You know, Greg, this is our fourth time
speaking and we've spoken every time
about OpenAI's product direction and I
think I'm starting to get it. Um, you
know, there was this conversation that a
super app was the wrong term for what
you were doing with um the app that
you're building bringing codeex which is
coding side of OpenAI's product browser
and chat GPT together. And when you use
the word super app, people would be
like, "No, a super app is actually
something that you can just use every
other app within." Um, and now as we've
seen these products come together,
actually super app might be the correct
term. You know, at least for us on the
outside, we're starting to see it that
when you need to do anything, um, you it
will start with a prompt in chat GPT and
then OpenAI's technology will use either
your browser or your computer to get
that done for you. Is that the right way
to think about it?
>> I I think that's a pretty good
perspective, right? And I think to
really zoom out, the thing we're
actually trying to build is in AGI,
right? That if you think about what
people have been using since chat GBT,
it's a language model, right? There's a
big gap between these.
It's amazing. You can talk to chat talks
back to you. Great. Wonderful. But when
we launched in 2022, there was no
memory, right? It's not hooked up to any
tools. Has no context. And so it really
is that this conversational intelligence
is only one part of what people really
need to get work done to be able to
achieve their goals. And where we're
going is to have an AI that's really
looking out for you, right? That you can
provide the goals, the directions, that
it's constantly thinking about what can
I do for Alex today? Uh that it's able
to go and solve super hard problems,
very mundane problems. you wake up your
inbox is organized but also if there's
like a health plan that you are thinking
about that it can help you help you
achieve that figure out medical
treatments uh or you know sort of back
and forth provide you with that kind of
information at least and I think that
the question of what's the interface you
want what is the product that you want
is what we spend a lot of time thinking
about and the answer is you want almost
no interface you want no product right
you want this to be like what's the
interface between you and me right just
being able to talk to a persistent
entity of some form firm that's able to
go and accomplish goals for you. And so
building that is hard. It will take
time, but we have a lot of the pieces,
right? We're increasingly bringing
together the product layer, trying to
make the models better, trying to make
the whole system just so there's less
like clicking buttons and toggles and
changing modes and all these things. Not
to say that there won't be some of those
along the way, but the long-term
trajectory is towards simplification,
unification. Yeah, it's very interesting
that you say the interface uh will melt
away. And so to go a little bit deeper
with my question, um many of us who use
products like chatbt today will see that
the bot will make a suggestion at the
end. You know, you ask it about
nutrition and it says should I make a
health plan for you or make a diet plan
for you? You ask it, sorry Ron John, we
just talked about travel, but you ask it
about travel and then it will give you
an agenda for instance. And so am I
hearing you right that what's going to
happen within chat GPT just to give an
example is you talk to it about your
health decisions and it might say you
know you probably need to you know go to
this specialist let me make an
appointment for you and then it will go
and actually take that action on your
behalf. So it goes from simply a
conversation interface to actually
understanding your intent and then going
out and accomplishing that for you. that
that I that's exactly right and I think
that if you've used codecs and by the
way how many people in the room have
used have used codecs
>> people yeah decent number of people um
and that our goal is to really bring the
power of codecs to everyone right to
bring agents to everyone you that
technology exists right now right you
can hook up like I hook up my codeex to
slack to my Gmail to my calendar and
there are many people within openai
nontechnical users you know it's got
code in the name but it's not really
about code. It's really about having
this general purpose tool using harness
an agent. And that the kinds of things,
for example, someone on our comm's team
does is um she was organizing an event
and it would just ask all of the uh
event attendees for their dietary
preferences, set up a whole seating
chart, kind of did all of that work uh
so that she could focus on the parts
that she wanted to and and really
thinking about the vision of what she
wanted to achieve. And I think that
we're going to see this across the
board. So the it's not sci-fi anymore to
think about an AI that's hooked up to
these tools. And I remember with you
know our very first attempt at tool use
in chatbt was 2023. I think in like
March or April or something we released
plugins. Do people remember plugins back
in early chat days?
>> That didn't work.
