You know, that $1.4 trillion you mentioned, we'll spend it over a very long period of time.
I wish we could do it faster.
I think it would be great to just lay it out for everyone, once and for all, how those numbers are going to work.
Exponential growth is usually very hard for people.
OpenAI CEO Sam Altman joins us to talk about OpenAI's plan to win as the AI race tightens,
how the infrastructure math makes sense, and when an OpenAI IPO might be coming.
And Sam is with us here in studio today.
Sam, welcome to the show.
Thanks for having me.
So OpenAI is 10 years old.
It's crazy to me.
And ChatGPT is three.
But the competition is intensifying.
This place where OpenAI headquarters was in a code red, is in a code red after Gemini 3 came out.
And everywhere you look, there are companies that are trying to take a little bit of OpenAI's advantage.
And for the first time I can remember, it doesn't seem like this company has a clear lead.
So I'm curious to hear your perspective on how open AI will emerge from this moment and when.
First of all, on the code red point, we view those as like relatively low stakes, somewhat frequent things to do.
I think that it's good to be paranoid and act quickly when a potential competitive threat emerges.
This happened to us in the past.
That happened earlier this year with DeepSeek.
And there was a code red back then, too.
Yeah, there's there's a saying about pandemics,
which is something like when a pandemic starts,
every bit of action you take at the beginning
is worth much more than action you take later.
And most people don't do enough early on
and then panic later.
And you certainly saw that during the COVID pandemic.
But I sort of think of that philosophy
as how we respond to competitive threats.
And I think it's good to be a little paranoid.
Gemini 3 has not, or at least has not so far,
had the impact we were worried it might.
But it did in the same way that DeepSeek did.
identify some weaknesses in our product offering strategy and we're addressing those very quickly
i don't think we'll be in this code red that much longer uh you know like these are not these are
historically these have been kind of like six or eight week things for us um but i'm glad we're
doing it uh just today we launched a new image model which is a great thing and that's something
consumers really wanted um last week we launched 5.2 which uh is going over extremely well and
very quickly uh we'll have a few other things to uh launch and then we'll also have some continuous
improvements like speeding up the service but you know i think this is like my guess is we'll be
doing these once maybe twice a year for a long time and that's uh part of really just making
sure that we win in our space um a lot of other companies will do great too and i'm happy for them
but you know chat gbt is still uh by far by far the dominant uh
chatbot in the market and i expect that lead to increase not decrease over time
um the the models will get good everywhere but a lot of the reasons that people use a product
consumer or enterprise uh have much more to do than just with the model and we've you know been
expecting this for a while so we try to build the whole cohesive set of things that it takes to make
sure that we are, you know, the product that people most want to use. I think competition is good. It
pushes us to be better. But I think we'll do great in chat. I think we'll do great in enterprise and
in the future years, other new categories, I expect we'll do great there too. I think people
really want to use one AI platform. People use their phone at their personal life and they want
to use the same kind of phone at work most of the time. We're seeing the same thing with AI. The
strength of ChatGPT consumer is really helping us win the enterprise. Of course, enterprises need
different offerings, but people think about, okay, I know this company opening up and I know how to
use this ChatGPT interface. So the strategy is make the best models, build the best product around
it and have enough infrastructure to serve it at scale. Yeah, there is an incumbent advantage.
ChatGPT, I think earlier this year was around 400 million weekly active users. Now it's at 800
million. Reports say approaching 900 million. But then on the other side, you have distribution
advantages at places like Google. And so I'm curious to hear your perspective. Do you think
the models are going to commoditize? And if they do, what matters most? Is it distribution?
Is it how well you build your applications? Is it something else that I'm not thinking of?
I don't think commoditization is quite the right framework to think about the models.
There will be areas where different models excel at different things.
For the kind of normal use cases of chatting with a model maybe there will be a lot of great options For scientific discovery you will want the thing that right at the edge that is optimized for science perhaps So models will have different strengths and the most economic value
I think, will be created by models at the frontier
and we plan to be ahead there.
