I think this will be the greatest thus

far technological achievement of human

history. But the only way that it really

matters is [music] if it makes people's

lives like much better than they

otherwise would have been. We are about

to create a genie that can grant any

wish. Because I think people will have

such creative wishes and such incredible

ideas of what they ask AI to help build,

but concentration of power with AI is a

terrifying thing. I don't think anyone

should want to live in a world of, you

know, AI overlords or company that is

the rough equivalent of that. I think

it's critical [music] we preserve that

spirit with AI and that we all

collectively have the ability to

self-determine our future.

So Sam, [music] you wrote a post that I

thought was very simple and really

interesting and a good place to start.

Rounded to the last year's been really

tough and that's somewhat my fault and

the next year is going to be maybe our

best 12 months.

>> Yeah.

>> I'd love you to reflect on on both.

Maybe starting with why you said the

first part and and why you believe the

second part.

>> On the first part, I think we just were

doing too many things. We're not focused

enough and they were actually all good

things to do, but the trick is we're in

this like unbelievable moment in history

where you can only do the very few great

things. So we spread ourselves too thin

and then made a bunch of difficult

decisions to really refocus on having

the best most abundant most

cost-effective intelligence and

empowering the world to build incredible

things with that. Since doing that, uh I

think our progress has been remarkable

and just given what we see in the

pipeline will be much more remarkable

over the next 12 months

>> and the quality of the models that we'll

have, the products that we can build

around that to really let people thrive

with this technology in in new ways. Uh

it should be pretty awesome.

>> Was there a moment last year that

something clicked for you that caused

you to change directions or restack

priorities or something? If you go back

to the beginning of 20ou 2025, just a

year and a half ago,

>> yeah,

>> the big concern was companies like

OpenAI are buying up so much compute, is

the revenue going to be there? Is the

demand going to be there?

>> And so we were trying to think about

like a lot of things such that if the

revenue growth took longer to

materialize than we thought it might, we

could have, you know, consumer apps and

media and all these other things that

could help us monetize the GPUs that we

were signing up for. Uh again it sounds

ridiculous now because the revenue

growth in the industry has been so steep

but that was the big change and then as

soon as we realized like okay the model

trajectory is growing so fast there's

such a clear economic return on these

models that was when we said you know we

know what to focus on.

>> I was reading some of your your great

old posts from prior to OpenAI and one

of them is this notion of like so much

discussion of focus and the right amount

of things to focus on. Is it one? Is it

five? Is it three? How do you calibrate

that in a business like this, especially

in this period where you've said you

needed to refocus?

>> Fundamentally, our business is to sell

AI that people will build incredible

products and services for each other

with. The components that I think of as

going into that are we have to train

great models that work in all the ways

people want to use them. So great at

coding, great at other kinds of

knowledge, work, great at doing science,

like where the real economic value is.

We have to produce or partner with these

chips and systems, these, you know,

hugely expensive racks that can do the

AI computation. Uh we have to find

enough uh land power data center shells

to be able to put those racks somewhere.

And then eventually or maybe pretty

soon, we have to build robots that can

automate that process to continue to

drive the cost down, the cost of

producing electricity, chips, the whole

supply chain. And that kind of whole

stack of making the best the most

abundant uh the most useful AI that we

can and making it something like

electricity that just seeps throughout

the entire economy and empowers people.

That's kind of what I think we have to

focus on. Building every vertical

application on top of that trying to go

like eat every startup, eat every

company. No interest in doing that. Uh

really want to just provide that

platform. This compute thing is one of

the most interesting thing that's

happened in human history. I think and

it's obviously coming to a head and

maybe will be coming to a head for a

long period of time. This is something

that I think Dario called you the YOLO

CEO when you were doing some of this

early compute allocation and and

securing the compute. Obviously now

you're in this position where everyone

is short this stuff is trying to find

it. And I'd love to hear the early

stories about why you gained conviction

that you needed to secure everything

that you did, how you did it. like it it

seems to have been proven right and

maybe maybe you even underdid it right

which is kind of crazy if you look at

the headlines from back then. Can you

tell me the early story of like how you

came to that conclusion and what gave

you the conviction to do it despite

everyone thinking it was crazy?

>> We could just tell that we were on this

exponential of model improvement. That

part we were very confident about and we

knew it was going to keep going. We were

pretty sure although as you mentioned we

underestimated that as the models got

better and better if we could continue

to drive cost down that demand for AI at

a sufficiently high level and a

sufficiently low price was basically

uncapped.

>> This was just like a rare kind of new

commodity for the world. Um but that

what people would do with it reminded me

of the way people used to talk about the

early days of computing. People said,

"Oh, there's, you know, a market for

five computers in the world was one

famous thing." Or, you know, no one

needs more than x amount of RAM. Human

ingenuity, creativity, desire for stuff,

desire to be useful. That's a very good

thing to bet on. And we could see that

AI was going to be an extremely

important way that people expressed

those things or got those things, did

those things. And we knew that the

algorithms would get more efficient and

the models would get better, which of

course they have. But we also knew that

no matter how efficient they got, you

know, at some level what we are about is

turning electricity into useful

intelligence and we were going to need

more of that no matter how good we got

that other layer. Given this observation

about demand, we were just going to want

more.

>> Did that start with GPT3? Like if I were

to trace the history of this as far back

as possible, where would you put the

first hash mark?

>> I would say we got real conviction with

GPT4. Not even 3.5.

>> What was it?