>> It didn't work at all because the models
the models weren't ready. Right. The
form factor is correct. Obviously you're
going to have an AI that's able to like
talk to your Gmail like no question. But
we could only like have three different
connectors exposed to the model at a
time or start forgetting. You know, we
had like 2K maybe 4K token context. Like
there's just no memory, right? It's kind
of like when you had early computers in
the 60s or 70s or something, right? You
know, that you had tiny little me memory
banks and today you have your phone
that's like better than any
supercomputer from that era. And I think
that's where we're going with these
models, right? That the rate of
improvement has been so steep. So now
you can have hundreds of different tools
accessible that we have the ability to
hook them up to whole file system. So
you can almost have the full power of
the internet and like almost any
application you want at the model's
fingertips. And it's smart, right? It's
got 52 million token context, depends
how you squint on it. And the capability
level is also getting so so powerful,
right? These models are now solving
unsolved math problems and physics
problems, right? And really helping
people be able to achieve things they
couldn't otherwise. like we are on the
era on the edge of this era of agents
really transforming how we all operate
whether it's in software engineering
finance legal sales and in our personal
lives too. So just to unpack that
example that you were giving there one
of your colleagues is chatting with Chad
GPT about an event and then suggest hey
you know uh how should we you know
contact event attendees about something
and instead of like saying okay I have
to do that and going into an event
program basically what happens is the
the interface will take over from there
once it says it's a good idea and you
agree and then hook into whatever tools
you're using and then do it for you.
>> Exactly. So it uses its you know Gmail
connector searches through your inbox to
find all the people who are attending
and then if you're on the like what
what'sever in dietary restriction sees
oh these people I already have their
dietary restrictions these people I do
not um drafts an email depending on
exactly how you have things set up it
might say hey I drafted these emails can
I send them if you have a connector that
doesn't even let it send emails said I
drafted it you need to send them And uh
in a different world, you could also
imagine that it's you've built enough
trust with the system where it says I
drafted the emails and I actually sent
them. And I think that this actually
points to a really important aspect of
the agentic era, which is trust, right?
That we need to really learn how to
build trust with these systems, where
they're good, where they're not. Figure
out what you want to delegate to them
and how you want to entrust them with
responsibility. And that's something we
view as earned, right? It's not
something that that we can we can grant
but by providing lots of tools and
control and oversight and supervision to
the operator to the person who that this
AI is operating on behalf of like we
think that that is going to be such an
important thing and so that's a key
product feature and differentiator.
>> Yeah. Yeah. And when you go back to some
of the early attempts at this, there was
this like move that OpenAI had to let
you call an Uber within Chat GPT. And it
it followed a long line of companies
that have tried to get you to take
action within chat, but it never really
took off. And the difference here might
be that the chatbot can take control of
your browser or take control of your
computer and then you don't necessarily
have to worry about like is this plug-in
going to work? it goes and accomplishes
that for you by taking over your
machine. So I wonder you know if you
expect a fight from the user interfaces
that we have today aka like all the
other apps all the software where to be
truly useful chat GPT will have to not
be blocked to be able to go out and
execute these actions on behalf of a
user. Well, look f first of all, I'd say
that it's this is not theoretical at
this point, right? That people have been
using codec. So, it's a separate
product, separate app. You have to
install it separately. Really starting
to focus on software engineering. But
the amount of non-software work that has
been happening codeex has been
absolutely exploding, right? It's been
this like incredible exponential curve,
exactly the thing that you you would
expect. And within OpenAI, we basically
have the same level of penetration now
in usage as Slack, right? It's like
everyone and OpenI is like an entirely
slack based company. We do not use email
for the most part. It's like really like
if you're not on Slack, you're not going
to do any work. Um, and it's kind of
feeling that way now with codeex app as
well. And that everyone's codex is
hooked up to all of these tools. Um, how
the ecos ecosystem evolves. I think it's
going to be a very nuanced thing because
I think one thing that is very important
is that we believe that there should be
an ecosystem that gets to be vibrant and
thriving and that people can really
build and see the benefits. And so we've
actually seen this from uh partner
companies where uh you know that we I
remember there's a couple different
partners where we said hey we really
want to train our AI to be really good
at using your software and we didn't
know what they would say and actually
the response we got is this is the most
partner friendly outreach we've ever had
right that the idea that you will make
your AI specifically good at using our
tool and they just see the opportunity
because their tool will be used just so
much more as a result and that everyone
is trying to think about how do they not
just survive as a company into the AI
era but thrive? like how do you really
get the advantages of the fact there's
going to be so much more activity and if
you don't have AI in there if you shut
it out then you're actually going to be
declining not thriving
>> right this this kind of makes open AI
puts open AAI so first of all you're
going to bring you talk about people
using codeex so one of your colleagues
shared and I think you've talked about
this too that you've brought chat GPT
into codeex so you can bring codeex into
chat GPT which is basically like if
we're users of chat GPT
this experience that we talked about of
chat GPT not only suggesting what you
might want to do next but going to do it
for you that's going to happen and so it
makes you effectively an operating
system don't you think but not the
operating system like an iOS where you
would like go open up the your phone and
then tap different apps it's almost as
if all interaction with all apps will
happen through this interface is that
the ambition
>> I think that you could describe it that
way but I think of it a little
differently. Like the way that I think
about this is that what is the ideal
interface to an AGI or we call it kind
of a personal AGI. And I think that it's
again the same interface that you and I
are using right now. You just want to
talk to an assistant, right? You want to
talk to something that can go and and
and work and and operate on your behalf.
