By 2030, it'll be inconceivable to buy a

car and not expect it to drive itself.

Every single one of our cars, we want to

have the ability for it to operate at

very high levels of autonomy. Radars are

extremely cheap. LARS are very cheap,

but the really expensive part of the

system is actually the onboard

inference. In order to imagine more

expensive than any of the perception

stack, [music] my view is EV adoption in

the United States is a reflection of the

lack of choice. As consumers, we need

lots of choices. We need to have

variety. We selfidentify [music]

with the thing we drive. The world

doesn't need another Model Y. The world

needs another choice.

>> Hi listeners, welcome back to No Priors.

Today I'm here with R.J. Scarring, the

founder and CEO of Rivian. We're here to

talk about their autonomy strategy,

proprietary chips, their coming R2

model, whether Americans want EVs, and

what our relationship to cars is going

to be in the age of AI. Let's get into

[music] it. AJ, thanks so much for doing

this. Thank you for having me.

>> So, Rivian's already uh an incredibly

cool company. How did you decide it was

going to become an autonomy company?

When did that happen?

>> I mean, from the beginning, we thought

of it as a transportation and mobility

company. And in fact, even before Rivian

became Rivian, when I was thinking about

what's the first products, it was

unclear what kind of car would be, but

or even if it was a car, but it was

always clear we wanted to be at the

front edge of helping to redefine what

does it mean to have access to personal

transportation. And so autonomy is

always been part of the strategy, but

it's now fully coming to life with the

technology that we're building.

>> And when you think about the function of

Rivian, there's transportation, there's

also the experience. Like when how long

ago did you guys start investing in the

autonomy strategy here?

>> Yes, we launched R1 in um very end of

2021.

>> Mh.

>> And we used what I'll broadly

characterize like a 1.0 approach to

autonomy. So we had a perception

platform. We used a a third party, a

front-facing camera that was essentially

a third-party solution that then plugged

into an overall framework that we built,

but it was all rules based. So, the

camera is fed a rulesbased planner. The

planner would then make a bunch of

decisions around the feeds from the

perception. And it was, you know, the

moment we launched, we knew it was the

wrong approach, but it was the thing we'

started working on uh well before the

launch. And so, at the end of 2021,

beginning of 2022, we made the decision

to completely reset the platform. And

>> was that hard as a decision?

>> No, cuz it was so clear when we made we

made the when you're building something

like this, you're you recognize you're

going to spend many many billions of

dollars creating it. So we knew this

like at the core of transportation is is

driving and at the core of that is a

shift to having the vehicle be capable

of driving itself. And so we made the

decision to redo it like clean sheet,

you know, no legacy of what we had built

in the Gen One. And that first launched

from a hardware point of view in the

middle of 2024. Uh so that was with our

gen two vehicles. You know, not a single

line of shared code, not a single piece

of common hardware on the perception on

the compute side. And uh and then we had

to build like the actual data flywheel.

So we had to grow the car park to build

enough of a data flywheel to then start

to train the model. And what we showed

in our economy day late last year, late

in 2025, was the beginnings of a series

of really like super exciting steps of

how this is going to grow and expand. I

say this all the time. I I think of not

just for Rivian, but I'd say for the

auto industry in general, the last three

years compared to the next three years

are going to look very different. So the

rate of progress that we saw in autonomy

between let's say 2020 and 2025 or 2021

and 2025 and what we're going to see

between today and let's say 2029 2030

are they're completely different slopes

and that really comes back to you know

entirely new architectures now being

used to develop self-driving actually

truly AI architectures whereas before

these were not AI architectures in the

in the true sense they were they were

using machine vision but really

rules-based environments that we defined

as as humans, you know, we codified

them, which is very different than how

Apple today.

>> You might actually have perfect timing

here in that uh I got to be part of

investing in sort of the first wave of

independent autonomy bets that were

working with the OEMs at my last

investing firm. Okay. But this is

>> I would say 8 10 years ago.

>> Yeah.