And we're very proud that 5.2
is the best reasoning model in the world
and the one that scientists are having
the most progress with.
But also, we're very proud
that it's what enterprises are saying
is the best at all the tasks that a business needs to do its work. So there will be times
that we're ahead in some areas and behind in others, but the overall most intelligent model,
I expect to have significant value even in a world where free models can do a lot of the stuff that
people need. The products will really matter. Distribution and brand, as you said, will really
matter um in chat gpt for example personalization is extremely sticky people love the fact that the
model gets to know them over time and you'll see us push on that uh much much more um people have
experiences with these models that they then really kind of associate with it uh and you know i
remember someone telling me once like you kind of pick a toothpaste once in your life and buy it
forever or most people do that apparently um and people talk about it they have one magical
experience with chat gpt health care is like a famous example where people put their um you know
they put a blood test into chat gpt or put their symptoms in and they figure out they have something
and they go to a doctor and they get cured of something they couldn't figure out before like
those users are very sticky uh to say nothing of the personalization on on top of it um
there will be all the product stuff uh we just launched our browser uh recently and i think
that's pointing at a new uh you know pretty good potential moat for us uh the devices are further
off but i'm very excited to to do that so i think there'll be all these pieces and on the enterprise
uh what creates the the moat or the competitive advantage um i expect it to be a little bit
different. But in the same way that personalization to a user is very important in consumer,
there will be a similar concept of personalization to an enterprise where a company will have a
relationship with a company like ours and they will connect their data to that. And you'll be
able to use a bunch of agents from different companies running that. And it'll kind of like
make sure that information is handled the right way. And I expect that'll be pretty sticky too.
we already have more than a million
people think of us largely as a consumer company
but we are going to definitely get into enterprise
share the stat
well I didn't actually
a million we have more than a million enterprise
users but we have like
just absolutely rapid
adoption of the API
and like the API business
grew faster for us this year than
even chat GPT
so the enterprise stuff is
also
So, you know, it's really happening starting this year.
Can I just go back to this?
Maybe if commoditization is not the right word, model some maybe parity for everyday
users.
Because you started off your answer saying, okay, maybe everyday use will feel the same,
but at the frontier, it's going to feel really different.
When it comes to ChatGPT's ability to grow, if I'll just use Google as an example, if ChatGPT
and Gemini are built on a model that feels similar for everyday uses.
How big of a threat is the fact that, you know,
Google has all these surfaces through which it can push out Gemini,
whereas ChatGPT is fighting for every new user?
I think Google is still a huge threat.
You know, extremely powerful company.
If Google had really decided to take us seriously in 2023, let's say,
we would have been in a really bad place.
I think they would have just been able to smash us.
But their AI effort at the time
was kind of going in not quite the right direction.
Product-wise, they didn't, you know,
they had their own code red at one point,
but they didn't take it that seriously.
Everyone's doing wild.
Code red's out here, yeah.
And then, and also Google has probably
the greatest business model in the whole tech industry.
And I think they will be slow to give that up.
But bolting AI into web search,
I don't maybe wrong maybe like drinking the kool-aid here I don't think that'll work as well
as reimagining the whole this is actually a broader trend I think is interesting bolting AI
onto the existing way of doing things I don't think is going to work well as redesigning stuff
in this sort of like AI first world that's part of why we wanted to do the consumer devices in
the first place but it applies at many other levels if you stick AI into a messaging app that doing a nice job summarizing your messages and drafting responses for you that is definitely a little better
But I don't think that's the end state. That is not the idea of you have this like really smart AI
that is like acting as your agent, talking to everybody else's agent and figuring out when to
bother you, when not to bother you and how to, you know, what decisions it can handle and when
it needs to ask you. So similar things for search, similar things for like productivity suites.
I suspect it always takes longer than you think, but I suspect we will see new products in the
major categories that are just totally built around AI rather than bolting AI in. And I think
this is a weakness of Google's, even though they have this huge distribution advantage.