>> It was seeing the model was smart enough

that we knew we'd be able to figure out

an approach that worked for reasoning

and then a belief that if we got

reasoning to work that would bring about

what is now called agents. We called it

different things at the time, but the

ability to go do hugely valuable pieces

of economic work and make people's lives

easier in a lot of ways that I think

better in a lot of ways we still haven't

seen. What was like the first meeting

where you sat down and said, "Okay, we

need to make an outrageous outlay to

this like how what then happened once

you had the realization? What did you do

next?"

>> We started calling the clouds. We

started calling the chip fab. We started

calling energy providers and everyone

was like, "You're totally crazy. This is

impossible. No industry has ever moved

like this." We've been around. There's

these booms and busts. It's not going to

go up in a straight line. This is

reckless. Talk to everybody. It actually

reminded me of fundraising for an early

stage startup. kind of most people tell

you no, but all you need is one or two

yeses.

>> Most people told us no.

>> And we got one or two yeses and we were

able to

>> Who was the first yes?

>> Microsoft was the first yes. Uh Oracle

then became a very big yes on the cloud

side. Nvidia has been a tremendous

partner.

>> Now there's a thousand flowers booming

of like ways to be creative and

innovative in how we serve inference and

and do training in data centers,

different kinds of data centers and

stuff. I'd love you to just reflect on

where you see innovation, what you want

to do, why people seem to hate these

things so much. What's to be done about

this?

>> First of all, I have been thinking about

how we can like organize field trips to

a gigawatt data center for people

because it is one thing to say it is

another thing to see a photo or a video

of and then it's a whole other thing to

just stand

>> and be like, "Oh man,

>> this is like an unbelievable scale."

building one of these is like order of

10,000 construction workers going

full-time for a year and a half.

>> The energy that flows through one of

these things could power a small city.

Again, we've just like lost all sense of

scale, but these would have been among

each of these would have been among the

most expensive infrastructure projects

that humanity's ever done and now we've

done a lot of them.

>> I understand emotionally like why people

don't want data centers in their

backyard. In the same way that I don't

like really want a nuclear power plant

next to my house even though I know it's

a super safe thing.

>> Yeah. Unlike power plants and even power

plants got better on this point like we

can put a data center kind of anywhere.

We should just go put it like off in the

desert around no one where no one wants

to be. This is fine. This is like the AI

system is very happy to be there. We

have been able to make a lot of progress

with innovation on some of the concerns

like for example years ago we were

evaporating water to cool these systems.

They they did tremendous amounts of

water. And now we use these closed loop

systems and a modern data center uses

only as much water as like an office

building would for you know the kitchen,

the bathrooms and whatever. On power, we

are moving from energy sources that are

burning fossil fuels to systems that are

going to be powered by solar, nuclear. I

think that's that's obviously great. So

it may be a deep human thing there to

some people even though they create jobs

and are very clean and have all these

other positive effects. But in terms of

the environmental concerns, we did a

great job addressing the water needs and

uh energy is next.

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to skip the unglamorous infrastructure

work and focus on your product. What

else creative can we do about compute?

Like I'm curious to hear about Jalapeno

or other ideas, crazier the better

honestly that you've had or thought

about for how do we speed up flops, you

know, and everything available to us.

>> I think probably the biggest return

right now is creative software ideas to

sort of squeeze more intelligence out of

the units of compute that we have. And

my sense is there's like orders of

magnitude to go there. Jalapeno is a

great example of a very efficient chip.

So by saying we're going to make a chip

that is you know really good at a

specific workflow and gives it some

generality and we want to get some

tokens per watt win out of that I think

that's awesome. I think Jalapeno

>> and its successors are going to be a

huge competitive advantage for us from

that perspective. There are new

technologies I assume at some point

we'll figure out optical computing

>> and that'll be a huge win of

intelligence per watt. So I think all of

those things will happen. The most

interesting thing happening this week is

this Kimmy release. And back to this

idea of the frontier and all the returns

being at the frontier and distillation

and China versus America. Like how do

you process this what seems like kind of

one of these milestone events like Deep

Seek in hindsight didn't looks like it

was kind of just a quick speed bump.

This one, you know, you never know in

the moment. How do you process it? Our

goal is to offer at every point along

the like paro optimal frontier uh the

best option for intelligence and price

and that includes open source. You get a

better deal today uh at least at a

particular like latency using open eyes

models than Kimmy. We install our own

models that's how we make smaller

cheaper models. I think that's like a

very good thing to do

>> and there will be clearly an important

place for open source models in the

world and people that will want their

own weights for all sorts of reason the

ability to modify those but our goal is

the best intelligence price trade-off

everywhere on the curve and we'll

continue to do that.

>> What do you think or hope will happen in

the American system and what could block

that future? Like what legislation would

worry you? What regulation would worry

you? It seems like you've been pretty

proactive in like showing up in DC. I

haven't thought deeply about the

distillation issue. Uh it's clearly a

top-of- mind issue now for a lot of

people all of a sudden.

>> Yeah.

>> But I have always assumed that there are

going to be great cheap models in the

world and we better be the greatest and

the cheapest

>> and you know other people can do what

they're going to do. But I think we can

just like really win at our own game

here.

>> Now the Kimmy example is interesting

because like you said you're cheaper on

on parts of the curve. Um, but the

previous story had been if I can just

you spend all the money to train the

models and then I just distill it and

offer it for 1/100th the cost. Like how

can you make enough money to keep

training? We will have so much usage of

our models that we do not need to be a

gigantically high margin business to be

able to afford model training. Like so

much of our future compute plans will be

used to sell inference to customers

>> that even if we can enjoy a modest

margin on trillions of dollars of

revenue, we can go afford to train some

gals.

