Chat GPT originally was entirely free

and the reason for that was that it was

intended to be a demo and we were going

to wind it down after a month.

>> We then realized that the demo went

viral and people loved the demo and it

was actually a product and but we

realized to be a product you can't take

the product down every time you're at

capacity. So we you ship subscriptions

simply because it could shape the

demand. It was a way of gracefully

turning users away when we had to turn

away someone. You know, you guys are at

900 million VT active users now, and

that growth has been incredible. The

next billion users, where they where are

they going to come from?

>> We've got about 10% of the world coming

to us now. 90% left to go, right?

There's so much more opportunity.

>> Well, Nick, so excited to have you here.

>> Thank you for having me, Peru. You've

had uh quite the journey from Germany to

the US for Brown.

>> That's true.

>> Most recently at Instacart delivering uh

groceries in 30 minutes to now

delivering AGI to billions. I'm sure

that was the plan all along.

>> Yeah, clearly total master plan.

>> Well, well, tell us about your journey.

What how did you get to OpenAI? I know

it's a fun fun story. And your your

three and a half years or so at OpenI,

how have they gone? The only through

light in how any sort of employment

decision has been entirely people based.

So I don't claim any credit for for uh

joining OpenAI um or predicting chat GPT

or anything like it. But um I uh um hit

up someone who I admire a lot who I got

got to know at Dropbox. um Joanne who uh

worked worked here at the time and she

uh I asked her to get off the Dolly 2

wait list and she told me I had an

interview if I wanted to get off the

wait list. So um I took the bait and got

totally nerd sniped in the process and

and here I am.

>> There you go. The Dolly 2 weight list

will get you.

>> It's a great recruiting tool.

>> Nice, nice, nice.

>> We should do more weight lists probably.

>> Yeah. Yeah. Yeah. Well, you know, the

big uh the big

super cycle we're in is is is Chad GPT.

Now, I assume over a billion users on

the monthly side, 900 million weekly

active users that recently reported up

from zero three and a half years ago. Uh

you could have, if I imagine what the

dashboard of Nick Turley looks like, it

could have users, it could have paying

subscribers, it could have daily active

users, it could have retention,

engagement. I mean, there's like 15

things, maybe all of them. What what is

your northstar? How do you how do you op

what are you optimizing for? Uh what is

Nick looking at in his daily dashboard?

>> It's it's funny, right? Because it's

such a young product. It's been to your

point three and a half years. And um

this kind of question, it kind of

changes as as as you evolve and you and

you grow up and you ask yourself, you

know, what are we really building here?

And uh to this day, right, then want to

build a super assistant that can

actually help people achieve their

goals.

>> And ultimately the thing we care about

is like is our product doing that? Is it

actually helping you do the thing that

you're, you know, um coming to the

product to do? And it's so different for

different people, right? Some people um

are are trying to get healthy, other

people are trying to start a company,

>> um um learn a new topic, um

>> do their taxes. There's all these

different things that you might be

doing. Um, and the true measure of

success is whether or not that we're

helping you do that. And um, obviously

we look at WAU in particular because,

you know, we want to know if you're

coming back to the product. We look at

retention. Um, but we, you know, we we

look at, um, all kinds of stuff in

aggregate because really there isn't

like this one single thing that you can

optimize for.

>> If you were to allocate a 100 units of

points

>> to these metrics, which metric can you

like distribute the 100 units across

these metrics in order of importance for

you right this second? It's a good

question. I I care a lot about long-term

retention and I would put all my points

there. Um because um I'm really proud of

the retention stats we have.

>> Huge,

>> but ultimately the sign of durable

values, whether or not people are coming

back in three months because that means

you're really solving their problems.

And

>> yeah,

>> I think things like revenue, they follow

from that. Um

>> yeah.

>> Um versus like, you know, trying to go

on those things directly. And we've

we've had a lot of success making very

principled decisions u on this stuff.

Like one good example is you GPD4 used

to be behind a pay pay wall because we

couldn't serve it to everyone and then

we had GPD4 which was a total

breakthrough

>> in um in in our ability to inference it

and um

>> so we just gave it away for free and

that ended up being totally revenue

positive and retention positive because

it just provided access to the tech and

I think when you make your decisions

that way and you focus on the customer

>> you end up with a great product and

revenue obviously follows too.

>> Phenomenal.

>> Yeah. Well, it uh it shows up in the

numbers. You know, I I posted this chart

uh yesterday on the data that we have,

you know, from a third party. The

retention curves for chat GPD are

smiling. Look at that. Just like that.

And that is a rare that is a very rare

occurrence, you know, as as we know. And

um why why do you think like if you were

to give us a narrative on that smile

curve? What is the why do these smile

curves exist? What are you seeing in

Chad GPD that has people who have who

have maybe turned off for a couple of

weeks or months coming back and why are

they coming back?

>> Look, there isn't one single thing. You

know, the way you build a retentive

product is lots and lots of little

things and really trying to make it

better systematically. I will say that

you know with AI and in particular chat

GBT I found that it takes people some

time to really understand all the parts

of their life they can delegate right

and I think many users for that it's a

multi-month process for them to

understand how can this thing help me

and what are all the different ways

>> that um I could plug chatbt into my life

>> um and um but you know when I think

about some of the breakthroughs and

levers we've had um things like search

and personalization they they have

helped solve those user problems because

um you know search provides way more

daily value to you.

>> Um it used to be that chat was a pretty

worky product. You know we'd see usage

go down on the weekend. We usage you

know go down during the summer months

when a lot of people were off from work

>> and um

>> today you know we're we're mobile first.

The vast majority is mobile and we see

all these personal use cases and I think

search was a big investment that got us

there

>> and personalization makes chat so much

more relevant for you right because it

gets to know you over time. you get to

know it. Um and um those are two things

that have materially moved um the way

that you know people come back to the

product. But um there's lots more to do.

>> Yeah.

>> Um and you know, as mentioned, I don't

I'm not resting on our um retention

stats even though, um we're obviously

very proud.

>> Nice, nice, nice. Um and you know, the

other thing that I I got wrong about

Chad GPT was this is two two and a half

years ago. I was like, well, you know,

let's look at who's going to win this

consumer AI race. You typically these

consumer markets are winner or take most

winner or take all. Look at search.

Google has near 90% plus market share.

Three and a half, four trillion market

cap.

>> Mobile, same thing with Apple. Social,

same thing with Meta. I was like, well,

AI, Meta has all the distribution.

Google's got all the distribution.

They've got 34 billion users. Well, it

would be a flick of a switch for them to

roll out their AI. But I was wrong.

