The Biggest AI Deployment Nobody Talks About | Samsara CEO Sanjit Biswas

These are not the tokens you're going to

find online. Like you can't crawl Reddit

and find out about what happened on a

construction site. The Samsara system in

a given day were driving 99% of the US

roads usually multiple times a day. I

was just in the field last week with a

large energy utility. And uh they shared

with me a really interesting stat. They

said over the last 125 years we built a

certain amount of grid capacity. In the

next 5 years, we're going to triple

that. We're talking about millions and

millions of vehicles. We believe we

helped prevent about 380,000 car

crashes, road accidents in the last

year.

>> Hi, I'm Matt Turk. Welcome to the Matt

podcast. My guest today is Sanjit

Biswas, co-founder and CEO of Sanssara,

the 20 billion company running what

might be the largest AI deployment in

the physical world. Millions of

vehicles, 25 trillion data points a

year, driving 99% of US roads every

single day. We talked about physical AI,

agents for truckers and frontline

workers, humanoids, autonomous trucks,

and why the AI boom is really an

infrastructure construction project. Oh,

and if you're enjoying this episode or

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and it really helps the podcast. Now,

here's Sanjit.

All right, Sanjit. On this podcast,

we've talked a lot about AI models and

software agents, but we have spoken a

little less about physical AI. So, this

feels like the episode where we're going

to talk about how uh AI is confronting

the physical reality of transportation

and construction and plants and

utilities. So, maybe let's start with

physical AI. That's a term that uh we

hear about more and more often these

days, typically in the context of like

humanoids and robot taxes. from your

perspective, what is physical AI?

>> Yeah, absolutely. Well, first Matt,

thanks for having me on your show. Um, I

would say physical AI is really the

application of AI to the physical world.

So, if you think about the

infrastructure of our planet, it's way

more than just the roads where you might

see a Whimo robo taxi. Uh, it's the

construction sites, it's the electrical

grid, it's really kind of like all the

plumbing that's under the street. It's

it's it's everything out there. The

interesting challenge, I think, with

physical AI is that it's not digitized.

Uh, right? like this is the frontier

where you don't have decades of bits

that you can reason over and tokenize

and and quickly ingest and that makes it

really fascinating because the amount of

value is is kind of that's trapped is

really significant. Um so we think about

it in a few different ways. We think

about um you know digitizing things like

location from GPS tracking. Of course we

think about using cameras as sensors. So

you can use that to understand the

physical world in a pretty rich way

especially when you ingest lots and lots

of basically video footage. Uh if you

think about how many frames you get from

that it's really significant. And then

there's a lot of other uh kind of data

sources uh whether it's like weather

sources of like what happened with

precipitation on all the roads. um you

can think about speed limit data like

there's lots and lots of physical

aspects of the world that uh when you

put them together when you fuse them

together uh there's a huge value unlock

>> and um maybe walk us through how AI

changes that whole discussion so uh you

know there was industrial automation

obviously that's been going on for

decades and perhaps centuries then there

was a whole wave of IoT and famously

your ticker as a public company is IoT

and Navas AI I um how how different is

the current moment?

>> Yeah. Well, it's interesting you

mentioned centuries because that is like

the right time scale to think about

physical infrastructure, right? All the

way back to like the Roman era, like

there's pretty significant

infrastructure out there. So many of

these processes of like how do you

maintain a roadway have been in place

for a very very long time. A lot of that

process was manual, right? Like let's go

inspect the condition of the road. let's

understand when it was last worked on,

you know, let let's kind of dig it up

and see what we find. If you think about

the ability to digitize that and then

use sensor data, it's a huge unlock. So

the question becomes how do you get the

data? Then how do you process it? And

then how do you come up with a

meaningful insight or really an action

like what should we do about it? Uh and

that's that's I think now possible. The

last call it two decades was around

reporting like how do we ingest the data

and give you a really cool like table so

you can look at it and reason about it,

figure it out. Now what's awesome really

in the last two three years is the AIS

are able to reason about this kind of

information. They can look for other

context clues and then give you the

insight and now we're actually seeing a

Gentic AI of course which is it can take

an action for you can maybe schedule the

work to be done or or start performing

some of the work itself.

>> Why hasn't um Silicon Valley been all

over that problem space? I mean it feels

like we've been talking about chatbots

and then agent more in the kind of

digital and software realm. Uh I mean

obviously there's Tesla there's there's

you know humanoids being being built. Um

but is that is that did all of that need

to happen before this could be applied

to the physical world or is or is the

physical world just like a different set

of challenges altogether? You know, I

think it makes complete sense where all

of this AI wave has started, which is in

the digital word world. We had all the

bits. We had, you know, you know,

pabytes of data to reason over and

there's a great training set. So, you

think about all the trillions of tokens

that were needed to get these models

bootstrapped. The physical world is much

messier and there's also a hardware

component to it. And there's kind of

this saying of like hardware is hard,

right? Like this stuff has to like hold

up in the environment. It's got to relay

the data over like unreliable networks.

uh it has to be deployed to the front

lines and that requires a lot of sort of

messy work, right? Um physical hardware

installations, getting millions of

frontline workers to adopt new

technologies like integrate it into

their day-to-day work and it's basically

not as much lowhanging fruit as what

we've seen kind of in the digital world.

All that being said, it's a massive part

of the global economy, right? All these

industries, they make up about 40 50% of

world GDP. And so it's an area where you

can have tremendous impact, but you have

to really roll up your sleeves and get

much more involved than uh kind of

connecting to a large database that may

have already existed.

>> Is it fair to say that it's also a much

more unforgiving environment where

mistakes are potentially much much more

consequential?

>> Absolutely. So of course you're dealing

with human life in a lot of physical

operations and that's an area for

tremendous impact. So if you can build

safety systems that keep workers safe,

that's a great thing. But you also have

to be careful that you don't somehow

introduce risk into the into the

picture. Um there are other sides of

that too which is like these are digital

technologies so we want to make sure

they're hardened from a cyber

perspective like they're not introducing

cyber security risk but really

practically um the physical world is a

pretty dangerous place. Think about a

construction job site, right? There's a

lot of like earthmoving equipment

multi-tonon, right? Like really

dangerous stuff. It's kind of low

visibility. Um and so you know the

operators that are are operating that

equipment are taking some risk. The

people on the site are taking a lot of

risk. So it's inherently a risky

environment and our question has been

can we find ways to make it less risky

using data. So we see that the risk as

the opportunity as opposed to the

challenge.

>> All right. So you alluded to to some of

this but um for contextual awareness

early in this conversation uh maybe give

us a 60cond on on on what sensor does.