>> And uh as you mentioned there's several

architectural revolutions since then.

Yeah. And so for companies to make that

shift from you know we're going to have

these separate perception and planning

systems to more endtoend neural networks

>> I I asked because I felt it was actually

quite a hard decision for people in

choosing their partners and from a from

a technical perspective.

>> Well I think it I mean you can see it.

So there's if you go back to the very

beginning of the idea of self-driving,

a lot of effort, a lot of spend happened

for companies to build these rules-based

environments and to build these more

classic systems. And when transform

based encoding came along, you just a

couple years ago and it shifted very

rapidly to it was clear that the future

state was going to be neural net based.

It was hard because if you're a company

that built all these systems, it's like

do I keep investing what I had? what do

I what do I do with all this work that

was was built before? And the reality is

is a lot of it is the vast majority of

it is going to be pure throwaway. Um

because it wasn't like a gradual shift.

It was a complete rethink of how things

are architected.

>> How did you decide that this was going

to be a an in-house effort versus a

partner effort that given most people

who made cars said we're going to go

partner or buy something here? I I guess

the emotional philosophical is on things

that are really important, we've taken

the approach of vertically integrating

them. So electronics, our software, all

the high voltage systems in the vehicle.

So things like motors, inverters,

uh all the power electronics, these are

all things we we develop and build

inhouse. And in a few cases, you know,

we had to start with something that was

either off the shelf or partially off

the shelf. But today, all of that's

completely in-house. And in the case of

self-driving, we knew that long-term it

needed to be something that was

developed internally. We started as I

said with a mobilecentric solution,

which a lot of folks did, right?

>> Particularly in like you that 2015 to

2021 time frame. But when you really

look at what's necessary to to be

successful in a neural net based

approach,

>> there's a core set of ingredients that

very few people have and I think we

uniquely have them. So first and

foremost, you need to have complete

control of a perception platform. You

have all the everything that the the the

system is capable of observing, whether

that's cameras, radars, or LAR, or some

combination of all three. You need to

have control of that. Meaning there's no

intermediary company that's like

processing some of the information. And

so that's powerful because you can then

feed raw signals into your system. The

system needs to be capable of triggering

unique or interesting or noteworthy

events that you can then use to train

that triggered. you know those triggered

moments need to then be captured saved

on the vehicle and then when the when

the time arises where you have Wi-Fi

ideally send it up and the reason I say

Wi-Fi these are this is a large a lot of

data so you could of course do it over

LTE but it's expensive as you have to

have a really robust data architecture

on the vehicle then you need to be able

to send it off offboard and use that

with a lot of uh training so with a lot

of GPUs to train a model companies that

are either developing independent

solutions that are not a car company

they typically don't have access to the

type of mileage that we do. So that the

huge amount of data that our vehicles

generate. Uh if you're developing this

from a sensor set point of view, you

typically don't have the vehicle

architecture and the vehicle car park.

So we just came to the view that we have

all these ingredients to do it really

well. And [clears throat]

>> it's like not an optional thing. It's

the companies that do this well will

exist. The companies that don't do this

well,

>> like I feel really strongly this. They

will not exist. They will shrink to

shrink to nothing. asmtoically approach

you know zero.

>> You think it can only be delivered in

really a vertical vertically integrated?

>> No, I think I think there's more than

one less than five companies outside of

China that have the necessary

ingredients to do this. The capital, the

GPUs, the the car park with you know

enough vehicles generate enough data. I

say more than one less than five. It's

probably

>> and the control of that whole training

loop you're doing.

>> It's probably like more than one less

than three maybe four. Like there's very

small number of companies that can do

this. I think the uni unique spot we are

in time right now is the 1.0. Can

>> I ask explicitly then? It's you, it's

Tesla, it's Whimo. Is that the three?

>> I would include all three of those.

Yeah. And there's maybe one or two

others in [clears throat] in the mix.

But I think

>> the challenge is you have to look at the

not just the moment in time for

performance where we are today.

