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>> One of the great companies of the AI era

is of course Cororeweave. They're

building massive infrastructure for

these hyperscalers. And in some ways,

Michael Intrader, welcome to the

program. You're the original

hyperscaler. you guys got in very early

and secured your I don't know which GPUs

you wound up getting but you were very

early to this trend. How did you get to

it so early and how did you build out

this you know first I guess at the time

neocloud? Yeah. So we we didn't really

start it as a Neocloud and I I uh I was

uh running an algorithmic hedge fund uh

focused on natural gas and uh when when

you build an algorithmic hedge fund um

once the algorithms are built you're

really just monitoring it and testing

different uh thesises and doing all

that. But there's also a lot of downtime

and we got super interested in crypto.

Um, and you know, we're pretty nerdy. We

kind of dig under the hood and we

started to get interested in the

security layer. Uh, we looked at Bitcoin

and the mining for Bitcoin and we didn't

like it. We just thought that like

there's some brilliant engineer that

built the ASIC and they're probably

going to be better at running it than we

are. So, we really began to focus on the

GPUs mostly because the GPUs were you

can mine Ethereum with them. uh but you

could also do all these other things and

really so right from the start we looked

at the compute as an option to be able

to deploy our computing power to

different use cases and so you know

began the company in 2017 uh you know um

spent the first kind of three years

mining crypto went through a couple of

crypto winters um because we had come

from a hedge fund were, you know, we we

have real chops in risk management and

how we think about uh capital and risk

exposure and allocation and all of that.

And so we were really careful around

that right from the start. So we

weathered crypto winter really well um

and began to scale the company and

immediately started to look for other

use cases that you could use this

compute for because crypto was pretty

volatile.

>> Yeah. And crypto was a question mark at

that time.

>> Absolutely.

>> Yeah. I mean Bitcoin was speculative and

there were many other specular projects.

the only other people using this type of

hardware quants

>> medical researchers.

>> So a good way to think about it is like

the progression of products that we kind

of started to work on. You know first

was uh um crypto but we immediately

moved from crypto to CGI rendering and

we built projects that would allow uh um

folks that were trying to animate and

render images um you know kind of what

makes the movies cool, right? and and uh

we started to work on that and then we

moved to batch computing and started to

look at medical research and different

ways of using the compute to be able to

drive science. Um, and we just kind of

kept moving up the stack in terms of

complexity uh uh on how GPUs could be

used. And ultimately uh in like call it

like 2020 2021 we started to really try

to figure out how you can go ahead and

use GPUs for neural networks and that

was not something that uh we knew how to

do. Um, and so we actually went out and

bought a bunch of A100s and donated them

to a uh a group that was working on uh a

Luther AI. They were working on an open-

source project with the thought that um

these guys are taking the GPU compute

because we're donating it. They can't

really get pissed at us if we're not

very good at it initially. And uh that

worked out really well because

>> they can't complain about the SLA.

>> They they kept telling us like we need

more of this, you got to work on this.

And that began to really uh uh give us

an understanding of what was necessary

to run scale parallelized computing. And

uh you know that that uh um we went

through it. I I I kind of feel like

buying those initial GPUs was the

tuition we paid to learn how to run this

business. And then one of the

interesting things is all of those guys

went back to their day jobs because they

were all volunteers working on this.

They were like-minded scientists. And

when they got to their day jobs, they

were all like, I want that

infrastructure. It's built the right

way. That's the way that researchers are

going to want to use it. And that

launched our our business. It was an

amazing story. And

>> so you went from crypto to these

researchers into academia and deep

research. What's the next card to turn

over in the poker game?

>> Yeah. So, so um what became very clear

to us very very early on was that the

scaling laws were going to drive and

remember this is really back in the you

know 2020 2021 before uh uh chatgpt

moment occurred and we began to

understand that like computing

decommoditizes at scale right like when

when you know anybody can run a GPU but

can you run a cluster that's large

enough to train a model that can change

the world and that's a different

question. And so we really began to

think about like how do you go about

scaling up your delivery of this

computing to clients, larger and larger

clients. And that was the next card to

turn is to think about it from a okay,

you know, there's a component of this

that is going to lean into uh our

ability to access the capital to be able

to deliver our solution to the broadest

possible audience to the most

sophisticated consumers of this compute.

And and that was really the next card is

thinking about it as a business rather

than as a engineering project to be able

to deliver the the uh uh the

infrastructure and the software and

really everything between you know when

you when you're thinking about what we

do, we kind of live above the Nvidia

GPUs but below the models. Yeah. and

everything in there, all the software,

the integration of software and

operations and uh observability and all

the things that you need to be able to

build uh a cloud that's purpose-built

for this one specific use case, right?

So, we don't we don't do everything. We

really focus on one use case which

allows

>> you want to do web servers different you

got AWS,

>> you know what they do a great job. It's

like it's a it's a great solution. It

was a brilliant solution to solve a

problem. We just looked at it and said

there's a new problem and let's go about

let's go about looking at this problem

and try and come up with a solution to

deliver compute that solves that

problem.

>> And when did the language model start

dialing and calling you for you know

capacity?

>> Yeah. So uh our our our first uh um well

our our first language model was really

a Luther. Um but uh our our first like

large commercial uh was inflection. Um

and so you know we work with Mustafa and

and and and Inflection and then we we

really diversified from there uh into

the hyperscalers into you know uh open

AAI across the the the the model uh the

foundation models across um you know and

and just kept scaling and scaling with

the belief that you know once again the

the the decommoditization

of compute the ability to to deliver a

solution and the solution is building

supercomputers that can change the world

and that's really what we began to focus

on. That was the lead into training and

now the world has gone through, you

know, this this moment where we've moved

from research into the productization of

this. It's it's it's beginning to work

its way in from the the uh the fringe of

organizations into the core of what they

do. And you can see that every day in

the uh in the amount of inference

compute that is being driven through you

know our uh infrastructure layer which

is just massive which is just like one

of the shows you people are consuming it

not just building models but they're

deploying them and and utilizing them.

>> I always think of inference as the

monetization

>> of the investment in artificial

intelligence. So when when when we when

we see our compute being used uh uh to

stand up the massive scale of inference

that's hitting our compute every day and

like you know inference is when people

ask the model a question it comes back

with an answer that's an inference or

when you ask the model a question and

then to go do something that's inference

right and that's actually where you're

you're you have the opportunity to

really drive value outside of the model

itself but into the real world and

that's really exciting for us. That's

what we like to watch. That's what I

like to watch in terms of gauging the

health.

>> What chips are those?

>> Um so so really uh you know we are we

are the tip of the spear in bringing um

the new architecture uh out of Nvidia uh

into uh um into commercial production at

scale. Yeah. And uh so when when you

know we were the first ones to bring the

H100s at scale, we were the first ones

to bring the H200s at scale, first ones

with the GB uh 200s, and now you've got

the GB300s. And one of the things that's

that's that's amazing and really

fascinating for us is is you know people

are using the bleeding edge GPUs to

train models as the new architectures

come out and then they take those GPUs

and they move them into different

experiments and then over time they move

them into inference and they continue to

use them in inference for a very very

long time.

