Open AI's CFO, Sarah Friar.

>> We're going to get right to it. You have

just completed what I regard [music] as

the most successful fundraising round in

history.

>> We're going to raise [music] actually

north of a hundred and twenty billion

dollars. We think AI is the biggest era

that we've seen [music] today. We're

just starting to understand what it's

going to mean for global productivity

and with that, you [music] know,

hopefully more affluence, better lives

for everyone. Luck is whatever the

preparation meets [music] opportunity,

but you got to grab it.

Long time listener, first time caller.

Quite exciting.

To get to hang out with all the bros

here. Hello.

>> [laughter]

>> We weren't sure how to start this off,

but I thought the best thing was to

allow our erstwhile crypto czar to maybe

>> erst

>> say a few comments and

>> I saw an article today. I think it might

have been in the Wall Street Journal

that the perception is that there's an

advantage to IPOing earlier if you're an

AI company. So now we know SpaceX is

going. And then the question is when's

when are Open AI and Anthropic going to

go? And I'm curious, how do you think

about that? Do you think there is a

little bit of a race on or, you know,

you haven't made a decision about that

yet?

>> Like in the end, an IPO, I say this to

the team all the time, it's a milestone.

It is not a destination. Do not run your

company as if that's some sort of

destination. It's just another way to

fund raise. We just did, you heard me on

on the the the sizzle reel, raise a

hundred and twenty-two billion dollars

in March and that was to give ourselves

maximum flexibility. I feel like my job

as a CFO is create optionality for this,

not just this company, but just this era

that we're living in.

>> Sarah, was that

that point in fundraising, is that the

biggest private or public up until the

SpaceX IPO?

>> It is.

>> Right.

>> It is by orders of magnitude. I think

the largest IPO to date was the Saudi

Aramco, which was about $30 billion.

So, it is actually incredible that

you're going to have potentially three

IPOs at a scale that will be bigger even

than 2001,

2000, that that time frame. There was a

lot that went on in the market, too. But

the market has grown. And by the way,

the other thing going on in the market

is like if you look at buybacks, M&A,

and so on, there's actually a lot of

capital keeps being returned back to

shareholders in cash. So, there is a lot

of money sitting on the sidelines. But

in the spirit of like the question,

David, I think in the end you want to

you'll be measured, right? It's the In

the end, the market is a weighing

machine, not a popularity machine. No

one remembers who won first, Google or

Yahoo, Lyft or Uber. And I say that not

because well, I want to be first or

second, but I just think it's you know,

the the press loves a bit of drama, but

in the end we're going to have to build

big, sustainable, durable companies. And

fundraising will be a key component of

doing exactly that.

>> Sarah, breaking news.

>> Oh my god, so many people coming at me.

Hi, Jason. [laughter]

>> I know. It is it is it is hard balancing

four interviewers at the same time.

>> This is my world, by the way, so I'm

good with this, Jason.

>> Anthropic just

uh confidentially filed their S-1. So,

does that mean you're third place in

terms of the filing?

>> It does not mean anything yet because

[laughter] you have to run now the

gauntlet of the SEC, and who knows how

long that takes for anyone.

>> Yeah, is it Is there though a

benefit to them going forward, and I

think unpacking the rivalry with

Anthropic is on everybody's minds. So,

just I I guess you can't talk too much

about IPOs, so I'll just pivot to

Anthropic was far behind, and now

they've really um I think everybody

would agree in the industry now blown

past OpenAI in terms of developers and

corporations, and it seems revenue.

So, did

How did that happen at OpenAI when you

had such a tremendous lead? How did

Anthropic blow past you guys?

>> So, let's talk a little bit about our

strategy. Our strategy's different,

right? So, we are building the AI layer,

the infrastructure, and it's really

important that there's a single

foundation, but then with many

interfaces out into the world. So,

ChatGPT is one to the consumer. Over 900

million people use ChatGPT weekly, and

it's become the noun and the verb. It's

how most people experience AI for the

first time. Kind of fun fact, our

economic research team just showed me

the fastest growing continents now are

Africa, probably not totally surprising

since it started at a small base.