>> Do you have the ingredients to continue

making progress at a very high like high

rate over the next four or five years?

>> And so a lot of the solutions that are

more 1.0 based and and are sort of stuck

in that framework I think have a like a

truly a 0% chance of progressing to be

competitive with a neural net based

approach and the neural net based

approach does take a lot of times you

have to build ton of inference on the

you have to have either buy it or build

it a lot of inference we decided to

build it so we built an in-house chip to

do this you need to have a car parked

this large

>> you just mean enough onboard compute to

actually run the models the car yeah in

the vehicle and so you could you could

buy that. Of course, Nvidia makes those.

Um, but you need to be able to do that

at scale and have it in every car. And

so, we took the decision to make our

chip in house.

>> Is that more a capability uh decision or

a cost decision?

>> It's a cost. And then like we want to

have it on everything. So, every single

one of our cars, we want to have the

ability for it to operate at very high

levels of autonomy. And so, we design,

spec, and build the cameras.

>> Radars are extremely cheap. LARS are

now, you know, very, very cheap. But the

really expensive part of the system is

actually the onboard inference.

>> And so that's like an order imagine more

expensive than any of the perception

stack. I think people focus on the

perception because it's the things we

can like visualize,

>> right?

>> But the brain is actually the most

expensive part. And so we brought that

in house as a way to remove cost from

the system so that we can easily deploy

this on on every car.

>> You are taking like a sort of know

step-by-step approach to levels of

autonomy. Yeah. and Rivian, how do you

think about um how quickly you approach

like level four or you know the safety

case around each of these things? How

fast your team goes against this?

>> Yeah, I mean this is even this question

is unique because just a few years ago

20 2019 2021 even there was like very

like very clearly delineated

ways to approach autonomy. There was a

level two approach which was camera

heavy maybe with a few radars

>> and then there was a level four approach

which was of course had cameras but had

a lot of lightars. It was sort of

inconceivable to think of the level two

system becoming a level four and

similarly the level four system was way

overbuilt to even like conceivably think

about putting that on every consumer

vehicle.

>> Well, you didn't want the the big want

all these parts. Yes. The tens of

thousands of dollars of perception. So

what's happened is those two worlds just

I think have just started to very

clearly merge where the delineation

between a level two, a level three and a

level four um in terms of perception and

and in terms of compute has started to

fade and it's now essentially just

remove like how capable the system is at

addressing all these corner cases.

And you know, this is what's hard for a

consumer to recognize. If you're driving

a level two system or a level three

system or a level four system for

99.9999,

like three or four nights, feels

identical,

>> right?

>> The difference is like the fifth or

sixth or seventh nine on that is these

like extreme corner cases. And so I

think it's actually led to a lot of

confusion where you'll be in a level two

system like the car could drive itself

and you're like yes it can under

>> most of the roads conditions except

these very unique corner cases. And so

to your point on safety cases, the

question then becomes is like how

confident are we in the system

capability in covering these really

obscure unlikely rare events which of

course if they're not covered well it

can lead to really uh you know terrible

outcome you know the vehicle in a bad

collision and so that's where the neural

net based approach has just changed

things a lot. So the the the

capabilities are so much stronger and

the ability now I think for us to deploy

on a lot more vehicles have a car park

that's very large. So we went from, you

know, few years ago state-of-the-art was

you'd have a test development fleet of

maybe maybe a few hundred vehicles,

maybe maybe like high hundreds of

vehicles to now like thousands and

thousands. Every single car on the road

is part of your data fleet that's

identifying these unique corner cases

and then running the model against them

to test.

>> And now of course we're simulating those

unique cases and we can do a lot there.

So the just the whole nature of it's

changed so dramatically that I mean I

think by by 2030 it'll be inconceivable

to buy a car and not expect it to drive

itself. You know maybe that's sooner.