>> Yeah. So Samsara is a technology company

serving the world of physical

operations. So think about those

construction companies, the energy

utilities, the supply chain and

logistics companies that power the

planet. Uh we help digitize their

operation. So that's a combination of

hardware. So think uh GPS trackers, dash

cameras, asset trackers, all kinds of

different devices. Uh cloud services to

ingest all the data and then now AI and

applications to really close the loop,

right? to help people take some kind of

action or ideally automate the action

that's needed. Um what we found is it's

helpful to start with just tangible real

world problems and then expand over

time. So where we started was around

fleets of vehicles almost all of these

industries that have you know tens of

thousands of vehicles that they need to

perform their work but uh over time

we've expanded now into those frontline

operations and we're able to fuse all

this data together from different

sources on our platform third party

sources and and unlock tremendous

amounts of value for the customer.

>> Okay. And uh you just crossed two

billion in AR is that is that correct?

>> That's right. with uh you're a

profitable company growing at 30%. Is

that that's correct the right metrics?

Okay. Just a beautiful beautiful

company. Any other metrics you can share

about the the kind of the volume of uh

data points you're seeing or just to

give people a sense for the scale of the

of the company?

>> Yeah. Um so on the data points side of

things, these are numbers that feel

abstract even to me and I live them

every day. But we're talking about 25

trillion data points. GPS, video,

thirdparty API integrations, all kinds

of data flowing into the system.

Millions and millions of vehicles, for

example. Uh we're talking about, you

know, millions of frontline workers that

are using our apps every day. Um and in

terms of impact, that's the other sort

of set of data points we look at, which

is like, well, how is all this

technology having impact in the world?

Uh we believe we helped prevent about

380,000 car crashes, uh you know, road

accidents in the last year. That's

meaningful to us because as engineers

and product builders, we are able to

have like significant impact in the

world this way. Uh we've helped uh avoid

the emission of billions of pounds of

CO2 by helping do things like optimize

routes and reducing you know engine

idling things that seem simple but uh

you technologically simple perhaps but

the execution matters a lot. What's cool

is you see that real world impact. Yeah,

that that's a crazy number. 380,000 and

that's because uh you're able to detect

uh whether uh a driver can get sleepy or

that kind of stuff.

>> You got it. Yeah. So there's so many

different factors that produce risk. And

you know, we're very excited about

autonomy and robo taxis and everything

that we're seeing sort of on the

frontiers, but there are a lot of these

industries like um in heavy duty

trucking or construction, people work

very long shift. Um you know, maybe

they've been out in the field for 10 12

hours, it's been hot and so they're

exhausted. So you know, fatigue is

definitely one of them. U you also tend

to see more accidents in general at

night, right? Because the roads are less

visible. you see accidents in foggy

conditions after it snows or rains,

things like that. So, we're able to help

prevent a lot of risk by warning the

driver of when we see, you know, kind of

the risk increasing. We can provide some

real-time feedback and that helps them

be much more alert, much more aware. And

we can also coach away some of the bad

habits that people develop. Um, this is

an interesting stat, but in the US,

approximately 10% of people don't

regularly wear their seatelt. And that,

uh, varies by state, it varies by

industry, but that's like one of the

biggest things you can do to improve

your risk outcome is just simply put on

the seat belt. And it makes sense

because sometimes people are doing a

quick trip or that, you know, they're

distracted. But that little reminder

helps save lives, right? So that's one

simple one. Putting down your mobile

phone is the other one, right? When you

take a look at your mobile phone, like

lots of people have this habit. um your

your car if you're driving can move the

length of a football field and that is

hard to think because you're like I'm

just taking a quick look to see what

that message was about but then you look

up and you've moved 100 yards, right?

That's the kind of risk avoidance that

we can create with real-time alerting.

>> Okay, great. I'd love to spend a few

minutes on on your entrepreneurial story

uh leading to the creation of the

company which I believe was Stanford to

MIT to Merke uh Moroi.

>> Yeah. This is like walk us through like

how it all came about. You started the

first company as a student or right

after your PhD.

>> That that's correct. Um actually during

our PhD. So my co-founder John and I we

met at MIT as PhD students uh over 20

years ago. I I now cap it because we're

just old. Uh but it was it was a fun

sort of research project that we worked

on which was um this was around the time

that Wi-Fi was emerging as a new

technology. We built a uh research

project called roofnet. We covered

essentially the city of Cambridge, the

area between MIT and Harvard with free

Wi-Fi in the early 2000s. So that was

really exciting. It's like a hands-on

kind of very practical research project.

We did a bunch of academic research on

routing protocols and you know how to

build the network. Um but the first

company Moroi came out of that project

which was we thought it was tremendously

cool this idea that Wi-Fi could connect

so many people just incredibly useful.

We wanted to help other people build big

networks. And so we essentially took

that research um and and now I would use

the word distill like we condensed it

down to uh you know run in a box that

other people could build networks out of

and then we started essentially making

that product available. So that was

Moroi. Um to be honest we kind of

thought of it as a project like we

weren't even thinking of it as a

company. Uh we kind of bootstrapped the

business in Boston. We ended up moving

to California. Um and it was fascinating

because this is 2006 like 20 years ago.

Wi-Fi was a brand new kind of naent

technology and there were some real

challenges, right? How do you do guest

access? How do you do networks at scale?

How do you deal with people starting to

use YouTube which was brand new back

then? Like hard to imagine, right? Um

but that kind of exposed us to how fun

it was to solve real problems. And um we

had a huge, you know, kind of deep

background in networking. We had a lot

of friends from grad school that we

recruited to start that company. And so

we got off the ground quickly. we

started seeing these devices get out in

the world and Moroi ended up kind of

just growing and growing and growing. It

was doubling in revenue every year. Um

so that was the beginning of our

entrepreneurial journey. It was a little

bit of an accidental start.

>> And when you started Samsara, you know,

as opposed to to what you did in Moro,

that was a brand new area where u as far

as could tell from what I read like you

guys didn't have a prior background in

that. So like how does one become an

expert as an entrepreneur in a domain

that they don't have a background in?