That's not what happened. Chad GPT turns

out uh you know you guys are at 900

million VT active users now and um that

growth has been incredible clearly

distribution was not enough right

>> so the same question for distribution

what are the levers for us that have

gotten us to the scale is it model

quality is it you know product quality

is it you know features is it the

experience or product improvements like

memory and personalization or search

like

>> same question like what would you say

drove historical

>> growth and and success

We've got about 10% of the world coming

to us now. 90% left to go, right? So

there's so much more opportunity to to

to re reach more people and introduce

them to the way that AI can can can

>> benefit them, right? But when I when I

look backwards and I only say that

because like the next billion users

might be very different in terms of like

how you um engage and reach and provide

value,

>> but when I look backward, it's been

roughly sort of

>> oneird one third one third between

sort of classic friction removal type of

work. Like one of our biggest um

um moments when you look at pure impact

um was you know removing the

authentication wall

>> and Sam will say I told you so because I

think that was his feedback from like

day one

>> u like can't you shouldn't have to log

into the chatbt but it's like stuff like

that that you do for any product and it

does matter some things never change

right

>> um and but you know and then you know

another third or so isort

>> you know are what I would sort of sort

core product investments and they're

really typically things that we've done

together between research and product.

So

>> search and personalization are really

good examples of that where we came

together and we figured out not just

UIUX evolution but also how to postrain

um these changes into the model and it

was really the moments when we came

together. Um um another recent example

is like we have these writing blocks

that render when you you know ask about

queries um where you're um you know

trying to write with the model

>> and like putting really good craft into

those experiences really matters and um

um you our users love it and then

>> another third of of the growth has been

just model improvements like step both

step changes like going from um GBD 3.5

back then to GPD4 then going from GPD4

behind a payw wall to O suddenly

available to everyone, right?

>> But a lot of it is also the iteration

that isn't splashy, that doesn't warrant

like, you know, a named release. You we

um I'm really excited about the updates

we just made um with 5.3, 5.4, etc.

>> Because um that is when we like take a

lot of user feedback and we you know,

methodically address it and obviously

that shows up um

>> in our retention as well. So sort of one

third one third one third between

classic um friction removal um and

access

>> um core product investments and then

pure model improvements.

>> And so the question that I've really

been waiting to ask you is how do we get

the next billion

>> and and you know talk about that a

little bit. There's a lot of it it seems

like at least from the outside fog of

war. If I was a consumer today in the

market to pick my super assistant, um

you would have a couple of great

options, you know, uh plot out there,

they're having some great traction last

couple of weeks.

>> Um Gemini mega distribution, Uber

distribution, um and us by the by the by

the leading product today, at least in

user numbers, the next billion users,

where they where they going to come

from? First of all, just to

contextualize that goal. You we care

about

um two things at the end of the day.

Obviously, reaching more people is

really important. It's the direct

manifestation of our mission um to the

world where you the more people we can

introduce the benefits of AI uh the

better that is. Um but we're also really

excited to go deeper. Um and that means

taking the same billion users that find

value in chat GPD today. Um and actually

providing more meaningful value in their

world like actually helping them achieve

their goals. not just answering

questions, right?

>> Um, so I'll talk about how we get to

more scale, but I think it's important

to remember that, you know, the way this

technology is evolving is, you know,

we're going to go beyond pure chat bots

um pretty fast.

>> Exciting. Um I think on on scale you

know it's shocked me how many people

have found value in chatbt as it works

today because I don't think delegation

is a natural skill for most

>> and chatbt is a pretty

it's a it's it's a power tool right you

come to it doesn't tell you what it's

for you kind of have to discover it on

your own and you have to use it and then

you'll learn about this prompt that was

really cool and then maybe you you're on

Twitter and you learn about another one

or you're on Instagram and you learn

another But the product it's it's like a

raw appliance. And I think to you know

on one thing we really need to nail as

we you know reach the next set of users

>> is a product that has a bit more of an

affordance.

>> U because I think for most people

they're very very busy.

>> Um and they everyone I think in the

world has intelligence constrained

problems like problems that more

intelligence could help with

>> but you need to frame that to people.

>> Yeah. Yeah.

>> And I still feel like we're a little bit

too much like a computer terminal and it

needs to feel more like software like or

um you know an operating system of

software,

>> right?

>> Um so that's one thing. Um another thing

um that gets at the same constraint is

is beginning to be proactive.

>> Um in a world where a lot of folks are,

you know, too busy to delegate their

problems to AI or don't quite know where

to start, I think being able to help you

proactively is really really important

as well.

Um, but you know, I think all these are

are product evolutions that we could

make on on top of the current tech. And

the thing that gets me particularly

excited is productizing our next

generation tech or reasoning models

because the truth is when you look at

reasoning and chatb today.

>> It's relevant to a very small group of

people. It's relevant for the people who

are trying to get the most out of chat

GPT.

>> But I fundamentally believe that

reasoning it's transformative. And if

you can figure out how to productize

reasoning in a way that works on

people's behalf without them even

knowing

>> um and that looks very much like you

know

>> the model doing long horizon tasks on

your behalf. It doesn't mean you

encounter the concept. It just means

it's benefiting you right.

>> Um so there's so much work to do and and

you know

>> the the the product certainly has to

evolve to to be relevant for for for

this kind of scale.

>> Yeah. One of the one of the things that

I've been hoping for a while and you

know Brad made a bet two years ago um

when can Chad GBD help me take actions.

>> Can Chad Guby help me be more proactive

>> and um I think his bet expired end of

last year. So he's he's we're very

curious.

>> When is that coming? And I'll frame that

for you because you know with with with

with search engines in Google you know

two decades ago you'd have gotten the 10

blue links. You could have spent an hour

getting the answer. you can now get the

answer instantly with chat GPT.

[clears throat]

>> Uh and it feels like the next step is is

actions

>> 100%.

>> And it feels like the next step is you

know with you know pulse is a is a great

proactive product.

>> Um that that that feels you know it's I

have a pulse that runs weekly

>> but what I would really like is like hey

Nick spoke about something and just just

find me make sure I know that Nick spoke

about this or or like hey this XYZ thing

happened that I cared about a lot.

>> Um when is when is one of that going to

get proactive? um what is the modality

going to look like?

>> Yeah. Yeah. So there's there's two

concepts I think. Um there there's chat

should be doing stuff rather than just

answering

>> and then there's chat should be being

proactive and I think when you put them

together you start feeling like it feels

like a super assistant. Um because I

think these things compound

>> um

>> on the action taking piece you strictly

speaking in chat

can do stuff today. Um the action space

is just very limited, right? It can

search the web, which means it can use

you search tool or browser in the same

way that a human would.

>> Um it can make images. It can do all the

all these things, right? But it doesn't

have clearly doesn't have the same

action space that a human with a

computer would have. And that is what we

aim to build. Um

>> and if you timing is everything on these

bets, right? And I don't pretend to be

great at timing either. U you look at

past attempts that we've made like the

chatbt agent for example,

>> which kind of has capabilities like

this. um it was just slightly too early.