Maybe like we hope it's sooner like

we're targeting a little sooner than

that but certainly in like a very very

near future like that will become a

mustave in a car. Sort of like it's hard

to imagine buying a car today without

airbags or buying a car today without

air conditioning. Um these things at a

moment in time were optional. I think in

not too not too much time, couple years,

it'll be hard to concede buying a car

that can't drop you at the airport or

pick up your kids from school.

>> I would argue that right now um most of

the biggest car makers do not have the

ingredients that you described to make

this a reality.

>> So, do you think that um that's going to

play out in the market where like

autonomy will be so important as a

driving feature, core feature of the car

that there's just going to be a big

market share shift to those those who

can figure it out. I I know you're

biased here, but I'm like,

>> "No, no, no. I think it's it's it's a

hard question to answer." So, I think

it's uh I I always characterize like

this.

>> I think it's inconceivable

for a car company to continue to operate

at scale like mass market. I think very

niche enthusiast realms sure, but like

at scale

>> without a software defined architecture,

which is even before you get to

autonomy, just like can you do OTAAS? Do

you have control of a of a

>> sorry can you define software define

architecture?

>> Yeah, that's like before we even get to

autonomous like these are like basics.

So the way car

>> the core thesis of

>> Yeah. Yeah. So the way car electronic

systems have been designed and built and

have evolved with the exception of Tesla

and Rivian every car on the road has

what is uh called a domain based

architecture. So you could also call it

a functionbased architecture. So all the

functions across the vehicle, let's say

chassis control or door system control

or uh eight track, your air conditioning

system, all have little computers

associated with them, right?

>> What we call ECUs, electronic control

units. And in a modern car, you might

have 100 to 150 of these. And each of

these run their own little island of

software. And that little island of

software is written by a supplier, more

likely a supplier to the supplier. So

you go to a a tier one and they hire a

tier two who writes the code base to run

your H.

>> That's why it's impossible to debug like

a software system. And

>> it's also why it's really hard to do an

update. So imagine you have a 100

different islands of software written by

100 different teams uh that all have to

coordinate. And so if you want a

feature, you know, something that

manifests as a feature often involves

combining functions from different

domains.

>> So a simple one to visualize is when you

walk up to your car to get into it, you

want it to automatically unlock. You

want the HVAC to go to your preset. You

want your seats to adjust. You want it

to make an audible noise in the outside.

You want the lights to do something.

>> You probably want the the audio system

to do something. Those are all different

little ECUs in a traditional car. And

the coordination cost in it is really

high. It's very unlikely that a car

company will make a change to that

sequence because it involves

coordinating amongst maybe 10 different

players. In contrast on a on a approach

where you build a zonal architecture

where you have a very small number of

computers ideally you know one two maybe

three depending on the size of the car

that are running one operating system

that control everything. It's very easy.

So that sequence you could make up

updates to you know in a matter of

minutes maybe an hour you could change

the whole sequence of what happens you

walk up to the car issue an overear

update and it's very straightforward.

How often does Rivian update?

>> We do about one a month and uh it's

typically, you know, we add a couple of

new features, we add refinements to

existing features. We're listening to

like what customers are seeing and

asking for, but you know, every month

the car gets like notably better and

it's created this really amazing dynamic

where customers are like excited for the

for the update. They're like, when's the

next OTAA going to drop? The irony of

all this is these domain based

architectures goes back to like how do

we arrive at this it actually goes back

to fuel injection systems. So up until

early 1960s like every car on the road

was completely analog. So there's no

computers at all in the cars 100% analog

and the first computers were there to

drive the fuel injection systems and car

companies said this isn't a core

competency. Let's push that little

computer to run the fuel injection

system to a supplier and the supplier

will make that. You know, this is where

you saw things like the Bosch fuel

injection systems and never planned.