>> What is the shelf life of a 100 right

now? That's been a big debate is I think

for your company for Microsoft and I

guess Michael Bur you know who you must

have known when you were a quant you

know saying oh my god the whole industry

is the sky's falling and then we all

know in the industry that people don't

just throw this hardware away that they

find uses for it the street finds its

own use for technology so what's the

reality of the lifespan of these things

>> so so my my take on the the uh uh the

GPU uh depreciation bait is that it's

nonsense Right. It's a debate that is

being uh brought to the forefront by uh

some traders that have a short position

in the stock and they're trying to uh

talk down. Look, here's what we know,

right? Um

when when we buy infrastructure, we're a

success based company, right? We're a

small company on a relative basis

compared to the enormous companies that

we're competing with. And so they come

our clients come into us and they buy

compute for five years, for six years.

Our average contract is 5 years. So any

commentary by anyone either inside or

outside of the industry that this stuff

becomes obsolete in 16 months or

whatever nonsense they're spewing, it's

it doesn't it doesn't in any way match

up with the facts on the ground. The

facts on the ground is they're buying it

for 5 years. Right? If and my approach

to this has always been if people are

willing to pay me for it,

>> it still has value.

>> Correct.

>> Pretty simple way of of approaching it.

We use a six-year depreciation. Um, we

believe that the GPUs will last in

excess of six years, but we felt like

that was a fair and reasonable approach

to a technology cycle that's moving at

this velocity. Um, the A100s, the ampers

this year, the price has appreciated

through the year.

>> And why is that? I I think it's because

one of the things that happens is as

more installed capacity becomes

available, you have new companies that

come into existence that have new use

cases that have different size models

that are trying to uh build new

commercial ventures that maybe have been

blocked out of the H100s and never had

an opportunity to run on that. I mean to

make a very simple example for the

audience like when you trade in your

iPhone after 3 or 4 years you're like

who's going to use an iPhone 12 and it's

like have you been to South America or

Africa where you go to the store and you

buy an iPhone 12 or you buy the Pixel 7

and it costs $50 that's still got great

life left in it.

>> Absolutely.

>> Yeah. you know,

>> and so look, you know, we we find these

amazing use cases, new companies that

have come into existence or existing

companies that have integrated new

models into their workflow that are able

to use the Ampierce and so they keep

buying any GPUs that we have available.

And once again, you know, the the

concept that a GPU

>> is no longer relevant or commercially

viable after 16 more 18 months or two

years.

>> Yeah, that's it just it just doesn't

make sense.

>> It's obviously far. I think sometimes

people get caught up in Moore's law or

in just how fast our industry is growing

and that there's so much at stake that

big companies are demanding the most

recent products. That doesn't mean that

the lifespan has gotten shorter. It

means the opportunity and the surface

area of the opportunity has gotten much

larger.

>> Yeah. Uh one of the things is is like

you know the the uh the the industry has

gotten so much attention for the

unprecedented scale of capital that is

coming to bear on this. And

because of that, there tends to be a

incredible focus on

the companies that are building on these

most advanced chipsets. And the truth of

the matter is is you know even within

those companies they have a long tale of

useful life

>> to provide inference horsepower to work

on other experiments to do less bleeding

edge activity but still needs to be done

>> and yeah I mean rendering comes to mind

as well or yeah we're making images on

nano banana like there there will be a

use for it. There is a moment in time

where maybe the compute to power ratio

doesn't make sense. My my expectation is

is obsolescence will be defined by the

moment in time where the power

in the data center for me will be able

to be repurposed for a higher margin

than the existing infrastructure

provides. And you know, like I said, I I

fully expect this infrastructure to last

in excess of 6 years, but the the the

standard in the in in in the space has

really been used with one exception,

which is Amazon, which is Yeah, it's 6

years. That's that seems like the right

schedule. I'm not making it up. That's

what everybody's using.

>> Yeah. And the energy cost is the

opportunity because hey, it's just a we

need that space. there's a better uh

reward here and that might get resold

that hardware to somebody else who wants

it a hobbyist or something. It's

available

>> and or it could be sent someplace else

where they have more capacity when they

can repurpose it there. But I I I um I

kind of feel like, you know, we'll we'll

deal with that part of the business when

we get there. What I know right now is

it is extraordinarily profitable. It's

very creative to my company to continue

to keep the infrastructure that's been

up and running, that's been on these

long-term contracts, and as it rolls

off, as it's been in use for 5 years,

you know, as it becomes available, I am

still able to sell it at a higher price

than it was at a year ago. There's

competition now. When you were buying

these from Jansen back in the day, yeah,

you could buy them and have them

shipped, I would assume, within 30 days

or less. nowadays what's the weight like

even for you a loyal old customer and is

there a bit of a battle is there

politics to who gets the servers like I

you see some like very big names talking

about they got to get an allocation is

it still a little bit crazy what's it

like to be in that category having to

buy something everybody wants

>> look uh you know I I uh I I think of it

as an affirmation of the business that

we're in right like the fact that we are

attracting competitors the the means

that the business is healthy and there's

a lot of people trying to deliver this

service because the need for this

infrastructure the need to integrate the

infrastructure you know into the

software layers to deliver it to

artificial intelligence uh either at the

model level or the inference level or

the application level or whatever you

know level of the five layer cake that

Jensen's you know focused on

the the fact that there are more people

coming into this it doesn't discourage

me um as far as getting access to the GP

CPUs, we show up like everybody else

with a um you know, we'd like to buy

here's a PO and we're ready to pay. Um

the one what's the wait time like? And

is it just really competitive or not?

Because I talked to Jensen about he said

I said, "How do you manage all these

like big egos and names and companies

trying to buy stuff?" And he said,

"Well, they order it and we give it to

them in the order in which they order

it."

>> Is it really like that?

>> It really is. Right. like you know he he

doesn't want to be in the position of

playing favorites or all like that that

just seems like a bad place to be with

your clients

>> or auctioning them off. Can you imagine?

>> Yeah, that would that that

>> that would be crazy.

>> Yeah.

I don't I'm not sure that would be good

for the long-term business. No. Yeah.

So, so our our our approach is

>> you might get some sovereigns coming in

and saying I'll pay double. They do that

with Ferraris too sometimes.

>> I guess these are the Ferraris of

computing, right?

>> In a way they are. Yeah. Bugattis. Our

>> our our approach is to work with

clients across the entire space to find

opportunities that are really

interesting companies that can fit into

our contraction contracting requirements

where we're going to be able to go out

and structure the debt that we require

in order to go out and and uh build

infrastructure at this scale. And um

>> how does all that debt work? I that is

something that you guys specialize in.

um corporate debt uh I'm in the venture

business people are like why should I be

in venture when corporate debt pays so

well corporate paper's so huge I'm

curious how this fits in and like what

uh interest rate people are paying on

you know a billion dollars in

infrastructure what do they pay on that

>> yeah so so coreweave has really been the

innovator around a lot of the financing

engines that have come to bear on this

we did the first GPU based uh loans. Um,

and like I I think it's important or I'm

going to try to explain this in a way

people can understand. So what we do is

we go out and we find a client. Let's

use Microsoft. You brought them up

before, right? And Microsoft comes to us

and says, "We'd like to buy some compute

for you." And we say, "Okay, great.