The models weren't quite good enough to

hit real escape velocity. And the

problem is if you don't have escape

velocity is that users don't learn to

trust it. They don't even try.

>> So when you look at a lot of things

people were doing in the original

version of chatb agent, it was the

things that happened to work like

migrating your uh uh file server into

the cloud or something like that. Useful

stuff but very niche.

>> Yeah. Um and um as this stuff gets

better um we just have to get it to a

point where people try to use it for

real meaningful problems in their life

because then we can start hill climbing

and this has been the magic of chatbt

where chatbt upon launch was good enough

to get real attempts at use cases even

if they didn't initially work like chat

was a pretty bad writer originally it

was a bad software engineer but people

tried and got enough value out of it

that we could take those use cases and

make them great

>> and I do think we're about to get to

that point with general purpose agents

where

>> it works well enough that you get at

least partial credit

>> and because you're getting partial

credit you get really good tasks back

and then the magic begins because um you

know once you have

>> a a set of use cases that you can um

climb the hill on we can make them

awesome. So on task I think we're close

but I think even people inside of open

would have had a hard time predicting

exactly when this gets good. Um we've

been excited about it for a while

>> on um proactivity.

>> Um Pulse was a really great first step

because

>> what we wanted to build was a form

factor where you're not prompting the

model like the model's prompting you.

>> Um for the reasons that I described

earlier, which is, you know, it's so

hard for people to delegate and to

figure out what their problems are. What

if the eye understood your your goals

and the things you're interested in and

just could start being proactive on your

behalf?

um pulse is limited in the value it can

provide for you because it's not

connected to your life and it can't take

action. So, it's producing information

for you

>> and people love that. You know, I love

that. I've got mine running, too.

>> But I think the magic begins when you

have actions and proactivity because

then it can begin speculatively

actually, you know, um detecting, hey,

you just landed where you were supposed

to go. Um you know, I'm I'm I'm going to

call a cab for you. or

>> um you know if you're at work it's like

hey I proactively ran this analysis

because I saw your metrics dropped

>> um so I think these things really

compound and we need to nail multiple of

the building blocks to really achieve

the transformation um and the form

factor that we hope for

>> as you were answering those questions I

now have uh 15 more questions for you

[laughter] so so so I hope you have 15

more minutes but um

>> okay one by one we'll start with what

you said on actions and tasks

>> got it on timing tough to Okay. But is

there a shape or ordinality of tasks and

or or agents that that you think, hey,

this is the kind of thing that's likely

to come first whenever it does?

>> I mean, the thing that's already come

first is the uh domain specific agents,

right? If you look at what's happening

in in code,

>> we're we're we're fully there.

>> You know, it's it's mindbending, but

we've got so many engineers um who who

don't open their IDE like ever. And

right

>> for me as someone who you know used to

code and then unfortunately got very

very busy it's brought me back in the

game.

>> So um codeex and you know products like

it is clearly a product that has escape

velocity where people are absolutely

using it for all kinds of agentic work

and if you just take what people are

doing

>> and make it work even better

>> you kind of get all the way there. Um,

you know, I won't be surprised if you

see this happen for other forms of sort

of quantitative knowledge work just

because it happens to have the

properties that code has. It's testable.

You know, if it worked or not,

>> um, it's, you know, very RL friendly.

>> Um,

>> but um, uh, the the domain specific ones

already work. I think the thing

everyone's working for is, you know,

general purpose agents that just

>> kind of work for anything.

>> Yeah. And I you know that's why I think

you need to to win a consumer because

it's very hard to train people into like

okay

>> um it can work like deep research was a

consumer product and it's really was our

first agentic

>> uh thing out there

>> um but I think what consumers want is I

can just ask it anything and we'll do

what

>> um what needs to be done without any

sort of retraining and um

>> we'll get there just a matter of time

>> at least a psychological goal is uh

flight bookings bookings restaurant

bookings, shopping,

>> all this stuff. Um there are so many

consumer problems and those are just the

type of things that you would kick off,

right?

>> Yeah.

>> The minute you have productivity,

there's there's things you don't even

think of as agentic tasks.

>> Um like you're trying to get in shape.

You don't think of that as a task you

would delegate.

>> Um unless you have a trainer, in which

case you do, but most people don't,

right? But if the EI knew that, it could

totally start working in the background

for you over very long periods of time

and and getting you, you know, um,

here's your fitness plan. Okay, I

actually signed you up for this thing.

You could imagine it being quite helpful

if it's aligned with your with your

long-term interest.

>> You're going to give Ozanic a run for

the money. [laughter]

>> We got to be careful what businesses we

get into, but uh, hopefully we can help.

>> That'll be great. Cannot wait. Cannot

wait. Uh the second thing you said was

um you know pro proactive users and that

might require us to go beyond chat bots.

>> What's an example of a modality that

might take chat GPD beyond a chatbot?

>> So chat will always be close to my

heart. It's the way we grew up. Um, and

it's an important modality to stay like

I I I think

>> it's less about chat and more about

natural language to me where you know

the fact that you can express yourself

to the machine in ways that are very

natural to you

>> whether or not that's text whether or

not that's voice whether or not that is

you know um structured UI that is

rendered by the model

>> that is just very very powerful and

that's here to stay but

>> u I think the thing

>> SA server

>> that's right uh uh for those that don't

know that's the name of our codebase um

short for super assistant server Um

because you know it's proof that this

was always the vision and is always the

vision. Um but uh you know the the the

thing that that'll change I think is

that chat is a great way of expressing

your intent. It's a good way of

communicating with the machine but it's

not a great output. Um where in many

cases what you want back is an artifact

like here's your your your you know plan

for your trip. Here is the analysis.

>> Here is um you know an outcome that I

delivered for you. you know, I just made

you five bucks.

>> Like, this is what I want my AI doing

for me, right? Yeah, totally. I mean,

this is what people care about, right?

And and and and I think chat will always

be there as the way that you sort of

disambiguate your intent and you kick

off the task,

>> but I don't think it's necessarily the

the the final deliverable. And I think

that's that's that's the way in which we

can evolve. So hopefully that's a very

graceful transition because I'm very

lucky and it's hard earned,

>> you know, to to have a billion people

coming to you weekly for a thing that

they love.

>> Yeah. But I think it's a great jumping

off point because we have so much

unsatisfied intent from people where

they're trying clearly trying to do

something and chat is helpful enough

>> but it could be so much more helpful. Um

and I think that's where we evolve.

>> Yeah. And you must be sitting on so much

of this data where people are showing up

to chat and attempting as you said uh

three years ago they were at least

making the attempt.

>> Yeah.

>> So you might have at least the frequency

histogram of like hey here are all the

things that people want to achieve with

us. We do uh we have like really awesome

you know um classifiers that run

automatically. It's fully privacy

preserving but gives us a sense of you

know what use cases people have

>> and it's important right because when

you make a new model you make a model

update you want to know what use cases

just got better what use cases got

worse.