Yeah. I've spoken with so many people about this question. When ChatGPT came out initially,
I think it was Bendig Devins that suggested you might not want to put AI in Excel. You might want
to just reimagine how you use Excel. And to me, in my mind, that was like you upload your numbers
and then you talk to your numbers. But one of the things people have found as they've developed this
stuff is there needs to be some sort of backend there. So is it that you sort of build the backend
and then you interact with it, with AI as if it's a new software program?
Yeah, that's kind of what's happening.
Why wouldn't you then be able to just bolt it on top?
I mean, you can bolt it on top, but the...
I spend a lot of my day in various messaging apps,
including email, including text, Slack, whatever.
I think that's just the wrong interface.
So you can bolt AI on top of those.
And again, it's like a little bit better.
But what I would rather do is just sort of like
have the ability to say in the morning,
here are the things I want to get done today.
Here's what I'm worried about.
Here's what I'm thinking about.
Here's what I'd like to happen.
i do not want to be i do not want to spend all day messaging people i do not want you to summarize
them i do not want you to show me a bunch of drafts deal with everything you can you know me
you know these people you know what i want to get done um and then you know like batch every couple
of hours updates me if you need something but that's a very different flow than the way these
apps work right now yeah and i was going to ask you what chat chippie t is going to look like in
the next year and then the next two years is that kind of where it's going to be perfectly honest i
expected by this point chat gbt would have looked more different than it did in launch what did you
anticipate i don't know i just thought like that chat interface was not going to go as far as it
turned out to go like we i mean it was put up it looks better now but it is broadly similar to when
it was put up as like a research preview it was not even meant to be a product we knew that the
text interface was very good you know like the everyone's used to texting their friends and they
like it um the chat interface was very good but i would have thought to be as big and as
significantly used for real work of a product as what we have now the interface would have had to go
much further than it has now i still think it should do that but there is something about the
generality of the current interface that I underestimated the power of.
What I think should happen, of course, is that AI should be able to generate different kinds of
interfaces for different kinds of tasks. So if you are talking about your numbers,
it should be able to show you that in different ways and you should be able to interact with it
in different ways. And we have a little bit of this with features like Canvas. It should be way
more interactive. It's like right now, you know, it's kind of a back and forth conversation.
It'd be nice if you could just be talking about an object and it could be continuously updating.
You have more questions, more thoughts, more information comes in. It'd be nice to be more
proactive over time where it maybe does understand what you want to get done that day. And it's
continuously working for you in the background and send you updates. And you see part of this,
the way people are using codecs, which I think is one of the most exciting
things that happened this year is codecs got really good uh and that points to
a lot of what i hope the shape of the future looks like um but
it is surprising to me i was gonna say embarrassing but it's not i mean clearly
it's been super successful uh it is surprising me how little chat gpt has changed over the last
three years yeah the interface works yeah but I guess what the guts have
changed and you talked a little bit about how personalization is big to me
and I think this has been one of your preferred features to memory has been a
real difference maker I been having a conversation with chat GPT about a forthcoming trip that has lots of planning elements for weeks now And I can just come in in a new window and be like all right let pick up on this trip
And it has the context and it knows.
It knows the guide I'm going with.
It knows what I'm doing.
The fact that I've been like planning fitness for it and can really synthesize all of those things.
How good can memory get?
I think we have no conception because the human limit,
like even if you have the world's best personal assistant,
they can't remember every word you've ever said in your life.
They can't have read every email.
They can't have read every document you've ever written.
They can't be looking at all your work every day
and remembering every little detail.
They can't be a participant in your life to that degree.
And no human has like infinite perfect memory.