We're going to sign a contract." Once I

have a contract in hand,

>> then what I do is I create something.

It's not a particularly creative name.

It's called the box. Yeah.

>> Right. And what I do with the box is I

take my contract with Microsoft and I

put it in the box. I go to Jensen and I

buy the GPUs, I put it in the box. I

take my data center contract, I put it

in the box. And now the box governs cash

flow.

>> And it has a waterfall of cash flow that

comes into it and goes out of it. And so

the way it works is then I build the

compute and then I deliver the compute

to Microsoft and they pay the box. They

don't pay me,

>> right? It goes into the box and the

first thing it does is it pays the data

center. It pays the power bill. It pays

the interest and the principal and then

whatever's left flows back to us, right?

And so it is an incredibly well

ststructured, time-tested,

pressure-etested vehicle to be able to

borrow money against client paper and

all of the other collateral around the

deal. which is why Corewave, which is a

company that many people haven't ever

heard of, was able to go out and raise

$35 billion in 18 months to build

infrastructure at scale. But what's

important to understand is the economics

in this box are such that within 2.5

years of a 5-year deal, we have paid for

everything.

>> The principal's been paid off. The well

the principal's been paid off, the

interest has been paid off. The return

into the box is such that we are able to

generate returns to our company at the

box level which gives the most

sophisticated lenders in the world

whether it's banks or private equity

funds or um you know whoever. confidence

that they're going to

be able to achieve the one rule of

lending, which is give me my money back.

>> Yes. Works better when that happens.

>> So, they look at this box and they're

like, "Wow, we're really confident we're

going to get our money back."

>> And maybe they want 10 boxes.

>> That's correct.

>> And if any one box um goes upside down,

you can deal with it and it's not as

acute.

>> That's correct. And they don't

cross-pollinate. They don't cause uh

contagion across the boxes. are all

independent and discreet. One, and

number two is as you do this and as you

show the lenders how this financing tool

and how this financing mechanism works,

what they do is they continue to lend

you money at progressively lower rates.

And so when you think about our cost of

capital over the last two years, we have

dropped our cost of capital by 600 basis

points.

>> Wow. It is enormous, right? And so

you're seeing a company that is driving

its cost of capital down towards where

the hyperscalers borrow, which will

enable us to be able to be competitive

with them over time. And we have been

extremely

uh militant and diligent about feeding,

watering, and caring for those boxes so

that we continue to have access to the

capital markets in a way that allows us

to build and drive our business.

>> Means you has to say no. You have to say

no to maybe some people who want to be

in the box.

>> Yeah. So, we we look at some deals and

we're just like, you know, they want to

buy GPUs for a year and I look at it and

say I I that's not a deal that I can do

because it's too short for me to am

advertise the expenses or and so I won't

do that. Right. Like once

>> and they can go to another provider who

maybe wants to take that risk on who has

extra capacity.

>> Absolutely. But our business is really

built about around the risk management

of being able to get to scale. Because

in my mind

during this period of disequilibrium

during this period where there are not

enough GPUs in the world to uh provide

the compute for all of the different use

cases in artificial intelligence the

part that's important for me and for my

company is to get enormously large so we

can drive down our cost of capital so

that we have information flow coming in

from all different parts of the market.

large language models, high-speed

trading, uh, uh, search, all of these

things. And they're feeding they're

feeding information back into us that is

letting us know what the next product we

need to build is or where, you know,

they need help uh, scaling or what type

of compute they need and all of that

information flow is incredibly valuable

to us.

>> What What can you tell us about demand?

There's been reports of, hey, maybe the

Oracle Starbase thing with OpenAI's been

downsized or maybe not and then you know

uh other folks Microsoft is going big

and Google's going big Meta's going big

and those people obviously have massive

cash flow Apple seems to be MIA they

don't seem to want to play you you

you've you've uh you've named a lot of

really big companies with really big

balance sheets that have the capacity to

drive a lot of demand look I I have been

truly steadfast in this

>> for years now for for for four

The depth of the demand for the service

we provide has been relentless and

overwhelms the global capacity of the

world to deliver enough compute to

enable all of the demand for artificial

intelligence to be sated and that has

been we have been relentless about that.

>> Sounds like Nick's tickets during the

Patrick Euing era like they got up to

50,000 people on the wait list. So if

magically the weight list went away, if

the if the constraint went away and we

just had a large amount of GPUs

available, lot of energy available, a

lot of data center available, how much

capacity would just all of a sudden come

out of the system.

>> So so or would be deployed I should say.

>> So remember how we build our our

business through this box

>> and it's a fiveyear box. So if we had an

air pocket, if if demand were suddenly

to disappear because of a technology

breakthrough, because of a uh a war,

anything, right? Like like the why from

a riskmanagement perspective does not

matter. You have to prepare your company

for the what happens if it happens.

Yeah. And so by entering into these

long-term contracts into entering into

contracts with counterparties that have

large balance sheets, you are or we are

protecting ourselves and our lenders.

Yeah.

>> So that we are confident and they are

confident because you can see how

confident they are by the rate that

they're charging us continuing to

decline that they're ultimately going to

get their money back. And that is the

one rule of lending. And so um you know

I if

>> but just in terms of the capacity if you

were unconstrained and Nvidia Jensen

says hey order as many as you want what

would happen

>> so um the the it's also important to

understand the constraints aren't just

GPUs right electricity it's it's power

shells it's memory it's storage it's

it's networking it's optics all of the

things and there's there's various

throttles that will limit the

>> memory is a throttle right now right

>> oh yeah it Oh yeah, it is.

>> Why? How did memory become the throttle?

>> If um

memory and uh it has historically been a

cyclical business, right? We have seen

these waves of demand driving up the

cost for memory and then it collapses

and then it drives it up. It's a very

boom and bust business. is cyclical in

its nature because the fabs are so

capital intensive that people invest in

the fabs, build a ton of capacity and

then overbuild if there's any type of

turndown. And that we've seen that cycle

again and again. What's happening right

now is the confluence of two things,

right? one is is

with all the demand for artificial

intelligence and the corresponding

demand for compute and the ancillary

services around the GPU, the demand is

through the roof. That's number one.

Number two is is that

>> there was probably an investment cycle

that needed to happen back in 2023

that would have brought on the necessary

fab capacity to be able to serve.

>> Impossible to predict what should happen

just with energy. It's impossible to

predict what just happened. And now

people are chasing energy. The data

centers are going where the energy is.

It's not based on real estate. It's

based on it's and

>> where's there's some wind.

>> And anytime you you have a uh very cap

not every any time, but many times when

you have a uh a capital inensive

business like you know building fabs,

you will get this boom and bust cycle

just like in energy they overbuild.

Yeah. And you know

>> fiber.

>> Yeah. I mean there's there's there's a

lot of examples of that our approach

>> in some ways when you look at that it's

a beautiful aspect of capitalism that

we're able to have a boom bus cycle that

we're able to weather it right if you

think just that capitalism from first

principles something like that happens

and we have too much fiber it creates an

opportunity for Google to buy it all up

or the next person

>> listen the the the um um you know it it

does it does a lot of things having a

boom cycle it clears out the underbrush.

will be able to survive and take

advantage of that and it sews the seeds

of future business.