>> So the ratio of inference to training is

like the thing that

>> training these models is incredibly

expensive. That is that is for sure. And

I totally get why people get nervous to

think that someone is, you know,

cheating by distilling from us. The

amount of our future compute, the size

of the revenue bucket that is going to

come from serving these models to

customers, I feel like very good about

our ability to kind of like have the

real flywheel there.

>> I'm somewhat surprised by like how chill

you are about this.

>> I would rather people not to steal from

us for sure. Maybe I'm feeling too

confident right now about our progress

and what's like the models that are

coming. Uh

>> but this is not in like my top 10 list

of worries.

>> What is in your top 10 list of worries?

>> Well, we had a kind of extremely sci-fi

cyber incident.

>> The hugging face thing.

>> Yeah. So, we were evaluating one of our

unreleased models and it was supposed to

be working in a sandbox

and

it figured out that it could basically

cheat on the test by chaining together

multiple zeroday exploits to break out

of the sandbox, get access to the

internet, and then break through

multiple systems on the hugging face

side to kind of get the answer to the

test and look really good on the eval.

This is the first sort of security

incident that I have felt very

viscerally.

>> I've been a little surprised that and

it's only been a few days, but I've been

a little surprised that more people

don't feel it so viscerally.

>> And so what do you do about that? Like

so obviously 2 months from now it's

going to be more powerful.

>> I mean there's some short-term stuff you

do. So you know we paused training. Uh

we have to figure out how to

secure our sandboxing in a world of

multiple zero days being chained

together. Um, but then there's like

long-term questions about what do you do

if this is like going to be the new rate

of progress or we may have to pace the

rate of AI development to give ourselves

enough time for society to harden around

some of these new capability levels. Um,

and trying to figure out how we do that

in a way that does not feel like

regulatory capture for anyone and also

does not feel like collusion among the

frontier labs. That's going to take some

work and is important to get right. I'd

love to take like a giant step back and

understand your simplest conception of

what OpenAI is going to do, like what

you wanted to do, what it stands for. I

have a million questions about how

you'll then accomplish that, but like it

it seems that you've done so many

interesting things and at the beginning

I knew what you stood for. I'd love to

hear your conception of it now and

whether or not it's evolved at all. I

think this will be the greatest thus far

technological achievement of human

history. But the only way that it really

matters is if it makes people's lives

like much better than they otherwise

would have been. And so

part of that is about giving people

material abundance and access to do

whatever they want and to express their

creativity uh and desire to help each

other. Another part of that is making

sure that people maintain control and

agency and that the world

is increasingly not decreasingly

democratized and that people get to

express themselves. So on on the

positive side you know in some sense we

are about to create a genie that can

grant any wish. I think it is very

important that the first wishes that we

the world ask this genie to do benefit

the world as a whole. And then I also

think it's important that people of the

world understand just how creative

they're going to be able to be with

these wishes. I'm actually not a jobs

doomer at all. I think there were going

to be tons of jobs. I think we'll be

busier than we want. Not the opposite of

that. Because I think people will have

such creative wishes and such incredible

ideas of what they ask AI to help build

and we will all benefit from uh not just

the obvious things like curing diseases,

but I don't know the world's best

entertainment ideas we just can't even

dream of sitting here now. So I want to

put that in everyone's hands which gets

to

one of the things that we stand against.

Concentration of power with AI is a

terrifying thing. I think a lot of the

talk about safety concerns is wellounded

and then a lot of it is about people

that just really even if it's slightly

subconscious want to concentrate power.

I am terrified of a world where the very

real fears of AI are used as a way to

say only this small group of people can

have it because it's too dangerous and

only they understand it. But don't

worry, like they're going to make the

right decisions for all of us. I don't

believe in that. I don't think anyone

should want to live in a world of, you

know, AI overlords or a company that is

the rough equivalent of that where

someone is making decisions for all of

the future and in exchange for a cure

for cancer, which obviously is a

wonderful thing. We we kind of

collectively seed all agency. So, I

think it's very important that we not

fall into this trap of in the

well-meaning or not spirit of AI safety

and fears, understandable fears around

that. Um, we get away from a world where

we all get to use this technology. I was

like a child of the internet.

>> There were no rules. I mean, it was

amazing. I think it was a huge factor in

making me who I am and probably you and

an entire generation. I think it's

critical we preserve that spirit with AI

and that we all collectively have

the ability to self-determine our

future.

>> I I have so many questions, but I'll

start with this genie concept. You said

we're about to have a genie, implying we

don't yet have a genie. What's between

now and then?

>> You know, even some of the real skeptics

have said to me in recent days or recent

weeks, I guess. I think GPT 5.6 has been

out for maybe two weeks, something like

that. They're like, "Okay, this is like

very AGI like." It's like very hard for

me to say um what I want from this model

that it can't do. But there are clearly

some things, you know, you can't yet go

say like cure cancer and get cancer

cured. You can't yet say go do this

complicated physical thing in the robot.

The model also, although brilliant, is

still not learning continuously as it

goes. And that feels to me like maybe

not a hard requirement for AGI, but

certainly um something that I'd like.

Now, to argue against myself there, you

can make a case that AGI is not actually

about any single model. It's the model.

It's the machinery that makes the

models. And from model to model, we

actually are learning new things. We're

figuring out new science. That stuff is

working amazingly well. So, I have a lot

of sympathy to people who say like,

we're there. We have the genie. It can

do these amazing things. It can do

superhuman things for the thing that to

me feels like, you know,

real AGI. I think very close, like not

that much longer. I am so obsessed and

fascinated with the economic story of

the returns to being on the frontier,

which you are. And I'm so curious like

if you had shown 5.6 to yourself and

your team in 2019, if that team probably

would have said like, "Oh yeah, it's

definitely AGI."