It's sort of like a field of weeds. Then

over the next like 7 60 70 years,

everything that became, you know,

computer controlled to any degree

suddenly started to have a little ECU, a

little computer associated with it. and

it just like grew into this absolute

disastrous mess that is a you know today

the the network architecture that's in

truly every car on the road with the

exception of of two companies that what

I just described is what underpins we

did a large uh software licensing deal a

$5.8 8 billion deal with Volkswagen

Group, the second largest car company in

the world to uh essentially leverage our

network architecture and ECU topology

uh for their you know all their various

brands and so it's an interesting final

point there on the on your first

question which is you know what happens

to market share so I think it's

inconceivable that car if to be at scale

that you don't have a software

definfined architecture that allows your

features to become better and better and

particularly thinking about how AI

starts to integrate into the features

that's number

Secondly, it's inconceivable to think

about a car company existing at scale

without the vehicles having very high

levels of autonomy. And so car companies

have a choice on both of those. They can

either accept that they're going to

shrink. That's choice one. Choice two is

go build it themselves, which is really

hard because they don't typically have

these skill sets. They're not software

electronics companies in terms of like

their organizational DNA. Or they can

find a third party to source it from.

And in both cases, there's not great

third parties to go to. Uh, and in the

case of autonomy, most of the third

parties that that did emerge over the

last 10 to 15 years tend to be very much

uh like classic rules-based what call

like AD or autonomous vehicle 1.0

solutions. And those work pretty well

for the business construct of selling

like a sensor and a function. But that

structure is really flawed when you want

to have like a large data flywheel and

it's constantly learning and evolving

and you're issuing updates constantly.

It's just um it's really hard to imagine

that with an arms length transaction.

And so I think the vertically integrated

stacks are going to naturally have some

big advantages.

>> So this might be an irrelevant question

but I'm curious. Um do you think that

the autonomy like the models that maybe

the three maybe the one maybe the five

companies that come up with this

>> uh develop are fundamentally different

over time because I spent a lot of time

in the AI ecosystem and the

>> let's say the languageoriented

foundation models like feel like they're

converging at this moment in time.

>> I I look at a Rivian I'm like

>> I don't know people adventure in that

thing. Do do you actually want it to do

different things, have different styles

or capabilities, or is it really just

like

as much autonomy as possible safety

case?

>> Well, first I This is a great this is a

great question. Um

>> I want my car to drive.

>> So like in the LLM world, it a lot of it

has converged because it's the training

data sets nearly the same. Yeah. So

we're taking the the breath of knowledge

that's contained on the internet and

we're training models off of that. In

the case of driving a vehicle, there is

no internet of driving data. And so you

need both a robust sensor set to be able

to capture the data and you need a car

park, you know, that has enough vehicles

in it. And so, of course, Tesla has the

largest car park of vehicles by far. Our

approach to this is we have a a higher

level of capability on our perception

stacks. We have better cameras, we have

radar, and of course with R2, we'll have

a LAR as well. A huge part of that

strategy is not only those cover corner

cases better. So the cameras have

incredible low light and you know bright

light performance. So the dynamic range

of the cameras is stronger. We have more

cameras, a lot more megapixels. Uh we

have radar which is great for object

detection. And the LAR which is it's a

very powerful tool for training the the

models. And so imagine

800 ft in front of us there's a little

speck into a camera. It's hard to figure

out what that is. And historically, what

we would do to train that is she would

have a LAR sitting on the vehicle on on

a like a ground truth fleet to help

train your cameras. Putting that on

every single one of our cars is turns

our entire fleet into this amazing

training platform, this data acquisition

machine. That was a core part of how we

thought about our strategy is we're

going to go, you know, not as heavy as

let's say a Whimo on perception,

>> but heavier than let's say Tesla to

build a really robust data platform on a

vehicle-by- vehicle basis and then with

a car park that's going to grow grow

significantly with the expansion with

R2. Yeah. So, I I think first and

foremost is there is no common internet

data. So the data sets that we're going

to be picking up though are going to be

very similar

>> but but you have to go acquire

>> but there's still different decisions

about what data you care about

acquiring. Yeah.

>> Well I think this is what to

[clears throat] like how does a car feel

ultimately it needs to be safe and the

differences in the way it drives or

feels are going to be more about like

what's the UI the user interface of it.