You put the fiber into the ground which

became the backbone of how you know we

watch movies every day and how we you

know uh communicate and how we hop on a

zoom and you know co and all of these

things were based on that infrastructure

that was available to be consumed. Yeah,

people don't recognize this fact if you

the the premise of YouTube from the

founders who I knew, Chad Hurley and his

other partner. They basically had the

realization at this curve storage is

coming down so quickly we could offer

free unlimited uploads and bandwidth is

coming down. So I guess we don't have to

charge people for sharing a video

online. Before that, if your video went

viral, people are going to have their

minds blown. But your server would turn

off and it would say this person, you

know, needs to pay their bill. Yes.

Because they were getting charged for

carriage by the megabit going out.

>> Yes. I mean it look and and you know

these these the business models change

and evolve and you know like you said

Moore's law and and and certainly Jensen

will talk about the fact that like what

what is going on within the the the

accelerated comput dwarfs

>> Moore's law right and all of that is

going to lead to

>> more opportunity to build more companies

that are going to do things like you two

did which has really changed the world.

>> Yeah. I mean the the concept that I I

don't know if it was like a million

hours being uploaded every hour or

minute but at some point Susan what

Jackie rest in peace said told me just

like how much was being uploaded every

minute and it made no logical sense and

she realized

>> well there's three billion people two or

three billion people in the service and

1% upload or 0.1 10 bit bips upload and

it's like okay one in a thousand people

upload it's a big it's a big denom

denominator like

>> I I was uh sitting on a a panel uh with

Sarah Frier, CFO of uh Open AAI and u um

she every once in a while uh um she she

really puts out like interesting uh

information and so she was talking about

the cost of a million tokens when ChatG3

came out and it was $32 and change and

now a million tokens cost nine cents.

>> Yeah.

>> Right. And so you you just see like like

the incredible power of how the capital

markets, how capitalism is

uh uh fueling engineering and fueling uh

uh competition.

>> It's become recursive now too. I mean

these models if you say to the model,

hey make yourself more efficient, spend

less money and lower the cost of tokens.

It'll be like okay captain.

>> Yeah.

>> I don't know if you saw Cararpathy's

recursive

>> thing last weekend but it's like now

civilians who've never worked in a

language model or done computer science

are like, I'm going to try to do

something recursive this weekend. You

know, it's one of the things that I that

uh uh I talked to, you know, the other

founders about, you know, and it's like

when you think about some of the things

that AI does, right, it's lowering the

barrier to operations. So if you have a

good idea or a great idea, you can open

up your model and you can tell your

model, you can vibe code it, you can do

all kinds of different things and create

things that never existed before. That's

amazing, right? like that's bringing

down this incredible barrier that kept

human creativity contained and now all

of a sudden this whole new vector of uh

uh you know medical research or

different approaches to you know

baseball cards or whatever you want if

you've got a great idea if you've got a

new creative idea that's the valuable

kernel right now that allows you to to

build new things and to create new

things and I just think that's

incredibly exciting like you're bringing

the minds of 8 billion in people a tool

that allows them to overcome what was

insurmountable for

forever

>> for humanity.

>> Yeah, it's a bright new future. Michael,

appreciate you sharing the uh uh

information with us and the vision. I am

really delighted to have Arvin Shri Nas

on the program.

>> Thank you for having me here Jason.

>> It's so great. I want to go through

three stages in which I fell in love

with your product. The first phase was I

could go in pick my language model if I

wanted to choose open AI, if I wanted to

use claude, whatever it was. That was

like a real unlock for me. And on the

sidebar sidebar, I noticed you had done

essentially like what Yahoo did in the

early days, finance, sports, and when I

pulled my nickname up, it gave me a live

version of that. When I pulled my stocks

up, it summarized the news in real time.

time and I was like, "Wow, this

execution's great." And I I kind of made

you my front door, two different models,

and it made it easier for me to check

it. Then you came out with the Comet

browser and I was like, "Holy cow, I can

give this a series of instructions. Go

to my LinkedIn, find everybody from this

company, put them into a Google sheet

and boom, you were the first out of the

gate with that." And then just the last

couple of weeks I had been claw pilled

in using openclaw but you came out with

computer and I started using computer

and boy it's good uh it's a really

strong start uh allowing me to do

repetitive tasks very similar in some

ways to co-work from claude uh or

basically an engineer or developer using

it. So

are are these the evolution of the

company and I should think about it that

way. But how do you look at perplexity

now? You have a very loyal fan base.

You're making a lot of money. I don't

know if you disclose it but I think it's

hundreds of millions to billions. You

can tell us but what is perplexity in

the face of wow Claude's having a great

run, OpenAI still doing strong. Grock

doing very well. Gemini coming on

strong. There's like six or seven of you

and uh you just happen to be one of my

top twos right now.

>> Thank you. So tell me first of all,

first of all, thank you. Thank you so

much. Perplexity has always been built

for people who are always looking for

the extra edge, the curious people. So

it's very natural that you are uh one of

our power users. Uh one common theme for

us uh for the last three and a half

years is accuracy. Plexity wants to be

the company that's building the most

accurate AI. So when you want to give

somebody answers, accuracy is very

essential for building trust because

only then the user is going to ask the

next set of questions. It turns out it

was a great idea to give AI access to

the internet to be accurate. So that's

the perplexity ask product. It turns out

it's a great idea for AI to have full

access to a browser so that it can be

accurate when you task it to go do

something that you would do yourself on

a browser. Aentic browsing comet. Now

the last phase is it turns out it's a

great idea for AI to be given a full

access to a computer so that it can do

whatever you do on a computer on its own

essentially becoming the computer

itself. an orchestra of everything AI

can do today. every single capability

each individual AI model has be it GPT

or cloud or Gemini or anything else an

orchestra of all those capabilities that

what that's what perplexity computer is

and all these sub agents that are

running inside computer are the

musicians the models are essentially the

instruments and they're like hundreds of

models out there each having their own

specialization some are good at coding

some are good at writing some are good

at multimodal visual synthesis is image

generation, video generation, audio, but

what matters is the end output, the

music you play. That's the work AI gets

done for you. And that's what perplexity

computers. The AI itself is the

computer. Now,

>> still lives inside of a browser. Have

you considered giving it desktop root

access? That feels like the next place

this is going, but that comes with a lot

of security issues, a lot of trust

issues. As you mentioned, trust is

paramount. getting the right answer is

what builds it, but also not getting

hacked and not having it delete your

files. So, how do you think about root

access to my Windows machine? Obviously,

iOS, they won't let you, but with an

Android phone, it would let you.

>> Yes.

>> So, do you have that in the works?

>> Yes. So, we announced something called

personal computer. Perplexity personal

computer that's essentially going to

take all the trust and reliability and

the server side execution of perplexity

computer but synchronize it with your

local computer so that you can use it

from your phone and we're going to do

this with the Mac Mini where you

synchronize your computer with the Mac

Mini so that becomes your local server

all the agent orchestration that has to

do with your local private data will run

on that local orchestration loop that

runtime with the Mac Mini. Not on your

servers, not on anthropics.

>> Exactly.

>> Yeah.

>> It could still ping Frontier models if

it needs to with your permission,

>> but it will be orchestrating everything

on your local hardware.

