There's many people making it

now.

Why did that one become the first one to

hit, you know, relative scale and for a

deployment?

>> Yeah, in nature, you know, a lot of

animals have four legs, so there's a

reason to that. So, for sure, you have a

great mobility. You can climb stairs.

You can go anywhere a person can go. So

dexterity and balance,

>> mobility, right? Balance, but also

stability. Four legs if you wide

footprint, a lot of footooth holes hold

on to because we work in nasty

environments, slippery floors, there's,

you know, rainforming, this snow falling

down, grass growing. So four legs is a

real good format.

>> Well, now this is a silly question, but

why don't we make cenotars

for the human versions when people are

making the Optimus, the Neo, the Atlas

from Boston Dynamics, those standup

robots.

with two legs. The concern is they're

always going to fall over. They

constantly fall over in demos and if

they fall over, they're going to break

somebody's ankle. Why not put four legs

on those?

>> You could absolutely. And it really

depends on the use case. If you need to

work, bring, you know, I don't know, in

a coffee shop, bring it to there's

narrower spaces, right? You want to work

in eye level. Maybe a humanoid is

better. In the facility that we work for

leg stability, there's enough space to

go around. Yeah. It's the perfect

format.

>> I don't buy I think all the cafes should

have are they were they cenotars in uh

Greek mythology.

>> It's a century.

I think that should be the new standard.

You found a really effective first use

case which is inspecting

really important infrastructure and now

you have thousands of these hundreds of

these for hundreds for deployed over the

last five years.

>> Yeah.

>> These are expensive. They're low

hundreds of thousands of dollars to buy

them. Yeah.

>> And to operate them, I'm assuming tens

of thousands a year in service

contracts.

>> So, they're not for home use.

>> These are industrial and they have a lot

of sensors on them. So, if you were

going to inspect, I don't know, a

pipeline with natural gas in it.

>> Right.

>> These things can go out in any weather

and they can sense things on that

pipeline that a human can't. Correct.

>> Yeah, that's right. For us, it's not

about labor replacement, right? It's

what can we do better? What can we do?

Super human. Inspection is a great

example. Our eyes and ears don't

perceive all the signals. Micro gas

leakages, temperature equipment

overheating with the cameras on the

robot, thermal cameras, acoustic, you

know, microphones, gas concentrations

and all of that. We pack it full of

sensors and AI and you can go way beyond

what a human can do. So, the monetary

benefit is avoiding downtime. These

assets, if they stop, they lose revenues

in hundreds of thousands per hour. So

every minute, every hour we can save

them essentially pays for the robots. So

that's why we can afford having really

expensive sensors, really expensive GPUs

on top of a robot.

>> Yeah, these have seriously powerful

compute on them,

>> right?

>> And they have to have a significant

amount of battery power then. So these

things can do a mission of what? An hour

or two?

>> Two hours an hour docking station to

come back charge. But they do this over

and over. Some of our customers run

these missions 40 times a day. 14

>> 14 five four because they're interested

in a specific point when the electric

arc furnace goes up. They want to know

in that minute what's happening.

>> Too dangerous to send in a person.

Thermal cameras burned. They need a

robot that right at that moment.

>> Got it. And they have to charge not hot

swapping the batteries.

>> No, you want hands-free autonomy. Nobody

should even be bothered that there's a

robot. They don't care about the robot.

Actually, they don't even want the

robot. They want the data. They want the

insights. The robot is a means to an end

to collect the data precisely. At what

point can you offload the very um power

hungry compute

>> and put it in the cloud?

>> We also do that. There's always two

parts. There's parts that need to run

real time on the robot because you also

cannot guarantee connectivity, obstacle

avoidance, data quality, making sure you

have the right thing. If you upload a

blurry image to the cloud, it's too

late.

>> But in the cloud, of course, you do

contextual analysis, historic downtime

analysis, etc.

>> Are people asking for these to be able

to operate for 24 hours yet or 12 hours?

No, for sure. So the maximum is in the I

8 hour range. So it has enough time for

charging. If you need to go beyond that,

that's rare. There's diminishing returns

to more frequently do it. But you have

to manage. They do it manually today,

maybe once or twice a day. And they get

eight, 15, 20 times now, right? So it's

already the frequency goes massively up.

>> Yeah.

>> Without putting people into harm's way,

plus the quality is so much higher.

>> What's the most fascinating science

fiction uh deployment you have currently

with these? Yeah. I mean, what's really

exciting any anything offshore, right?

People fly out with helicopters. Every

helicopter flight costs in the tens of

thousands. So, but if you're offshore,

it's very tricky, right? It needs to

work. There's almost no people around.

It needs to be fly.

>> These are oil rigs.

>> Oil and and wind energy offshore as

well.

>> Ah, yes.

>> But wait a second. These things don't

operate in the water. So, how do they

work with windmills in the ocean?

>> There's windmills around hundreds of

them. They come together to a

transformer station. Ah

>> that transforms to AC to DC before it

trans and that's a manned facility

typically a big converter. This is where

the robot operates.

>> Got it. Can they operate like in severe

conditions like the Antarctic and stuff

like that and have you deployed them

there yet?

>> Well in Norway for sure. So that's - 20°

in deserts plus 40 50 60°. Right. So

that's exactly the point where you want

to send in a robot. temperatures, dust,

humidity, but most importantly, we have

a robot now that goes into explosive

atmospheres where there's, you know, in

oil and gas and chemicals, methane in

the air. You're not allowed to, you

know, create a spark. So, we built a

special robot that's guaranteed not to

create a spark.

>> This is where you don't want to have

people, but for a machine, that's a

perfect case, right? Dangerous

environment. This is where we're sending

robots in.

>> That's fascinating. So if you're in the

Peran basin and something's leaking,

>> that is one of the most dangerous these

oil rigs and gas leaks. This is where

people seriously die.

>> Yes. And you don't want you want to know

when it's happening, but you don't want

to create a problem. So that's a perfect

case.

>> I mean, I'm going to keep going sci-fi,

but dropping these things into the

bottom of the ocean seems like a

no-brainer at some point.

>> Well, there's submarines, right? We we

don't do that right now, but I agree,

right? Robots should work in

environments where people shouldn't be

dangerous, remotes, right? boring

repetitive task. This is what we

>> that's a different form factor right

now. But there are people creating on

the surface and then under the surface

slightly under the surface, right,

robots that are doing essentially not

inspections but monitoring systems for

obviously the military. Um well, if

you're out there inspecting and there's

a gas leak

and it's dangerous to

um send humans out there, when are you

going to put some uh equipment on these

to fix the goddamn leak while you're out

there? And that must be the holy grail,

is it not?

>> Yeah. Once you can detect a problem,

customer ask, can you solve it? Can you

fix it? Can you turn? Not today. You

know, in a demo, yes. But in reality

getting to 99.9% reliability in

explosive atmosphere that's still in

development first step is closed levers

open cabinets eventually you want to

have by manual manipulation maybe three

four arms to fix the machine right

that's still you know AI will help us

there still a lot of work ahead of us so

a lot of the demos you see of humanoids

folding laundry that's a very controlled

environment once you're outdoor in a

hail storm right um freezing

temperatures it's different also for

perception but eventually we foresee the

future that this will be solved

>> what percentage of Your robot is sourced

from China.