Fastest growing languages are

Azerbaijani and

what Kazakhstani? What is it's Kazakh.

>> Kazakh.

>> Um which is kind of incredible to talk

about where it's going. So, multiple

interfaces, ChatGPT, of course there's

Codex, um just hit 5 million over the

weekend. We're really proud of that

coming from almost zero in January.

>> users.

>> Go Codex.

Um helped me prepare for this little

special up here, too.

Um there's of course Frontier, our

enterprise offering, and everything

every other way that we can get out

there to reach businesses of all sizes.

That is a very different strategy. We

think that because it's served up on one

model, there's a compounding element of

advantage that comes from that. More

users, more data, more ability to

personalize. ChatGPT acts as a front

door. As we as models get bigger,

there's more efficiency. That should

lower the overall cost to give you a

token in the world. That should compound

to higher gross margins, ultimately more

ways to pay for compute, and then access

to compute is one of the really big

competitive advantages at the moment.

So, you know, we have to all run our own

races, but we all have to recognize

we're part of an ecosystem that also

needs to bring people along

collectively.

>> Did you spread a little bit too thin to

many projects? People are talking about

this new gadget, Sora, and and then

maybe not enough focus on enterprise. Is

that a fair assessment of if there was a

mistake in the last year? That was it?

>> No, I I think that the world loves to go

to binaryisms. Like, are you a consumer

company, Sarah? Are you an enterprise

company? The reality is we're very much

both. We're not one or the other. Right

now, our revenue's getting pretty

balanced, about 50/50. We are incredibly

focused on the enterprise. Like, I spend

so much of my time with I mean, just

even in the last week, I could tell you

I've been to see Thermo Fisher in

Boston. I was with a bunch of banks in

New York. I was on the phone with

Travelers on Friday. I spent this

morning on the phone with a tech

company. Doesn't matter the vertical.

People are really moving on AI right

now. Our new head of revenue, Denise

Dresser, in seat since December, she is

a force of nature. And so, I think the

enterprise, broadly speaking, is really

firing on all cylinders. But, we don't

want to leave the consumer behind.

Remember our mission at OpenAI is AGI

for the benefit of humanity, not for the

benefit of humanity who can pay, or for

the benefit of humanity who live in an

enterprise, but very broad-based.

Um it's why we offer so much free,

because we want people to get a taste.

Once they get a taste of intelligence,

the ability to come up a commitment

curve is incredible. Our free users do

about seven turn seven questions a day.

Our first paid tier do double that,

about 15. Our Our real paid tier, the

plus, 20 bucks, hopefully you're all on

it or higher, about 3x. And pro, about

um 11x

over a free user. So, remember when you

got your flip phone, and you're like,

yeah, I don't know what it does, make

some calls. Now, that same phone, think

of all the things it does for you.

That's the path we're on with

intelligence right now. Sorry, Chuck.

>> You said

>> very influential. I think it was about

18 months ago for a lot of us in the

industry where you framed a very simple

economic trade-off which was gigawatts

to cash. And I think you said 1 gigawatt

is roughly equivalent to about 10

billion dollars a year of revenue to

OpenAI.

So, comment number one was this 1

gigawatt equals 10 billion dollars a

year of revenue for you. But, it's not

just you cuz you can probably

extrapolate that to Anthropic and other

folks Gemini.

But, then you were really at the

forefront of getting access to power and

data centers and powered land. It seemed

a little crazy, but now it looks like

hold on, there's a huge deficit of

supply. Can you just unpack all of that

and explain

both the spectrum of where we are and

then those specific economics and if

that's changed?

So, first of all, yes, compute is a very

scarce resource at the moment. We what

we see in our business, we're going up

that kind of vertical wall of demand

right now and there's just not enough

tokens available. So, I'm very grateful

that I got to work alongside Greg and

Sam. I think we really put our press

hand on this. And last year, we were

definitely taking some, you know, arrows

in the back about why are they out there

buying all this computes? And I think

thank God we did because in 2026, we

still won't have enough compute. Um,

where are we on the compute continuum?