>> Yeah, you're you're very right. We had

never spent time like in a loading dock

or warehouse or like in a construction

yard. Um but we were always fascinated

by them and I think that was really the

key is uh this is just like nerdy

curiosity of like well how does

electrical grid really work? Right? I

have an electrical engineering

background. I was just always kind of

like fascinated by this. um or like

supply chain if you're just curious

about like well that Amazon package like

how how far did it travel like you know

where where were the goods stored like

all of those kinds of questions were

fascinating to us and um similar to Maro

we weren't intending to start this

company right out of Cisco like we had

been kind of on this pretty intense run

uh but the curiosity kind of got the

better of us and we started reading lots

of books about this and you know like

just trying to learn about the world um

the challenge with physical operation

though is you can't learn about it in a

book like you actually need to go on

site and to do that you need a reason

you need an excuse essentially and so we

said well maybe we can be helpful to

these industries right because um like I

was saying earlier it's the

infrastructure of our plan so massive

there have to be interesting problems to

solve there so we kind of started this

company market first very steep learning

curve and I have to say as a second time

through entrepreneur I'm really glad we

had that experience because had we gone

back and it we probably would have

overweighted our prior experience and

said, "Hey, this is how it's done or

this is how we did it at Moroi." Uh,

with Samsara, it's a different customer.

We serve the world of operations much

more than the kind of technical buyer.

Um, we sell direct, so we interact

directly with our customers versus via

channel. And that reset was enough for

us to kind of go back to beginner's

mind, which I think is also very

important for most companies.

>> Okay. All right. Thank you for all of

this.

uh let's deep dive uh into the product

itself. So uh based on what we've said

so far um you have a hardware layer

which is the sensors. So uh just to use

an analogy and and and stop me if that

doesn't seem right but that would be the

the sensors so the ears and the eyes.

>> Then you have a software layer

>> I guess now with AI which would be the

the brain. Mhm.

>> And you just added recently and we're

going to talk a bunch about that you

added an energetic layer which would be

uh the the arms for the action. Is that

is that directionally uh how you think

about it?

>> Yeah. And I would say um you know every

single one of those layers has some

connective tissue attached to it. So if

you think about the hardware, I've got

hardware on my desk of course and so

this would be an example of one of our

sensors.

>> So what is this? What is this?

>> Uh this is what we call an asset tag.

So, you could put this on a piece of

construction equipment, right? Um, it's

got an accelerometer in there, so it can

tell, you know, how much it's been

moving. It's got a Bluetooth radio

that's a little bit more powerful than

what you've probably used on the

consumer side. So, you know, your

AirPods have Bluetooth. This is a

industrial-grade Bluetooth. Um, it's got

a battery inside and then it's built to

be super tough. So, you can like beat

this thing up. You can drive over it

with the truck and u, you know, it'll

it'll continue to operate that layer. It

has hardware, but also has firmware

that's running it on it on it. It's got

network connectivity. I mentioned

Bluetooth. So, this connects to the

millions of Samsara gateways, tens of

millions of phones and handsets that can

act as kind of a relay point for us. And

then we we're able to get that to the

cloud in a secure way. So, that's the

data capture side, right? Going from

motion like the accelerometer into the

Bluetooth layer into the cloud. Um, but

from there, you need to organize it

because like you got signals coming from

all over. uh you need to be able to like

operate on it in a pretty methodical way

and that's what's going to feed the AI

because if you give the AI pretty noisy

data you'll get you know it's like less

signal noise ratio so we need to get get

clean data in um and then to use your

analogy that's the brain right like

that's where we we store it we operate

on it um you can surface those insights

to the end user or the agentic piece is

you can just take an action right um

maybe change a safety setting right like

say hey we're gonna ask our entire fleet

in uh New York because it's raining to

increase the following distance versus

on a bright sunny day, right? Um that

kind of change would have normally

required a human in the loop. We're now

finding that the AI can do it very

consistently and uh can do it at scale

that people wouldn't be able to get to

because you'd need someone just sitting

there monitoring all the settings for

thousands of vehicles. Not very

practical. So, it just doesn't get done.

And that's the maybe the arms the kind

of action side of things.

>> Okay, great. All right. So that's the

overall architecture. Um, so going back

to that hardware layer. So you showed us

an asset tracker. Uh, you said it

connects via Bluetooth. That's

Bluetooth. Uh, it's been a while since I

looked at at at all the things, but like

it's not like Laura one and that that

kind of uh frameworks and

>> Yes, this is it's Bluetooth low energy.

uh if you're familiar with the BLE that

you you know would see on your uh

fitness device or your AirPods, that

kind of thing. So, uh Bluetooth has come

a long way over the years, it's it's

kind of gotten added on to and it's it's

picked up a lot of the great

characteristics that many of these other

standards had. So, we can get a lot of

range out of these trackers. And then we

add a layer on top of that of security.

How do we make sure that uh we preserve

the privacy and the security of the the

the tag that it's being applied to?

>> And it's powered by battery. You said

like how much autonomy would a tracker

have? Like how long does it last?

>> Uh this specific one would last about uh

3 years. We have others that last 6 plus

years. Uh we even have a really small

form factor one. I've got these on my

desk as well. And I don't know if you

can see them, but this is like a

tracking label. So we're talking about a

sticker.

>> That's the one you just launched in

Vegas a few weeks ago. Okay.

>> Exactly. So these last about uh 45 days

or so. Um so long enough for shipments

to kind of go one way. And then these

are disposable. So, um, they don't have

lithium-ion batteries in them, for

example. So, you can just, uh, peel

them, stick them, track them, and then

dispose of them.

>> But, show them again on camera if if you

will.

>> Yeah. This is a

>> So, this So, this is literally a

sticker. So, what's what's in it?

>> What's in it? Um, it's hard to make out

on camera, but there's basically some

batteries and then, of course, the

Bluetooth chip, right, as running our

firmware. Um and that is how it it

beakens up essentially its signal of

where it is.

>> How are you able to get such a flat and

small um like form factor? Is that is

that where the innovation is like just

miniaturization or like what what's the

I

>> I would say for us it's systems

innovation. We did not build the

battery. We don't make the silicon or

the chip but we work with partners to

integrate all this together. And then we

have the network which is essentially

think about um you know the millions of

vehicles on the road all the people

running the Samsara stack they form a

community and relay signals for each

other. Um and you've actually probably

seen this in the consumer side with the

Apple Air Tag kind of that concept of an

ecosystem. We apply basically the

industrial strength version of that.

>> Okay. Very cool stuff. So what else do

you have at the hardware layer? Uh I

read some more vehicle gateways.

What what what does this what do those

do?

>> Yeah. Unfortunately, I don't have all

the hardware that we make on my desk,

but the vehicle gateway, think of it as

a black box that goes on a truck or a

piece of construction equipment or

basically any kind of moving asset. Um,

that is a a different type of collector,

right? So, it collects diagnostic

information from the engine computers

and that's everything from uh you know,

how much fuel is it consuming to does it

have fault codes to uh was the driver's

foot on the accelerator or the brake.

That's all there on the diagnostic port.

So we're able to ingest that

information.

>> It's like a long time series of some

sort.