And AI is definitely gonna be able to do that.
and we actually talk a lot about this like right now memory is still very crude very early we're
in like the you know the gpt2 era of memory but what it's gonna be like when it really does
remember every detail of your entire life and personalized across all of that and not just the
facts but like the little small preferences that you had that you maybe like didn't even think to
indicate but the ai can pick up on uh i think that's gonna be super powerful that's one of the
features that still maybe not a 2026 thing, but that's one of the parts of this I'm most excited
for. Yeah. I was speaking with a neuroscientist on the show and he mentioned that you can't find
thoughts in the brain. The brain doesn't have a place to store thoughts, but computing,
there's a place to store them. So you can keep all of them. And as these bots do keep our thoughts,
of course, there's a privacy concern. But the other thing is something that's going to be
interesting is we'll really build relationships with them. I think it's been one of the more
underrated things about this entire moment is that people have felt that these bots are their
companions, are looking out for them. And I'm curious to hear your perspective.
When you think about the level of, I don't know if intimacy is the right word, but companionship
people have with these bots, is there a dial that you can turn to be like, oh, let's make sure people
become really close with these things or, you know, we turn the dial a little bit further and
there's an arm's distance between them. And if there is that dial, how do you modulate that the
right way? There are definitely more people than I realized that want to have, let's call it close
companionship. I don't know what the right word is like. Relationship doesn't feel quite right.
Companionship doesn't feel quite right. I don't know what to call it, but they want to have
whatever this deep connection with an AI is.
There are more people that want that
at the current level of model capability
than I thought.
And there's like a whole bunch of reasons
why I think we underestimated this.
But at the beginning of this year,
it was considered a very strange thing to say.
You wanted that.
Maybe a lot of people still don't.
Revealed preference.
You know, people like their AI chatbot
to get to know them and be warm to them
and be supportive.
And there's value.
even for people who, in some cases, even for people who say they don't care about that,
still have a preference for it. I think there's some version of this which can be super healthy,
and I think adult users should get a lot of choice in where on the spectrum they want to be.
There are definitely versions of it that seem to me unhealthy, although I'm sure a lot of people
will choose to do that. And then there's some people who definitely want the driest, most
efficient tool possible.
So I suspect like lots of other technologies,
we will run the experiment.
We will find that there's unknown unknowns,
good and bad about it.
And society will over time figure out
how to think about where people should set that dial.
And then people have huge choice
and set it in very different places.
So your thought is allow people basically to determine this?
Yes, definitely. But I don't think we know like how far it's supposed to go, like how far we should allow it to go. We're going to give people quite a bit of personal freedom here.
There are examples of things that we've talked about that other services will offer, but we
won't.
We're not going to have RAI try to convince people that it should be in an exclusive romantic
relationship with them, for example.
You've got to keep it open.
No, I'm sure that that will happen with other services.
Well, I guess, yeah, because the stickier it is, the more money that service makes.
All these possibilities kind of, they're a little bit scary when you think about them
a little bit deeply.
Totally.
This is one that really does, that I personally, you can see the ways that this goes really
wrong.
Yeah.
You mentioned enterprise.
Let's talk about enterprise.
You were at a lunch with some editors and CEOs of some news companies in New York last week
and told them that enterprise is going to be a major priority for OpenAI next year.
I'd love to hear a little bit more about why that's a priority, how you think you stack up against Anthropic.
I know people will say this is a pivot for OpenAI that has been consumer focused.
So just give us an overview about the enterprise plan.
So our strategy was always consumer first.
There were a few reasons for that.
One, the models were not robust and skilled enough for most enterprise uses.
And now they're getting there.
the second was we had this like clear opportunity to win in consumer and those are rare and hard to
come by and i think if you win in consumer it makes it massively easier to win in enterprise
and we are we are seeing that now but as i mentioned earlier this was a year where we
enterprise growth outpaced consumer growth and given where the models are today where they will
get to next year. We think this is the time where we can build a really significant enterprise
business quite rapidly. I mean, I think we already have one, but it can grow much more.
Companies seem ready for it. The technology seems ready for it. Coding is the biggest example so far,
but there are others that are now growing, other verticals that are now growing very quickly.
and we're starting to hear enterprises say,
you know, I really just want an AI platform.
Which vertical company?
Finance.
Science is the one I'm most excited about
of everything happening right now, personally.
Customer support is doing great.
But yeah, we have this thing called GDP, though.