>> Zero. So that's

>> 0%.

>> Yeah.

>> And is that because in the EU and Norway

it's banned or that's a choice?

>> That happened just historically that we

source locally and you get chips from

the US etc. And for some of our

customers it's important and we built a

lot ourselves right because we started

10 years ago. So a lot of the

architecture nowadays you get cheaper

components around the globe. So it's

about being smart where you get

components from, which one are active,

which one are just metals. Um, so for

sure it's a it's a hard world to

navigate, but tapping into the

commoditization of certain hardware that

makes sense for us costwise.

>> Who's specializing in that outside of

China now? Is it Vietnam, India, Taiwan?

Where can you source like the actuators

and and a lot of this

>> for sure? China is number one pushing.

There's good companies in Europe, right?

In the US as well. So these three

regions for sure if it's just about

labor assembly you can go elsewhere as

well but you want to get the core

expertise somebody who builds that

component.

>> Got it. And how do you look at China

now? They've been stealing the IP. I'm

assuming they've stolen yours already.

Um and certainly other people's IP is

being stolen at scale in China and

they're building robots that are going

to be 80% cheaper and they're going to

try to deploy them to the same customer

base. I am certain. How are you thinking

about the threat of Chinese robotics?

>> If you look at the robot from China

today, that device is a piece of

hardware that can walk beautifully.

Great engineering. Love it. Do back

flips. Yeah. But they're not solving the

problem. Our customers don't compare a

platform to the full solution that we

have. Do you need autonomy, inspection,

intelligence, the workflow integration,

so much more, right? It's just a

hardware difference.

>> So the harness, the wrapper, the

services around it, they're not

providing that.

>> And then the trust in the data, right?

We call very sensitive data. We have ISO

certification for cyber security, all

these topics, right? So that's how we

compete.

>> So you might not want to send the

nuclear power plants latest uh data to

the Chinese Communist Party. You're

saying

>> you don't want to have 15 cameras in

your critical infrastructure that

somebody else controls.

>> Yeah, I'm being a bit facicious, but uh

>> it's happening today. Yeah,

>> but it's there's data leakage. Talk to

me about military applications.

>> Yeah,

>> NATO is uh having to arm itself. I

apologize on behalf of the United States

uh for our stance with NATO, but you

guys have to pay up and pay your fair

share. You've agreed to do that, but I

think there's a perception in Europe,

you can tell me if I'm wrong, and in

NATO that you may have to go it maybe

without the United States. You may need

to build your own military uh products

and services.

Do you not need to be in the military

space? And do you not to take the same

applications and build military

applications? But are you doing that

yet?

>> Yeah. So I think there's a

responsibility in Europe to build

technologies to be able to in

>> you believe that personally.

>> Yes. However, for any botics we built

and we went down one track there's

tremendous poll. So today we're not

doing it not intend to do it right and

it's also a different product at that

stage probably right. It sounds very

easy just take four legs and do military

you need to go a couple of steps for

what exactly you're doing different

communications different autonomy. So

we're not doing it but I mean I think

there's a responsibility to do it for

others. Is it never say never for you uh

or is it you're dead set on like you

have a mission you're not going to build

military product

>> for us today the mission is clear we

started with non-military this is where

we're headed

>> got it but if the EU asks you and you

>> ask I mean we get you know requests but

it's also honest truth are we solving

actually the problem just shipping a

robot to the military doesn't solve the

problem yet we really need to go deep so

you would need a different team to do

that our team

>> really you need a different team

>> well seems Like you could do the same

team and build military applications.

>> No, autonomy is very different, right?

So for example, we do autonomy. You have

time to set up a robot and it does

inspections all of that. In military,

it's about millisecond being in right

remote control human in the loop.

Different communications, different

autonomy. Then everything on top

application software very different.

>> Yes, you could lose a four-legged robot

to also go into a house.

>> That's about it, right? The rest is

different.

>> How do you think about robots that are

armed? Clearly, China has done

demonstrations of these same type of,

you know, four-legged robots with guns

on them. And obviously with AI, these

Terminator scenarios are here.

>> Yeah,

>> they're being built in China already.

Yeah,

>> we've seen drones on the battlefield in

Ukraine. Norway is not far away from

Russia. It's

>> it's not that close, but it's not that

far away either. How do you think about

the fact that communist countries are

building these robots that have weapons

on them?

>> Yeah,

>> I personally don't like it. I hate it. I

think it's concerned, right? I mean, as

an engineer, you should have pride,

right, to build technology for good.

Defense is one part. The active attack,

putting a gun on it, it's just risky.

These technologies getting mature, but

they're not that mature that you would

put somebody else in in harm's way.

>> Yeah, it is. the enemy we're going to be

faced is going to do this and we need to

monitor it. What is the buzz inside the

industry about this? When you're out

with other people in the industry,

>> you know, what do you know that we don't

know about what's happening in those

authoritarian countries with robotics

and the military?

>> I think these are all very early tests.

If I look at those videos, these are

demonstrations. Got it.

>> I've not seen these types of robot act

drones. Yes, Ukraine. that came out of

necessity that that was a mature

category that was used in robotics.

Actually to the people I speak to I mean

four years ago we wrote a letter

together with our friends at Boston

Dynamics and others right who condemn

the weaponization of robots for exactly

that reason that

>> as engineers we don't want to see it

being used and we think it's just

dangerous and risky and stupid.

>> Yeah. All right. Listen, continued

success. All right everybody, really

excited to have Bert Borick here. He is

the founder and CEO of 1X. If you know

1X, they make the Neo. The Neo is a

household robot. You've sold a lot of

pre-orders and you guaranteed people

this would make it and would ship in

2026 into their homes. What does it

cost? And are you going to hit your

self-imposed deadline?

>> You got to keep your promises. Okay.

>> So, we will ship in 2026.

>> Okay.

>> Now, expectation managing here. It'll be

slow in the beginning. We want to do it

right. Yes. But there will be a handful

of customers that get their Neo in 2026

and I'm so excited and I can't wait.

>> What is the cost of the Neo?