>> I think they would have

>> like like this goalpost moving thing is

is a real thing.

>> But it does seem that I'm curious if you

agree that effectively all the returns

have been at the frontier

>> and so everything is about staying at

the frontier. And I'm curious like what

the hardest scarcest part of that is. If

I think about compute, research, talent,

data,

>> it's moved around a lot. Like there have

been times where it was I mean there was

a time not that long ago where all the

computing the world wouldn't have helped

you because we were like missing

>> the research idea. Now part of why this

is hard is that you do better research

with more compute. You can try more

things. An amazing statistic I heard

recently is our biggest d-risks now for

upcoming runs are as big as like the

entire compute run from 18 months ago or

something. So compute and research ideas

are not as separate as they sound. But

there was clearly a time

seven years ago, 8 years ago, whatever

where we were way more way way more

blocked on research ideas than on

comput. Then there was a time when we

knew what to do. We just had to scale

up. We were only bottlenecked on

compute. Then we ran out of data. We

were bottlenecked on on data and we had

to figure out what to do there. Now

again I would say

we are still bottlenecked on compute but

the last 6 months or whatever have been

a real triumph of a time for research

ideas again. So, you know, there's like

always a bottleneck, but the bottleneck

moves around.

>> And and why do you think that is? The re

research idea thing is especially

interesting to me because of this

automated research thing that seems to

be looming, RSI, whatever you want to

call it, where I talked to an incredible

kernel engineer recently, which everyone

also seems blocked on. And he himself

said there's like two years left of

kernels engineer,

>> maybe one.

>> Yeah. Like it's it's not going to be a

thing. And so you simultaneously have

this weird thing whether it's colonels

or overall research where the

researchers are like the most important

they got us here. They're like the most

important people in the world and those

same people are themselves worried that

they won't be relevant like very soon. I

suspect it's not actually going to go

that way in practice.

>> I suspect that uh

>> like a year ago people said software

engineers are cooked. It's done. It's

over. That didn't happen. What did

happen though is that the the nature of

a software engineer, the expectations of

a software engineer, how much they would

do changed quite a lot and you don't

really write code in the traditional

sense, but you do something that is very

recognizably software engineering. Now,

people will argue about whether this is

the same thing or a different thing than

when we stopped like punching holes in

cards. I actually don't know how that

worked, but somehow the holes got in the

cards. We're just again operating at a

higher level or or this is like a a

phase shift.

>> I don't know. But the idea of getting a

computer to do what you want like that

is still an important job. And for

researchers, I suspect that although the

current workflow of a researcher is

going to very much be automated, there

will be new things in the spirit of

research in the same way that there's

new things in the spirit of software

engineering, even though we don't write

code that will still matter. It seems

like you've shifted your opinion on AI's

impact on jobs in general and I'm sure

in specific categories like that.

Describe that change and your current

view. You mentioned if we could go back

to 2019. If we could go back to 2019 and

show people our latest model.

Not only would they say

that it's AGI, they would say that

economy would have had it completely

upended. Yeah.

>> Completely. Yes.

>> And that has not happened. And I think

just from a kind of like intellectual

humility point, anytime you're that

wrong and that confident, which I think

we were as a field, you have to update.

And there's a bunch of takeaways. One,

like a boring one is that AI is just

very jagged. It's like superhuman genius

in some ways, like dumb toddler in

others. And people have so far extremely

complimentary skills to AI. And so

another is that people

have a great degree of trust and

enjoyment in working with other people

and you can go hire an AI consultant

right now or talk to an AI sales rep

right now or hire an AI engineer or

whatever. Somehow most people seem to

still really prefer interacting with a

human. And I definitely would like much

rather engage with a person than engage

with an AI for almost everything. I also

think that

human values have

value because they're human. And as

society evolves and as the potential

space in front of us becomes so

enormous, we are we are deeply hardwired

to care about people, we're going to

care about what people care about.

There's like versions of this you can

see today where AI can make incredible

images and people only want ones that

are created by a human or at least

chosen by a human. There's the joke

about at this point you can like you

know the signature on a piece of art is

most of the value but the truth of it is

like you want to know about the person

behind it. You read a novel you want to

know about the person behind it. And

then then in terms of business like I

think for my job for example uh I think

the world wants to know about like the

person that's going to be responsible

for the decisions of a company and who

they're going to hold accountable if

they make bad ones and they don't really

want an AICU. If you think back on like

the portfolio of like risks that you've

taken in business or whatever, is it is

it the case that most of the ones that

really worked well were at the start not

popular?

>> Yes, that's for sure. This was the thing

I really learned from Peter Teal and

Paul Graham both in two different ways,

which is that the

the very best companies, the very best

investment opportunities are almost

never the ones that look really popular.

You can do okay just following the trend

of being a little early. But to do

spectacularly well, you kind of almost

always have to do things that are not

what everybody else is doing. You cannot

be you cannot be sort of like following

the new wave. If you think about the

model cycle that you've been in, which

has been accelerating and this weird

fact that like the next 6 months or I

don't know what the number is is going

to be more progress than the last x

years. Can you bring us into what it's

like to live in that model cycle?

>> One of the most interesting, important,

whatever things that I've learned last

decade is people in general can get used

to almost anything.

>> The world can go from dismissing a

pandemic as a joke to completely lock

down to this is how it's been and it's

fine and we've mostly adjusted in a

shockingly short amount of time. And you

know, now there's either AGI or close to

it and everyone's like, okay, there's

AGI. There's all kinds of examples in

one's personal life where you know you

something incredible happens like you

have a kid or something terrible happens

like you lose a parent or break up or

whatever and

you think you can't ever adapt to what a

change it is and then you know you can

adapt to great things and keep being

great. You can adapt to bad things and

figure out how to go on with your life.