>> Yes. Uh well it's we have to collect it

and organize it but it forms a long time

series of many many different signals.

And even something that sounds as simple

as a fault code really there's a lot of

depth and richness to that too because

if you read the fault codes very

carefully you can understand very

specific dynamics about different kinds

of engines and fuel types and you know

air pressures and so on. So we we take

all of that in. Um we have

>> uh and then you have uh AI dash cams.

What are those?

>> That's right. So I think you're familiar

with dash cams. You've seen them in

Ubers, right? Like um and super

valuable, useful for drivers because if

something happens on the road, you can

exonerate yourself very quickly. Um so

we have a connected version of that. So

it records HD video, it has some

storage, it's got the ability to send

that to the cloud. Um and we run AI

models at the edge. So we can do things

like um provide you feedback to increase

your following distance like I said

based on the weather condition. Um now

we're also seeing the driver side of

that camera. So it's like you know

outward and inward facing. The driver

side of the camera can do things like

detect fatigue or mobile phone usage

provide real-time feedback to the

driver. And the idea is they can

self-correct uh self coach and that is

like the aha is it breaks the the cycle

or the bad feedback loop of I'm going to

look at my phone. If you get a sort of

like audio alert in the moment and it

happens many many times you tend to

break the habit because it's sort of

negative reinforcement. Um that was a

huge breakthrough for us um six seven

years ago uh as we introduced AI at the

edge and now we're kind of going further

with that concept finding other forms of

risk and so on

>> right because it used to be a safety

device and now it's an interface uh that

the driver can can communicate with the

AI.

>> Exactly. Because now that you've got the

technology in the cab, what else could

you do with it? Could you give the

driver uh driver a briefing in the

morning as they start their shift about

where all they're going to go and

traffic conditions and weather? Um, we

have a little button. They can use that

to uh, you know, call dispatch, for

example, and say, "Hey, I'm going to be

late because I need to uh go pick up

some tools or something like that." So,

the that connectivity layer just got

enhanced with the presence of all this

technology

>> and all of this. And I know you have

other sensors for like temperature and

that kind of stuff. uh all of this is is

built by you. So you mentioned not all

the components but like all of this is

is propos

the system and it's designed as an open

system by the way because a lot of these

newer assets they have uh APIs

effectively right so a lot of newer

trucks for example we can do a

cloud-tocloud connection so you don't

necessarily need the black box but you

want the data and you want it like

organized and and seamless with all of

your other assets uh because typically

in operations you'll have Ford trucks

and GM trucks and Caterpillar and

Freightlininer and all kinds of other

equipment coming together. Um, so we act

as that orchestration layer. Uh, so the

hardware is very much part of the story,

but we also have software in interfaces

coming into the system.

>> All right. So you got all of this and

then uh you move it to the cloud and

then uh what do you have there? Like you

have a gigantic data warehouse and like

ETL kind of data transformation. Uh is

that how it works?

>> Um that's right. From an ingestion

perspective, I think you got it. Um, so

just massive amounts of data storage.

Uh, this is all kind of sitting in

modern hyperscaler cloud. So it's not

that we have like one big warehouse/data

center, but um, you know, pretty large

system. Um, so we ingest the data, we're

storing it and organizing it, and then

we like basically have a bunch of

processes that are that are um

automating like sort of automatically

working on top of that data as well. And

then you have the UIUX collaboration

interface where you're a customer

whether you're a dispatcher or a truck

driver or uh the business owner like

everybody has access.

>> That's right. And and there many

different personas. So you named a few

of the the key ones. Um the drivers in

the front line are are very much like

just regular users of the system. Um you

do have the dispatchers. You'll have

other people who'd be like safety

managers or uh in certain cases if they

have to do paperwork like uh essentially

compliance managers and but you also

have people that do maintenance for

example and so they want to know what is

the health of all these assets in the

field which truck will I need to

maintain at the end of the day when it

comes back to the yard. Um and then you

do have the executives and all these

other business uh minded people who want

to know well did we show up on time

right like what is the efficiency of our

fleet and how do we do this at scale

many of our customers actually most of

our customers are large enterprises so

think operations have thousands tens of

thousands of people and tens of

thousands of assets and so on

>> as a thought uh so selling to a bunch of

different personas especially in like

more traditional industries uh

especially as you've added this AI layer

recently how do you go about it like how

do you convince people in you know

typically non technology industry to to

buy?

>> Well, you know, I think the the great

part about this technology is very

tangible and um it's the kind of thing

that when you see it, you get it very

quickly. So, what we do is we tend to go

on site, we will demo the technology and

do trials. So, you can easily these are

plug-and-play so you can easily try it

out in your environment, in your

industry. And um like I said, there's so

many challenges in physical operations.

It tends to never just be one thing. So

yes, we want to reduce the number of

accidents we get into, but I think we're

also like leaving our trucks idling a

lot because it's just a bad habit or

we're leaving tools behind at the job

site and we'd like to get those back cuz

we spend millions of dollars replacing

them. So we will often find multiple

challenges like that. Then we

demonstrate to them at small scale like

maybe a team or you know a region or or

something like that that this works and

when they see it they get it

immediately. These are people who are

experts in their industry. So they would

say, "I immediately see the value or the

ROI." Uh, but they have to see it in

that kind of tangible way. They're not

just buying it because it's AI or big

data or something like that. They're

like, "No, if this solves problems for

us in our construction business, great.

Let's do it."

>> And when you think about the long-term

defensibility of the the business, uh,

especially in a world where models may

or may not commoditize, I think most

people would say they are commoditizing.

Mhm.

>> Is it the data layer that you that you

feel protects sensara or how do you

think about modes?

>> Yeah, there are a few things. Um, we see

whether it commoditizes or not, these

models are incredible, right? And uh the

amount of value they can unlock with

their ability to ingest the data and

reason is is awesome. Um, so we're very

excited about what we're seeing on that

front. Um, the operational data that I

was talking about, the physical world

kind of digitization side of thing is

where we come in, right? These are not

the tokens you're going to find online.

Like you can't crawl Reddit and find out

about what happened on a construction

site, right? Nor can you do, you know,

test time reasoning about it. You can't

just you you can simulate all kinds of

environments, but really what our

customers need to know is like what was

going on in that specific environment at

that time, right? Um that requires this

interplay of hardware and software, but

also the change management. How do you

get this out into the field and that

partnership? So that's a unique area for

us that we focus on. Um this is what

we've been doing for the last decade

plus and it takes a lot of work. I have

to I have to emphasize that too is like

we get out in the field with our

customers, understand their business and

work backwards. So it's not something

that can purely be like just you know uh

one click uh deployed. It really

requires a kind of a nuanced approach.