And so that yes, like that agent, that
AGI, that AI will have its own computer,
right? It'll have its own access to
things. maybe can you know like ideal
co-orker would be they can come over and
type things on your computer too. So
some access some delegated access to
your own own system and you know maybe
you delegate access to your inbox
sometimes maybe it has its own inbox
with some sort of uh some sort of you
know window into into the things that it
needs you forward emails to it. Th these
are not actually if you if you think
about this is not unprecedented, right?
It's like the way that you work with an
assistant who's a person that we've
we've actually or any co-orker really,
we've spent a lot of time really
thinking about how do you build these
trust boundaries and make sure that
you're able to operate together. And so
I think of it as just a different thing.
It's not it you could think of it as an
operating system, but an operating
system is almost something from a
different time, right? It's a different
layer of the stack. This is really more
about how do you interface with
technology broadly. And I think that the
beautiful thing about AI is it's really
about bringing the machine closer to the
human rather than us having to contort
ourselves into like files and folders
and like all these details that somehow
are not natural, right? That are more
about how the machine operates rather
than how we operate.
>> Yeah. Talking about a personal
intelligence, it sort of um I don't did
you watch WWDC last week?
>> Uh no, no, I missed it. I was I was
banned, but um I watched it on TV. Um
come on, Apple. Anyway, it does look
like you and and and Siri, the new Siri
are going to come kind of into
competition, right? Because they're an
app that's going to sit or an
intelligence that will sit on top of all
of your apps and let you take action.
And Chacht will be an app on the iPhone.
So then talk a little bit about whether
that positioning is going to be
difficult for open AI and how you're
thinking about that strategically.
>> Well, I just think again think of it a
little differently. Like I think that
we're in the beginning of this new
agentic era and the way that this has
always gone in AI is that when you have
a new level of capability, it means you
have an opportunity to rethink
everything, right? Rethink how
>> people interface, how like what the
techn is capable of. And I think that
this is no no different, right? In my
mind, like the kinds of things that I
see on the horizon,
for example, AI for solving
scientific problems, right? And I think
we're starting to see the inklings of
this. Like for example, today we
announced
we have in uh peer-reviewed literature
people doctors who are using 03.
Remember 03?
>> Yep.
>> That was like forever ago now, right?
That was like one of our earliest
reasoning models using that to find
diagnosis for people who I had no no
answers from doctors for many many
years. You know, there's an example of
someone who had spent 20 years with a
mysterious ailment. Finally, it's been
diagnosed through the use of this
technology. And if you're like, okay,
you've got models that can do that, they
can do that. And then it's really about
like, you know, the same like
distribution and like, you know, can you
get access to an app? You know, to me
it's it doesn't type check. It's like we
have something fundamentally new. And so
that's not to say that there won't be
competition. I actually think that there
will be and it's going to be great for
everyone, but I just think that the ways
in which you're going to use this
technology, the things it will be
capable of and what it'll make you
capable of doing are just totally
different from anything we've seen
before.
>> You know, I was going to ask you um
well, does it mean that you'll have to
um you know, create your own device
assuming that like my concept is is you
know that you're going to have to go
through Apple to get to the user. Um
assuming that's somewhat valid. But the
answer is you already are right. You're
so OpenAI is working on a device right
now.
>> It certainly has been publicly reported.
>> I I was in your office in December and
Sam told me that this is happening. It's
it's multiple devices. Um so if you
think about the way that again you're
going to interface with um with these
AIs, how does that device play in or
series of devices? Well, look, I I think
again I I would just step back and say
that I think this is the beginning of
something very new and that I think
about the way that
I think I want to say like I think the
biggest shift that has happened in terms
of interface again it's not even about
devices and and and things like that.
It's really about the shift from
conversational intelligence like kind of
the chat paradigm where it's like kind
of you have an AI that's personalized
enough to you that it's worth reading
its output, right? You ask it a
question, you get an answer, it's
something that's useful to you to agents
where they're capable enough to actually
do things for you. Like that is a big
shift and that that
implies a difference in how you want to
interact. And so you kind of are just
going to want a single agent that has
access to your context. And this will be
true in personal life. This will be true
in a business context, right? You
imagine, for example, having a uh you
know, imagine you have a PhD in every
field co-worker, you know, Nobel prizes,
multiple of them, and you hire one of
these, you hire a hundred of them, and
you don't invite them to any meetings.