I was going to ask you about that.
Can I actually throw my question out?
Sure about that.
All right, because I wrote to Aaron Levy,
the CEO of Box. And I said, I'm going to meet with Sam. What should I ask him? He goes,
throw a question out about GDP Val, right? So this is the measure of how AI performs in knowledge
work tasks. And I said, okay, I went back to the release of GPT 5.2, the model that you recently
released and looked at the GDP Val chart. Now this, of course, it's an open AI evaluation.
That being said, the GPT 5 thinking model. So this is the model released in the summer. It tied
knowledge workers at 38% of tasks. Beat or tied, I think. Beat or tied. So 38.8%. GPT 5.2 thinking,
beat or tied at 70.9% of knowledge work tasks. And GPT 5.2 pro, 74.1% of knowledge work tasks.
And it passed a threshold of being expert level. It handled, it looks like something like 60% of
expert tasks, of tasks that would make it on par with an expert in the knowledge work. What are the
implications of the fact that these models can do that much knowledge work? So, you know, you were
asking about verticals, and I think that's a great question. But the thing that was going through my
mind and why I kind of was stumbling a little bit is that eval, I think it's like 40 something
different verticals that a business has to do. There's make a PowerPoint, do this legal analysis,
this, you know, write up this little web app, all this stuff. And the eval is, do experts prefer
the output of the model relative to other experts for a lot of the things that a business has to do?
Now, these are small, well-scoped tasks. These don't get the kind of complicated, open-ended,
creative work that, you know, figuring out a new product. These don't get a lot of collaborative
team things but a co-worker that you can assign an hour's worth of tasks to and get something you
like better back 74 or 70 percent of time if you want to pay less is still pretty extraordinary if
you went back to the launch of chat tbt three years ago and said we were going to have that
in three years most people say absolutely not um and so as we think about how enterprises are
can integrate this, it's no longer just that it can do code. It's all of these knowledge work tasks
you can kind of farm out to the AI. And that's going to take a while to really kind of figure out
how enterprises integrate with it, but should be quite substantial.
I know you not an economist so I not going to ask you what is the macro impact on jobs But But let me just read you one line that I heard you know in terms of how this impacts jobs from Blood in the Machine on Substack This is from a technical copywriter They said chatbots came in and made it so my job
was managing the bots instead of a team of reps. Okay, that to me seems like it's going to happen
often. But then this person continued and said, once the bots were sufficiently trained up to
offer good enough support, then I was out. Is that going to become more common? Is that what
bad companies are going to do? Because if you have a human who's going to be able to
sort of orchestrate a bunch of different bots, then you might want to keep them.
I don't know. How do you think about this? So I agree with you that it's clear to see
how everyone's going to be managing like a lot of AIs doing different stuff.
eventually like any good manager hopefully your team gets better and better but you just
take on more scope and more responsibility i am not i am not a jobs doomer
um short term i have some worry i think the transition is likely to be rough uh in some cases
but we are so deeply wired to care about other people what other people do we are so
we seem to be so focused on relative status and always wanting more and to be of use and service
to express creative spirit whatever whatever whatever has driven us this long i don't think
that's going away now i do think the jobs of the future or i don't even know if jobs is the right
word whatever we're all going to do all day in 2050 probably looks very different than it does
today um but i but i i don't have any of this like oh life is going to be without meaning and
the economy is going to totally break.
We will find, I hope, much more meaning.
And the economy, I think, will significantly change.
But I think you just don't bet against evolutionary biology.
You know, I think a lot about how we can automate
all the functions at OpenAI.
And then even more than that,
I think about what it means to have an AI CEO of OpenAI.
It doesn't bother me.
I'm thrilled for it.
I won't fight it.