>> Yeah.

>> And if it needs to run on the server

side hardware, if you don't want very

complicated, longunning stask to be

running on your local hardware. Yeah.

>> You can delegate it to run on your

server side computer, which is again

only accessible to you and you alone. So

that way we're going to bring this

perfect hybrid of trustworthy

uh hybrid between local and server side

and you

>> and you'll make it easy to do. It just

be abstracted. You install one

executable, boom, it's done.

>> It's it's like open claw for dummies.

Nobody needs to learn how to use it.

Nobody needs to manage API keys. Nobody

needs to manage separate billing across

like 100 different services. Figure out

what you can give access to and not

access to. We take care of that.

>> So it's a Steve Jobs way of doing it.

you know, end to end integration

>> and and how do you think about local

models? I have started running Kimmy 2.5

on a Mac Studio.

>> It's not as good as Claude or Gemini or

Grock, but you can probably do about 80%

there for free.

>> Yeah.

>> Essentially.

>> Yeah.

>> Uh and so that's quite compelling

considering some of my other bills,

Claude and and stuff were getting

expensive.

>> So, do you have one of those? You

started testing on your local Mac

Studio. I assume you have a Mac Studio

and you're doing this yourself. Yeah.

>> Or now, I don't know if you saw uh Dell

and Nvidia announced a giant

workstation. Um is it 3,800?

>> Something like that.

>> Something like that with 750 gigs of

RAM. So,

>> what do you think about the desktop

going back to workstation/server?

>> Yeah.

>> Status.

>> I think it's very promising. Um my my

prediction is it'll initially start off

as a sub agent. So whatever you need to

go uh like your tax returns, your

personal photos, your emails, your your

calendar, all that stuff, those local

apps, your personal notes, very personal

notes. You can make sure that the models

that access those tokens will be running

on your local hardware if you want to,

if you're that privacy conscious.

uh and more complicated stuff that

accesses your data that's already on the

server side. Example, your Google

calendar, yeah, your Gmail. This is

personal data still, but an AI runtime

can access that through your connector,

your Google calendar connector, your

Google Workspace connector, and that

could run on the server side because

anyway, the data is on the servers. It's

not even lying on your device.

>> So, that sort of hybrid orchestration is

where we're headed to. I don't think

it's a dichotomy between fully local

versus fully server. Uh it's all about

choice. And anyway, when you're on your

phone, uh you want to you don't care

actually which server that workloads

running from because it's not going to

be able to run on your phone anyway. The

chips need to exist on a Mac Studio or a

Mac Mini and or on the server

>> or this new Dell that's coming out. And

I I really think the idea of spending

$10,000 on a powerful desktop will

appeal to people if it lowers their $500

a month

>> claude bill. This is an incredible

savings. Plus, you get the benefit

>> of privacy and not educating the

language models on your personal data.

>> Yes. And it's going to be it's going to

be like you're buying a refrigerator,

your your your internet modem. Like the

cost for these will eventually go down.

>> Yeah. But it's not going to feel like

you're wasting your money. Uh every

every home has a lot of other sensors.

>> Yeah.

>> That runs your home that'll also be part

of this orchestration loop.

>> Yeah.

>> So, so that's where it gets exciting

because now you can just dictate

something to your phone and that can

control your entire home.

>> So that's the dream that everybody has

and all that orchestration loop can run

on your local hardware, no problem. And

I'm curious what you think of the

operating system. What's eventually

going to be the operating system of this

workstation?

>> AI is the operating system. Like earlier

in the traditional operating system, you

execute programmatically.

Now you start with objectives, not

specific instructions.

>> Right?

>> You come up with a highlevel objective.

go build this website for me that you

know takes all the transcripts of all in

podcast and tracks the stock price just

before the podcast and after. Yeah.

>> And charted for the max 7.

>> Yeah.

>> And and charted over time you can so

that's the objective but individually

it's running a file system a code

sandbox access to the internet. It's

having like its own HTML tools and like

so I think that's basically where you

know models systems and files and

connectors are all coming together. You

would think of that as an OS

>> except you're operating at an

abstraction about that where you're

thinking in terms of objectives.

>> Yeah. And does it need to eventually

become its own operating system in your

mind?

>> It could be like people could think

about like yeah I have a my perplexity

computer running all the time whether it

essentially it runs on Linux machines

right now. Every server side computer is

a Linux machine. Yeah.

>> So, I think Mark Anderson tweeted this

right after our release that turns out

Linux computers was the right idea.

Desktop desktop Linux computers are

finally going to work.

>> Yeah. I mean, they're stable. They're

customizable and you're not at the mercy

of Apple's desire to contain the

experience or Microsoft surface area as

for hackers.

>> Exactly.

>> You build something rock solid and it

does feel like Linux might actually

become the correct

>> the eventual winner. It may not need to

have a front end.

>> That's the thing. You could you could

access the Linux machine on your phone.

>> You could be running iOS or Android. It

doesn't matter.

>> The actual valuable runtime is running

on Linux on the server.

>> You've done great as a consumer company.

Lot of love there. Now I'm starting to

see uh corporations with computer

starting engaging it. In fact, you'll be

happy to know this. Last week, I took

two people in my back office and I said,

"Stop working on OpenClaw. Your job is

to do the back office automation at our

venture firm only using Perplexity." And

they were like, "Perplexity computer."

And they were like, "Oh, okay. Um, it

doesn't talk well in Slack. It doesn't

have an agent in Slack." I was like, "It

will. I'm going to see AR and I'll talk

to him about that." So, we need a really

strong Slack connector.

>> It's already out.

>> It is. Okay, great. computer exists as a

Slackbot right now.

>> Okay,

>> that you can add to your Slack workspace

on enterprise plan

>> and our entire company works like that.

People are talking more to computer on

Slack to other than to other people.

>> In our first volley, we were sending

reports in, but it wasn't interactive.

That's perfect.

So now you've got your company going in

two different directions. This

incredible consumer run you have. How

many people are using the product every

month?

>> Several tens of millions. So tens of

millions of people that's very much

similar to the trajectory of the Google

and Yahoo consumer business. Now you've

got corporate. How are you doing on the

corporate side? Thousands of companies.

>> The fastest growing business for us. Ah

>> it's growing faster than the consumer in

revenue and things like computer unlock

entirely new possibilities. For example,

we've saved more than $und00 million for

our uh enterprise max customers who are

on the highest tier of enterprise.

>> Explain what that is. What does it cost?

200 a month per person.

>> So there are two tiers. One is the

enterprise pro which is $40 a month and

there's the enterprise max which is $400

a month. And that that and and and and

on a computer after you run out of your

credits you would pay for the tokens.

You pay for the usage.

>> Are you making money on the $400 a

month, $5,000 a year one or at this

point in time are people going so crazy?

Our uh one thing that Perplexity has is

every revenue we make, unlike certain

other rapper companies, every revenue

Perplexity makes has positive gross

margins.

>> Got it.

>> Because uh we're not just selling

tokens,

>> right?

>> Most of our revenue is recurring because

people are paying a subscription fee

>> and because we route through multiple

different models, we're very efficient

in terms of how we spend on the tokens.

because we have all this advantage with

rag and orchestration and search. We

don't actually need to blow up the

context window of the models.