>> Do you have a concept of a data network

effect or data flywheel across

customers? So does something that you

learn in the context of a dash cam with

uh customer X in geography Y also apply

to customer Z in a different geography

like in terms of learnings. uh very

much. So, you know, on the dash cam side

of things, the key insight there is, um,

while these may be all different

companies, we're all driving on the same

roads, right? Um, the Samsara system,

uh, in a given day, we're driving 99% of

the US roads, usually multiple times a

day. So, you can use that of course on

the risk side. So, we can understand

where are the risky intersections or

where weather conditions bad and and how

do we warn other drivers. So, there's a

network effect there. Um, but there are

other sort of uh side effects, right? Um

because we drive all the roads and we

have cameras, we can tell you where all

the potholes are, right? And that is

super useful for the city. So like this,

you know, city of Chicago for example,

they want to know which potholes are,

you know, happen after the winter

weather season. What order should we go

after the men in terms of severity? You

can use the camera data for that, the

accelerometer data from the GPS tracker

that I mentioned. So every time you see

that big bump in the road, um you look

at the video. And the cool part about

that is not only do you know where the

pothole is, but we can see what's

happening to it over time. Is it getting

bigger? Is it, you know, cracking? All

that kind of stuff. So that is another

sort of data network effect that we get.

And then maybe the third, since we

talked about asset trackers early, you

have these millions of vehicles driving

around. A Bluetooth tracker on its own

probably doesn't get picked up, right?

Because think about a construction site.

It could be acres and acres of land. But

if you have, you know, one company

delivering building materials, another

company performing construction, the

electrical contractor, one of those guys

may pick it up and that is another

network effect that you get with

millions of these vehicles and then tens

of millions of handsets like the mobile

devices. Uh it's a incredible kind of

mesh network that forms.

>> All right. So going back to the the

product and and the eye stuff. So you

mentioned uh edge and cloud. Where do

you guys do what in what proportion? We

could spend an hour just talking about

where where what is going on. Um if I

had to generalize, I would say at the

edge we're typically running inference

and data collection. So the data

collection of course gets us the

training data. The inference is

essentially running models where we have

the weights and we send them down from

the cloud and they're running at many

frames per second at the edge. And this

is how we do the the kind of real-time

or the low latency detections and closed

loop alerting to the driver. Um the

reason we do it at the edge is is

practical, right? So sometimes you don't

have a great sell signal. Uh many of our

customers are operating in the middle of

nowhere. Um and then also the latency

matters. If you can get uh feedback to

the driver, you know, really very soon

after something happened, it's much more

likely they'll they'll change that

behavior. So uh again very kind of

practical architecture for us. It's

worked really well um in terms of how

robust it is and how it holds up. Um but

that being said, it's not fixed. So if

that means we need to do some inference

in the cloud, we are set up to do that.

We have real-time tunnels that that

connect these devices.

>> Presumably what you run at the edge

would be smaller models. Are those um

your your

traditional quote unquote uh

convolutional neural networks that are

trained for images like a very specific

tasks versus um you know other forms of

like more modern generative AI. There

are lots of different model types. So,

uh, convolutional neural networks are

very much where we started. That's

basically from the imageet era of like,

okay, can we detect a mobile phone,

right? Um, from there these models have

become more sophisticated. So, we run

basically a model backbone with many

different classifiers and and heads or

attention heads. So, um, basically once

we see a device in somebody's hand, what

is it? Is it a phone? Is it a vape pen?

Is it a sandwich? um you know what is

the activity that's going on with it.

That's not a single shot detection. It

it tends to be a little more nuanced

than that. Same thing when we look

outward. Uh we have cameras that point

out at the road. We have some cameras

that point at the sides or or to the

back and we're trying to do things like

uh estimate depth, right? So, am I

likely to run into a lamp post or a

mailbox or something like that? That's a

different kind of model than like a what

a CNN would be able to do. And then what

has um uh generative AI fundamentally

changed for for you guys? Um is that

video reasoning? What what what do you

use for for what?

>> We use generative in a few different

ways. I would say if we think about the

overall class of models, yes, you can

now reason about video. So uh what would

have required a human in the loop

reviewer um or you know and that's the

kind of work that might have been done

overseas in lowerc cost geos or

something like that you can now do at

much more volume um in the cloud using

these models. So uh for example if

someone slams on the brakes right while

they're driving their truck um the the

naive thing to assume is like hey the

driver was distracted and they they kind

of woke up. The more nuanced thing is

that driver might have been avoiding a

deer or a dog or, you know, some kind of

uh defensive event. If you can watch

that as a video clip, you can now say,

"Hey, we're actually going to give the

driver some positive feedback because

they did a really good thing." The VLMs

are able to effectively do what I just

said, right? Um, similarly, like if you

want to understand, did someone run a a

red light, right? These things happen.

you need to have a pretty sophisticated

model that understands the geometry of

the road and all the conditions and and

so on. So that would be like a a JEPA

style model for example. So we're able

to use a few different model families.

On the generative side of like actually

being able to create video that's also

very interesting because from a coaching

perspective most of our customers are

bottlenecked on the number of humanto

human interactions they can have. Like

for me to sit down with you Matt and say

hey we need to talk about your driving

from last week. We could probably do

that for a small fraction, but I can't

do that for every driver. It's not

practical. Um, you may have seen like,

you know, AI generated avatars like AI

generated people. We can generate a

coach and that can resemble the VP of

safety from the company or, you know, a

celebrity or who knows, you know,

whatever the customer wants, but that

can be a very effective way to deliver

end of week coaching. So, that's a form

of generative video that frankly we

couldn't have even dreamed of five years

ago. Do you build some of your own

models or do you take stuff off the

shelf and customize it? Are you an open

source shop? Are you in OpenAI anthropic

shop, Gemini shop? Um what what do you

use?

>> We are universally accepting of models

in the sense of there's so much

innovation happening. Um so yes, the

frontier labs are doing great work,

right? The and we'll use multiple models

from different labs uh simultaneously.

Um the open source models are are pretty

compelling. I think it's having its

moment now. But we've been seeing u and

this is really I think from the academic

communities open source and really open

weights models really give you a lot of

operational freedom. So we can do things

like distill them uh for example to

shrink them down to fit on a device. Um

so we use models like that and then

there's others that we train from

scratch. Those might be smaller models

call it tens of millions of parameters

but very specific to something we need

to do in the field.

>> All right let's talk about agents. So

that's the that was the big launch that

you guys had at the um your Beyond 2026

conference in Las Vegas just at the end

of June. So less than a month ago now.