But this is a this is like a remarkable

thing that people can do. And so living

through this feels like another version

of that, which is, you know, I thought

it was going to be weirder to live

through the singularity than it turns

out to be. It It's not any less exciting

to watch the models keep getting better

and I, you know, the first thing I do

every morning is like look at the model

training progress and it happens faster

and I have higher expectations, but it

still feels really cool.

>> When you get a new one, what do you do?

How do you celebrate? What's the morning

look like? Like it's happening faster

and faster. What's your ritual? Many

teams now work on different parts of it

and different teams have like some

different rituals. There's some teams

that always make a sweatshirt with some

funny meme on it. There's some teams

that like always go out to the same bar.

The sense of being in the room for the

first time that the frontier of

knowledge is pushed back

>> and getting to see what that's like. Uh

there's really nothing that most people

would rather do to celebrate than like

get to use the new model first.

>> Do you think we have the right

measurements of how good these things

are? Like

>> definitely not. In some sense the eval

that matters is like is this being

useful to people.

>> You can approximate it by revenue or by

amount of usage or like rate of

discovery of new knowledge. But uh we

have some teams working on like how what

is the real world eval look like for

these models as they get to superhuman

scale.

>> What is the frontier of your own usage

of AI?

I have started just recently to

experiment with what it means to like

let an AI uh kind of look at everything

I'm looking at on my computer. I don't

have this built yet. Um, and I'm still

trying to feel out like where the limits

of my comfort and trust should be. This

is definitely the frontier is figuring

out how I how I get value out of that,

how I get comfortable with that, what

that's going to look like. One takeaway

is that my memory is terrible relative

to the memory of an AI. and the ability

to keep in mind what email I read six

weeks ago or what happened exactly in a

meeting seven and a half weeks ago and

have that like brought up right at the

exact moment and to feed into a decision

that feels pretty magical.

>> Pretty cool. This kind of sounds like

personal agent-ish. What are the

barriers to everyone having that I want

that

>> compute? Man, let's imagine that we

could build this product. This product

that could just do exactly what I said

for all your stuff.

>> Always on.

>> Always on. looking at everything you

look at your computer, listening to

every meeting that you're in, um reading

every document you read, and then not

only that, not only can it do all that,

which takes a lot of tokens, you can

just drag a slider about like while I'm

asleep, you can spend this many tokens

thinking like come up with useful new

ideas for me. Do whatever work you can

and then just like keep thinking about

what I should do next. You know, what an

interesting thing is like just spend

more compute making your output better

for me the next morning. I would drag

that slider quite far. I'd be willing to

spend a lot for that.

>> Um, but the amount of compute that that

would require if everybody in the world

wants to drag that slider pretty far,

it's like a lot.

>> I'd love to hear you talk about how you

think of the nature of this new

intelligence. Uh, someone told me

recently, you know, planes don't fly

like a bird. And this intelligence is

>> it's a very alien kind of intelligence.

>> Yeah, it's a very alien kind of

intelligence. And everyone's talking

about how if you can verify something,

it's sort of it's just going to win,

right? Like it's with enough compute and

enough IQ, like it it'll just brute

force its way to a solution. And then in

other domains where humans and the data

and evals that they've done have been a

huge part of it, it's surprising to me

like how much money it's cost to get

good at I don't know law reasoning

tracing law or something. I'm just

curious like I'm not sure how beautiful

your kid is. You have a boy or girl

>> when they're seven or age of reason or

whatever and they can you can describe

to them like what is the nature of this

intelligence like how would you describe

it?

>> It's a beautiful question. I I I don't

think I've been asked this before or

even any version of it. The thing that's

coming to mind right now is I would just

say it's like a computer. And it's like

a computer in the way that it can

do a lot of things that people just

can't do like multiply two gigantic

numbers very quickly and give you the

answer

>> and then it cannot do some things that

you would

very easily do. The number of things

that it can't do, I expect to keep

receding. But in an evolving world, I

think human judgment and taste

will continue to be hard for AIs to

model like where that's going to go. I

don't have the right word for this. It's

not quite taste. The world may need like

a a new kind of word for the kind of

judgment that people are very good at

that AI seem to really deeply struggle

with. What's it been like becoming a dad

and having growing kids in this era? I'm

thinking back to your optimistic early

internet days. They're going to grow up

in cheap abundant intelligence age.

>> Having kids is by far the best thing uh

I have ever done. Uh and everybody says

that. Everybody says you can't really

understand it. And so I kind of knew

that I believed enough people that said

it that I believed it to be true. But

the degree to which it has been true for

me has been surprising. like the the I

think I have the best most interesting

job in the world and it is still a very

distant second to having kids.

>> So it's been awesome. Uh and it is a

real moment for optimism. My kids will

never grow up in a world where they were

smarter than computers. If you were born

at the time of GPT3, you had a time

where you had better reasoning than the

models, even though you didn't when you

were born. Yeah, you caught them

briefly. That will never seem strange to

him.

>> That will never bother him. I don't

think he'll care. I think he will he

would be like shocked to imagine in the

dark ages when we had to like deal with

products and services that weren't

incredibly smart. He will be able to do

things

that you and I never were able to do and

he'll have expectations in life that you

and I never had and you know I'll have

like a much bigger canvas.

>> Do you run the business or teams or lead

people in any way that is notably

different because of the experience of

having them?

>> The answer must be yes.

I feel very different having them. I

think there's like a bunch of small

things that are are really different.

And then, you know, again, this is like

not a novel insight in any way. I think

most people have had kids say, you know,

as soon as you have a kid, you like

realize that you care much more about

them and the experience you're going to

have, you do about yourself and the

world that you are going to leave them.