There's kind of choke points everywhere.

And and I think they will continue to

move back and forth. I mean, you all

talk about this and know this.

As well as anyone I'm here, whether it's

energy first and foremost, um, land,

power, how we get regulatory, um,

environments such that we can build

quickly. Um, when you get into the racks

and chips themselves, clearly, do we

have enough, um, in that supply chain?

Memory spike is is on at the moment.

Access to great talent. Um, do we have

enough people coming through our

education system? I really worry about

this right now. I'm a trustee at

Stanford and you know, I see just that

you know, we need to keep the focus on

education and science.

And then trust. I mean, I actually put

that as part of the supply chain.

Sam right now is in Saline, Michigan.

He's going to be cutting the ribbon in

about 2 hours. So you are getting a

sneak preview, but they told me it was

okay to say it in the room.

That will be, you know, sticking shovels

in the ground on a 1 gigawatt data

center, which is part of our Oracle

complex. Really important there on the

trust side that we don't leave

communities behind. I spent 7 years of

my life working at Nextdoor doing the

hard work of what it means to be local.

And you cannot tell people from top down

what they need cuz they will tell you,

"Thank you, but no thank you. I will

tell you what I need." And so in a data

center like that, we're actually

spending a lot of time in the community

saying, "Number one, we're not going to

raise your electricity bills. We're

going to pay for our infrastructure and

our power. It will not be the ratepayer

that has to pay. Number two, we're going

to bring jobs. 2,500 union jobs.

Good jobs. Like electricians, HVAC, and

so on.

We are going to pay our taxes. A billion

dollars in taxes just for that data

center into Michigan. And on top of

that, we're going to invest 45 million

dollars going into education for Codex

credits to do what you all talked about

this weekend. It's like anyone who's not

like coming in fossil to their new job.

I have teenagers using Codex. It would

be like I would never hire a finance

person didn't know how to use Excel. And

I pretty much probably wouldn't hire a

finance person today that doesn't know

how to use a tool like Codex. So that,

you know, so when I think about

investment, we're having to invest ahead

of demand. That means we need to both be

able to find all of the compute and all

the pieces and then pay for it. So that

goes back to your capital question on

IPO. And then on the other side on the

economics, look, the economics do

continue to get better. They're getting

better on multiple fronts. I think we

are doing a better job of actually

showing true value to our customers. And

I think you get beyond kind of a cost

plus type pricing into something that

feels more akin to the value being

created. Now, scarcity of tokens helps

cuz it's causing a bit of a compression

in time.

>> about that in just like without specific

names, where you know the landscape

exists today in terms of all the power

that's available and all the demand that

exists across everybody?

>> Yep.

>> What's going to happen over the next

year just at the current course and

speed of what is available? Of the data

centers that's available, of the tokens

that's available, of the infrastructure

that's available for everybody because I

you know I told this story last week but

you know I'll use Anthropic and one of

the frustrating things is at some point

it just says you know 10:30 it's like

all right Chamath see you at 2:30.

>> Yeah.

>> And that's not a viable experience.

>> Right.

Um

>> And in fairness to ChatGPT actually I've

never had that with

>> Yeah, we we're quite generous with our

tokens and again on purpose we're trying

to drive access so people understand cuz

if you're on that free tier not actually

getting the latest model but we're

trying to put it in your hands so you

get a sense for it by the way because

you know if you're a kid um doing

homework like I think about when I grew

up and the encyclopedias Britannicas

showed up at the front door in Northern

Ireland in a tiny little community in

the middle of the troubles it was like

the clouds parted and so we want to make

sure that people get that feeling by the

way. But the landscape right now in 2026

if you want to buy more compute good

luck to you. Like tell me cuz I don't

know where else to find it. I mean as

you know Elon has some. Well I was going

to say Elon ironically ended up being

the one person that had too much compute

in a way um but good job on like

figuring out how to sell that off.