>> So that's an interesting one because it

depends a bit. Um I mean when we

launched the pre-order we had two

different payment models. You had a kind

of like early adopter upfront full

payment um and then we had a

subscription fee and the product of

course is going through a lot of

development. So how this subscription

model will look and these things are

kind of like still evolving

>> and we want to figure that out also a

bit together with our customers in the

beginning but another big one now is we

haven't really announced this yet but uh

I've dripped it in a bit

>> which is we are going to allow a lot of

people to build on Neo so we are also

launching Neo as a platform

>> yes I'm thinking app store of such or a

skill store so if I have it in my home

and I want to make a salad you as a

hacker could make the salad salad skill

and I can buy and subscribe to your

salad skill. Yeah,

>> that will be part of it. But to me, NEO

and 1X is about so much more than just

consumer, right? So consumer is an

incredibly important market, but 1X has

always been about how do we create an

abundance of labor across society

through these humanoids. And I sincerely

believe that we have a platform now

which is so uniquely capable and so well

situated that allowing people to build

on this will open up how to use Neo

across all of our society not just in

homes right

>> but it will also benefit the consumer

because this will mean there will be

more things developed on Neo and part of

that will be an app store targeted

towards consumer which we're very

excited about but also it will just be

in general how do you create a bigger

ecosystem that can just accelerate the

autonomy and accelerate the path to

actually having a fully autonomous agent

at home that can do it.

>> What was the pre-order? 20K or

something. I'm trying to remember.

>> So, we have we haven't given out

official numbers, but it's pretty

significant. We s we sold out the first

10K in uh the first few days.

>> Oh, so people put a deposit down for

that. They'll have the ability to fully

uh so sort of like the Tesla $500

deposit or 500 a month, a,000 a month,

something in that range. Yeah, 500 a

month.

>> 500 a month. So, this is for, if I were

to think of a parallel Google glasses or

the Vision Pro. This is for high-end

folks who are the Vanguard, who are the

earliest of the early adopters. Yeah,

>> 100%. I mean, we tried to be very

transparent about this. Getting a home

humanoid in 2026 is going to be rough

around the edges,

>> right? They're going to fall.

>> They're going to fall, right? Uh but I

am very happy to say that I think we

will actually be able to ship something

that's very close to full autonomy

>> which we did not want to promise when we

launched this because it was too early

but and I I'm not going to fully promise

it yet but the way it's trending now it

looks like we will be able to ship an

experience that is fully autonomous

>> and that is still quite useful. Now, if

you want everything to just work out of

the box day one, then there will be some

teleoperation involved or some guidance

of the system. But a thing that really

excites me these days is that we're

seeing the path now to actually shipping

something that if you want it, it can be

a fully autonomous experience

>> and it's getting pretty darn good. The

tea operating is fascinating to me. I

don't know if you saw this, but in New

York there was a chicken sandwich shop.

couldn't find um a cashier. So, they

hired somebody in Manila in the

Philippines for, you know, $3 an hour,

which is a huge salary for a for a

cashier in the Philippines. And they had

her on a Zoom call. They just popped up

Zoom, acted it themselves,

>> and you could order and if you had a

customer service issue, you just talked

to her and she was like, "Hey, I'm right

here."

>> That is in some ways what you'll be able

to do with your robot. You'll have

somebody in the Philippines who you'll

be able to tap into who'll be able to

turn it on and when you say, "Hey, pour

me a glass of orange juice." That person

will be able to remotely do that task.

Is is that what I'm envisioning here

correctly or incorrectly?

>> I think it will all happen. So so so

back to how the platform works, right?

So let me just back up and spend like

two minutes on that. So if you think

about Neo as a platform, so if you want

to build your orange shop around this

orange juice shop that okay, you buy a

bunch of Neos, you get Neos, you get the

robot operating system with like the

fleet management and all that. You also

get the data collection equipment which

is gloves that have the same tactile

sensors as Neos, the same vision system,

and you can gather data in your shop,

>> right? fine-tune our model with within

our system where we kind of like we do

all the cap dense captioning of the data

for we you like we do all that you fine

tune your model you deploy this and you

get this working and now you have a

fully automated shop and you're very

happy that's one path

maybe that doesn't quite work so you say

ah I'm going to have someone intervene

sometimes in tallyop and then your data

gets better that's one way of doing it

right there's many ways of gathering

data

>> or maybe you're just saying like you

know what this is super complicated I

just want it fully talked. That's also

fine. Depends on how you want to apply

this.

>> Um, and the platform goes all the way

from like these kind of like developers

that just want to automate their

workflow all the way to the more

foundation labs that want to deploy

their models. So there's also a world

where you can run someone else's model

on Neo. We're going to allow that, I

think. Uh,

>> so you're going to be an open platform.

You'll be in a way headless to the

knowledge inside of it. You'll be able

to plug in if OpenAI has a world model

or Claude or some of the other

independent world models, they'll be

able to be plugged in. Yeah,

>> 100%. Now, I sincerely believe that our

model will be the best one.

>> Sure.

>> And I believe in competition. So, if we

that actually control everything from

the manufacturing all the way up to the

product can't make the best model, then

we kind of failed.

>> Yeah.

>> Uh but will we allow other people's to

build on this? 100%. And one of the big

reasons for this is that currently if

you look at where this where the field

is, there is no one general model that

solves everything for robotics. It's not

there yet,

>> right? And if we are stuck in our

customers kind of like backyards helping

them integrate towards ERP solutions and

everything else the next couple years,

we are not going to get there. What we

want to do is to work on the general

problem. How do we solve embodied AGI so

we can actually create an abundance of

labor? And

>> this requires us to focus on the general

problem and then allow other people to

also help apply what is available today

and to help build the ecosystem. Right?

If we get this enormous robotics

ecosystem, we all benefit.

>> Yeah. And I could see some applications

where one TA operator, let's say this

was a convenience store robot that just

help you carry stuff out to your car.

That might only happen once every hour.

You could have one tea operator or maybe

you have 10 of them that are monitoring

30 40 Neos and they control them

remotely and help people move the

groceries to their car. Yeah,

>> personally actually I'm I'm like I have

a use case for Neo. Okay,

>> in Talop, which is I'm part of the time

in Norway, mostly in San Francisco area

now, but part of the time in Norway and

I'm also kind of like conventions like

this, right? And when I'm out traveling,

I want to be able to be present and run

my company through Neo.

>> Yes.

>> Put the hat on Neo. I am Neo. And that's

actually pretty magical. And you can I

can go around. I can pick up the parts.

I can look at the parts. I can talk to

people. I can be in the meetings. Right.

And so that's one application of

teleoperation that I think actually will

never go away. Like no matter how good

your autonomy is, that will still be

there.

>> Yeah. Your avatar at your factory in

Shenzhen.

>> 100%.

>> Yeah. And you know there are other

applications like this where

remote power stations where there's no

one within like an hour of driving. You

have a robot standing in the closet and

something goes wrong and you go out and

you like flip the old switches and you

do the things

>> like you're likely not going to automate

that because it's kind of like a one-off

thing that happens every few months.

Right.

>> Right. So,

>> but it's worth having that robot in that

space out in the middle of the forest

near, you know, those power lines or

power converters. They can go out

within, you know, minutes and and work.

>> Essentially like what used to be called

like expert in place, like this concept

of like you can take the world's best

expert and tell teleport them to

anywhere in the world to help solve a

situation

>> like a surgeon. Yeah.

>> Yeah. It's it's super useful. I do think

that what we've experienced over the

last year is first of all that

Neo has become so capable especially

with the new hands that teleoperation

does not fully use the hardware like

you're not able to get the tele

operation to be good enough to fully

utilize the hardware.