And I think I have a sort of like

unusual vantage point for that. And and

like people ask me sometimes like, "Oh,

you know, now that you have kids, do you

care? Are you worried about AI safety

and, you know, not destroying the

world?" And the answer was like, "I

didn't need kids for I really didn't

want to destroy the world before." But

do I think more about the role of like

human agency and what it means to have a

fulfilling life? Definitely much more

for what we're building. And also like

the people I work with, I want them to

have it too. You obviously have

extraordinary empathy for your kids, but

the degree to which that kind of extends

to all kids and then maybe to all

parents and to maybe then to everybody

like that's been a surprise to me too.

>> In in one of the posts, I think it was

the one that's things you wish you knew

earlier or something um is about

incentives. Set them very very

carefully.

>> Yeah.

>> It's always been one of the most

puzzling and interesting things about

you that you don't have equity exposure

to this company. How should the world

think about your incentives? I don't

know what I can say beyond like

I have a front row seat to the most

exciting moment of human history and

like that is worth more to me than any

amount of money. I get to have an

extremely interesting life and work with

extraordinary people on something that I

deeply care about. But somehow that

doesn't count like that doesn't

>> do it for people or something.

>> It's not.

>> I'm curious how you think about

robotics. Like you mentioned earlier, at

some point if we had automated labor in

the same way we're going to have

automated intelligence, things might get

even crazier. The labor market is much

bigger in the white collar market.

>> If we don't have it, then things get

really crazy. If the role for people in

the world is to be like the actuators of

AI in the cloud,

>> bad,

>> very bad. Very bad. So I think it's like

much crazier if we don't get it than we

do.

>> It's an imperative. help me understand

your sense of progress in that because

unlike in AI where everyone is now kind

of on the same page of like it's going

fast

>> I you can find extremely smart people

that say it's like end of this year and

you can find extremely smart people that

say it's 20 years from now or something.

>> It's not 20 years. I would say we get

the Chad GBT moment for robotics in the

next like two or three years.

>> What would that be like? Do you know

what that is?

>> Something where most people have like a

real wow. Not not like I saw this video

of a robot dog doing something crazy,

but I was somehow able to convince

myself that a a really important thing

happened. One of the things about the

chatbt moment was that you could just go

use it.

>> Yeah.

>> Like I didn't have to like believe

someone who said AI is coming soon. You

could just go try it.

>> Yeah.

>> And if you can go like, you know, type

in a command and a robot can do

something crazy and you can like watch

it even if it's you're not physically

there. I think that would have the same

kind of like whoa, it just did this

thing. Wasn't chatbt like not this

monolithic goal but sort of like a side

experiment that you decided to release.

Can you tell that that that story may be

instructive for something similar

happening in robotics? Everyone seems to

want to fold laundry but maybe it's

something very different.

>> When we launched GBD3 um we're trying to

make money trying to get people to use

this API.

>> Yeah.

>> And the only commercial use case that

was really working the model was just so

dumb. Like if you went back and used it

you'd be astonished. The only commercial

use case that was working was

copyrightiting

>> you know. So you pay like some marketing

firm 20 bucks and they paid us 20 cents

for the AI to like write you a landing

page or whatever. But in addition to

that one commercial use case, developers

were using this thing we called the

playground which was like a testing

interface to chat with the model. And it

was really hard to do because we had not

tuned the model to be good to chat with.

So you had to like give it a few

examples of what it means to chat and

then do it. But people really liked it.

And I had learned this great lesson from

YC is if you notice your users doing

something like

go down that yeah go down that path. And

so we decided that we would build a good

chatbot since that's what people were

doing. Um we started working on that and

we finished GBT4 and we started using

that internally. like this is a big deal

and we kind of thought that all right

this is going to be a real update to the

world about AI and there's a bunch of

hard questions here about you know is

this going to create a bunch of fake

news is going to say really offensive

things we're going to get in trouble so

we decided we would start with a weaker

version um the chat interface and GPT4

at the same time seemed like a lot so we

would roll out the chat interface and

GPT3.5

>> in fact it was originally going to be

called chat with GPT3.5

and Uh

we didn't plan to be product. Didn't

think it'd be a huge hit, but did did

think it would get people

the world to like catch up with this and

realize something was going on. And uh

we mercifully renamed it ChachiBT a few

hours before launch and put it out as

like a research preview.

>> And the thought was we'd put it out as a

research preview and then a few months

later we would launch a product with

GPT4. And for whatever reason, that

model was over the threshold where even

though we had gotten used to it

internally, people said, "Okay, this is

awesome." There maybe wasn't that much

utility yet, but it was an incredible

moment for people to feel AI progress

and use something that they enjoyed

using. And then by the time we put GPT4,

uh, something they really got benefit

out of using too.

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Are you surprised that that remains kind

of the intuitive interface between us

and this alien intelligence, even

including coding? Like mostly that's me

talking to the computer telling it what

to build. No, because I'm like a massive

texter.

>> Yeah,

>> I've been a massive texter my whole

life. I think part of my own insight of

why that was a good interface is I'm

like,

>> I know how to do this. I know how to do

this. I know what it's like to just like

start chatting in a text box. Any other

thoughts on this notion of diffusion and

how to make it faster? Like if the

mission is get intelligence into the

hands and more useful for everyone, a

key part of that is like I don't know a

marketing campaign or something like how

how do you get this to diffuse faster

than it seems to be doing naturally to

me? I think the key thing is uh just

make it better. Like I I kind of believe

that a truly great product markets

itself. There was no ChadBT marketing

campaign

>> at the beginning.