Uh and you launched agent studio. So

maybe walk us through this and I think

in the past you describe a progression

from uh connecting operations to

understanding them to taking action. So

how does that all fit together?

>> Yeah. Well, a few years ago, we did

introduce LLMs into our product. We

called it the Samsar Assistant. So,

think of it as like a chatbot tied to

your operational data. Um, it became

very popular. We saw customers asking

all kinds of, you know, practical

questions like who are my safest drivers

or which trucks need maintenance, things

like that. Um, those tended to be, you

know, single turn or maybe like a few

turn interactions like you just go back

and forth with the the chatbot window on

the side. The breakthrough that of

course happened last year is these

agents can operate over much longer time

horizons. So instead of an AI responding

to a question in a second, it can like

go do some work on its own, develop a

plan and go after it. We've seen the

impact of that in the coding world, but

there's also a lot of implication for

the operational world, right? So um the

one of the demos I did on stage is we

have a warranty agent. when it sees a

fault code, it can basically crack the

service manual, look at your uh specific

like OEM negotiated warranty agreements

and then correlate the two and say,

"Yes, this specific issue given the age

or you know the number of miles that

have been driven on this vehicle is

actually covered under warranty." Then

it can open a work order, put in the

steps and also tell you, hey, do any of

the other trucks have that issue? That

is a, you know, like what would have

been like an hour or two of human labor

that we've been able to automate down to

like under a minute. That is the huge

kind of breakthrough unlock. And I I

just went very deep on warranties. But

you can see how that would apply to

reporting, how it would apply to, you

know, briefing a driver at the beginning

of the day. You can use in all kinds of

creative ways.

>> It sounds like u for all perhaps the

obvious reasons you're starting with um

non-risky kind of use cases. Is that is

that how you guys think about it? Like

something where if you make the wrong

warranty claim, you know, it's not

great, but uh

>> yeah,

>> you know, nobody dies.

>> Yeah. Um I I think of it as we're just

starting with the most practical areas

we can have impact. The the reality, by

the way, is most of those warranty

claims just are unfulfilled, right?

Nobody has the time to go do all that

work that I just mentioned and and do

the paperwork. So that's an area of of

tremendous interest for our customers is

you know hey I have all this extra work

that I know would be useful but I'm not

able to get to so how do I do that? Um

and then you know over time I think the

idea will be how do we really like

autonomously

run parts of the operation for the

customer if that's like replanning the

route for example before uh every

morning shift we now have the technology

to do that and again it's not super

risky but it requires a lot of business

judgment of you know that route actually

is run by this person because they have

a relationship they've been you know

seeing that customer for 10 years you

need to have all that context So in that

sense we are starting with things where

we know we can have an impact and then

we're working with customers to figure

out what else could we do.

>> You use the word um autonomously. I'm

I'm I'm always uh fascinated for people

that that build real agents that work in

the real world just like you guys do.

What would you say is the proportion of

um sort of a gentic reasoning versus

having some good old you know code

enough code workflow and rules that's

built into it for for ultimate success

like you know ultimately who cares you

know what what does what as long as it

works but like what is the recipe to

make it work is that is a combination or

are we at a stage where just agent

reasoning can do so much so that you

don't need that much like rules built

into the overall solution

>> I I think agent reasoning was a huge

unlock like I said this ability to build

plans and work over long time horizons

but you do need to outline what is it

that I want the agent to do right and

that is in in some lightweight sense

like the workflow it's also the

guardrails like at at what point do you

say hey agent you should you know ask me

for some help or agent I don't want you

to go down that rabbit hole right like

we we have to kind of keep it on track

so it's some combination of operational

context which comes through workflow and

guardrail

with also this now kind of new agentic

reasoning ability. I don't think either

really works well in sort of isolation

and the workflow side of things. By the

way, we had elements of that in our

product. Um I'll give you a very simple

example in u in most commercial

industries there's a walkound inspection

that you do at the beginning of your

shift and end of your shift. We see

about 300 350 million of those a year.

that has historically just been a

workflow on a mobile device, right? Like

you're kind of going step by step,

taking some pictures, saying something's

safe. If you combine that with the

diagnostic information, the location

information, who last did the check,

like you know, what was in the picture,

that is is a huge unlock. So that's kind

of what we mean by combining these two

things.

>> What do you think agents are not able to

do just yet?

>> Oh boy, that that ceiling question, it

changes like, you know, I feel like

every week. Um, and you there's there's

some nuance to this. So, for example,

these new models like the kind of Fable

5 class and and GPD soul, it's hard to

figure out where the practical ceiling

is, but sometimes you do see them go and

get um distracted or like lost in a loop

somewhere, right? So, I think there is

some aspect of like they may find the

answer eventually, but can they find the

answer in um 10 seconds or 1 minute or

even 1 hour, right? So that's one area

where I think there is still a practical

ceiling and my guess is as these models

become more and more powerful more

sophisticated that will shrink and then

these algorithms are getting more

efficient. So maybe the compute combined

with the algorithm combined with just

like smarter model architectures will

make what would have been like a one-day

task a 1-hour task or you know even

faster than that. And if you suspend

disbelief a little bit, what do you

think you would be able to do

uh in like a year or two? You know, not

10 because obviously who knows. Um but

uh given the progress, so right now

you're able to do um uh you give the

example of warranty, you give the

example of uh sort of pre-planning a day

>> uh for for a driver. What what else do

you think you can do in the next year? I

I think a lot of our ability to predict

what happens next year or two is by

looking at what is like barely possible

now and then what will the sort of um

cost curves look like or or capability

curves look like and um something that

we talked about at our beyond conference

last month was this idea of a of a

driver ride along. So, in operations,

it's quite common to basically have a

manager sit with you over the course of

your day, like drive around with you for

8 hours. And what they're doing is

they're not looking for, you know, uh,

how fast you're going. They're looking

for your habits of like, do you check

your mirrors, like, you know, are you

alert and are you aware? That kind of

thing. That's basically a massive amount

of, uh, video computation, right? So,

you can run a tokenizer. It just turns

into like a lot of compute. You can do

that today. It's it's pretty expensive

and costly, but it it works and it's

doable. We're pretty optimistic that the

you know cost per million tokens is

dropping so fast and the capabilities

are rising that we can deliver that at

better and better cost over time to our

customer. So I think in a year or two

that will be possible and I have to say

like you know just over last weekend I

was playing um Sarah which is one of

those big like wafer scale chip

companies. they have uh an inference

model that you can run on their chip

which is basically Gemma 4 but like

hyper accelerated like that's a great

example of like that is nonlinear in

terms of jump right like what you get

out of these big models today if you run

Gemma 4 on your GP you might get like

100 tokens per second if you have a fast

card if you run it in their cloud you

get anywhere from 800 to 1500 tokens per

second so call it 10x faster um that is

the kind of thing where it unlocks these

new use cases that we couldn't get to

because it would have been too either

expensive or too slow.