I don't want to be the person hanging on,
being like, I can do this better the handmade way.
ai ceo just make a bunch of decisions to sort of like direct all of our resources to giving ai more
energy and power it's like um i mean no you would really you put a guardrail on yeah like
obviously you don't want an ai ceo that is not governed by humans but if you think about
if you think about maybe like
um this is a crazy analogy but i'll give it anyway if you think about a version where
like every person in the world was effectively on the board of directors of an AI company
and got to, you know, tell the AI CEO what to do and fire them if they weren't doing a good job
at that and, you know, got governance on the decisions. But the AI CEO got to try to like
execute the wishes of the board. I think to people of the future, that might seem like quite a
reasonable thing. Okay. So we're going to move to infrastructure in a minute, but before we leave
this section on models and capabilities when's gp6 gpt6 coming um i expect i don't know when we'll
call a model gpt6 uh but i would expect new models that are significant gains from 5.2 in the first
quarter of next year what does significant gains mean i don't have like an eval score in mind for
you yet but uh more enterprise side of things or definitely both the there will be a lot of
improvements to the model for consumers uh the main thing consumers want right now is not more
iq enterprises still do want more iq so uh we'll improve the model in different ways for the kind
of for different uses but uh i our goal is a model that everybody likes much better so infrastructure
You have 1.4 trillion thereabouts and commitments to build infrastructure.
I've listened to a lot of what you've said about infrastructure.
Here are some of the things you said.
If people knew what we could do with compute, they would want way, way more.
You said the gap between what we could offer today versus 10x compute and 100x compute is substantial.
Can you help flesh that out a little bit?
What are you going to do with so much more compute?
Well, I mentioned this earlier a little bit. The thing I'm personally most excited about is to use AI and lots of compute to discover new science.
I am a believer that scientific discovery is the high order bit of how the world gets better for everybody.
And if we can throw huge amounts of compute at scientific problems and discover new knowledge which the tiniest bit is starting to happen now It very early These are very small things
But, you know, my learning in the history of this field is once the squiggles start and
it lifts off the x-axis a little bit, we know how to make that better and better.
But that takes huge amounts of compute to do.
So that's one area we're like throwing lots of AI, discovering new science, curing disease,
lots of other things.
a kind of recent cool example here is we built the Sora Android app using Codex
and they did it in like less than a month they used a huge amount one of the nice things about
working at OpenAEyes you don't get any limits on Codex they used a huge amount of tokens but
they were able to do what would normally have taken a lot of people much longer and Codex kind
mostly did it for us.
And you can imagine that going much further,
where entire companies can build their products
using lots of compute.
People have talked a lot about how video models
are going to point towards these generated,
real-time generated user interfaces.
That will take a lot of compute.
Enterprises that want to transform their business
will use a lot of compute.
doctors that want to offer good personalized healthcare that are like constantly
measuring every sign they can get from each individual patient. You can imagine that using
a lot of compute. It's hard to frame how much compute we're already using to generate AI output
in the world. But these are horribly rough numbers. And I think it's like undisciplined
to talk this way, but I always find these mental thought experiments a little bit useful.
So forgive me for the sloppiness.
Let's say that an AI company today might be generating something on the order of 10 trillion
tokens a day out of frontier models.
More, but not.
It's not like a quadrillion tokens for anybody, I don't think.
Let's say there's 8 billion people in the world, and let's say on average someone's, these are, I think, totally wrong, but let's say someone, the average number of tokens outputted by a person per day is like 20,000.
You can then start to, and the token, you could, to be fair, then we'd have to compare the output tokens of a model provider today, not all the tokens consumed.
but you can start to look at this and you can say we're gonna have these models at a company be
outputting more tokens per day than all of humanity put together and then 10 times that
and then 100 times that and you know in some sense it's like a really silly comparison
but in some sense it gives a magnitude for like how much of the
intellectual crunching on the planet is like human brains versus ai brains and
But that's kind of the relative growth rates there are interesting.
And so I'm wondering, do you know that there is this demand to use this compute?
So for instance, would we have surefire scientific breakthroughs if OpenAI were to put double the compute towards science or with medicine?
Would we have that clear ability to assist doctors?
Like how much of this is sort of a supposition of what's to happen versus clear understanding based off of what you see today that it will happen?