Um in 2027 it's pretty limited as well

frankly. Now there's a couple of things

shifting around. When we talk about

compute, there's training that mostly

still all happens here in the United

States for USG reasons, for making sure

that a national asset in effect is

happening on US soil. For inference, we

want that to be global. And I think

particularly in an agentic world, you

want much more kind of real-time. Even

for things like Sora and video, which by

the way, yeah, we have you know, we had

to make a really tough choice cuz we

didn't have enough compute.

>> And it uses a lot.

>> Right now, yeah, video does. But video

is not over. Like in particular, when

you start to think about where AI is

taking us into more multimodality. So,

remember, we've all been taught by the

last generation of technology to talk

with our thumbs. It's a disease. You

walk around, everyone's looking down,

they don't look up anymore. Teenagers

sit on my sofa at night and talk to each

other with their thumbs. I'm like, "Who

are you talking to?" And then my son

will be like, "Him." I'm like, "Okay."

Talk. Multimodality is here. Um

hopefully, I think you all talked about

it this weekend. You're talking to your

tool. I talk to Codex every day. And so

that is changing rapidly, but that is

going to need much more kind of

real-time compute cuz it's an odd

experience. If I was talking to Chamath

>> Johnny Ive's this puck and his ear

pieces. So, maybe tell us a little bit

about that project. You've admitted it

now.

>> If I if I tell you it's an ear piece,

Johnny will come and steal my teenage

son. I might give it to him, give him to

him.

>> you

>> Uh but

>> We You believe that there should be

>> We're changing into a consumer substrate

that I cannot tell you what it is, but

by the end of this year, we will unveil

it. Early next year, you'll be able to

buy it. I have seen it. I've tried it. I

am a hand talker. Right now, I'm sitting

on my hands.

>> Did you have a Did you have a paradigm

shift? When when Yeah, when you used it,

was it like having an iPhone for the

first time?

>> It's very

What Johnny and team are really good at

is

bringing humanity to devices and I don't

really know how to explain that well,

but when you see it, you feel it.

>> It feels natural in some way?

>> It feels very natural, but it feels very

lovable.

>> Really?

>> And I can't really explain what that

emotion is cuz so much

>> Intimate in some way in terms of

>> technology is

>> Not taking your phone out and it's it's

seamless is what I've heard from people

who played with it.

>> is very um can be very mechanistic, but

we all know great design just makes

everything fade away, right? It's what

um at the time, you know, the simple is

hard.

>> Yeah.

>> Um

>> I think this is a very

this story just going back to the

earlier question, so putting on

the CFO hat, help us understand the

capital allocation model that you use.

Cuz a lot of businesses over the last

decade, two decades that have kind of

been these outsized returners have found

some unique way to deploy capital at a

higher ROC than anyone else and then you

end up plowing all your capital into

that higher ROC bucket.

>> Yeah.

>> What is that for you guys and how do you

think about that led that portfolio

approach to having more of these kind of

big returner shots and is there an

engine where that gets better over time?

>> There has to be because

in the end, the durable, high value

companies created in this era, I don't

think they're not going to be magical.

They're going to look like the great

companies of prior eras. They're going

to create customer value. Starts with

the customer um and really helps the

customer do something different, better,

more revenue, more efficiency, right?

Thermo Fisher wants to be able to get um

patient screening done faster so they

get FDA approval faster. That's really

important. Like if you have a form of

cancer where you have weeks to live, the

difference between a breakthrough in

four weeks and two weeks can literally

be life or death.

They also have, I'm going to misquote

this, but something like 30,000, 38,000

people in the field selling those

amazing like if you walk into any lab in

the country, you'll just see Thermo

Fisher plastered all over every device.

Those people want to be more efficient

going to work. Like the the fastest

takeoff of Codex within Open AI right

now is actually in our go-to-market

team. Our devs are there, but like if

you look at the pace of growth kind of

month over month, it's all in GTM. So,

they want more productivity out of their

GTM team. And of course, um they're

doing things in areas like finance,

which I get really excited about. But

so, customer value first. From that, now

you need to get to a great gross margin.

So, how do you get to a great gross

margin? You're looking at like the cost

of revenue. The main input is compute.