>> Not to promote the podcast on the

podcast, but by the time we release this

uh the prior episode will be precisely

an episode with Andrew Feldman of Serial

Brush if anybody missed it

>> and of and of commercial. I'm I'm glad

you mentioned the the the ride along

because I think there's a fascinating

aspect to uh the whole dash cam almost

from a

societal standpoint. uh in in that uh uh

it could be like an interesting

blueprint in terms of like how we

professionally interact with AI uh not

just when we query it through AI

chatbots but like having AI live with us

on a on a on a on a permanent basis. So,

you know, sort of the obvious question

is that this there's an element of like

arguably big brother is watching you.

You know, AI is watching every single

move that you make. Um, and you know,

for your own good, but it's also looking

at what you may not do well. I'm curious

about what you've learned from the

perspective of uh uh making everyone

happy, if there's such a thing, whether

that's the the customer, the driver, and

you know, people for people not ripping

out the the camera in rage.

>> Yeah. So, uh, you know, a couple of

thoughts there. The first is we actually

do spend a lot of time on the front line

with drivers and other, you know,

frontline workers. So, it's very

important for us to get their

perspective because they're the primary

users and and really beneficiaries of

the system. Something people don't often

think of is, you know, if you have a

dash camera, what is it used for? The

majority use case is actually

exoneration. So, what I mean by that is

helping explain what happened if there

was an accident. Because for example, if

you are the Home Depot, you're a very

well-known brand. They're a customer of

ours. Um, lots of claims, auto claims

are placed against you because they'll

say, "Hey, a Home Depot truck backed

into my car on this highway." Right? And

that is something that really upsets a

driver because they'll say, "Look, I was

doing my job great. Like, I didn't run

into that guy." Now, you can basically

produce HD video evidence of where you

were and if there was an accident, who

caused it, all that stuff. That's it.

eliminates all the ambiguity, right?

Like now you can just resolve it and

look, if there was an accident, the

company may choose to just settle it out

and and pay it out. But if there wasn't,

which is like a very common case, now

you can really fight it and say, "Look,

we we know exactly what happened."

Drivers love that because I I have to

say 90% of the time they're doing a

great job and nobody's seeing it, right?

And so that has been a huge unlock is

this positive reinforcement of we're

analyzing the whole drive. we're seeing

all these good behaviors, defensive

driving or exonerations, things like

that. That I think is what is is sort of

the counterwe to the hey what is all

this for? Like how is it being used? If

you have that in your culture, if you

are kind of doing the equivalent of

employee of the month but showcasing

really great work, I think people get

really excited about this. Also uh from

a safety perspective we should remember

in physical industries the risk uh of

injury is on the person right so in

other words like we want everyone to go

home the same way they came to work

right that is an important concept that

I think people don't don't think about

if you're working construction you're

working oil field services or something

like that you take a lot of risk when

you do your job every day so it's

actually in the what's in it for me it's

like we are trying to keep you safe uh

if you have that and you do it in a

transparent, thoughtful, you know,

respectful from a privacy perspective

way, it it goes a very long way with the

front line.

>> Very interesting. And that makes a lot

of sense. Just to push a little bit, if

I if I may, um

um in in some ways the eye also becomes

a judge of the quality of your work.

>> I don't know if you agree or disagree.

Uh I'm curious if there's any uh kind of

like safeguards about how that happens

or should happen in the future and

perhaps it's a fact of life. You know,

we just had the World Cup and like we we

now familiar with the VAR review and it

is what it is. You were offside and

that's just what it is and technology is

here to tell everyone that you are

offside. Curious about like what what

you've learned. It seems like such an

important current topic.

>> Yeah, very much important. I think

transparency again is like the key word

here. It's not and by the way our

cameras are not hidden cameras. They're

they're quite visible. So it's not like

a secret sort of recording device. Uh

and the the whole idea is to bring the

front line along. So we call this change

management, right? Like hey, we're

introducing these things. What do they

do? What are they for? How can you use

them? How can they be useful to you? If

you have that conversation early in a

transparent way, it tends to be quite

constructive because um I mentioned Home

Depot earlier. they saw like a 65%

reduction in their claims like auto

claims. That was a huge win for that

organization both at the sort of

executive level but more importantly at

the sort of like regional level. Um

those kinds of wins are what we want to

like help create. Now could you use this

like in a bad guy kind of way? You could

but that would be like who is sitting

there watching like each driver? It's

like super boring by the way to like sit

and watch drivers, right? Um so when

when you kind of are transparent about

what the system's doing and what it's

not doing and then how the data is used

that is like how you get the buy in and

and you earn the trust uh of of that

entire organization.

>> All right. So you sit at the very

forefront of of of all of this in in

physical AI. Um I'm curious where you

see the world going as we maybe uh take

a step back. Are we going towards a

world of like mixed fleets of of just

people and just robots and is that is

that what you're seeing?

>> Yeah, this is the like if we kind of

imagine 5 10 years out like where does

this go? I I do think yes. Um we expect

there to be a lot more robots sort of

involved in physical operations. You see

this actually if you go into a warehouse

today, right? So if you go into either

manufacturing or fulfillment center,

there's actually a lot of automation

robotics going on. And the the cool part

about that is it reduces risk of injury

to a lot of the human workers like

lifting injuries were very common uh 10

20 years ago. They're way less common

these days because quite literally the

robots doing the heavy lifting. Now

there's still humans working there

because there's kind of um all the

handoffs and you know there there's some

nuance to the operation but we expect

something similar to happen out in the

field right so think about a

construction site or maybe a company

building a roadway or you know uh

modernizing the grid. There's a lot of

kind of repetitive work that has to

happen. Imagine you're grading a site

like you're making it level. Could that

happen during the third shift between

midnight and 8 am right? That could be a

really cool way to do productive work on

the side of the road where you're just

going for like five miles making it

flat, right? Um we we see robots being

able to do that in the next 5 years.

Now, all the rest of it though, it's

still pretty messy and construction has

like exception after exception. Like

you're solving problems constantly.

That's where I think the humans offer a

lot of experience and judgment of well,

how should this work? And I'm waiting on

this building material while I can

perform this other thing. Meanwhile, the

robots like making the road flat, right?