>> Uh so the fidelity of the hand is

greater than a teller operator is able

to leverage.

>> Yes. Right. The teleoperator will not

feel the same as the robot is feeling

for example. Right. then you need to

build full haptic systems and they're

going to slow you down and be slow and

clunky and like so we're increasingly

seeing that gathering data with humans

just wearing the sensors of the robot in

as transparent a manner as possible. So

like they should not disturb what you

are doing right that's the most useful

data to solve kind of baseline dexterity

on the robot but even more importantly

the big bet that we made which is this

decade long bet in 1x is if you get the

robot to be similar enough to a human

then you can train on all of the

available video data out there of

humans.

>> Yes.

>> And we're starting to see some very good

proof that this is actually working

incredibly well. And that's the reason

we started the WX World Model Lab

because we now finally have the scaling

loss on that. And we're seeing that this

>> take us inside that take us inside the

lab. You are you having people in

factories wear glasses, wear your hands,

and do their tasks over and over again?

Are you working with the micro ones of

the world to go do you know real world

stuff and outsourcing like unique

proprietary data that you can have that

other companies don't? How does the

world model get built at scale?

>> So so so first of all yes we do that and

if you but that's not the main point. So

I think ultimately it's very simple

right the model is going to be as good

as the data.

>> Yeah. And if you think about the data

pyramid then on the top you have like

tele operation data very high quality

small fine tuned data set where actually

what we do is you will have the operator

try to do the task very well and very

fast and they will often fail and then

just try again and then we pick the good

samples where they did the task as good

as a human would right

>> you don't need a lot of that data it's

just to align your model then you have

the data which is what you're talking

about with like put the sensors on the

human go and gather data.

>> Yeah,

>> you have more of that and it's very

close to the robot but it's not the

robot. The telea is the robot. This is

not the robot but it's close.

>> Then you have egocentric video from

humans point of view. So that is further

away from the robot but it's still quite

close because the robot hands is the

same as human hands and like it looks

the same and so it's quite close.

>> And then you have general video data.

>> Yes.

>> Of the world. or the world in general

and of people, right? And because Neo is

so similar to a human, we can actually

utilize all of that data. Now, the

bottom layer in the pyramid, which is

this video data, general video data is

absolutely

ludicrously immense compared to anything

else.

>> It's YouTube, it's everything. So if you

look at what is needed to actually

achieve true intelligence,

>> you need multiple orders of magnitude

more data than anyone is even close to

collecting over the next few years with

egocentric data or with this sensor

data.

>> Got it?

>> And all of the major breakthroughs that

we've seen as far as I'm aware of in AI

have been because someone figured out

how to use a huge new data source that

previously we were not able to use. you

unlock some new set of data and now your

model capability greatly improves.

>> Well, you've got a lot of people out

there trying to find data like that is

like one of the

>> gonna take years. So, it's it's like a

catch 22. So, our big bet is you have to

be able to utilize the general video

data out there.

>> Yeah.

>> And the only way to do that is you have

to care about every single tiny detail

of the robot to be as close to human as

possible. Like you know like the flesh

and tissue and skin Yeah.

>> is highly nonlinear. So like how much

force for it to deform? What's the

friction?

>> Like what is the impact energy when

touching the table?

>> And people have different size hands. I

mean literally in the NBA there's a

wingspan as a concept and people with a

wide wingspan, longer arms than the

average person get paid 20% more for

having that extra two or three inches of

wingspan. It's pretty fascinating when

you think about it.

>> That that's a really good way of saying

it. Wingspan. We've always we've always

called it for the the the gorilla

coefficient. Yes. Long arms.

>> Yeah.

>> Yeah. If you

>> But but anyway, yeah. So, my point is,

yes, we do all of these things, but

ultimately what differentiates 1X from

all of the other robotics companies is

that we are all in on pre-training our

own models on this video data on the

internet.

>> Yes.

>> And that our cross embodiment is not

another robot. Our cross embodiment is

the human.

>> And we want to be as close to that as

possible because that solves the catch

22. In the end, all the data will be

robotics data because a robotic data has

it has the actions, it has the tactile,

it has the forces, it's better. But the

only way to get all of that data is to

create a base model that is good enough

that you can deploy all these robots

across society and they will do useful

things that people pay for and also

gather the data. When do the robots

become recursive in nature and they are

teaching themselves, building themselves

and like we're seeing with large

language models now where people

creating agents instead of giving it

prompts and instructions we're now

starting to say well here are the goals

here's a loop you are one agent that you

know identifies for a business potential

customers okay you're the agent that

does customer success and here's what

that looks like you're the agent that uh

you know does pricing of products and

those agents start working in concert.

We're starting to see that in knowledge

work. When does that come to robotics

where you don't have to actually worry

about making the robots better? They're

sentient enough to use a word. Perhaps

not accurate, but they know what their

mission is. You've given them the goal.

Hey, you're working in a Michelin

starred restaurant. your goal is to make

the most delightful food with this level

of fidelity and perfection. Um, and here

are the outcomes. And it says, "Okay,

I've just got to get better at, you

know, uh, poaching these eggs to to

really be great at this."

>> It's kind of sci-fi.

>> No, no, it's not sci-fi. It's actually

something we think a lot about, but it's

also incredibly hard to answer because,

you know,

>> the development now is going like this

and you're here on the curve. So when

you asked me a year ago, I was way more

bearish on how long far along we would

be today on the AI

>> and like every time I kind of sample

things have moved faster than I think.

So it's easy to get like carried away,

right? But I think if I try to answer it

broadly, I am extremely sure that we're

less than a decade away from hard

takeoff. And when I say hard takeoff, I

mean robots building the robots, the

data centers, the chip fabs, doing the

mining and refining.

actually a true abundance of labor, a

self-sufficient system that is just

>> under 10 years.

>> Under 10 years, my current bet would be

3 years.

>> Got it.

>> But like if it takes 10 like in the in

the history of humanity, right? It's

still like a blip. It doesn't really

matter. That gets back to like what is

1x, right? Because

>> and you call this the industry term hard

launch or

>> hard takeoff.

>> Hard takeover.

>> Takeoff. Not take over. We're going to

do it right. So it's going to be hard

take off. Hard takeover. Yes.

>> But you know

I've heard the term right this is a

industry term

>> RG and you can't really get this without

the physical part right like the digital

intelligence can never create its own

substrate you need the physical part

>> right

>> and I think also this is going to have

>> incredible impact on humanity with

respect to for example progressing

science right

>> like a lot of the demand that we're

seeing now on our platform is people who

want to automate lab work

>> because if your if your AI model can't

actually build and carry out his

experiments and observe the results. How

are they going to progress science?

Right.

>> So all of these things will happen in

the coming years as AI becomes physical

and exact timeline is a bit hard but

it's years not decades.

>> Yeah. I mean, if you if you believe it's

three, and I know you're an optimist,

you have to be to do what you're doing,

a crazy optimist for sure, and you think

the outer, you know, uh, estimate is 10,

you know, we'll we'll we'll be fine with

five, six or seven, uh, burnt, you've

got to catch a flight. This is amazing.