You know like even we just updated some

of our features. We have three settings

for how the vehicle drives. Mild,

medium, and spicy.

>> Spicy is the highest one. Yeah. And so

this is like a little bit more

aggressive over time and we've spent

time thinking about this. I think this

will start to become part of a key

decision is how does the vehicle behave

and there's work we're doing to to think

about how the vehicle can behave in a

way that against a set of heruristics

>> drives like you.

>> So overall the overall model is trained

on how to performs in a safe way but it

actually learns some of your you learn

some of your driving preferences and

creates a model around you. Of course,

in a world where you never drive the car

because you're just it's always driving

for you. There's a way for you to set.

I'd like it to aggressively change

lanes. I'd like it to reside in the

right hand lane. Like those kinds of

decisions and those are those are less

around the tech, more on what's the the

the product or the UI if you like.

>> Right. The ability to collect those

preferences.

>> Yeah. Preference based. And I think we

will see that

>> and that'll be a decision like a Tesla

makes that may be different than how

Rivian makes it. you know, it's hard to

say today.

>> Can we talk about what the R2 means for

like the company and some some of the

key design decisions here? I was just

talking to Jonathan, one of your lead

designers, about the constraints and,

you know, aiming for more mass market

and more volume here.

>> Uh, I mean, yeah, you said it. It's uh

so R1, it's a flagship product. It's

average selling price is around $90,000.

It's the best selling, the R1S is the

best selling premium electric SUV in the

country. So it's electric SUVs over

$70,000 and we're the bestselling

premium SUV electric or non-elect

electric in the state of California. So

it sells really well. You know, it out

sells everything in its class like a

model Tesla Model X. It out sells like 2

to1. But um because of the price, it's

just limiting in terms of how much

volume we can achieve with that

platform. And so R2 is the our first

truly mass market product with pricing

that's as we've said going to start at

45 and allows people that are in that

you know the average price of a new car

in the United States is $50,000 in that

like $45 to $55,000 price range. Uh I

think to have a really great choice and

to date there haven't been a lot of

great choices there. You know there's

I'd say there's like sort of singular

set of great choices with a model 3

model Y. Uh and of course that's that's

shown through the extreme market share

capture of 50% roughly market share goes

up or down but around that call it half

the EV market is Model 3 or Model Y. So

there's just such an untapped

opportunity to pull customers out of ICE

vehicles out of internal combustion

vehicles with a choice that's you know

has characteristics that are different

and unique relative to a Tesla. These

are like too substantive to be rapid

fire questions, but they're they're

important for me to ask you. Do

Americans want EVs? Like why haven't

they adopted them faster?

>> What?

>> Yeah, I think to the last question, I

think causality is always a hard thing

to,

you know, really understand, but let's

zoom out here. The the overall adoption

rate in the United States of EVs is

around 8%. The vast majority of vehicle

buyers are buying vehicles that are

under $70,000 with the average sale

price of about 50. And so if you look at

the number of vehicle choices you have

at a price point that's under $70,000

depending on the year. This of course

changes year to year. There's well in

excess of 300 different vehicle model

line choices. Putting aside trims and

performance packages but just in terms

of like overall vehicle types. And so

you can buy hatchbacks, minivans, SUVs,

you know, two-seaters, convertibles. I

mean there's a whole array of different

things you can buy. And in the EV space,

I think, and this is I think there's

more than one, less than three great

choices. And I'd say Tesla with the

Model 3, Model Y is absolutely one of

those. But there's so few choices that

if you are looking for a form factor

that's not a Tesla.

>> So, you think it's just missing product

set that people want? Yeah.

>> An extreme lack of choice is how you put

it. Um, like a shocking lack of choice.

And this is what gets into interesting

like corporate psychology, but because

of the success of the Model Y in

particular, the EV choices that do exist

that are outside of Tesla are often very

similar to a Model Y. Sure.