>> Yeah.

>> Um and that's not always always trivial

to figure out unless you have really

good analytics on the system. But so

much of my learning is actually

qualitative where I will just you know I

have a habit of reaching out to a fairly

random set of users to just figure out

what they're doing

>> and I've never worked on a product where

three and a half years later you're

still learning every time because

usually by that time you know what the

use cases are that your product can you

know deliver on

>> but our tech is so unusual in the fact

that I keep learning about something

crazy I didn't know was possible.

>> Wow that's awesome. But basically a

billion users, I suspect a small

fraction of them are power users

>> who are uh getting maybe thousands maybe

maybe maybe tens of thousands of of of

value on on their $200 subscription.

>> Yeah.

>> Um the vast majority is is you know

middle of the pack and then and and a

few call it casual users who are you

know start using Chad GPD as search

maybe or or or teach me about AI or or

help me with my homework. Um what is

your focus like maybe in those

constituents power users, casual users

and and early users or however you frame

it, what is our focus on for each of

those three factions?

>> Yeah. Yeah. Well, first of all, I feel

accountable to our entire user base. In

fact, our non-users too because you

products like like Chachet can have real

externalities on on all humans.

>> Yeah. But when I think about sort of the

way we build,

it's really useful to imagine the

extremes. Um, one extreme being a user

who doesn't care about AI at all.

>> Um, who has a busy life. Um, and um, um,

needs to be convinced of, you know, the

value that we can provide because that

forces you to really nail the interface

and to expose the capabilities uh, that

are hidden in the model in a way that

people can actually gro. And then the

other useful extreme is you know um our

our power user base because power users

are the users who teach us what's

possible. Um

>> um it's actually impossible for us to do

all the product discovery

um on our own

>> simply by because of how empirical

>> this technology is um and how much you

actually learn post launch.

>> So building you know um for each of

those extremes can be valuable. Uh but

our user base is incredibly diverse and

um people have so many different use

cases and this is why you know I like to

look look at all kinds of different

segmentations not just frequency but

also you know what use cases are you

coming to us for. Um

>> but um definitely huge variety in the HP

user base.

>> Yeah

>> I look up to Mac OS for example as an

example where it really works for people

who don't understand technology at all.

it's entirely magical but if you are a

power user you've got terminal you got

settings you configure almost anything

in Mac OS

>> and it's really beautifully done where

the complexity is progressively

disclosed

>> so um you can interact with it and love

love the simplicity of it all but you

can also got all the knobs and

developers love it right

>> and so I think this is kind of the

inspiration for how we want to be in

chatbt that doesn't mean we always live

up to it but um it means that building

for powers is extremely important

>> and you know that's not just a property

that I'm think is sort of aesthetically

exciting. It's also really important in

AI because it's the power users who show

you what's possible.

>> They are actually doing the product

discovery

>> because it would be impossible for us

with such an empirical tech to do all

the product discovery on our own.

>> So the type of user who subscribes to

Chad GPD Pro who used codeex before it

quite worked

>> who is now the strongest advocate of of

cool tools tokens and teaching us what's

what's possible. um that is an

incredible valuable incredibly valuable

member of the community

>> and it might not show up in your you

know um weekly active users as just one

number right um but this is exactly why

there isn't like a single north star

>> and you really need to need need to take

these different segments very seriously

so um I love building for power users

>> um

>> and uh you know you you asked on you

know token consumption etc it's so

fascinating to see there's people who

get incredible value um out of these

products and u watching what they do is

very informative.

>> Okay. So, so we're very focused on the

entire user base. Um learn a lot from

the power users. Um

>> you know the other thing I might say is

the power users right now are getting um

>> a lot of value almost too much value

>> and a lot of

>> no such thing.

>> No such thing. the the the analog that

is most common is the Uber and Lyft of

the 2015 era,

>> right?

>> And you know, it took it took a while,

but I know you were thinking about it a

lot. I know you guys are thinking about

pricing quite a bit.

>> Yeah.

>> Um maybe tell us a little bit about

pricing. Um

>> you know, right now pricing is pretty

simple. uh is there a is there a path

for for for folks who are getting a lot

of great value to price that product

differently and and and you know meet

them where they are and and and and and

the other way on the other side

>> I mean pricing is there's no world in

which pricing doesn't significantly

evolve when

>> the technology is changing this quickly

right chat GPT originally was entirely

free and the reason for that was that it

was intended to be a demo

>> and we were going to wind it down after

a month

>> we then realized that the demo went

viral And people loved the demo and it

was actually a product and but we

realized to be a product you can't take

the product down every time you're at

capacity. So we you know ship

subscriptions simply because it could

shape the demand. It

>> was a way of gracefully turning users

away when we had to turn away someone

and it felt like the fairest and most

equitable way of doing so is saying hey

you know if you really need this product

pay a subscription fee and you got it.

Then we figured out how to make the

product stable and we had the choice of

do we keep the subscription thing or do

we go back to free and we realized we

had consistently more tech that we

couldn't scale. GPD4 being the first

example because we had way too many free

users to serve GPD4 and we put it behind

the plus plan. And so, you know, the way

we stumbled into subscriptions was was

sort of accidental by trying to just

solve for the user. Um, and it felt like

the right way at the time um to to

provide maximal access to our to to to

our tech. Um,

since then we've had so many other

breakthroughs including test time

compute where you can scale up

>> um um intelligence

>> um um kind of um as much as you want.

Yeah,

>> more or less. And um you know, it took

us, you know, in the entire industry a

little bit of time to turn that into

product value, but we're here now where,

you know, our our our our

power users want to use more and more

and more intelligence. And you it's

possible that, you know, in in the

current era having unlimited plan is

like having unlimited electricity plan.

You know, it just doesn't make sense

because like, you know, people may need

a lot a lot of electricity and they're

getting a lot of value out of that.

There's a reason you can't buy that,

right? So, um, obviously I want to be

really thoughtful about the way that we

evolve our our plans and SKs and

subscriptions, but you would be

incredibly sub, you know, surprised if

it didn't change given the magnitude and

profoundness of of the technical

breakthroughs that we've had and the

product breakthroughs that follow.

>> Yeah. Um, and you know, relatedly, so so

I I imagine you're going to have

something for the power users.

>> Mhm.

>> Um, what about the other side? How do we

get um the casual users into the into

the wheel and and still uh monetize

them?

>> As mentioned, you know, our our business

model is will evolve and the northstar

is access, right? We we we would like to

pick a um way of of of of you providing

an off we want to pro provide an

offering that maximizes um the number of

people can who can access our most

powerful tools. I think for the longest

time that has been subscriptions.