>> Yeah.

>> As a result of that, we have positive

gross margins on all the revenue. Every

single penny we make, we make profits on

that. But the overall the company is

still yet to be profitable, but we're

working towards that.

>> You've had the opportunity to exit. A

lot of rumors, Apple, other people were

like, "Hey, this is a great team." How

many people on the team now?

>> About 400.

>> Yeah. You you've got a very coveted

team. You obviously understand consumer.

You obviously understand business. It's

a product driven organization. Reports

are you declined,

but the world's getting hyper

competitive here. How do you keep up as

a 400 person organization when you got

Sam Alman over here raising a hundred

billion dollars, you know, and then you

have Elon putting data centers in space

and merging with SpaceX and Twitter. You

have Google with unlimited resources.

Amazon getting in the game and obviously

Gemini uh very strong product and Google

really good at consumer. I think we'd

all agree Facebook and Meta haven't

figured it out yet except maybe for

serving us better ads, but they they

haven't figured out the consumer case

yet, but they'll copy it. They always

do.

How do you look at the playing field?

Because the degree of difficulty, this

isn't playing checkers or this is like

playing against the 10 best chess

players in the world. That's what you

have to do every day.

>> So, how do you think about it? Long-term

and independent company. Do you think

you'll need to join forces at some

point?

>> Well, and why didn't you take the deal?

This deals were incredible that you got

offered.

>> So, one advantage we have that all these

companies you mentioned don't have is

the multimodel orchestration. We're like

Switzerland. We don't have to have one

horse in the race. If GPT wins, Gemini

wins, Claude wins, Llama wins, it

doesn't matter to us. Uh or even open

source models can win, no problem.

>> And you have them on the service. You

have DeepSeek and Kimmy.

>> We have Kimmy, we have Neotron, and we

have uh a lot of usage of Quen, Alibaba

Quen.

>> Yeah.

>> Silently under the hood. So for us like

that advantage of being able to take the

best in each model and give the user the

orchestra of everything they can do. I

don't think any of the companies you

mentioned can do that

>> right nor would they

>> nor would they it makes no sense for

them. It would be an admission that all

the data centers and capex they've built

out mean still couldn't produce them the

best model. And uh Daario uh CEO of

Anthropic said recently in an interview

uh that models are specializing. Towards

the beginning of last year people

thought models are going to commoditize

but towards the end of last year people

models started specializing. Even within

coding

u cloud code and codeex have very

different capabilities. Our iOS

engineers love using codeex. Our backend

engineers love using cloud code.

>> Yeah. So even within a specialization

like coding, models have their own

unique specialtities and there are many

other use cases outside coding where

different models are good at different

things. Which means the orchestra

conductor that has no one model to the

horse in the race can win by providing a

very unique value and service to the

customer that each of these amazing

names that you mentioned cannot. And so

you're buying tokens wholesale from them

and then you'll charge customers to do

it or do you think it's all

>> we're going to take care of all that

orchestration?

>> Yeah.

>> So you don't have to manage tokens

across different models

>> cuz I authenticate I a couple of my

different accounts my pro accounts into

perplexity. But does it I I I don't have

enough knowledge to know if you're

abstracting that and people can just

search across them and it's part of

their perplexity subscription. No, we're

not bundling subscriptions from into

other AIS.

>> We just ping the models directly.

>> Got it.

>> Uh what you get in us is the perplexity

or orchestration.

>> Got it.

>> The harness,

>> right?

>> So the when when when when models are

kind of specializing the there's a

bigger value in the one who knows how to

build a great harness,

>> right?

>> That can take the best in each model.

>> Does it auto route today or do you still

have the drop down somebody's got to

pick

>> it? It definitely auto routes the best

model for each prompt,

>> but we also give users the flexibility

to pick whatever model they want.

>> What do you think of I've seen a bunch

of startups hack this together, but

doing the same query across multiple

>> We built a thing called model council.

>> Model council. Yeah.

>> Yeah. So that's one of the one of the

modes and perplexity where I saw Jensen

say in one of the interviews that he he

puts the same prompt in five different

AIs and sees what each of them says.

>> Yes.

>> Like everybody does that. Yeah. But then

you still have to apply your biological

computers

about your trust or your

>> five different doctors.

>> Five different doctors trying to figure

it out.

>> Exactly. So it's dumb.

>> So the model council is a feature we

built where it will not just give you

the answers of each model, but it will

tell you exactly where they agree, where

they disagree, and where the nuances

are.

>> And that's in the interface. Model

council, I didn't know it was there.

>> It's there.

>> I mean, you you released product at a

pretty great cadence, huh? How where did

you learn that and what's your

philosophy of shipping product?

>> Our philosophy is like speed is our

mode. Like you know again one of the

things that big companies cannot do is

move at the speed we do serve customers

at the speed and qual it's it's very

hard to maintain quality speed and trust

at the same time.

>> Yeah.

>> Like Apple takes a long time to ship

anything

>> because they're very worried about

people not trusting them.

>> Yeah.

>> Uh and so some companies are

bureaucratic and they just take forever

to ship something. They don't maintain

what they ship. They may make a big deal

about an event but nobody even knows how

to go and use that feature.

>> Yeah. They get abandoned.

>> Exactly. So, Perplexity has those

advantages for being very small. And

towards the end of last year, we found

that like AI coding tools have made it

much faster for us to ship things

>> which is honestly one of the reasons why

we built computer because now even

non-engineers are shipping code here by

just pinging a slack bot and asking it

to fix bugs.

>> So, this the the iteration has just been

like exponential. The the moment I had

where I became clawilled was when I was

working with it and I was like, "Hey, I

want to build my network. I know these

20 people in Japan. I had dinner with

them during my recent trip. I want to

know who they know. So, check out

LinkedIn and other things and who

they're associated with and make me like

a mind map of it. And then the next trip

I want to meet with the next circle of,

you know, those connections." So, I

started asking like, "Okay, I got the

results." I was like, "Great."

Um, and they said, "Where do you want me

to put them?" And uh, I was like, "Well,

where can you put them?" And it said,

"Well, I can put it in a Google sheet. I

can put it in notion table. I can put it

here. I can give you a PDF. I can give

you a CSV file. Or I could write you a

CRM." And I was like, "Yeah, sure. Make

me a CRM system." And it may a CRM

system.

>> And I think that becomes, and I think

maybe one out of a thousand people

working with AI have had that

experience. Maybe it's one in 10,000.

Where your agent says, I'll make you

bespoke software.

>> Yeah.

>> Have you had that yet? And and do you

see that as a part of computer that when

a person needs a spreadsheet, you don't

launch Excel or Google Sheets, you just

pop up a spreadsheet?

>> Yeah. Well, we have a board meeting

tomorrow.

>> Okay, I'll come.

>> And and so

>> I'll pitch it to the board.

>> Sure.

>> Uh our computer computer made the memo.

>> Oh, wow.

>> Yeah. And um we had a partner meeting to

pitch a partnership idea and uh earlier

we would have a design team do the whole

deck.

>> Yeah.

>> Computer just oneshotted it. Uh I had a

press briefing with a bunch of

journalists. My comm's person would

>> Sorry about that. Brutal.