The good news on compute is that there

is a massive deflationary curve on cost.

Right from Chat GPT uh 5 to 5.4, I think

the deprecation cost was something like

97%. It's like a kind of an amazing

curve. Actually, I'm slightly from 4 to

5.4, it was 97%. But that happened in

like 2 years. It's kind of wowing,

right?

>> That's incredible.

>> Um even our newest model, if you look at

5.5 that we just released, we're trying

to now translate that back to the

customer. So, we actually raised prices

on 5.5 2x. But if you look at what the

cost of the customer is, they're

probably still getting a break of about

20 to 30% cost reduction per token

because it's just much more efficient

per token. So, there's a lot to do in

that envelope.

>> Yep.

>> And and part of making an a capital

allocation decision is having to

if you make it on today's cost profile,

you actually might misprice the

outcomes. You have to lean in a little

on the cost profile. And then as we

think about like the builds, yeah, you

are having to make like really my focus

today on compute is what's the compute I

can buy for '28 onwards. Like that

Michigan data center in Saline, I don't

think we will be getting compute out of

it until probably end of 27 or early 28.

So, that's where you're starting to make

your bets. And in fact, where I feel

most short of compute right now is

starting to look at 30, 31, 32. So,

you're having to create a business

model. Now, the good news is each year

goes by, we get more confidence in the

build. We're seeing it massively

outperform. And so, that's giving us

more and more confidence. And the market

is coming towards us much more.

>> All right. So, how are you making the

compute needs forecast multiple years

out, accounting for all of the

architectural and model advancements

that are happening, where quality value

or utility per unit of power is going

up? And help us understand how you kind

of estimate that given that there's a

lot of technology development going on

that has a high kind of variance to it.

>> Yeah, yeah. So, we we do have to make

multiple

assumptions both on the compute itself.

So, we assume right now that compute it

actually on a per gigawatt is getting

more expensive cuz power is getting more

expensive, memory is getting more

expensive, and so on. However, the the

intelligence that we get on the other

side out because of the deprecation on

the chip side is is more than making up

for that. So, in a terms of a per unit

sold to a customer, it should actually

get a lot less expensive.

>> improvement in that. So, that's just

>> Yeah, exactly. That's just the chip

itself. We don't want to overestimate on

the model side cuz sometimes like 5.5 is

an incredibly good model on the

efficiency side. But if you look at

something like 5.4, the prior model, it

was a really large pre-trained model. It

was very expensive. It was actually hard

to serve. And sometimes we want to do

that really big pre-trained moment. And

then we take multiple model turns to be

able to kind of drive down on the cost

side. I mean, in the in the near term,

like in 26 and 27, I clearly build a

model that's bottom-up. So, I know what

my products are, I have a sense of what

the pricing will be.

You know, P times you know, consumer P

times Q, how many wilds do I think I

have? I can see what the shape of the

line is. How many of them will

subscribe? Advertising coming in is also

still related to how many weekly

actives, how many dailies, how many

messages, and so on. So, you can you can

do actually a pretty good model job in

26 and 27. That said, the shape of the

line keeps taking us by surprise to the

upside. When you get into the outer

years, you're actually looking more at

the compute you've bought and almost

just doing an algorithm the other way

that's saying this amount of compute

should equate somewhat this amount of

revenue. I don't know for certain

exactly where it will all come from.

Like a year ago, I built a model for

investors that showed a gentic revenue.

And the story was, we're going to have

this thing, we're going to be in the

agentic era, we're going to hand it to a

developer with natural language. They're

going to be able to build and we think

they will pay upwards of maybe $2,000 a

month for it, which is kind of laughable

in hindsight. But nobody believed. They

were like, I don't even know what she's

talking about. There's no way that will

happen. And $2,000 a month? Remember

when people were losing their minds over

chat GPT Pro being at $200? Like, oh my

god, nobody will ever pay for that.

Yeah.

>> So, why 122 billion? Does it take you to

2031, 2032? Like, how do you get the

calculus on the capital needs as you do

that modeling?