They're not going to be very useful. And
so there's something about how do you
get context into the AI and not just
statically but dynamically right as
context evolves as your business
processes evolve how do you have a
context layer that is accessible to an
AI that lets the AI operate to the
extent of that raw intelligence and so
finding ways to make that AI be
accessible so available in your meetings
to make it very ergonomic it's very easy
to get access to Um, I think all of
that's going to require a rethink, but I
think it again it's just the core for me
starts from thinking about the agentic
form factor and then working backwards
to how do you just make this have the
context it needs and again the trust is
going to be such a core part of making
this whole equation work.
>> So kind of like having this this device
with you at all times and being like I
need to get that done and it goes and
does it for you. And I think that that
will be part of it. But I almost even
think if you don't have a device like
that, it's not like you're going to be
out of the game, right? Because it's
this a this AI. It's not because there's
one thing there's one version of it
where you think of it where it's like
the device is the AI and you want your
phone to be the AI. You want, you know,
whatever whatever you know custom device
you're you're thinking about to be the
AI, but it's not going to be like that.
It's going to be more like an interface.
Like no more than your phone is you,
right? It's an interface to you. It's a
way that I can sort of, you know, call
you up whenever I need you. uh whenever
I want to ask you a question and there's
different ways of accessing right
there's like synchronous phone call I
can text you I can email you and I think
that we're going to be much the same
with how we interact with our agents uh
there's been some reports that open is
working on these like birectional voice
models I think we've talked about that
in the past like the goal is to have
like an AI that you can you can speak
with and we'll be able to process that
and speak back with you in a much more
natural way can you share anything about
that
No, [laughter]
>> but no, more seriously, um I mean, look,
I think that the that the general shape
of the of the technology, like the way
that like we had we had we've had voice
models, um you know, kind of a really
cool voice experience for, you know,
year and a half, two years now. Um you
know, we first demoed it back in March,
April of 2024. Um brought it to market,
um you know, maybe late that late that
year. And the way that it works and the
way that everyone's models work is that
you basically chain together um well the
original way that these things worked
was that you would chain together a text
to or a speechtoext model then you do a
texttoext model and then you would do a
texttospech model. Horribleness, right?
Like these three things chained
together. Um it still has been the case
that even if you have one unified model
that's able to kind of take in input and
then you know able to output a response,
you still have this problem of turn
taking, right? Imagine that like we have
this like you cannot overlap, you cannot
interrupt. It's just like once you you
you speak to me in a turn and then you
got to wait for me to finish my whole
response. That is not how human
conversation works. And so that we we
basically have like a hack where we have
these models that determine oh it seems
like the turn has ended and oh it seems
like the turn has started and we're like
why are we talking about turns, right?
Like turns again are so unnatural. This
is the humans contorting ourselves to
the machine and its limitations. And so
the obvious thing that you want to
accomplish is a model in AI that works
much more like you and I do, right?
That's able to process input at the same
time it's processing output. And all of
that is of course something that many
people in this field are trying to to
run towards. Um I think it's going to be
very very exciting as you move to these
natural very human fluid like
conversational interfaces. No one's seen
anything like it. Like one thing that
that I think about is the the current
interaction with you know chat GBT
voice. many ways it's magical, right? So
many people use it on their commute,
able to ask all these questions, but it
also is so frustrating, right? Whenever
it breaks the magic because it's like
you realize, oh, I want to like add some
followup and it keeps talking over you
and it didn't it's just like that is
just it doesn't make sense. And so I
think that part of what we need part of
like the whole point of this AI is to be
something that you can interact with
interact with fluidly and naturally. And
by the way, I think it's not just going
to be about the sort of use case, like
we kind of think about the the personal
use case, but it's also really the work
use case. And I think some of the most
magical experiences that that I've had
with codecs have been when operating it
through voice. Like many people, we have
a voice built in. Some people use third
party apps for it. Um, and that you just
get a very different experience when you
start to realize that like typing a
quick message to give some feedback,
easy, but like writing out a whole
paragraph and everything you want,
horrible. No one wants to do that,
right? You just want to be like saying
things and you want the real-time
feedback loop and all of that is going
to happen and it's going to be amazing.
So, let's talk about model improvement
briefly. Um, so there was a discussion a
couple years ago that large language
models were about to hit a wall. um that
was wrong. And um something you know
that I'm thinking about is I think we're
all thinking about it is how much better
can these models get and when will the
improvement stop? Any thoughts? Well, I
think that this is a place where when
you're kind of building these models,
you get kind of a sense and an intuition
that I think is harder to get from the
outside because we see all the data
points and we see also the work that
goes into these improvements. And so
that there's two parts to the answer.