Continued success. If people want to

order a Neo and give you $500 a month to

be part of this absolute lunacy that

you're doing, what do they do? How do

they get in?

Well, you go to our website and you

order a Neo.

>> That's it. It's that simple. That It's

2026. It should be that simple.

>> It It kind of should, right? If you can

order a Tesla online, you can order a

Neo online.

>> Transparent pricing.

>> I like it. Yeah.

>> Uh Burn continued success.

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>> All right, everybody. We're really

lucky. We have Amanda McMaster here. Not

McMasters. McMaster.

>> Just McMaster. No.

>> Just McMaster. No McMasters. Uh, you're

the interim CEO of Boston Dynamics, the

OG, the original robotics company. The

robots we've seen for decades doing back

flips, doing kung fu, getting kicked and

beaten, and getting back up. We have

been having a hard time remembering

who owns this company now because it was

an independent company, venturebacked,

then Sergey and Larry bought it. It was

part of Google, then it got sold. I

think Masayoshi owned it at some point,

but I believe Hyundai owns it now.

>> That's correct.

>> Did I get that whole history correct?

>> You did. You nailed it.

>> Okay. So, apparently I read way too much

industry news, but now you're in charge

of this.

>> Yes.

>> It's changed hands many times and you

went from being essentially one of one

really in humanoid robotics to one of

many. We're here at this uh Machina

Summit in Paris and you see many

contemporaries now. So, what is Boston

Dynamics working on now? Is it still a

research project or are you going into

the real world and applying these

robots? Because I think you guys got

there early, but you have to now deal

with fierce competition. Yeah.

>> Yeah, we are big on deploying robots.

So, it's no longer an AI lab experiment.

It's not a research and development

company anymore. We're now focused on um

real world deployment. So, we started

with our spot robot, which many people

know. That's our mobile quadroed um in

industrial

>> famously in uh Black Mirror chasing

people down, not yours.

>> Oh, you can own it, right? There's

always going to be a dystopian version

and a utopian version. You're obviously

pursuing the utopian, but that is a

really cool robot that has been for

deployed.

>> Yes, it has been deployed in real

customer sites. It's pre providing

really customer value. Um at this point

we have over 500 customers over 46

countries. Wow. Um it is the um it is

the mobile um autonomous robot that's

used more than any other on the planet

right now.

>> Wow. So it is the most deployed and most

utilized.

>> Yes. So real

>> why and who's what is the number one use

case for it? Like is it security? Is it

inspections? What do people use that dog

format for?

>> Yes.

>> Or pony. What do you like to call it?

Pony dog.

>> We like to think of as a dog. I mean, I

think it moves like that. But, um, you

know, we're using this, uh, the

customers are finding a lot of value in

industrial inspection. So, they're using

it for both, you know, acoustic um,

gauge reading, vibration detection. So,

assets that, you know, if they have

expensive assets in their facility and

they want to monitor them, this allows

for them to do that. Now, it can do that

during the day and then it can do

security perimeter work at night. Um, so

the answer is yes, we do all of that.

And, um, and the real inflection point

was customer ROI, right? We want

customers to find value in this to do

really useful work. It's not just about

yes, it's cute and it dances, but it's

long past dancing at this point. It's

now doing real work. And um and

customers need to see your ROI in under

two years.

>> And those inspections, if they were even

being done, were being done by humans.

>> Yes,

>> humans, as we all know, being them are

fallible. We make mistakes. And these

ones were just out there now as little

puppies running around a water treatment

facility, a bridge, whatever it happens

to be, infrastructure pipelines.

>> And it can record many different

sensors, video, obviously, vibrations,

all radar, I'm assuming, all different

tie acoustics you mentioned.

>> Y

>> what do those robots cost? What's the

range of the hardware cost? And then

what's your business model with these?

People buy them and rent the brain. They

rent it by the hour. What do you think

of as the CEO will be the business model

and what is the business model with

these hundreds or dozens of customers

deploying hundreds of these?

>> Yeah, so we um we we went with a capex

model to start with spot. Um we'll be

doing a probably a robot as a service

model likely with Atlas. Um we

understand with the humanoid form factor

folks may want to spin up at different

times and then and have the ability to

to do decrease um with spot it's been

pretty effective in capex. Um it's the

way these industrial customers think

about industrial tools. So they

generally want to spend capex for this.

Um it depends on their configuration.

You know it ranges anywhere between you

know $100,000 for the base robot all the

way up to 300,000 oneear fully loaded

with services integration deploy. It's

the price of a Tesla to a Ferrari

depending on how you equip it.

>> But what people need to understand is

the lifespan of these is greater than 5

years I would think. Like these are

you're known for industrial. So if it

can run I'm assuming you can run 20

hours a day, 22 hours a day with

charging.

>> Yep. So we're we're at um we think about

in terms of meanime between intervention

and we're um at over 3,000 hours. So

only only a couple times a year does a

human have to be involved and it has a

charging station. So, battery runs for

about 90 minutes. Um, usually we'd have

two, comes back, sits down and charges,

and the next one can take over.

>> Does it automatically swap the batteries

or

>> It just sits down onto its charging

part. Perfect.

>> Yeah.

>> Uh, Atlas has swappable batteries,

though.

>> Yes. But that the hot swap is a human

has to do it.

>> No. Uh, Atlas does it itself.

>> Oh, it does it.

>> So, Atlas will have two batteries. So,

it turns it torso around and you replace

one and put it with the other one. It

always has a backup. So, battery life's

not

>> perfect. So, for the humanoid one, it

can do it itself. Obviously, the dog

gets charged. So, realistically, they

could be in the field for close to 24

hours, maybe 18,

>> 20.

>> And so, that puts the operations at a

couple of dollars an hour. And has that

changed how people look at the use case,

the dramatic lowering of cost, cuz I'm

assuming union workers inspecting, you

know, pipelines, they're getting paid

40, 50, 60 bucks an hour fully baked

with their benefits, their pension,

whatever else. it it's quite expensive.

>> We haven't necessarily looked at labor

replacement for spot. Um while while

that is a metric you might look at. We

thought about you know how do we bring

spots in there to augment human labor.

One humans weren't doing the task even

if they were tasked with it they weren't

actually doing it. And two like we're

just trying to figure out ways that

humans can do more you know knowledge

worker tasks as opposed to going and

doing inspection. So yes one of the

metrics a customer might look might look

like for ROI is is labor replacement.

We're leaning more into how much do we

save you? So we found an air leak in

your facility and that was a would have

been $3 million a day at one time.

>> Yeah. The outcomes matter.

>> Yes. So what is the value that we're

driving?

>> But it's is it still delicate in the

industry to talk about labor

replacement. So you have to be very

thoughtful about that in this moment of

time.