>> And then my comms person would usually u

give me a memo what to say.

>> Computer one-shoted him.

>> So

>> it's crazy. And it's the context is so

good because the memor is getting

better. Yeah. Yeah.

>> So it's like I know that journalist from

the last time.

>> I know the board meeting. I have all the

previous decks.

>> When did that happen?

>> I think it it happened with Opus 45

>> Opus 45. That was a inflection point

when models were started being amazingly

good at orchestration and reasoning and

tool calls and cloud code brought in

this new idea in AI that everything can

happen inside a sandbox, a console, a

terminal with access to tools where

tools are just command line tools.

>> Yeah,

>> they don't even need to have graphical

user interface. So when you did that and

when you organize around files and sub

aents and skills and CLIs, the model

started be becoming very good at

handling the context. So the context

window no longer became a problem. It

just put whatever necessary into the

context whenever it wanted to and dumped

dumped them away when it wanted to.

>> Yeah.

>> And that made it like suddenly so good

at doing very long orchestration tasks.

>> Yeah. It's it's pretty crazy. I have

every episode of this week in startups,

all the transcripts and then all of all

in

>> that was one of the tasks I did by the

way I can send it to you. I asked it I

want you to download every all-in

podcast.

>> Yeah.

>> U since the beginning and I want you to

take a mention of all the public

companies they mentioned during the

episode.

>> Yes.

>> I want you to have a histogram of the

counts and I also want you to chart it

across time and then I want you to

analyze the impact on the stock price

>> and the sentiment of what we said.

Exactly. And it did like it clearly

said,

>> "Are we moving stocks

>> around Google's stock going up?"

>> Yes.

>> Prior to that, you guys were talking a

lot about Google.

>> Yes.

>> And it clearly

>> And I said I made a bet publicly on the

thing. I said, "I am buying a bunch of

Google because I believe even though

they're behind,

>> it's because they're too precious." You

were kind of mentioning a company that

might be too precious at times and

doesn't release.

>> I was like, "That's that company. They

need to release more." Yeah.

>> And uh I told Sergey, I was like,

>> like

>> give us the good stuff. started giving

us the good stuff.

>> It literally gives you the timestamps of

every single and then I can go click on

it and actually hear

>> exactly

>> that moment.

>> Yeah.

>> Sweet.

>> Yeah. So that's when that's when I was

like damn like

>> this I would have had somebody do this

as a weekl long project.

>> It would have been 10 hours a week of

researcher. I I'm experiencing the same

thing when I do research notes. I've

created my own uh like mega prompt.

>> Yeah. and it will go and like tell me

where you worked before and who's in

your circle, who your competitors are,

who your friends are, blah blah blah,

and then go find I try to find old

podcast is one of my secrets. If you're

an interviewer watching, I try to find

what was the person talking about 5

years ago, 10 years ago, and then over

10 years ago. And I've gone into

interviews now with Michael Dell and

talked about things he was talking about

in the '9s. Yeah.

>> And it finds me some ancient stuff. Like

you would pay a research or a producer,

>> you know, $70,000 a year, $80,000 a year

to do this and they would have done a

third of the job in 10 times longer.

>> It's really gotten weird just in the

last 6 months. What do you think the

next 6 months looks like?

>> I think the the dream that what we are

going to try to do is help businesses

run as autonomously as possible. You

know, everybody talks about this AI is

going to create this one person $1

billion company. Some people say it's

already happened because people pay

researchers like 1 billion, but it's not

truly moving the GDP by 1 billion. It's

not truly creating new value. So the

best way to do that is to actually help

a small business people who would

otherwise drive Ubers for like yes

>> extra passive income to like buy like a

Mac mini set up perplexity personal

computer and run their business on that

or like run it on the server it doesn't

matter uh and actually make real money.

>> Yeah.

>> Hundreds of thousands or even millions a

year

>> and uh grow it.

>> Have computer go and run your ad

campaigns on Instagram or Google. I mean

>> integrate with SEM and SEO tools, find

new users and uh integrate with Stripe,

charge them, ship new features, have

your own like intercom integration for

customer support and like have this all

working well. You can be sipping wine in

Napa. That's the dream that you know it

feels awesome to say. Everybody thinks

AI is already there. It's not there yet.

Someone has to do that hard work.

>> Yeah,

>> that's what we want to do. Yeah, it it's

a great vision because

when I watched startups 20 years ago,

there were so many check boxes they had

to do. I have to find an office space. I

got to put up a bunch of servers. I I

got to hire hire an HR firm. I I got to

hire a PR person. All this stuff. And

now I talk to young founders. They got a

three-person team. They've come out of

A16Z, my program, Launch Accelerator,

whatever it is, Y Combinator. And I'm

like, "Okay, you raised a half million,

you raised a million. Who are you

hiring?" And they're like, "Um, I don't

know if we need to hire anybody." I'm

like, "If you could hire somebody, would

you hire?" They're like, "Well, I do my

own HR. I have this partner." And

they're I'm like, "How are you doing

hiring anyway?" And they're like, "Well,

I put out an ad and then uh it sorts and

ranks the candidates and then it emails

the top 10, asks them a bunch of

questions, and then I meet with the last

two." And I'm like, "That's what a

recruiter did."

>> Like, the entire recruiting job has been

abstracted. And like a a tool like

computer is going to make that even

faster.

>> Much work to do. Uh lot of connectors, a

lot of specific workflows. People don't

want to like learn how to write like,

you know, essay long prompts. You know,

it needs to be so quick and fast and

autonomous. You just set it up and done.

>> And you have an idea, you can turn it

into a business and start making money.

>> Yeah. It's it's an incredible future. Uh

and it feels like it's right here. Do

you how do you think about job

displacement? is you're actually making

the tool that enables people

>> to be a solo entrepreneur and get to a

million in revenue, but it's also the

same tool that doesn't require them to

hire. And we've had this debate a

million times on the podcast.

>> Do you

I'm wondering if like me, you have

moments where you're like, "Oh my god,

this is really terrifying." Yeah.

>> A lot of people are going to lose their

jobs really fast.

>> Yeah.

>> And then, oh my god, you can learn any

skill you want and all the things that

were hard are now easy.

>> Yeah. I I go back and forth. I'm 70 80%

super positive about this, but I do

worry about like 20% of the time I'm a

little worried. Yeah. Where do you sit?

>> I mean, America has always been about

like entrepreneur entrepreneurship,

right? Like we we've been about like

trying to build new things, discover new

things, go explore.

>> Uh I think this whole like Henry Ford

came and built factories and brought in

jobs and things like that and like put

people into a box. But u I think the

reality is people most people don't

enjoy their jobs. They're doing it for

they hate them.

>> Exactly.

>> So there is suddenly a new possibility a

new opportunity to go use these tools,

learn them and start your own mini

business. And if it pays for your needs

for year or multiple years and lets you

have a high quality life and good work

life balance and true feeling of agency

and ownership and passion to like get

your ideas out there. I think that is

even if there is temporary job

displacement to deal with that sort of

glorious future is what we should look

forward to.

>> I I I think you're exactly right. If

there will be some displacement, but

then there's also going to be so many

opportunities open up and it requires

the individual to not be passive.