One is I think that the fundamental
science is one of the most mysterious
and important
just scientific discoveries and
empirical observations that that I can
that I that I'm aware of that I can
imagine right that we are able to
actually build these models and that the
scaling laws continue right that it just
is the case that you can just keep
training these models more data
more compute better architectures and
there's a lot of improvements that go
in. But every time we've kind of run
into a like, oh, this isn't quite
scaling the way we expect, it's we have
a problem, we have a bug, that our math
wasn't quite right, that oh, our
implementation isn't isn't isn't quite
matching the math, whatever the thing
is. And that is, I think, a very
important thing to to sort of
internalize. And actually if you we've
done studies where you go back to the
beginning of the field right that neural
nets themselves were designed in like
1940s right before computers right as a
model of maybe this is maybe this how
the brain processes information first
hardware implementation was 1959 with
the perceptron and if you look at
landmark results in the field that the
landmark results follow this incredibly
smooth deterministic path of more
compute being poured into them and so 70
years of people maybe 80 years now of
people saying this stuff is never going
to work, never going to scale, going to
hit the wall. Hasn't hit the wall yet.
There's still no wall in sight. And so I
think that the fundamentals allow it.
Now the practicality is hard, right?
Actually building these massive
supercomputers. It's hard. It's
expensive. It's not easy, right? That we
have teams that just like work so hard
to solve these incredibly hard technical
problems. We have our own network
protocol that we've had to design. um
that we have people who look at every
single layer of the stack that there's
weird wiggles in the graph and you the
way to think about these neural nets is
that there's like there's no
abstractions, right? It's almost like
any little piece that's wrong can have a
ripple effect that only shows up down
there. And so you need people to deeply
understand all of it. And yet if you get
the right team together, put the right
mission in front of people and people do
that grind, the outcome, it's worth it,
right? And it's achievable and it's
possible. And so I think that for those
reasons the progress will continue.
>> So then I'd love to hear your
perspective if if models can basically
progress much further from where they
are today. Um let's say let's say open
AI builds the best model and it the
equivalent of like something with like
15 PhDs with excellent emotional
intelligence that doesn't complain and
goes out and does stuff for you. Um, and
then the the next model maker will build
a a less good, but it has 13 PhDs and
it's like pretty good uh, you know, EQ
and we'll still go and do things for
you. So, where does the differentiation
come in when you get to that level of
intelligence? Because we've seen the
model makers kind of move in lock step.
One makes an advance, the next one comes
in and makes the advance. So, they all
become that smart. Do they is it
possible to differentiate? Well, I think
there are several dimensions to the
answer. Um, number one is I do think
there's a bit of an attractor state
where just like from a business model
perspective, every provider sells out
all their compute.
Okay. Right. I think that is just like
the world that we're heading towards
where there just is not going to be
enough compute to serve all the demand.
Right? That we're heading to this
compute powered economy that everyone's
going to be using these models all the
time to be able to accomplish tasks of
interest. And we just see it. It's like
right now we're talking about compute
constraints and like like the number of
people using these agents is like order
of 10 million 20 million maybe you know
it's like we're not at planet scale
chatbt is like a billion users right but
we haven't brought the agentic power
there yet so you're just looking at
these factors and the depth of usage is
also tiny compared to where we're going
and so I think that we're just going to
be in a world where even if you have
different vendors different capability
level open source models all these
things these neoclouds like I think that
compute is just going to be the scarce
resource and I think that it's going to
go go to use. Um so to some extent I
think that the the like is this a good
business to be in and for new entrance
to come into and things like that. My
answer is actually yes. I think that
there is like a huge market um that we
are just not going to be able to address
and we need much more energy momentum
there. Um but a second thing is that it
also misses the fact that intelligence
is not a undimensional thing right that
that If you really zoom in, being good
at different domains is something where
even if you you have a lot of raw
intelligence, getting good if you've
never practiced like you've never
actually done a pitch or something like
you're not going to be good at it your
first time, right? And that there's lots
of different you've never operated a
spreadsheet, right? You're not going to
be able to succeed at doing some complex
modeling. And so I think that there is
something that we have been
internalizing which is that we look
across different industries and
different domains and we have to
prioritize. We can't possibly be great
at every single area at once. There is
definitely a lot of like, hey, you just
get the general intelligence up and
it'll experience a lot of these things,
but to really become a domain expert, to
really be that PhD, and to really be
something that can help push forward the
ambition of a field, like that's hard.
And and by the way, one thing I also
want to say is that I think
understanding what happens when you
successfully do that. I think that
having a good mental model of that is
important, which is you look at
something like rewind to AlphaGo, right?
you remember move 37, this move that
like changed people's understanding of
the game and then now more people play
go than ever, right? And that it
actually inspired people to do even
more. I think we're just going to see
that. And so I think that the depth is
never going to stop, right? How deep can
you go on science, right? Like I I think
that people have thought sometimes that,
hey, we found out all the physics, it's
all good. We're all done. And I don't
think that that's the future we're
signed up for. I think we're signed up
for one where we're got to keep find
every time you unlock one mystery,
>> every every time you solve one mystery,
it unlocks like 10 more, right? So, I
think that there's just going to be so
much more to do and tons of room for
differentiation across different
companies.