>> Um, subscriptions have the downside of

the fact that, you know, in many markets

people don't

>> have credit cards or they don't use

credit cards to subscribe to software.

Um, and we're interested uh in other

ways that can maximize access of of the

tech. Um,

>> our ads pilots are in that spirit. You

know, we we really view it as a tool of

bringing chatt and our intelligence most

broadly

>> to anyone around the world. M

>> um and it is an example of how we

constantly need to evolve and figure out

the best way to to you know bring the

demand in line with what we are able to

offer.

>> Makes sense. Makes sense. You know the

ads the ads piece is um is is has been a

tricky one because you know Sam has

historically expressed reluctance about

ads and um

>> you know we've got to maintain a lot of

trust um with while delivering that. So,

um, I guess what changed?

>> I think we've talked about this several

times in my my history at OpenAI, and

every time it came up, we said if if we

were to do ads, we'd have to be really

thoughtful about the way we do it. Um,

so the first thing we did, you know,

starting, you know, end of last year was

to really engage the company on if we

put ads in chat GPT, how should we

approach it? What should the principles

weigh? what be how do you preserve the

things that are magical about chat GBT

while getting the benefits of ads which

you know is is is our ability to bring

>> um our most advanced tech to to to

anyone um regardless of their ability to

pay

>> and um I really love where we ended up

on the principal side

>> on the experience side

>> um we're very very early but on the

principal side I feel really proud

because you know it's it's very

important that the answer of chatbt be

independent as an example

>> respecting user privacy is very

important and there's a lot to learn um

from you know the way that tech has

evolved over the last few years

>> um or really last decade um

>> um and I I like that the principles are

out there before we've even really

gotten started um like we're very early

with our pilots

>> um you know it it's kind of interesting

I was obviously very anxiously and

eagerly looking at um our support um

inbounds and data and um the most common

inquiry about ads is not you know how do

disable ads or turn off ads, but it's

like how do I run an ad?

>> Um because the entire ecosystem is

really excited to be part of the story

and to figure out a way to talk to chatb

users. So,

>> um there's a lot more to come. Um but I

I I'm I'm very eager to get this right.

>> Yeah. Yeah. I'm sure you guys will. Um

switching gears, Nick, um something you

and I have spoken about a little bit is,

you know, distribution and partnerships.

Uh there's a couple of big partnerships

last year. Um Apple Reliance with with

Gemini, those are two big user bases,

right? A lot of India, a lot of the iOS

users. Um talk tell us a little bit how

you think about partnerships for Chad

GPT to to to meet the meet the user

base. Um and maybe specifically on those

two as well.

Look, um I think partnerships are a

great way to to bring two two two two

products together and to you know

>> um expose

um something like chatt to um people who

might not other otherwise have

encountered it.

>> Um the thing that I care about most when

considering something um like a

partnership is what is the what is the

user experience and can we make it

amazing? Um because at the end of the

day um when you look at um what's going

on in the market um you can get users to

click on things, you can get them to tap

any sort of product um especially if it

looks like a product they recognize etc.

>> Um but if the experience isn't truly

awesome um you people will churn or they

will you know at least not retain in the

way that you know we've been lucky to

retain them on chatbt. So for that

reason you know I'm super interested in

in paths like that. Um but it needs to

be great. Uh it needs to acrue to the

user. Um we are very lucky to have um a

great brand and a recognizable product

um for many many folks and I want to

make sure that anything we do is

accretive to um um to all that.

>> Nick, you are uh you're a master of

trade-offs. You must be making a lot of

trade-offs right now.

>> Uh um tell us about tell us about some

of the trade-offs you're making. tell us

about, you know, an a trade-off that you

might be making that people don't

appreciate uh from the outside.

>> There are a lot of trade-offs um indeed

and um for different reasons, right? Um

um the what I encounter a lot is trading

off

delivering on the people on on the use

cases that exist in the product today

and making them better versus

productizing you know step change

technology that's going to generate a

whole another set of use cases

>> because when you think about how chatb

came to be it was a totally open-ended

product. It was basically a user

experience around a technical

breakthrough.

>> And we couldn't have told you all the

ways that people find it valuable,

>> but putting it out there was really

important because it allowed us us to

discover and the world to discover what

we can do.

>> And then postg

um we can obviously very systematically

go and improve on the things that people

actually want to use it for. And when

you're at a company in this moment where

you both have such amazing traction with

what exists today and the most

mind-bending breakthroughs on the

research side, the balance you have to

strike is making the core product you

have better today with all the things

that matter, latency, um reliability, um

making the use cases really great that

people come to

>> with um you know providing access to to

the step change um And um uh you we try

to get the balance right, but we're a

small team and we don't always get it

right. And for that reason, it's one of

the most difficult um trade-offs that I

have to deal with.

>> Nick, I imagine one of the hardest

trade-offs you guys make here is those

uh GPUs that are melting between chat

GPT, between codecs research. Uh how do

you guys uh allocate the GPUs?

>> That is a very good question. Um and

I'll let you know when I figure it out.

Just kidding. you know, we we've gotten

we've gotten a lot better at this. Um, I

really hope, by the way, to be at a

point one day, and I've yet to reach

that point, where

>> we don't have to face this trade-off

because it's really painful to have real

user demand

>> uh for products that you can't serve.

>> Um, like you know, if if you only ever

worked in software, that's entirely

unusual dynamic, right? Where you just,

you know, or you were limited by this

zero sum resource out there.

>> Um,

>> the marketplaces have it. Um um but you

know I think pure software doesn't

doesn't really have the dynamic right.

>> Yeah.

>> So um you one thing we try to do

obviously we prioritize our existing

users um first we want to provide a fast

reliable product um and that is critical

and table stakes.

>> Then when you look at new capabilities

the sort of na naive

>> u business school thing to do would be

to probably you know look at revenue

incremental revenue per GPU or something

like that. Yeah,

>> but this is where it's more an art than

a science because we often have new

breakthrough capabilities that are

entirely zero to one. Deep research was

one of those you know we couldn't have

told you is there going to be demand for

you know consumer demand for a research

product but if you don't productize it

uh um to find out

>> um you you will never know. So you know

this is where we have to be a little bit

thoughtful on how we balance you know um

things that are no-brainers that people

are really going to love with things

that are um brand new ideas. Then

obviously on the research side, there's

a reason that Mark has the job he has

because a big part of um his his job is

is figuring out what research to fund

and um um you know, obviously GPU is a

big part of that. So um very nuanced

topic that we're continuously getting

better at, but um for me the priority is

always on our users.

>> Yeah. The other takeaway that I had is

you can you don't have line of sight to

a time when you won't have that problem.