>> Exactly.

>> They have to be rugged individualists.

They have to be resilient. Yeah.

>> And they have to be resourceful. And I

think once you start playing with these

tools, that's what happens.

>> Exactly. you you all of a sudden feel

like

>> it brings out the best in you if you

truly are in a good space.

>> Yeah.

>> Yeah.

>> I today uh Comet for iOS is out.

>> Yeah.

>> I'm a Comet super fan. I required

everybody. You were nice enough when I I

emailed you. I was like, "Can you send

me some licenses?" You sent You don't

may not remember. You sent me a bunch of

licenses. I said, "Everybody put this on

because it was $300 a month when you

first came out with the common browser.

Now it's free, I think, for all users.

>> Highly recommend it. Highly recommend

getting a pro account. It's only 20

bucks a month to get into perplexity,

which is a joke. So, you can get on

board for nothing, less than a dollar a

day.

>> But what does iOS allow me to do? And

and how does it connect to computer?

Because that's another thing I'm having.

>> Yeah.

>> Cloud code. Uh computer, there's not a

good enough integration with this mobile

device yet.

>> Yeah. So, computer is already on the

perplexity app. So, you can just toggle

the computer and start using it.

uh comet's uniqueness and perplexity for

the company uh and and and the strategy

is the fact that you can control the

browser. So the browser also becomes a

tool for computer

>> just like your Google workspace and all

these other things. uh until the whole

world is organized around CLI and tools.

>> Yeah,

>> there's still a lot of tasks we have to

do manually on the web on the browser.

Open tabs, fill up forms, click on

things, upload stuff, all that stuff. If

you want to automate, you need a

browser. You need an AI that can

natively control the browser. So that is

comet. And that's why no matter how many

other tools in the market exist like

open claw or like claw co-work

>> executing tasks on a browser on the

server side along with all the other

things is something uniquely perplexity

can do.

>> Yeah. My dream is that you'll create an

Android app that roots my Android phone.

>> Yeah.

>> And that you just take over and see

everything because one of the blockers I

have now is some of the websites have

gotten a little pnicity.

>> Yeah. I don't want to mention too many,

but Reddit, LinkedIn.

>> Yeah.

>> And like they're just I I am a great

Reddit user. I'm a great LinkedIn

supporter, but sometimes like I need to

get my inmail.

>> Yeah.

>> From my LinkedIn and I just need to, you

know, find seven people at company. I is

there going to be a solution

>> between the LinkedIn and Reddits of the

world and the claws and perplexities? Is

how is that

>> I mean

>> negotiation going? You don't have to

speak about any specific ones unless you

want to,

>> but it feels like there's got to be a

solution

>> and I'm willing to pay for it as a user.

I'm willing to play Reddit to allow my

bot to show up and behave properly.

>> Well, I I I cannot speak about any

particular company, but we are happy to

work with anyone, right? So, um I think

with with Comet, our idea is to give

people the flexibility to set things up

on their own.

>> Yeah. and uh any um official APIs that

anyone's willing to offer, we're always

happy to put that as part of computer.

Here's what I think should happen. Let

me see if you agree. Um and this is for

Steve Huffman at Reddit.

I go on Reddit. I do a pro account for

20 bucks a month. And when I do that, I

can authenticate whatever tool I want um

to do a series of well- behaved things a

certain number of times a day.

>> Yeah.

>> So, it's not unlimited. I'm not going to

scrape the whole site, but I would like

it to just let Perplexi or computer go

and just tell me, hey,

>> what are people saying on the this

weekend startups and all-in subreddits?

Summarize it for me so I get the

customer feedback. And I would literally

name my

uh agent and I would say I it won't post

on my behalf. It won't vote on my

behalf. Just needed to do a couple of

little readonly things. This would be an

easy solution. Or LinkedIn I would like

if you I have I already pay LinkedIn

like 50 bucks a month. Like they should

just let the $50 a month one work with

computer.

>> Yeah, absolutely. I mean,

>> okay, this is for Satia Nadella. Let

LinkedIn work with Perplexity and the

other players and we'll pay you extra.

>> Perfect. It's a revenue stream. Don't

you think API access for our customers

is a revenue stream?

>> I think so. I think so. I think I think

fundamentally giving users a choice

>> and setting it up as a win-win for both

the business and the user

>> Yeah.

>> is where the world should head to.

>> And and I I would say the same thing

applies to any any website in the world.

Like if if you want an AI to use it on

your behalf, it should be okay for cuz

that's what the user wants.

>> I mean, I have a paid New York Times

subscription. like let me go in there

and do you know whatever 100 searches a

day, a week, a month, whatever they

choose, but that would make the

subscription that much more sticky.

>> Exactly.

>> Uh all right, Arvin, love the product.

Anybody at home,

>> it's just tremendous. Go learn computer

and get the Comet browser. It has

changed my business for the last two

years. Love the product and we'll have

you back soon when you launch your

operating system and come up with your

own server and desktop server but

business is the focus. Yeah.

>> Yes.

>> All right. Great seeing you.

>> We have an amazing guest Arthur

Manchester here the CEO of Mistral AI.

How are you doing sir?

>> Great. Thank you for having me.

>> And so you're here at Nvidia's big

conference,

big announcement. You're going to be

working with Nvidia to build models.

uh to open source them. What is the uh

big announcement here?

>> Well, we're announcing that we are going

to be training the next generation of

frontier models with uh with Nvidia. Um

it's something that we've been doing

before with Nvidia with MLMO, something

we did like 18 months ago. And the point

for us is really to be able to produce

the best open source models out there so

that we can actually use those assets to

specialize them through products that we

do for our customers like Forge that

helps us customize the models for the

enterprise we work with in engineering

in physics in science uh in making them

better at certain languages when we work

with governments etc.

>> And and Michel obviously based in uh

France you're the leading AI company

there. What's it like running the

company and building a large language

model in Europe? Obviously, there's

regulations and all kinds of

considerations. Privacy, the French are

known for protecting privacy. In the

United States, we're known for taking it

away. How is the landscape there and

what do you have to deal with there that

maybe you wouldn't have to deal with in

America? And what's the pros and the

cons? I'd say first, we have 25% of our

business in the US. Uh, and 25% of our

researchers are actually here. So I

actually spend a lot of time here as

well as in France as well as in the UK

in Singapore where we are. So of course

it's it's different markets. Uh it's

markets where you have language which is

a topic uh where there's much more

manufact manufacturing is a bigger piece

of the cake than it is here. uh and I'd

say the our strength has been to also

work with European companies that are a

bit lagging behind uh and that wants to

adopt the technology to to leap forward

and we've been able to do that through a

forward deployment engineering

engagement through our forge product for

our studio product that allows to deploy

agents that do end to end automation but

on top of that the thing that we have

announced today like forge is something

that is actually being used today uh

with customers in the US because they

come to us with uh needs for post

training for making mod specifically

good at financial services and what's

happening is that we have this product

and we can bring the models to

specialize them as well.

>> And so your belief is specialized

verticalized models healthcare finance

engineering different verticals will win

the day or a a global model will win the

day that does everything.

>> Well you need general purpose models to

do the orchestration parts etc. But at

some point you enterprises sits on a lot

of intellectual property on a lot of