>> So, I think I'm reading you right and
that your belief is um maybe there's a
way that everybody can scale up these
models, but ultimately the company with
the most compute is going to win. And
you know, we spoke a couple months ago
and you had mentioned that like you were
asked internally, how much compute
should we buy? And you said, "All of
it." And they said, "No, really, how
much should we buy?" And you said, "No,
buy all of it." And and OpenAI is
definitely the leader in in buying
compute. I mean, we see the the the
money going out. Obviously, a lot of
money coming in through investment and
and now you you're you've built a
business with customers, but there's a
lot of money going out. Do you ever
wonder, hey,
are like, do you ever wonder maybe we're
not going to be able to pay all this
money back because it's a brand new
category? Well, the way that I look at
it is on the fundamentals, right? You
you need to really look at the fact that
the way that compute goes is that it's
multiple years out before compute
actually arrives, right? Depending on
exactly what you're doing. For example,
we've been investing in our own chip
program now for multiple years. And
super exciting progress like you know,
we'll we'll we'll have more to announce
uh actually pretty soon. Um but the fact
that we're able to do that is something
very unique, right? really think about
the full vertical integration of the
supply chain and I think that the world
we're heading towards is one where again
there's just not going to be enough
compute in the world to satisfy all the
demand and we see this very concretely
like you look at the exponential I mean
rewind to the exponential of chatbt um
look at the exponentials we're on now uh
you think about the problems that we
that we are able to solve you know
it's it's actually kind of interesting
that yeah we just yesterday announced um
it was two days ago announced a new
result in uh uh basically chemistry and
being able to synthesize you know new
new improved reaction and all of this is
without much attention you know the
thing I just said of if you go deep in a
domain you can really transform it and
we're not even scratching the surface
yet and so the way to think about is the
economy is so massive right and we see
it very concretely in terms of our own
growth in terms of what people are
willing to pay and kind of the size and
growth of this whole industry and so I
think that the thing that I think about
the most is how do we meet the demand
and how do you actually have something
that can help support all of the work
that people want to do in the economy?
And I think that is such a vast thing. I
don't think any of us have internalized
it yet. Hi everyone, Alex Canitz here. I
want to tell you about a documentary
I've made with Gravity to explore the
future of AI agent security. To find out
if we're truly ready for autonomous
agents, I sat down with MIT professor
Romesh Rosker, former White House CIO
Terresa Payton, Michelin's group chief
data and AI officer Ambbea Roger Gopal,
and Sharon Guy, a former executive at
Alibaba. [music]
They each offer unique insights into
this evolving landscape. We conclude
with Rory Blendell, CEO of Gravity, to
discuss the path forward. with gravity
leading the way. Join us on this
journey. You [music] can watch the full
documentary at the link in the show
notes.
[music]
Yeah, but if I may, there is a price war
brewing. I mean, at least that's
according to the reports. It's great to
have you here to talk about it. The Wall
Street Journal recently had a report
that an upcoming OpenAI model might have
significant price cuts. And so again,
like how can you know if it requires so
much resources to serve this demand and
it is growing demand um in an
environment where there might be price
cuts, how do you make that math work?
>> Well, again, I look at it from a
different angle. So if you look at the
whole history of what we've done, we
actually have been increasing the
intelligence cutting price right for a
fixed amount of intelligence and people
somehow just like like the Japanese
paradox just keeps happening. And so I
think frontier intelligence will always
be something that is going to be you
know it's always going to be the
priciest thing but I think that a year
from now that level of intelligence is
going to feel pretty mundane and like
you know going to be much more
available. Um, and I think that that the
the world that we're in is one where
people are starting to really think
about value. And it's actually been a
very interesting shift where over the
past, you know, first quarter, maybe up
until now, people have just been like
this AI agent stuff, it's all new. We
need to bring into our enterprise. Like,
we don't want to be left behind. How do
we be part of this this future? And now
people are like, okay, like let's make
sure this actually delivering ROI and
value. And I actually think that's a
great place to be, right? because people
are asking the right questions. And I
hear this I I had some customer meetings
today where people were saying exactly
this. They were like, "How can we have
even just like good spend controls? How
can we have observability?" And I I
think we literally today just released
spend controls. So, you know, it's like
>> Okay.