>> And let's be honest. I mean there's

going to be an element of labor

replacement for this as a metric because

it's easy. You know how many bodies are

in the world and how can you imagine a

total addressable market relative to

that. I just don't think it's the only

conversation we should be having, right?

Just an element of it.

>> And hopefully we're getting rid of the

the dangerous jobs and the ones people

might find um oppressive.

>> Yeah.

>> Uh

>> dull, dirty, dirty, dangerous.

>> Dull, dirty, dangerous.

>> Yeah. We don't want that hurting their

body.

>> Yeah. We We only get one human body.

Yeah.

>> Uh the Atlas, how do you think about

onboard compute versus remote when you

put the amount of brains? Uh my

understanding is you have the brains on

the robot.

>> Yep.

>> That means crazy battery drain. What do

you think about the option of having,

you know, the uh brains in the cloud and

having these be more lightweight if

they're in an area that has extremely

high speed Wi-Fi, etc. And do you offer

that yet or is it all, hey, you got to

have a robot with a lot of brains on it

cuz that's what the customers want. And

that seems to be a paradigm shift that's

occurring now.

>> Yeah.

>> So, how do you gro that or how should we

think about it?

>> We think about two brains, right? It's

my simplified version of telling the

stories. There's two brains. Okay.

There's the the brain that controls the

physicality of the robot, which is what

Boston Dynamics is known for. Know the

dynamic movement, reliability, the way

it manipulates things in the world that

lives on the robot. The reasoning layer

that under that gives you the semantic

understanding of its environment that

can be in the cloud. That's things that

we might partner with Google Deepine or

we may partner with other AR partners or

who will build some of this oursel and

then the wrapper around all of that is

the very specific information that a

particular customer needs around their

own workflows. You know the way that

they think about the um the job

processes that they have and the tools

that exist in their facility and how

this robot will interact with it. That's

going to live somewhere in between. So

it could be on on robot if you needed it

to. It could be in the cloud. Um we'll

figure out the wrapper for that. What

percentage of the robot is built in the

United States or outside of China and

Taiwan today?

>> 100% of the robots.

>> 100%. So there's no issue with the

sovereignty of robots in the United

States. We're seeing a lot of cheap

robots coming out of China.

>> Yeah.

>> Your personal opinion as the CEO of this

company and as an American, under any

circumstances, should we allow humanoid

robotics from China in the United

States?

>> No.

>> No. Why?

>> It's not safe. Right. We've already

we've already heard um about leaks that

are happening with some of the

quadripeds that you're seeing um in the

United States and it's being back

channelled back to China. Listen, we we

have seen what happens if we let China

win in the semiconductor space. You

know, we can't do that with robotics.

So, we need to have a concerted effort

to protect our IP to um make sure that

we are bringing manufacturing of of this

ecosystem into the United States or into

our allied countries. And that means

that we need to take our national

robotic strategy. We're lucky enough

that we get to sit at the table in some

of these discussions. Um I'm hoping that

more companies in the US join us um in

in taking taking up this mission.

>> Yeah, we have to be pretty serious about

this. It's an existential issue because

these

>> not only do we have to win this, we have

to make sure that the rest of the world

uses our platform rather than China's.

How do you think about the military

application of these? Obviously military

is uh you know the the the field has

been changed with drones in a way and at

a velocity no pun intended that I don't

think anybody anticipated because of

what's happened in Ukraine and now we

see in the Middle East with the war with

Iran. How do you think about Atlas and

Spot in the battlefield? Where are they

at in terms of deployment uh in the

military? Yeah. So, we've been we've

been pretty public about the fact that

we have an anti-weaponization stance. Um

but listen, um I think that um for what

we're trying to do right now um in

industrial use cases, it's a distraction

for our business, you know.

>> So, focus.

>> It's focused.

>> It's not philosophical.

>> It's I mean, it depends on who you ask

in there. As the CFO CEO, you know, I'm

going to look at this and say I'm all

about focus right now. We need to be

focused on the markets that we think

we're going to win in. Um, and certainly

we have great ties with the government

and we're happy to do any

non-weaponization work with them. Um,

and we do do that today.

>> Okay. So, you'll have them in or you do

have them in the field. Maybe if it had

to go collect a soldier or bring a med

pack, you'd be okay with that. Disarming

a bomb, you're okay with that.

>> EOD, we EOD is one of, you know,

explosive ordinance disposal is

something that's a great use case for

robots.

>> And you're doing that currently.

>> We do that currently. So, we're okay

with that. Um, what we don't want is

Terminator robots, right? It's

>> not good for the market,

>> but China's building them. So if China's

building them and we don't,

>> right?

>> You're kind of obligated if you're

Boston Dynamics to build them. So if

China puts these into the field, will

you build them to protect America?

>> I think that's a tough that's a tough

question and I think we're going to have

to answer it when the time comes and

hopefully it never comes.

>> The time is going to come. I can assure

you.

>> And I can assure you what your answer

will be when President Trump calls. You

will say, "Sir, yes, sir." or else your

company will be nationalized. I mean,

this is the reality of it. I mean, I'm

being a little facicious and playful

with you. But

>> they're going to deploy these and

they're going to deploy them and they

already have shown.

>> You've seen them put AK-47s on these.

>> Yes. Not on our robots. Not on yours, on

theirs.

>> Yes. And on And listen, it's terrifying.

Terrifying.

>> I think, listen, I know that we have the

best robot and the most capable robot in

the world. You know, if and when that

time came that we had to make a tough

decision, we would make the right one.

Um, but today we don't have to make that

decision. So, I'm going to keep everyone

focused on the application space that

makes a lot of sense for us to make

money. And

>> I'm going to tell you a secret.

>> Don't tell anybody.

>> The CIA, the FBI, and the Department of

War have many of your robots with many

weapons attached to them currently.

>> Don't tell anybody. All right. Listen, I

know you got to go. Continued success.

This is such an important American

company and uh I hope you take the job

and become full-time. I know you're

interim right now. Interim right now.

>> Uh so I I wish you great luck with it.

If people want to come work at Boston

Dynamics,

>> please tell

>> where are you based?

>> Um so we're in Waltham, so right outside

of Boston. Um but we're open to some

remote work and um we're considering

coming to the West Coast. So

>> I was about to say, you know, I mean, I

know it's in the name Boston Dynamics,

but I assume with all that talent

accumulating in the Bay Area, you're

going to need to pop up a a space there.

Yeah.

>> Yeah, we're consistent.

>> All right. Listen, continued success.

Thank you so much. All right, everybody.

Our next guest is Professor Jonathan

Hurst. He's the co-founder and chief

robotic officer or chief robot officer

at Agility Robotics. You have a PhD

>> in robotics

>> from 2008.

>> Yeah.

>> So, you've been at this for over 20

years.

>> Well over 20 years.

>> Things seem to have heated up

>> in the last 36 months. Maybe you could

for the audience at before we get into

your product line level set what you've

seen in the past 20 years.

>> Yeah.

>> And how the last two years compares to

the previous 20.

>> Yeah. I mean 20 years ago when we were

doing this, it really was an unknown in

industry, right? Robotics was more about

automation systems. Yeah.