>> It's it's been so fascinating. Um

because

you know we obviously have been

incredibly lucky to uh encounter more

and more users who want to use our

technology but then the value that we're

able to provide for each user is going

up as well

>> and um you know GP or GPU consumption

you know it correlates pretty well with

that value

>> um and uh when you just look at token

consumption per user um especially in

the enterprise too which is you know um

a a massive opportunity

>> um you see um a lot of very GPU GPU

hungry hungry workflows and um um yes

demand keeps going up even as prices go

down.

>> This is a fascinating insight. People

used to think that humans were you know

you can't kind of make more humans well

on a takes nine months and then 19 years

[laughter]

but uh but you're saying well that's

actually more more less finite resource

than GPUs. Yeah, I mean on the human

side um you can hire uh more humans and

obviously we've been doing busy doing

that and bring the best talent um across

functions to OpenAI. Um in the world

with agents you can also get more

leverage per human um um you can make

your humans very effective um at their

job to do more

>> but GPUs are zero sum um and if you

don't have more GPUs you really have to

figure out how do you make very very

hard trades and hate making hard trades

for users.

>> Yeah.

um hence the desire to um um have more

GPUs. But uh it's it's useful to start

with the most zero sum trade-off when

you do your planning. So I think

starting working backwards from GPUs um

is usually pretty pretty good idea.

>> Yeah. You know, one of the we have all

these external data sources for charts

of users and usage and

>> act activity and retention all those

things. What we don't have is tokens per

user over time.

>> And I bet that chart is like a sweet

line going this way. I think internal is

pretty good. Our internal uh employees

is a pretty good indicator for what's

about to happen. And um yes um the

charts are are are mindboggling.

>> Yeah. Yeah. Yeah. Fascinating. Uh okay,

a couple of quick ones on the present

before we go into the the landscape,

which is, you know, shopping. Uh you

know, we we just moved into a new house.

We took some photos and we were hoping

that all our furniture would magically

appear that Chad Gibbly helped us paint.

But

>> you know a lot of a lot of uh recent

updates on chat GPD shopping. Tell us

tell us about it. What are you thinking?

Yeah. On on shopping as Chad GP as a

shopping assistant. Shopping is one of

those use cases that exist organically

in chat GBT today and they work. Um you

can ask chat GPT about any purchase you

might be planning and get pretty

excellent advice. Uh but it's also one

of those cases where the experience that

exists in chat um today is it's not it's

not the perfect experience that you

would want um because shopping is very

visual for example. So you're going to

want to actually see products and images

and be able to compare and contrast not

just read you know walls of text.

>> Um people care about the sources of you

know um um um you know where can I learn

more about you know a given product etc.

Mhm.

>> Um and um so there's a lot of work to do

to make this discovery really really

good and allowing people to use chat as

a as a um as an assistant to find the

right product to buy.

>> Um and that's where our focus lies is

making that really great and making that

really great in a way that works for our

retail partners as well. Um because as I

mentioned earlier, there's huge appetite

from the ecosystem to be part of the the

ChachiBT journey. Um and um nailing the

discovery piece is has has been um the

most promising um focus here to date.

>> Nick on chat GPT you must see a breath

of information. You must see a breath of

uh use cases that people are doing with

Chad GPT and and tell us something about

you know what what does the world

underestimate about Chad GPT that you

have maybe been surprised by or or or

listener might be sub surprised by?

there's been a real change in the way

that people think of chat GPT

>> um over the last year or so where um

it's increasingly like a true thought

partner to people

>> um it's not just a thing that you know

answers your question um but it's a

thing that you can it's a sparring

partner that you can actually think

things through with

>> and u that shows up in all kinds of

domains ranging from life advice where

you know if you got a

>> relationship problem you can actually

get a lot of value chat is helping you

think through how to handle it and how

to talk to you your partner about it. um

all the way to a work setting where you

know you're you're working on um um an

analysis or you're like trying to figure

out how to frame something or you're you

know trying to build something and Chad

GPT really shows up as um as a um second

brain um of sorts and

>> uh I think that's qualitatively

different in terms of how the mental

model it occupies with people

>> and you see in the usage patterns and

the use cases that exist and I think the

more we nail things like you know

proactivity which we talked about

earlier in task etc. I think the more

it's going to feel like a teammate in

the workplace and like a super assistant

at home

>> and uh I think that's going to um

meaningfully change the the use cases

that people come for.

>> Yeah. You know, I've been the the most

highstakes thing I do with Chad GPT is

uh we have a we have a new baby

>> and when the baby's uh crying at 3 in

the morning,

>> Chad GPT,

>> what you know what's what's going on?

>> First of all, congrats. Second of all,

I've heard this from all parents in my

life. the chat has become indispensable

indispensable

>> as a thought partner and it makes sense

right if you have a really specific

scenario or you think it's a specific

scenario to you chat really comes

through and can um you know help you you

know um um build confidence and I think

that's such an empowering thing

>> right like and I imagine new parents

aren't always the most confident about

what is the right thing to do and if

chaty can make you um you know feel like

you you you are you have agency and

control and you know you know uh I think

I think it's really valuable.

>> Yeah, it's huge. Well, thank you for

making Chad GPT. It's literally getting

me an extra hour of sleep every day.

>> It took a village. But that is a great

metric. You know, that should be the

northstar metric is like incremental

hours of sleep.

>> That's a great one.

>> Incremental hours of sleep, incremental

hours of joy.

>> There you go.

>> I mean, you joke, but like we talk about

this a lot and um because spiritually

that that that is pretty close to what

we

>> hope we can do, right? is is is help you

re reach whatever you consider

self-actualization.

>> Yeah.

>> Whether that that's sleep or joy or any

other goal you might have.

>> Yeah. Yeah. Yeah. Well, thank you for

thank you to the village.

>> We're going to switch gears and talk

about the landscape.

>> Sure.

>> There's a lot going on on the field. Um

you know, how would you frame Chad GPT's

differentiation

uh to to to people out there? Um there's

a lot of different products out there.

Look, it's the best time in history to

be a consumer of technology.

>> It is indeed.

>> Um because uh you got options and the

competition is intense. And I think

that's beautiful and it's actually good

for us too because

>> if you were to premortem why a company

like OpenAI does not achieve its

mission, it's probably focus because of

the sheer number of opportunities that

become possible when you approach AGI,

right? and having competition and and

options out there, I think um it it

forces us to focus on our customers too

um and on the things that really matter

which aren't always the most flashy

things, right? Sometimes it's latency,

reliability, um the quality of the user

experience.

>> So, uh I think it's a really good thing.

>> Yeah.

>> I think the biggest differentiation of

Jet Beauty is the team behind it

because, you know, we're not static,

right? anything we build will get copied

and you know sometimes in in in in ways

that are high craft sometimes in ways

that um you know are sort of um you know

um check boxes and um it's really

important to us that we evolve the

category um and um build the super

assistant that we've always imagined and

I think the way the reason that I have

confidence that that's possible at a

speed that outpaces you know um um

the the dynamic of being copied

is that we have an amazing team. Um, and

that we have an amazing team across

research and engineering and design and

all the different functions that it

takes to make something amazing. And I

think our unique ability has been to

bring those functions together to build

something that is sort of at the

intersection of useful and possible

>> right in that moment.