>> Exactly. We're we are really investing
hard in enterprise readiness and the
tools that our customers are telling us
that they need. And I think that that
for me is the shift that we've also been
going through as a company is really not
just thinking about hey we're just going
to release models and you know have a
model really thinking about the endto
end of the business. How do we bring
this into solving real problems for real
customers and that is happening so
quickly across every single industry and
the number of different companies that
still feel like they're wrapping their
mind around how to best make use of
these models we're learning at the same
time. I think it's just so early in this
whole game to me the the like the
absolute size of the market growing so
quickly, our own revenue ramp growing so
quickly. I think it's still just like
none of us are anticipating how steep
that's all going to go.
>> Are you going to cut prices?
>> So again, the answer is always yes,
right? But it's about like I think that
what's going to keep happening is that
we're going to have frontier models. Um
I don't think there's going to be like a
massive shift in in the short term. I
don't think that that is the kind of
thing that's going to happen. But I
think the thing you should anticipate is
that over a year-long time horizon to
get to today's level of intelligence
that feels very premier, it's going to
be much cheaper. But there's going to be
a new thing that is going to be so much
better and you're going to be like, why
would I ever use this other one, right?
It's [clears throat] just it's just how
it's always going to be.
>> So Sadia Nadell has had some interesting
tweets and interviews recently. He
recently said, "The model is becoming a
commodity and the valuable asset is
company," or this might be a paraphrase,
"The valuable asset is a company
specific AI system that continually
learns from your data." Um, what do you
think about that? And is it weird to be
competing with Microsoft now?
>> Well, look, I don't think that there's
any layer of the stack here that is
going to just kind of be removed from
the value chain. I think that these
things multiplied together. And if you
think about the most the base layer of
compute right that is something where
it's just like no compute no AI
and to some extent you could say oh
comput is commoditized it's just flops
who cares about it but in reality like
you look at you look at today's chip
stocks you look at the uh you know
people who are selling compute kind of
what what is what the market is valuing
people at and they see that there's a
fundamental asset here that is just so
critical and I think that is because it
is a revenue center it is something that
anyone who's building AI has to rely on
and that there's a bunch of very
interesting dynamics in terms of the
efficiencies that you can squeeze out
and the margins, all these things. But
fundamentally, even though it's like you
can kind of squint it and say it's
commoditized, it's not. It's it's it's
not that that the value goes away. It's
not that the margins go away. It's like
something that the market will reward
because it has fundamental value and
that the importance of it's going to go
up over time. You can see that with some
of the prices that people are paying for
H100s, right? Hoppers are, you know,
kind of, you know, not not obsolete,
right? They're they're they're a a
previous gen chip and uh nor in any
normal situation where you're not
totally supply constrained, no one would
be buying them. But instead, the market
prices are up relative to to where they
were before. So there's this inversion
that's happening and again I think it's
going to keep happening where because
everyone has this avalanche of demand
that you're going to see prices and
margins and all of these things AC
continuing to increase at various levels
of the stack. And
>> I think the same kind of applies for
models where the models themselves are
also again they're not there's there's a
lot of competition there and I think
that's very good. I think it's good for
the enterprise. I think it's good for
customers, consumers. Um but I think
that there's a lot of areas where for
example our models have always been the
sort of smartest ones, right? The ones
that are able to solve these incredibly
hard problems. I think we're just
starting to reach a phase where you're
going to see the transformative impact
from that, right? It's like if we're
really able to speed up science through
models, the smarter the model, the
faster it's going to go. And it's very
very different from a model that has a
conversational interface that you're
able to, you know, is able to book your
travel, right? Or organize your
calendar. So that's also a dimension I
think we're going to do a very good job
in. But I'm just saying it's it's a
different area. Um and then I think that
the question of well how do you actually
connect the intelligence to your own
customers right to real value to you
have all these enterprises that have
built incredible businesses in different
domains and it's a huge thing and it's
not something where if you don't have
domain expertise that you're just going
to be able to do right and part of it is
that you need you think about regulated
industries you think about any area
where there's like you know think about
education where you have a parent you
have a teacher you have student you have
these different parties that need to
interact in very thoughtful ways for all
of these areas, all of these domains
that there's a lot of value to be built
by being in that area and thinking about
how the workflow should work, how these
models should be orchestrated. And so I
I really think that there's more than
enough to go around and I think that we
have to work together as a whole
ecosystem in order to deliver the kind
of value that I think is possible from
these systems.
>> Okay, just to go back to the SA point
one more time, uh he's called models a
commodity. He's trying to build his own
frontier intelligence. He's telling
potentially your customers, hey, you got
to come work with us because we're going
to help build these loops that will
learn from your data. Um, he's got
access to your IP, I think, till 2032.
So, how does it make you feel to hear
this coming from Saudia?
>> Look, I think that the most important
thing that is happening right now is the
usage of AI in the economy to really
transform the economy and to uplift
everyone. And so, I think that that is
something that I'm really focused on.
And the more that people are trying to
make that happen, I think that that's
better for everyone.
>> Um, GPT 5.6 is rumored to be on its way.