So, that's kind of what we see in the

next few years. The same thing applies,

I believe, to logistics and supply

chain. So, um now like there's a lot of

kind of last mile complication that

happens and you have to physically pick

up deliver package. Some of our

customers, they stock the shelves in the

grocery store. Maybe we get there with

humanoids and you know, never say never

like this stuff always is evolving. But

in the meantime, could you automate the

longhaul segment between Dallas and

Phoenix, right, of all of those, you

know, beverage cans coming in or

something like that? So, we see this as

an exciting like and uh in terms of what

the future looks like. And what we've

seen in operations is very diverse, lots

of different types of equipment, lots of

different types of labor. So, it's going

to be, you know, different makes and

models and different makes and models of

different kinds of robots is my guess.

And from your perspective as a business,

you would just par it all. Uh I guess

where would automated trucks and

humanoids on the construction site fit

in the overall picture at Simsar?

>> Well, practically speaking, most of our

customers would tell you they're supply

limited in terms of labor, right? So

these are uh labor intensive asset heavy

industries. So they welcome this idea of

like could I automate some of this labor

so we can basically perform more. So

that's kind of like our customers are

are going to be around. they have like a

lot of work to do. What we want to do is

provide the sort of uh single pane of

glass so they can orchestrate the whole

operation, trigger the workflow of like

okay that truck is arriving from Dallas.

Let's get queued up so the warehouse

systems ready to go and accept it. Um

let's make sure that we're notifying our

end customer and so on. And then I

showed the tracking label earlier. You

could put that on the pallet of goods.

So you can track it end to end as it's

changes hands through the supply chain

as it makes its way to a job site um as

it's installed. This is all very opaque

today. Like if you think about your, you

know, shipments that you receive as a

consumer, you might get like five five

updates, right? Like left this left the

facility, you know, out out on the road,

out for delivery, etc. We see like

hundreds and hundreds of pings. That's

going to be really important when things

are moving on their own. you know, where

is that um aerospace assembly, right?

Like we have customers who want to know

where this really expensive asset is

that they need to perform their job

right now. That's sort of like

unttracked.

>> Do you think uh automated uh you know

self-driving trucking is just around the

corner? It seems to be around the corner

for cars. I mean obviously it's already

happening with Whimos uh and and Teslas

now and automated pilot. In three years

are we at 10% uh self-driving trucks?

Are we at 75%? What's your gut?

>> I think on the robo taxi side it's going

to happen a bit faster because the

operations are much more regional. Um

they're much more um similar, right?

Like the way that you and I ride in a

taxi across town is going to be quite

similar. And so that's I think where

you're going to see the biggest sort of

like visible impact of autonomous

vehicles. Um, on the trucking side, it's

also important to realize like there's

long haul trucking and kind of like

moving stuff from point A to point B.

That tends to be a minority fraction of

what the commercial vehicles on the road

are doing. Most of the commercial

vehicles are um in like industries like

field service, right? So, they're HVAC

technicians or plumbers, electricians,

so people performing some work. Um, and

they're also they're either doing

something like that or they're in

industries like construction where

they're building the road. that tends to

be where current day sort of like

autonomy doesn't work so well. It's like

the really messy long tail. So for that

reason, we think the adoption might be a

bit slower, but it's not like a no. It's

just it might take 10 20 years. And

these are industries where again the

equipment's highly specialized. Like if

you look at cement mixers or you know

garbage trucks, like these are

customuilt. So for um for the autonomy

systems to make their way out to that

edge is just going to be a longer

diffusion curve than you know for a

sedan which is or or a van or something

like that which is very much the same.

>> Great. Maybe to to close um I'm very

very curious like you're at the heart of

this real economy as we said at the at

the beginning of this conversation

transportation and utilities and

manufacturing and all those you know

fundamentally important things. What

what's your sense of the reality of

American industrial

power today? Are you seeing same level

of velocity? Are you seeing an

acceleration?

Uh is the AI boom and like the data

centers like having a real impact on

your customers? What what's your um

sense of the level of just um velocity?

>> Yeah, very much. So I I think our

customers have it's very clear they're

busier than they've ever been before. So

from like an American economy

perspective, like we're seeing a lot of

intensity. Um I was just in the field

last week with a large energy utility.

Um and they've been involved in grid

modernization and uh they shared with me

a really interesting stat. They said,

you know, over the last 125 years, we

built a certain amount of grid capacity

in in megawatts or or gigawatts really.

Um in the next 5 years, we're going to

triple that. Like they as a company are

going to 3x the amount of power they

deliver. And that's like in five years

versus 125 years. So that requires a

tremendous amount of infrastructure

build even with new technologies. It's

like they can't work fast enough. U we

are seeing that across so many different

kinds of industries. And in that case

like they also shared you know 90% of

that demand is data center related. So

as the data center demand continues to

skyrocket the energy needs or all these

like different bottlenecks that have

been appearing. So many of our physical

operations companies uh customer

companies are involved in in that

directly. I know a lot of people that

your customers employ are trades people.

What what's your take on evolution of

trade? Like the the the idea that you

know we've seen we've all seen in the

last like couple of years is that uh

actually being a plumber uh becoming a

plumber might be a great idea if your

lawyer job is going to get automated.

Some of it is some level joke but I'm

I'm curious about uh what what you what

what you take is

>> Yeah. I I I will warn the lawyers

thinking about becoming plumbers. It's

it's a pretty messy job, right? So,

>> it's much harder.

>> It's it's pretty hard stuff. Um, so yes,

we've been continuing to see that

there's a basically a labor shortage,

labor bottleneck in a number of

different trades, but also things like

long haul trucking, commercial driver's

license holders, things like that. So,

uh, in general, I think there's a lot of

growing demand for these uh,

professions. And this data center boom

is a great example. There are just like

not enough electricians, uh, out there

right now. So you see companies like

Meta doing initiatives to like reskill

people, train them on how to become, you

know, a good electrician and then how

can you take the people who are trained

and make their jobs as efficient as

possible. So you don't want them like

waiting on materials at a job site like

you want to put them to work to perform

uh you know wherever their skills are

needed kind of thing. So very much kind

of a bottleneck, but the trades are in

incredible demand. And I also think that

their jobs are getting um more

modernized as well because if you think

about it as an electrician, a lot of it

is getting to the job site and having

the materials and knowing what you're

going to do. If an AI can kind of help

you with that, it takes a lot of the

mental load off and you can focus on on

the really unique trade kind of value

you have.

>> Well, Sanj, it's been a fantastic

conversation. Thank you so much. very

excited about uh what you guys are

continuing to build and like its sheer

importance in the overall economy and

it's been wonderful to learn more about

it. So, thank you.

>> Thank you. It's been fun.

>> Hi, it's Matt Turk again. Thanks for

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