>> Um, so you know, my best answer for you

is we keep pushing forward and we hope

to be um um expanding what people think

of this product as. you know, last uh

winter we had obviously uh you know,

what was called Code Red. Google had a a

great model. Uh there was a lot of, you

know, talk about it. Mark Beni off

switching very vocally to to to to

Gemini and us delaying ads and health

agents and shopping. Basically hit pause

on everything, making tragedy better.

>> Talk talk to us about that moment. Um

both both about what led to that um and

what was happening in that moment.

>> Yeah. Um so first off code reds are a

tool we use um to create focus and as

you can imagine when you're in a place

like open and this is what makes us

special to um special to work here is is

is

>> there are so many different things

>> going on it's a research lab we are

pursuing many different ideas right

>> and there's been these moments where u

we've wanted the company to come

together um um to solve a problem

>> across boundaries you know no matter

what your project might have And

>> end of last year, we had one of those

moments where we felt like we we need to

show up for our users. We need to focus

the things focus on the basics. Um like

reliability, performance, um the way

that talking to the model feels um

making personalization really great. um

all these elements that you know our our

users care about and um I loved it um

because it was really an opportunity to

work with a bunch of folks who I don't

normally get to work with on making the

product great

>> and we just exited the code red um

>> which we knew we would um with the

launch of 5.3 which you know is is a

great model for the everyday user um

it's great to talk to and um 5.4 four

which is um workhorse if you're trying

to do real knowledge work

>> and um you know undoubtedly we're going

to continue to use the tool um um of a

code red whenever we want to create

focus um but u I'm excited because I

think um chat GBT um is is is in a great

spot

>> yeah so code red is over now

>> that's correct

>> it's not the new normal

>> um it's not the new normal uh we want it

to be a special thing but it is a tool I

I suspect we will continue to use

>> that's great that's great and Um maybe

tangibly if you were to point at how did

code red change chat GPT uh or maybe the

ops or or how the team operates.

>> The thing I try to get like you know

foster with the team is focus. Um so we

certainly more focused than we were six

months ago

>> on um you know the

>> uh the things we really want to nail and

some of those things are very behind the

scenes like

>> latency reliability

>> those kind of things.

>> Okay. And some of those things are like

very considered efforts like you

involving chatbt into the super

assistant and um

>> so focus is the main lasting artifact.

Um and as you imagine um

>> um it's hard to stay focused sometimes

when there's so much going on in the

space but um that's the hard job and you

asked me about trade-offs earlier.

>> Um um getting getting the team to focus

on the things that really matter to

users is certainly one of them. Um

that's always worth it.

>> Yeah. you know, in the back of my mind

as I ask you that question

>> is all the other founders that are that

are in the arena right now. Uh and and

just a reminder that hey, Code Red is a

tool for for for you. Wartime at balance

as we used to call it is is a tool.

>> Yeah, I think you know every company

does it differently um in terms of how

you how you get stuff done. Um, but I

think it it's really valuable to have

terminology that um, you know, means

something

>> um, that, you know, signals to people

it's okay to drop your other stuff and

it's okay to, you know, focus on this

thing together even if that wasn't your

original job.

>> Yeah.

>> Um, so I think it worked really well um,

at a place like OpenAI. Um, but I

imagine startups would have an

equivalent.

>> Yeah. You know, one of the things that

caught everybody's imagination uh, on

our team was uh, what Peter was doing at

OpenClaw.

>> Mhm.

>> Incredibly potent to put all the tools

together. Obviously, Peter's a great

builder. Congrats on on on bringing on

Peter to the team. Um, tell us a little

bit about what what what Peter's working

on and when when might the uh uh the

billions on Chad GPT have something to

to to see there.

>> Well, first of all, I'm very excited for

for Peter to be here. Um, I uh um um was

excited to have another German speaker

in the house. He's he's Austrian. I'm

German. So, we were we were um

exchanging Guten Morgans. Um but the uh

um yeah the the open claw is so

inspiring because um it brought to life

in in many ways um a vision that that

that you we'd had um um in different

forms know admittedly

>> um around this kind of AI that is fully

embodied that you know exists across

different UIs that can do stuff for you

that has state that has an interaction

pattern that feels a little bit more

like talking to a human, you know,

because you um you open cloud allows you

to you interact in a very very natural

way where you can you send many texts

back and forth and it's very curt and so

there's a lot of elements of of of of

OpenCloud that I think were very

clarifying to to folks across the

industry and um but the best you know I

I'm I'm super excited to just like learn

learn from Peter and bring in uh into

the company and figure out what we can

do together. So there there's there's a

lot more to come. All right. So, now on

to the most fun section. Rapid fire.

>> All right.

>> You ready?

>> Sure.

>> Well, we we'll start with my most my my

my favorite game, which is long short.

Uh, pick an idea, a startup, a business,

a product that you love, you think you

you're very bullish on. Yeah, I'm I'm if

I were starting a company today, I I I'm

really excited about these companies

that are going into companies and

getting extremely hands-on and doing

effect effectively professional services

>> um with AI because we've saturated all

the emails

>> um and you need to get proximate to the

problems. So um that's it's it's those

companies that that um I'm I'm paying

attention to.

>> Fascinating. So this is, you know, this

is an example. This would be like, hey,

you're going and either acquiring or or

or going inside an operating firm that

has scale and and and and and a humming

engine.

>> Exactly.

>> And making that a more efficient engine.

>> Yeah. Or or or just like, you know,

you're doing contracts for for for for

customers that have really hard problems

and you're actually going in and

committing to solving the problem.

>> Um outcomes.

>> Yeah. because like you know there

there's a reason I think that we made so

much progress on math and coding um but

not on many other domains because those

are domains we are approximate to we as

people who work in labs and

>> there's all kinds of other domains that

we are not as proximate to and if you

get proximate I think you can you know

build something transformative and I

think this is more important now

>> um precisely because um you know the

easy problems have been solved um the

obvious problems have been solved by the

models

>> credit where credit is due I think not

notebook spoke LM is awesome and

differentiated and helps me learn new

stuff. I think it's great.

>> It's so good.

>> Yeah,

>> it's so good.

>> I think this is the example of you can

innovate and you can build something um

totally different. It's awesome.

>> Yeah. Yeah. Yeah. It's so good.

Particularly for uh I found it for some

more technical uh uh learning to be a

very approachable way to to

>> totally learn. And it's really cool. I

feel like AI an underrated capability of

AI is to just transform things into a

different medium.