>> Right. And you're maybe even more

specific. So, the estimates I've seen is

that to stand up 1 gigawatt of AI

compute costs about $50

in capital. Land, power, shell, chips,

everything. All in around 50 billion.

Do you have to front all of that money

when you create a new data center? Or

how much of it do you do? How much of it

can you get debt for? Does a 100 billion

raise only get you 2 gigawatts or does

it get you 5? Like, what does it get

you?

>> It's It's a great question. So, if you

look at our compute strategy,

um and it's crazy how fast the world has

changed. So, just 2 years ago, we were

literally one. We had one CSP we worked

with, Microsoft Azure. Um we we sat on

one chip, Nvidia. We had one product,

ChatGPT. One price point, $20 a month.

So, I often use a Rubik's Cube as kind

of my metaphor. So, we were like one

cube in the bottom. Today, if you look

at our strategy, it's being to go, first

of all, multi multiple CSPs. Because

what CSPs do for us, in effect, is they

shift capex into opex. So, you pay as

you get the revenue, so as you're

actually utilizing the data centers. So,

in effect, we are riding somewhat on

their ability to build and have capex

and um financing. So, today, we sit on

top of every CSP, Oracle, um

CoreWeave, um Microsoft, GCP, AWS, and a

bunch of small neoscalers.

On the chip side, we've also um gone for

a program of being multi-chip. Um cuz we

want to make sure you're always on the

frontier. I think if you're only on one

chip, there's just inherently a moment

where you can't be on the frontier

because there's some leapfrogging that

happens. So, today, Nvidia remains our

absolute priority partner. They have the

frontier chip. Our next big trading run

in the fall will be done on Vera Rubins.

We're really excited about that. And now

we're plotting kind of the Simon series

that's coming. But, we also now have

chips in the pipeline from AMD. Um

Cerebras is already online. It's been an

incredible low-latency chip, great for

devs, for example, that want real-time

coding. And there's our own chip that

we're working on with Broadcom. And

then, beyond that, there's other ways

we've diversified. So, now think about

that Rubik's Cube. It's become much more

multi-dimensional. And it allows us to

effectively utilize investment-grade

CSPs in order to be able to go fast and

push it back to be more opex not capex.

Now, we are starting to shift gears into

more of a built-to-suit type

environment. We announced a data center

we're building

with SoftBank Energy um down in Texas.

That's the beginning of something that's

beyond a CSP. There's a little bit more

capex required there. And then finally,

I think as the world progresses,

remember we've done all that just in 2

years. The reason I like a Rubik's Cube

is again, please chat GPT this, but I

think a Rubik's Cube has something like

a quintillion different um forms it can

come up with. And so it just gives us a

lot of optionality. So remember what I

said, my job is maximum optionality. And

in a moment where I'm not yet an

investment-grade type of entity where I

can go get lower cost debt financing,

being able to work with partners to do

that is really important.

>> think that in 5 years from now the stack

is just merged together? What do I mean?

In traditional or historical markets,

you'd have Nvidia sell the chips, but

that's all they do. And then you'd have,

you know, Microsoft just run a cloud.

That's all they would do. And then you

would have a consumer app. That's all

you would do. But now we see everybody

doing everything. You know, you guys

have silicon that you're spinning. You

have models that you make.

You may or may not eventually decide

that you need to be some form of a neo

cloud yourself. If you look at Nvidia,

they have incredible silicon, but they

also have their own open-source models.

They're increasingly becoming an

off-taker.

Google is a cloud company first, but

they also have a chip. Now they have

models. So it's all

merging.

Is if that continues to happen, does

that make

the competitive landscape simpler or

easier?

>> I mean, I think where everyone is trying

to make sure they reside is the layer

that is closest to the customer where

usually you take the the largest portion

of the profits of the ecosystem, right?

No one wants to find themselves trapped

away.

>> Absolutely. And so, that's why today,

when I think about our positioning,

comes back to where I started, why we

want to be that AI intelligence layer,

is because a year ago people talked

about the commoditization of the LLMs.