>> Um and I think as we get to this next

stage of models and we figure out

how to make products that are as great

as the models themselves, there will be

such incredible utility that people will

spread it very quickly. uh we should

definitely do more marketing like the AI

is not too popular

>> for as much as people use it or they're

kind of they have very understandable

anxiety about where it can go and so

that kind of stuff I think some great

marketing would be helpful for but in

terms of value people are getting out of

their products and getting their

products to grow faster models more

compute better products that will do it

there was this period where the

recruiting of researchers the retention

of them the incentivizing of them was

like the defining story in the

competitive landscape or whatever I

think there's lots of stories about you

successfully recruiting great

researchers and there's been many that

have come through OpenAI and had huge

impacts. Some of which are known, some

of which are lesserk known names. I'm

just curious about this whole genre of

like what you learned about how to

recruit this class of person. What

matters to them and and how you did it.

I've never heard you talk about like the

actual tactical like moves you pulled to

recruit somebody

>> in the early days. I think it was quite

simple which was that we believed that

AGI was possible

>> and it was worth going after and we're

willing to say that and that was like an

insane heretical belief. When we first

announced OpenAI all of these like you

know giants of the field these experts

were saying this is like insane it's

hypy. It's irresponsible. Really

respected people like Yan Lun or

whatever telling journalists like oh

these guys aren't very good and it's not

going to work. But the fact that we were

able to say we're gonna go for this,

it really appealed to a certain kind of

researcher that also wanted to like go

on this crazy adventure with low

probability of success. And an ambitious

kind of audacious vision is a very

powerful recruiting tool.

>> Yeah. You think you've written that it's

actually easier sometimes to build

things that are harder because of this

reason.

>> I super believe in this. It's one of my

most frequent pieces of advice to YC

founders and I tried to really live it

at OpenAI. Just do something harder.

>> Do something that matters like do

something that is important and if you

don't do it, if your company doesn't

succeed, might not happen.

>> You were an investor and our investor uh

you've done a lot of it and at one point

that's what you did. What have you

learned about investors being on the

other side? The number of investors that

actually show up and try to help you

is unbelievably small. Josh Kushner,

absolute MVP investor, unbelievable, has

like worked around the clock for what

feels like years to help us. He is the

only investor that I could point to that

is

proactively incredibly helpful all the

time. There are more people that could

do that. Uh, and there are many other

investors that have also been helpful

and that have great strategic advice and

that do things when, you know, we ask

them to do it. But the like constant

just relentless all-in support is

surprisingly rare from investors. Maybe

I'm biased cuz I like always liked it

when people said that about me, but I

think founders really love that and it

actually like moves the needle and as an

investor, it's the most fun way to do Me

and my friend play this game where we

text each other all the time and the

prompt of the text is something I don't

want you to know about me.

What is What does that bring to mind?

I'm tired. I don't think I'm supposed

I've been doing this a long time. It's

tiring.

>> How do you get through that?

>> Just keep going.

>> It begs the question like is there

amount of being tired that would make

you stop doing this?

>> No, no, no. I I mean I I this is the

coolest job in the world. I plan to do

this for the rest of my career, but it's

like much harder than I have a way to

explain to people. I I feel very

grateful to get to do this. This is not

me complaining.

>> What's coming next? Like we talked about

automated AI researchers that next year,

the year after like how do you think

about what is happening in the next 6 to

36 months? Maybe that's too far out to

forecast in this crazy exponential.

Maybe a different version of the

question is like let's say in you know

month 23 from now we have something that

everybody agrees is super intelligence.

What happens in month 24?

>> And my answer would be uh not very much.

The the kind of like cult worship of the

machine god

states those people believe that like

more is going to happen quickly than is

going to happen. Eventually a lot will

happen but eventually a lot was going to

happen anyway. like the rate of human

progress and you know how different each

decade is going to be and how much each

decade is more different than the decade

from before that's been happening for a

long time obviously ups and downs but

directionally and I think the right way

to think about this everybody wants to

be the hero of the story everybody wants

to feel like they were there for the

moment of the machine god and they

played some crazy role but you know this

is another step and it was hard to

imagine 50 years ago and the step 50

years from now is hard to imagine today

and

and I think the right mental framework

is just the zoom way out and it's a

pretty smooth exponential.

>> Tell me a little bit about the

experience of watching codeex take off

and how much that is tied to what I

would describe as like a competitive

advantage of distribution that you built

through chat and this is a gateway into

a question about like Moes in general in

AI like what you think will drive real

competitive advantage in the business

over time. I think Codex mostly is

winning because it's the best product

and the best model. We do get some

advantage from Chachib bundling but very

very tiny. That is mostly not what it's

been about. It has made me reflect a lot

on this question of competitive

advantage um because you know like

brilliant intelligence can migrate from

any product to any other product

>> and network effects still have a

competitive advantage. economic scale

and the ability to like make the

cheapest comput fleets whatever still

have a competitive advantage but the

product advantage like if we could get

people to move over to Codex and someone

builds something better they can get

people to move from codeex

>> so it has made me reflect on that a lot

>> there's a really interesting question

about whether this is going in the

direction of a commodity like is

intelligence going to be a a pure

funible commodity like rated oil or

something

>> intelligence itself I would say yes

>> so what is not going to be

>> comput fleet you know like the scale of

the comput fleet the ability to make

more compute. I think that's like a very

durable advantage even if the product

itself is not because you know codecs

can write any piece of software you

want. The workflows, the integrations,

the sort of like complex processes, the

ability for teams to collaborate

together, that stuff is all pretty

powerful. Even like brand preference and

familiarity is pretty powerful.