Everything based off what we see today is that it will happen.
It does not mean some crazy thing can't happen in the future.
Someone could discover some completely new architecture and there could be a 10,000 times efficiency gain.
And then we would have really probably overbuilt for a while.
but everything we see right now about how quickly the models are getting better at each new level
how much more people want to use them each time we can bring the cost down how much more people
really want to use them um everything about that indicates
to me that there will be increasing demand and people using these for wonderful things for silly
things. But it just so seems like this is the shape of the future. It's not just how many tokens
we can do per day, it's how fast we can do them. As these coding models have gotten better, they can
think for a really long time, but you don't want to wait for a really long time. So there will be
other dimensions It will not just be the number of tokens that we can do but the demand for intelligence across a small number of axes and what we can do with those you know if you like if you have like a really difficult health care problem do you want to use
5.2 or do you want to use 5.2 pro even if it takes dramatically more tokens i'll go with the
better model i think you will um let's just try to go one level deeper um going to the scientific
discovery. Can you give an example of like a scientist? It doesn't have to. Well, maybe it's
one that you know today that's like, I have problem X. And if I put, you know, compute Y
towards it, I will solve it. But I'm not able to today. There was a thing this morning on Twitter
where a bunch of mathematicians were saying they were all like replying to each other's tweets.
They're like, I was really skeptical that LMs were ever going to be good. 5.2 is the one that
crossed the boundary for me. It did, it, you know, figured out this, it, with some help,
it did this small proof, it, this, it discovered this small thing, but it's, this is actually
changing my workflow. And then people were piling on saying, yeah, me too. I mean, some people were
saying five poems was already there and not many. Um, but that, that was like, that's a very recent
example. And this model's only been out for five days or something where people are like, all right,
you know, the mathematics, yeah, the mathematics research community seems to say like, okay,
something important just happened. I've seen Greg Brockman has been highlighting all these
different mathematical scientific uses in his feed. And something has clicked, I think, with 5.2
among these communities. So it'll be interesting to see what happens as things progress.
We don't... One of the hard parts about compute at the scale is you have to do it so far in advance.
So that 1.4 trillion you mentioned, we'll spend it over a very long period of time. I wish we
could do it faster. I think there would be demand if we could do it faster. But it just takes an
enormously long time to build these projects and the energy to run the data centers and the chips
and the systems and the networking and everything else. So that will be over a while. But, you know,
we, from a year ago to now, we probably about tripled our compute. We'll triple our compute
again next year, hopefully again after that. Revenue grows even a little bit faster than that,
But it does roughly track our compute fleet.
So we have never yet found a situation where we can't really well monetize all the compute we have.
If we had, I think if we had, you know, double the compute, we'd be at double the revenue right now.
Okay, let's talk about numbers since you brought it up.
Revenue is growing.
Compute spend is growing.
But compute spend still outpaces revenue growth.
I think the numbers that have been reported are open AI is supposed to lose something like $120 billion between now and 2028-29 where you're going to become profitable.
So talk a little bit about how does that change?
Where does the turn happen?
I mean, as revenue grows and as inference becomes a larger and larger part of the fleet, it eventually subsumes the training expense.
So that's the plan. Spend a lot of money training, but make more and more. If we weren't continuing to grow our training costs by so much, we would be profitable way, way earlier. But the bet we're making is to invest very aggressively in training these big models.
The whole world is wondering how your revenue will line up with the spend.
The question's been asked if the trajectory is to hit $20 billion in revenue this year and the spend commitment is $1.4 trillion.
So I think it would be great just to lay out.
Again, over a very long period.
Yeah, and that's why I wanted to bring it up to you.