>> So if you were to like draw like an

outline, if you looked at the side view

profile of a lot of its alternatives and

draw a profile and then put it next to a

Model Y, it's almost identical. There's

a design sketch over here of basically

the Model Y and all its competitors.

>> They're all basically the same. It's

like if you want a Model Y, buy a Model

Y versus getting

>> you want something different.

>> Yes. You have all these companies are

trying to create their own version of

Model Y. And it's like it's unfortunate

because they didn't say, "Well, what can

we do that's unique and different?" And

so for us, we think the Model Y is a

great car. I've owned one. Many folks on

our team have owned one. But the world

doesn't need another Model Y. The world

needs another choice. And so I think uh

this is a reframing of just how we look

at transportation is it's such a big

space. It's such an area of personal

expression that we need as as consumers

we need lots of choices. We need to have

variety. We selfidentify with the thing

we drive. We just haven't had it. So I

think my view is the EV adoption in the

United States is a reflection of the

lack of choice. Uh there's one set of

really great choices with Model 3, Model

Y. I think there needs to be many more.

And so even looking at our partnership

with Volkswagen Group, a big motivator

for that which ties to our mission was

can we take our technology platform

>> and allow that to be expressed through a

variety of really interesting uh and

very story brands and different form

factors, different price points um of

course different segments. And I I think

the more choices we have, the more it's

going to lead to broader based adoption

of electric vehicles, which

creates, I think, a a very positive

level of momentum around the space. It's

it's worth noting on that point when we

look at how we develop a car like take

R2, we don't think of it as this is

someone who's going to buy an EV, let's

make it good. We think of it as let's

make the best possible vehicle, you

know, we can imagine. So incredible

performance and you know great range,

great uh dynamics, tons of storage and

the person buying it will be drawn into

electrification because the car is just

the best choice they have.

>> And we took that same view with R1 and

on R1 the vast majority of our customers

are first time ever owning an EV is a

Rivian which is which is really good. If

if all we were doing is moving customers

between

>> one or two brands it wouldn't be

accomplishing the goal. We have to

create new EV customers with products

that are so compelling that it just

draws people in.

>> So that leads into my very last question

here. I grew up thinking like a car is a

huge part of my identity.

>> Love cards. Drew them.

>> Still think they're pretty cool. Uh and

you know as they become more like

utilitarian services with uh the rise of

robo taxis as a concept of like you know

serving some of the function which your

car did before. How do you think our

relationship with cars changes or

vehicles over time?

>> I do think it's we're going to see a

shift. It's an interesting like

philosoph philosophical question. Why

why are cars such a part of our society

and

>> why do we have this affinity for them in

a way that we don't have that feeling

for other things in our life that are

really important? Like I don't I don't

look at my refrigerator and think I

really love that. Um in the same way

that I do with a car

>> and I think part of it is a car enables

personal freedom. It allows you to

explore. Um, it's it's something that

you not only ride in, but it be becomes

part of an expression of self. And I

think that's probably going to continue

to some degree, but it is going to

evolve. and and the way we look at it uh

with our products and even how we've

laid out and contemplated the the

purpose of the brand. We really look at

it through the lens of the vehicles and

the products we make need to both enable

people to go do the kinds of things you

know that they would hope to have

memories of years to come. So we we

often say the kinds of things you'd want

to take photographs of but more than

just enabling it which is a functional

requirement like can it drive their you

know can it fit the stuff your your pets

your gear your friends your your all of

your stuff more than just enabling it

can it inspire it and so can the brand

and the way we present what we're

building and the way we make design

decisions inspire you to go do the

things you want to remember for years to

come and so there's little like design

decisions we take that link to that. So

a flashlight in the door

>> is a invitation to explore. It's

invitation to go look at things the

night. Uh the

>> or the treehouse.

>> Yeah, there's exactly. So there's all

these little decisions you made

throughout the whole car that are just

designed to like engage that element of

inspiring people to go like imagine that

life they want to have.

>> Awesome. Thank you so much, [music] R.J.

Congrats on the R2 and uh on the

autonomy program.

>> Thank you.

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