>> And in the research community, we're

doing things like humanoid robots, like

autonomous, you know, mobile robots. uh

really trying to build the intelligence

and then build the hardware that can cap

be make it capable um and that's really

started to break through now into the

real world into having direct impact

beyond being a research topic and then

the universities have seen this demand

and this growth and people love robots

there's a lot of demand from students

who want to do it so the number of

programs has grown and it's just

exponentially growing very very exciting

very exciting

>> we've had a lot of false starts with

humanoid robot which you're specializing

in

>> and AI

>> and AI.

>> They call it the AI winters, you know,

human multiple ones.

>> This time is real

>> quite obviously. explain to the audience

why this time is different and why you

believe this time we're going to see

robotics and humanoid robotics

specifically deployed at a scale that I

think we can both agree will be maybe in

the next 20 30 years onetoone with

humans on the planet

>> very impactful

>> yeah why why is this time different

>> yeah well I would say generally it is

very easy to make a robot that looks

like a person

>> and that's why we've seen humanoid for

100 years in one. It's very hard to make

a robot that can do useful things in

human spaces.

>> And we're starting to see that today and

that's the difference. So even if it

doesn't look exactly like a human, but

maybe a little bit humanoid, but it's

doing useful work,

>> that's where the impact matters.

>> And because of large language models,

>> a lot of things have now become free.

When these robots look at a table here,

>> Yeah.

>> and you say, "What's on the table?" It

knows that's a phone. It knows this is

paper, tea, water. It probably knows how

many ounces are in each.

>> Yeah.

>> If it were sitting here 3 or 4 years

ago,

>> it wouldn't actually know

>> what was in the world. You would have to

program it in a very narrow way. Yeah.

>> Yeah. Perception was incredibly

difficult. And the fact that perception

is all but solved at this point is a

really, really huge inflection point. I

mean, you know, I said, yes, robots

doing useful things, but also people can

now see the future of generality. AI is

really enabling that much more broad um

you know context awareness for these

robots so people can see that this is

going to be useful gen generally doing

many useful things very soon.

>> So there's perception the robot has to

understand the world.

>> Yep. But then there always seemed to be

this blocker with getting the robot out

of a very confined narrow task like you

know in a factory

>> and I my perception is it was the

communication and the training level.

Maybe we can unpack that a bit because

my understanding was previously you

basically had to hardcode the robot if

you were going to make a cup of coffee.

We have a company I invested in Cafe X

and it is a robotic arm.

>> Mhm. makes a cup of coffee perfectly

every time, can draft a beer, all that

stuff, but it had to be manually coded.

Now, the instruction set because of

perception, because of language models,

having trained on every video on the

internet, every coffee recipe that also

seems to be for free. Am I wrong or

>> not yet? It's actually quite different.

So, language models, think of it like

it's a it's now becoming kind of a

commodity like the internet. It's

available to everybody. It's this

amazing rising tide. But these language

models are trained off of the entire

data on the internet and that data does

not exist for robot control. You know

what's the example for your robot of all

the torqus all the torque commands to

every motor given all the sensor input.

There's no training set of data. So you

have to generate and create that somehow

>> and there's a lot of different

approaches and ways people are are going

about this. And some of these AI tools

again think of AI not as a blackbox but

as a big tent of many different very

different useful computational tools

right in order to control a robot you

can do these things by learning from

demonstration you can give it you can

tellyoperate the robot start to train

from that data um you can give it

animation input or motion capture input

or any number of different things but

that's also got a real hard limit

because a person controlling a robot is

not really getting to what the robot can

do if if it were optimal and how its

behavior could work that of a robot

needs to practice

>> you And that's where you get into world

models and sim to real transfer and all

of these kinds of things.

>> And world models are the next frontier.

People are literally putting

>> gloves on humans

>> uh and

>> having them control robots remotely

>> to actually chop and make a salad to

pour water

>> and that's being done today by many

different companies. the the world

models will solve this problem

>> or

>> they are part of the part of the

solution. As with all of these things,

there is no silver bullet,

>> right?

>> So the world models, as I understand it,

are, you know, can you model an entire

warehouse and all of the physics of all

of the objects inside of it so that then

simulations of these robots can go

practice in the world model without

breaking things in the real world and

you know compress so you can do, you

know, a million iterations within days

and computationally and things like

that. But there's always a massive simto

toreal gap. Things aren't simulated

perfectly. And then you know as you pick

up something in the real world and the

there's wave dynamics and there's

condensation on the glass and the

dynamics of the robot are not perfectly

modeled. All these things are still very

very difficult. That takes real practice

in real life with robots in order to

>> Yeah. So,

>> is there going to be a singularity or a

crossing over moment where recursive

learning, just putting the robot in the

kitchen, Yeah. letting it make its own

mistakes and then saying do the next

test, do the next test, which is how we

taught it how to win at chess or go. We

didn't tell it like here's how to

castle. We just brute force it and said

try every computation and it was able to

figure it out. Now with these recursive

loops, what will get us there quicker?

Somebody builds a world model, says go

get recursive, puts the robots into a

kitchen, and you know, breaks a lot of

China. Or is it going to be these world

model companies very refinedly working

human alongside robot in a Michelin

starred, you know, kitchen to to make

that sule.

>> I mean, that's it's not a very

satisfying answer maybe, but it's all of

the tools. all of them, right? There's

not um a silver bullet at all here. I

don't believe that there's this

singularity. I do believe that things

are going to get better and better.

Think of it more like a snowball picking

up steam going down a hill. Got it?

>> But the reason that it's snowballing

like this is because people are putting

money and resources and engineering time

and engineering effort in as they

explore everything and start to figure

all of this stuff out.

>> All right. So,

>> but humans, for example, we've evolved

to learn. We are very good at learning

and it takes very little data to show us

how to do something. And then we

practice and practice and iterate.

Robots are not very good at learning

yet. Robots take so much more data, so

many more examples than a person. We're

still figuring out how to teach robots

how to learn. Um, but then one of the

benefits that robots have in the long

run is they've got Wi-Fi. You know, when

you learn how to play the violin, you

can't just load that to somebody else

and then they learn how to play the vi

know how to play the violin based on

your learnings. Robots will be

>> one robot learns to play violin. All

robots know how to play violin

>> or all robots of that type know how to

play the violin. Right. Yes.

>> And then minor variations for the next

type and the next piece of hardware.

>> So you are actually deploying your

product is called Digit. Digit is I

think 4.0. You're going to release 5.0.

You've got let's say dozens uh in

different applications out there in the

real world.