Um and frankly, it's gone the opposite

because as you start building an agentic

layer, and we all started use this word

harness, but the harness is what brings

the context, the memory, right? It I

have in my Codex, I have a whole

ginormous memory file where it knows

that I'm me. It knows I'm the CFO of

OpenAI. It knows how I like to write

things, well, how I like to say things.

It knows what I'm interested in. It

actually also knows that I'm a mom

teenagers. I mean, it just carries all

this memory.

And that makes the model more powerful

for me. Now, think about what happens

when that memory and that context is

brought into an actual enterprise

environment. So, now it's not just even

about the data that resides there, but I

always think about the the intuition of

like back when I worked on Wall Street,

right? There was all the data in the

world that told you what a stock should

do post an earnings call.

But, give me 1 second.

Then you called your trader. And the

trader would be like, "Yeah, stock's not

going up, Sarah." Now, I'm like, "What

are you talking about? Like all the

numbers say it did this, did this, did

this." And he's like, "Yeah, no, but I

know this fund is under pressure and

they need to sell down their book and

that is going to kill the stock for the

next week." Right? That is the intuition

of an enterprise. Like it's the best

example I always think of cuz I came out

of a financing world, but there's this

intuition in every walk of life. And

that's where I think the models are now

getting very connected to the memory and

context and intuition of your company.

And that's what gets CEOs and C-suite

really excited cuz they're like, "Okay,

now I really see how this is going to

add value to drive my revenue line, my

top line, but also, you know, I can

think about it as an efficiency play as

well." And so, back to what you're

asking, I think what people want to make

sure is they stay as close to that value

as possible.

>> And be flexible enough to pivot as you

continue to wrap.

>> But

>> Sorry, Jason.

>> It's quite all right. Um

been wonderful, and and you've been so

great with the details. One final detail

question, rapid-fire. Three great

greatest consumer businesses of our

lifetime, iPhone, Meta

advertising network, and Google's

advertising network. Two of those three

are ad-based, and and even Apple has a

sprinkling of that. It's

Haven't heard you talk about ads much.

People tell me they're seeing some ads

in the experiment in the free version.

What is your commitment to the ad

version? You guys got a little

uh trolled by Anthropic during the Super

Bowl. Oh, you're going to have ads. But

is ads the solution to making this free

for the world?

>> Yeah. So, first of all, on the ad front,

you know, we want to stick by our

principles. We want to make sure that

you know you're always getting the best

result based on the model, not by

something that was sponsored. So, that

has to hold true. And I think the second

thing is that we'll always provide a

free a tier, sorry, an ad-free tier for

people that just don't want ads. But

>> If they pay.

>> If you took If you took what I Fiji says

this really well. If, you know, Google

and Meta had a baby, it would be

ChatGPT. Cuz what you have in Google

Search, and by the way, we know we have

at least 11% of the search market. It's

a lot more because actually, when you do

a Google search and the page refreshes,

that counts as one. In ChatGPT, when you

do a whole conversation where you might

ask 50 questions, that also only counts

as one. So, in reality, we have a much

higher portion. Very high intent. That

is great for advertisers because I'm

effectively telling you what I'm doing,

right? I want really cool shoes to sit

on the stage. I'm telling you what I

want to go buy. In Meta's case, right,

they use this like people like you sort

of intent, so they have the demographic.

We have more than that cuz we have

memory, right? I just told you it knows

who I am. So, imagine putting memory and

context next to intent, you should have

a very potent ad platform, which gives

you an ability to offer up massive

access to the world writ large because

now you can pay for it. And I think back

to a question you asked Freeberg, like

if you look at them the revenue per

token right now. If I was optimizing

only for today, I would give every token

to the API.

>> Right.

>> Every token to the API. Order of

magnitude more than to the consumer.

However, I told you we're playing our

own game. We have a strategy where we

believe there's an AI infrastructure

layer utility like electricity, and in a

future state, you'll want to be able to

serve the world writ large, consumers,

small businesses, large enterprises,

governments. That's our strategy.

>> Ladies and gentlemen, the CFO of Open

AI, Sarah Friar.

>> Well done.

>> Fabulous. [music]

>> [music]

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