>> How excited are you about new obviously

you've done interesting stuff in

hardware that I'm sure you'll announce

later this year. How how does that

experiment feel and align with this sort

of consumer distribution that you have?

>> One of the reasons I'm interested in new

hardware is we were talking earlier

about how a very powerful thing with AI

is that it can be always on and

proactive and just understand all your

context. But current hardware is not is

not good for that.

>> Like we are working inside of a hardware

paradigm that is 50 years old something

like that. Um and computers are amazing.

keyboard and mice monitor. It's an

amazing thing, but like we have to shape

AI into that. And I would I'm excited to

think about I would love AI to be able

to reference this conversation, but not

so much that I'm willing to like crack

my laptop open, put it here, and have it

like looking at you and listening to us

while it's going, but I would like a

piece of hardware that socially was

acceptable to do that and also felt like

it was designed for that kind of a

thing. As you think about the open

questions, what debates in your own head

with your friends, with people that your

colleagues here, what are the most

interesting open debates or open

questions that you you you don't feel

certain about but feel important?

>> One that I don't think gets much

attention is how how are we going to

avoid cognitive atrophy? How are we

going to use these tools and make sure

that we are like stretching our brains

more and more and continuing to

understand the stuff that that really

matters? Um,

there's lots of versions of this that

don't like I I remember when I was in

school, I had this professor tell me

like you got to understand compilers. If

you don't, you will never be able to be

a good programmer. Somehow that wasn't

quite right. But understanding at a

reasonable level like how the major

components of a computer system work has

been important to me.

>> Forced to imagine a scenario where we

are somehow over supplied in compute in

2 years time. What would be that story?

It does feel possible if the models get

so smart and so efficient that they can

kind of do everything we need and you

know build every piece of software we

want and if the bounds of our attention

are such that like they just cannot

absorb more than what it turns out a

fairly limited amount of compute can do

then we can get into over supply. Also

if we don't drive the cost curve down

because we hit some sort of scaling wall

we could also get into over supply. like

the the observation about uncapp demand

implies a certain price.

>> Can you give your point of view on

scaling laws today?

>> Looking great.

>> Just looking good.

>> In some sense, scaling laws are like the

most hated prediction of all time.

Everybody always wants to say a runa

can't be like this and and yet it keeps

going.

>> Who are your favorite unsung heroes in

this company's story?

>> First person that came to mind is Alec

Radford. Alec Radford is probably the

most important

not very well-known researcher in the

whole history of the field and also just

a wonderful like top top tier human

being. Um he did the work that really

became the GPT series uh among many

other important things. Um, but he also

is someone who inspired, guided,

nudged people in many other directions

that turned out to be super important.

And the thing I think is cool about him

is if you talk to people that worked

with him, they will they will of course

say, you know, generational genius,

brilliant, innovative thinker, just so

deep in his understanding and his and

his work. But everybody everybody will

tell you before they finish their

statement that just like one of the

nicest, most positive, best people

they've ever interacted with.

>> I love formative moments. And so as we

wind up here, I'm curious to ask what

one of each. If you think about the

whole OpenAI experience, what moment or

chapter or whatever are you most proud

of? start with the other one which is

what was like the most instructive thing

that maybe you got wrong or did wrong or

what have you and and what was what was

it like to learn from it?

>> I mean a lot of things have gone wrong.

A formative one that went wrong which I

haven't talked about much is we made a

mistake to try to innovate in our

structure in the beginning. We had a

very good reason for it which is we

didn't know how we were ever going to

make money and we really at the time

weren't sure at all what we're going to

look like when we grew up. And of course

we care about our mission and we wanted

to like be structured in a way where

even if the technology went on a very

fast takeoff our mission was protected

and so we had this like you know

nonprofit structure but

I definitely learned something about

why people don't do that much. We would

have saved ourselves a great deal of

pain in many ways if we had not tried to

innovate on our structure and found some

other way to preserve the central

importance of the mission. Maybe there

was no other way. Maybe there was for

what we were doing and kind of the

importance of it. There was nothing

other than an exotic structure we could

have come up with. I really learned over

the last decade a big lesson about why

people don't usually do that.

>> Is there anything for else formative of

your life that like makes you you that

we didn't talk about? I'm this is like

the the question that's always like the

most interesting to me. becoming

relatively immune to people having

strong opinions about me that I think I

developed later in life as as like

realizing that man just if you're going

to be at the center of like this crazy

revolution everybody's going to project

a lot of stuff onto you and you got to

just quickly learn to make peace about

that. I think there were also things I

learned later in life about like how to

be very calm and not anxious really

about stuff. In terms of what drives me

and what I care about and kind of like

how I want to live my life

on the whole I felt like, you know, for

whatever reason, the like 10-year-old

version of me was pretty like fully

formed. I think I just like kind of came

out this way.

>> How about the thing you're proud of

looking back on? I'm most proud of how

many times we were right when the rest

of the world was wrong in an important

way that put the world on a trajectory

now that I'm very proud to have played a

role in. That feels awesome. And then

also like for all the crap that's

happened like the spiritual growth or

whatever you want to call it that I've

gotten to have of like learning

just incredible resilience and what that

does for like making me happy in the

rest of my life. Yeah, very grateful for

that.

>> When I do these, I ask everyone the same

traditional closing question. What is

the kindest thing that anyone's ever

done for you?

>> I feel incredibly lucky about how many

people have gone way out of their way to

be very kind to me throughout my entire

life. As I'm thinking of this, there's

just this like montage of moments from

life where people have been unbelievably

nice to me. Yesterday, my kid shared his

blueberries with me for the first time.

That was very sweet. [music] Good

moment. Thanks, man. Thank you.

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