Supposed to be
this just a Twitter rumor, but I'm going
to read it to you. Um, always the best
rumors. Three times cheaper than Fable
up to 1.5 million token context. Uh,
stronger agentic coding workflows. Um,
how much of that is true? What should we
expect for GPT 5.6?
>> [laughter]
>> I mean, look, you should always expect
better, faster, smarter, the whole
thing.
>> So, everything confirmed.
[laughter]
>> Definitely believe everything you read
on Twitter. Yeah, maybe not.
>> That has actually been a source of
problems in my personal life. Um, okay.
So, uh, I want to end on health. Um, you
brought it up a couple times. You
actually had a question in the audience
about it earlier. Um, you know,
sometimes there's a story and you read
it and you say to yourself, I know this
person is speaking to the media and I
know that what they're saying sounds
like maybe it's true. Um, but there's
something wrong with the story and we're
not going to see more of it. Um, and
I've read a couple of those recently.
Um, one is I think is it your friend the
GitLab CEO um, Sid Sberage? He had um he
he got cancer and used uh he got all the
diagnostic testing uh he could have so
just went out and tested like crazy and
fed that data into chatt with the
assistance of some people who had built
purpose-built application for it and was
able I don't know if cure is the right
word but to beat back the cancer to a
degree. There was also this dog Rosie
the dog in Australia. You guys heard of
Rosie? like the craziest story where
this guy I'm gonna get some detail wrong
but a guy um biopsied his dog which had
cancer um ran the mutations across of
across Alphafold and then was able to
design an mRNA vaccine that he injected
into the dog um with the assistance of
chatbots to build this thing which ended
up being able to jump over tables again
and the tumor shrunk. Um, when we think
about the future of AI and health, um,
help us sort out the truth with this
question. Are these a couple of outliers
that made good headlines, but there was
something about the story we weren't
hearing, or is this going to become
standard in the future?
>> Absolutely going to become standard.
Absolutely. And it's I I personally have
a number of friends who have done very
similar things of get the data right
your health diagnostics
and use codecs right use these models to
get insights from them and I think that
there are many people like I think that
there's about 230 million people each
week who use chat GBT for health queries
right and that's been that's this is
like a staggering scale Right. And this
is pe these are people sometimes you
upload a scan. Sometimes you have
doctors who are telling you conflicting
information. And I think that we've been
in a world where patients are not
empowered, right? Patients have to be
the doctor, right? You're the decider.
You are accountable, right? You know,
doctor makes a mistake and you're going
to be paying the price for the rest of
your life. Like it's just it's a very
different kind of incentive. And this is
very personal for me. you know, my wife
has a number of health conditions and I
think that we've just been we've not
like I don't even know how we'd be able
to manage many of her conditions right
now without the use of of chat. And I
think we're just at the beginning of
this journey, right? That I think that
the degree to which
even if you have the best medical team,
the best access, the best best experts,
there's only so much that can be done,
right? That you think about the things
that are just outside of the reach of
humanity or even just sometimes it's
like someone didn't even read the chart,
right? and kind of missed a detail. All
of that we should be able to improve
massively through these tools. And so I
think that the personalized medicine and
sometimes it's going to be about drugs
and drug discovery that are of you know
for for mass market but sometimes it'll
be even for the kind of NF1 things like
the the disease um diagnoses that I
mentioned earlier today. Sometimes it
will be for just like trying to
understand conditions and trying to come
up with with new potential therapeutics.
All of that we're seeing it happening
right now in front of our eyes. It's not
theoretical. It's really happening. And
so one of the most I think it's like one
of the most astounding possibilities of
AI is how much it can improve our
health. And you think about the ripple
effects of the system, right? Where so
much spending on the health care system
happens right now. that's a massive part
of the economy and that if you're
actually able to help people prevent
issues, right, to to get ahead of
potential,
you know, health health problems, that's
something that actually then alleviates
a lot of burden, a lot of strain. And
we're in a world where doctors are
burned out, nurses are burned out, like
there's like a real crisis that's
happening in front of us. And I think AI
will be able to help with all of that.
Like we have that potential if we deploy
it and use it wisely and well. And so I
think that applying AI to medicine, like
that's something that I is really a
personal motivation for [music] me in in
thinking about this whole journey of of
what we're building, what we're trying
to do with Open AI. And I'm hopeful that
we as a as a world and a community can
make the most of that.
>> Let's hope. I think we will. I'm very
very confident.
>> Greg, thank you so much.
>> Thank you. [applause]
Great. Thank you. Thank you so much.
[applause]
Oh my god. Thank you everyone. You have
a good time today.
[applause]
>> Thank you. Should we do it again next
year?
[applause]
>> You going to come? Yeah.
>> All