>> Give us an idea of what the forward

deploy looks like today

>> and where you think it will be in a year

or two. So today it's doing these sort

of multi-purpose workflows that are

still reasonably well scoped like

picking up bins and totes and carrying

them around. And the reason we do that

is because you need two arms to pick up

big things. You need this whole body

control to be dextrous in how you're

manipulating and moving those. You need

to be balancing to lift them to top of a

tall shelf in narrow space. So it kind

of justifies the form factor for this

one use case. But the real useful aspect

of a humanoid is its versatility. So

when we do the each picking and you know

fill a bin and carry it somewhere and

palletizing and depalitizing and are

expanding out into more and more use

cases is when it really starts to

escalate. And Digit V5 which is coming

out later this year is the first time

that a humanoid robot a robot which is

balancing can step out of a work cell

and does not need a physical barrier

between the robot and the person to

maintain safety in this warehouse. So

when Digit V5 is out there that's kind

of the scaling moment for us. Yeah, this

is a key moment that maybe people don't

appreciate, but if you've ever been to

one of Elon's factories or Toyota's

factories,

>> there are lines.

>> There's a line

>> and if you cross that line, the

>> everything shuts down.

>> Everything shuts down. And I I've taken

many of these tours with Elon and

>> they're like, "Seriously, please don't

cross that line cuz it's going to cost a

million dollars if you do at the Tesla

factory cuz it's it's cranking. We're

starting to feel comfortable enough that

these robots are not going to fall over

and break somebody's ankle.

>> Well, it's been a very very intentional

process over the past 2 or 3 years,

right?

>> Where you know, this is our experience

with Amazon when we deployed and the

robots are doing the task and they're

like great, you know, it it solves all

the R&D uh, you know, goals we had and

we're like, great, let's go deploy. And

they're like, oh no, no, we can't deploy

>> um because, you know, they they they're

not they don't they don't meet our

safety requirements. It's like, okay,

how do we meet that? Well, it turns out

that's super hard.

>> And so, it's been a bottom to top design

of this machine. Holistically, the whole

every system of the robot is touched to

figure out how to make it safe.

>> When we look at an industrial shrank

robot like yours,

>> bill of materials,

>> uh-huh.

>> Tens of thousands of dollars each. Yeah.

>> I mean, we're not discussing bills of

materials. We know that the costs are

coming down and down and down over time.

We'll be selling robots, you know, in

the vicinity of costs of cars and things

like that.

um the the real like what is the value

that they produce is the question to ask

when you have a robot that's working 24

hours a day and has a 5year life you

know what's the value and it's quite a

lot

>> yeah it would be uh if we were to think

about it from first principles

>> they can reasonably run 20 22 hours a

day and then they have to charge and

just

>> that's right be so we take 20 hours a

day

>> 300 by the way 20 out of 24 hours for

our digit V5 robot on because of the

very fast charge it generation that's

gone in this battery.

>> Yeah. So we get we have 20 hours 365

days a year, you know, now you're in

that 78,000 hours a year. Let's put it

at 8,000 5 years 40,000 hours of work.

>> It adds up.

>> Yeah. And people tend to think these

things are going to cost 20, 30,

$40,000.

>> They will at some point.

>> Yeah.

>> It's going to need to go through the

scaling and have 100,000 robots out

there before that actually is real.

>> So that's a dollar an hour. These people

are being paid in factories currently

$40 an hour.

>> Yeah.

>> Maybe in uh some other countries $10 an

hour, but let's put it at 20 bucks an

hour. You've got 90% compression in cost

at some point when these things hit the

market, which gives you plenty of room

to charge an Amazon or Toyota, other

partners on an hourly basis. Is that the

current plan to charge per hour of

utilization? You own the robot. They

>> We do both. We do a capex for customers

that prefer that. We also do robot as a

service for customers that prefer that.

It's really lower barrier to entry and

lower risk for them.

>> What's the price of a robot per hour?

>> We're not talking about that right now.

But I will say like as obviously as the

robots get better and better and better

at what they do, their value goes up and

up and up.

>> And that's at the same time that the

costs to build the robot are going down.

And the value for these robots is really

set by the human labor and what does it

cost to pay people to do these jobs. So

it's a very inelastic price for a very

long time.

>> So between a a bill of materials, tens

of thousands of dollars, currently

people in factories getting paid 20, 30

or $40 per hour in the Western

Hemisphere in the modern world,

>> which is a pretty big market.

>> Yeah, pretty big market. Plenty of room

for you to save them money and for you

to make enough profit,

>> build an actual business, you know,

>> to build an actual business. Yeah. So

let's take the conversation to what do

you think the time frame is if I were to

ask you in Amazon factories or if we

want to take Amazon out because they're

a partner don't want to get you in

trouble but an Amazon or Target like

company

>> at what point will the majority of

workers in a factory be robotic when

will that flip happen to 51%

knowing what you know Jonathan

>> I mean already in a lot of these

applications the majority of the workers

are robots.

>> Sure.

>> Right. There's a lot of AMRs, there's a

lot of conveyor belts, there's a lot of

industrial robot arms and that's not

changing. That's continuing to grow.

Sure. And this is just a new form of

automation like all of the others that's

uh helping to increase and build that

productivity.

>> So like how do you know how do we in the

United States anyway, how do we build

our GDP? It's not a growing population.

No,

>> it's increased efficiency and

capability. And the only way we could do

that is more and more

>> especially not with the anti-immigration

vibes we have in the in the country

right now or even in the western

hemisphere. Um

>> well let me phrase the question another

way. At what point if there were a

million people working in factories

sorting packages does it go down to

500,000? Is that a three, four, five

year?

>> I think we've already done that,

>> right? But looking for and but with

these new

>> it's going to just continue. You know,

someday there's going to be an

autonomous truck that drives up and have

a completely lights out autonomous

package sortation factory and then you

know an autonomous truck leaving again.

And at that point, it's probably

specialty automation doing those things

because it's just 24/7 doing it. And a

humanoid doesn't make sense. It's not

the most efficient thing for that

specific task. A humanoid is useful for

walking into human environments doing

human workflows. So by the time this one

factory is entirely automated, there's

also a whole bunch of other factories

that still are, you know, legacy and

still, you know, need automation where

humans were. But then we're also working

now in retail and grocery stores and

hospitals and construction sites and

delivering packages to your front door,

which is a forever human environment,

right? Print yards, uh, and that kind of

thing.

>> That's going to be an interesting one.

>> Yeah.

>> Because it's fairly obvious to anybody

who has even looked at the latest

generation of humanoid robots that the

factories are going lights out. Most

people are incapable at this point of

imagining

a Whimo robo taxi, an Uber self-driving

car,

>> and a robot getting out.

>> Yeah.

>> And bringing the packages to your

doorstep.

>> That's going to happen.

>> Absolutely. Going back.

>> Are you working with

>> folks on that? You don't have to say

who, but

>> you know what? That was one of our very

first use cases that we explored with

Ford. And there's a nice video online of

our very first digit robot getting out

of a vehicle, walking up to someone's

front porch and dropping a package

there, stairs and everything. So, we

could do that like this was seven years

ago, something like that.

>> Uh, but I don't think it's the best

first use case or the best first market.

So, it's on our road map for sure.

>> But such a big market for deploying with

what we're doing right now. We're going

to start there. How do you when when you

look at applications,

we know applications that seem obvious

to us not being in the industry, but