>> Mhm. And I think that's so important for

learning like we just la um um launched

these like dynamic um math blocks which

allow you to visually understand math

inside chatb learning is obviously a big

use case for us too

>> and I think just being able to transform

>> things from text to visual you know soon

from you know visual to video and like

all these different media is amazing

because people have such different ways

of processing information um and some

people are like auditory learners some

people are visual some people like

reading uh so I think that's really

magical Um and um a great great angle to

take.

>> Yeah. Amazing. Amazing. Amazing. Uh you

know, one of the things I think about a

lot is education and and and education

for for for kids now in school. Um the

world's changing so fast.

>> I'm not sure our education system is

changing that fast.

>> Yeah.

>> What advice would you have for for

students who are in school now? Um you

know, who might have to adapt faster

than the system around them might adapt?

It's a really good question and

something that I've thought a lot about

um myself

and you know the I think the most

important perma skill in this era is uh

curiosity I think because

>> if the machine can answer all your

questions you better have good

questions.

Um, and the only way to have good

questions, I think, is to

>> pursue the things you were actually

excited about from an early age and

throughout your entire life.

>> Yeah.

>> And, um, I reflect on this because the

only reason I'm here and working on this

stuff is because I thought it was neat

when I got, you know, nerd sniped in

>> um, in the interview process, right? And

it was like,

>> that's right.

>> This is so cool. And and so no matter

what you're doing, I think that's an

important skill is to be curious and

learn to stay curious. And I think I'm

confident that um if you foster that

skill um you will know how to adapt to

you know an evolving

>> landscape of tools and AIs and and jobs.

Um so that would be my advice.

>> Yeah, curiosity is has always been the

poorest scale. Our friend uh Bill Gurley

wrote about it in his book Running Down

a Dream. Um but curiosity have to check

that out.

>> Yeah. Um, what what is a job that uh

gets more valuable, not less, as uh AI

gets better as as AGI arrives?

>> Well, I think maybe the easy answer is

being an entrepreneur.

>> Um um because it's the best time to

build ever

>> in terms of like being able to

self-actualize your

>> Yeah.

>> your idea. Yeah,

>> but maybe maybe one that is maybe

nonobvious is I think writing actually

>> um is very important and it's not

because the AI can't write you know AI

will become amazing at writing just like

any other domains but because I think

the skill of writing

>> forces you to be very clear

>> of what you have to say

>> and even though prompt engineering is

obviously going to go away and has gone

away to much extent

>> the idea of expressing what you want

>> to a machine

>> requires you to be a pretty good writer

and a very precise um um writer. So I I

would say that that is a you know any

profession that involves very clear

writing and therefore thinking I think

is is is um um well set up. Yeah, 100%.

Honestly, I mean there this is the whole

thing about slot, right? There's so much

>> that's the other thing. I think there's

going to be a permanent need for high

quality, trusted, authoritative content

and tools like CHAP can help you

discover that content.

>> Yeah.

>> But I think the need for amazing um um

content is is is also here to stay.

>> Uh and final question, what has been

your AGI feel the AGI moment? When did

you feel it? I've had so many honestly.

Um, and it's

it's definitely not stopped. Um,

a few weeks or so after I joined Open

AI, GP24 had finished training

>> and I remember trying it out and it

actually it didn't impress me at all.

Um, nor anyone else that week because it

kind of didn't work. And it's because we

hadn't figured out how to post train it.

And uh I think seeing it go from

kind of wait is this really a thing or

was GPT3 kind of it to wow actually this

is an entire step change with what felt

to me at the time who didn't understand

much about AI at all as like just some

tweaks or some a little bit of final

stretch work

>> was profoundly humbling because you can

realize that you might it might not look

like we are close

>> to really powerful useful AI But we

probably are. Um,

>> and then the moment that, you know,

really, you know, there was two things

that GBD4 did that

felt like AGI to me. One is it could do

poetry and I didn't think it was

possible for AI model to do poetry. It

just kind of

>> fundamental philosophically, it just

didn't feel like

>> in scope

>> and then the other one was it could

produce code that actually worked and

compiled. And then my next moment where

I stared at the ceiling just in awe was

when I realized GPD4 could just simulate

an entire computer terminal um um like a

full computer with commands etc. And I'm

like, wait, how would this be imbued in

a in a in a language model? And there's

been so many moments since then,

honestly. Like reasoning was a moment.

One of the moments was when I think Mark

and I were uh uh giving a demo of

reasoning uh in front of the um whole

company. Um and um this was a moment

where we were still trying to kind of

find use cases that were hard enough for

the AI for the reasoning to, you know,

make it make a difference. We're way

past that point, we know. But at the

time, you know, I think we were having

to do a puzzle in front of everyone and

um I think one of the moments that made

me totally feel the AGI is like um we

were in the middle of the demo and

everyone started laughing and I was

like, "Wait, what what is funny?" And

then I stared at the screen because we

were showing this chain of thought as it

was streaming out of the model. Um and

the model swore and said like, "Oh, damn

it. May I have to adjust because I

realized I had made a mistake in the

puzzle." And uh the fact that they did

that, but in particular the fact that it

did that in a way that was entirely

emergent from the you know RL process

completely blew my mind um and you know

made me you know um uh feel quite humble

about what else these models might be

able to do. So um that was one of those

moments.

>> Yeah.

>> Uh and then most recently watching

people use codecs like like watching

people have

>> walk around with their computer open uh

because they don't want the task to end.

Um yeah uh watching people who have

never coded in their life make stuff and

bring ideas uh to life that feel feels

like an AGM. So honestly it just it's

just accelerating for me and it doesn't

wear off at all. Um and uh everyone has

a different thing obviously but those

are were some of mine.

>> Yeah. You know it's uh 10 years ago

there was a product called kite. I don't

know if you remember it was for software

engineers. It was like an AI coding

product. Mhm.

>> That's when I that's when I felt the

hunger for for for for for personal AI

and you know nothing happened for 10

years and then everything happened in

the last 10 months.

>> The timing thing is really hard because

it's it's actually quite

>> possible to predict where things will

end up I think um in terms of the kind

of product form factors you're going to

have but to know when it happens it's

really hard for me to make statements on

anything between

>> sort of eventually and in three months.

>> Yeah. because of all the ambiguity

around, you know, um um

>> well, that's a tight enough window, you

know, now and three months is a tight

enough window.

>> Three months is pretty pretty okay. Try

to stick to the three-month plan more or

less. Um um though, you know, my team

would probably tell me we don't, but I I

try. But yeah, anything in between three

months and eventually is is is

difficult. Um

>> yeah. Yeah. Yeah. Well, thanks for doing

it. You've got a lot going on. This was

a total treat. We so excited to see all

the great products you release for us.

Um,

we can do anything to be of help, let us

know.

>> Awesome. Thanks very much. Thanks for

having me.

>> Of course, man. This was fun.

As a reminder to everybody, just our

opinions, not investment advice.