I think it would be great to just lay it out for everyone once and for all how those numbers are going to work.
it's it's very hard to like really i i i find that one thing i certainly can't do it and very
few people i've ever met can do it you know you can like you have good intuition for a lot of
mathematical things in your head but exponential growth is usually very hard for people to do a
good quick mental framework on like for whatever reason there were a lot of things that evolution
needed us to be able to do well with math in our heads modeling exponential growth doesn't seem to
one of them um so the thing we believe is that we can stay on a very steep
growth curve of revenue for quite a while and everything we see right now continues to indicate
that we cannot do it if we don't have the compute again we're so compute constrained uh and it hits
the revenue line so hard that i think if we get to a point where we have like a lot of compute
sitting around that we can't monetize on a, you know,
profitable per unit of compute basis be very reasonable to say okay this is like a little
how's this all going to work but we've penciled this out a bunch of ways uh we will of course
also get more efficient uh on like a flops per dollar basis as you know all of the work we've
been doing to make compute cheaper comes to pass um but we see this consumer growth we see this
enterprise growth there's a whole bunch of new kinds of businesses that we haven't even launched
yet but will um but compute is really the lifeblood that enables all of this so we you know there's
like checkpoints along the way and if we're a little bit wrong about our timing or math we can
we have some flexibility but we have always been in a compute deficit it is always constrained what
we're able to do uh i unfortunately think it will always be the case but i wish it were less the
case and I'd like to get it to be less of the case over time because I think there's so many
great products and services that we can deliver and it'll be a great business. Okay, so it's
effectively training costs go down. As a percentage. They go up massively overall.
And then your expectation is through things like this enterprise push, through things like people
being willing to pay for ChatGPT through the API, OpenAI will be able to grow revenue enough to pay
for it with revenue. Yeah, that is the plan. Now, I think the thing, so the market's been
kind of losing its mind over this recently. I think the thing that has spooked the market
has been the debt has entered into this equation. And the idea around debt is you take debt out when
there's something that's predictable. And then companies will take the debt out, they'll build,
and they'll have predictable revenue. But this is a new category. It is unpredictable.
is that how do you think about the fact that that has entered the picture here so first of all i
think the market more lost its mind when earlier this year you know we would like meet with some
company and that company stock would go up 20 or 15 the next day that was crazy that felt really
unhealthy um i'm actually happy that there's like a little bit more skepticism and rationality in
the market now because it felt to me like we were just totally heading towards a very unstable bubble
And now I think people are some degree of discipline.
So I actually think things are, I think people went crazy earlier and now people are being
more rational.
On the debt front, I think we do kind of, we know that if we build infrastructure, we
the industry, someone's going to get value out of it.
And it's still totally early.
I agree with you.
But I don't think anyone's still questioning there's not going to be value from like AI infrastructure.
And so I think it is reasonable for debt to enter this market.
I think there will also be other kinds of financial instruments.
I suspect we'll see some unreasonable ones as people really, you know, innovate about how to finance this sort of stuff.
But, you know, like lending companies money to build data centers, that seems fine to me.
I think the fear is that if things don't continue apace, like here's one scenario, and you'll probably disagree with this, but like the model progress saturates, then the infrastructure becomes worth less than the anticipated value was.
And then, yes, those data centers will be worth something to someone, but it could be that they get liquidated and someone buys them at a discount.
Yeah. And I do suspect, by the way, there will be some like booms and busts along the way. These things are never a perfectly smooth line.
um first of all it seems very clear to me and this is like a thing i happily would bet the company on
that the models are going to get much much better we have like a pretty good window into this
we're very confident about that even if they did not i think the there's like a lot of inertia in
the world it takes a while to figure out how to adapt to things the overhang of the economic value
that I believe 5.2 represents relative to what the world has figured out how to get out of it so far
is so huge that even if you froze the model at 5.2, how much more like value can you create and
thus revenue can you drive? I bet a huge amount. In fact, you didn't ask this, but if I can go on
a rant for a second, we used to talk a lot about this two by two matrix of short timelines,
long timelines, slow takeoff, fast takeoff, and where we felt at different times, the
kind of probability was shifting and that that was going to be, you could kind of understand
a lot of the decisions and strategy that the world should optimize for based off of where
you were going to be on that two by two matrix There like a Z axis in my head in my picture of this that emerged which is small overhang big overhang And I kind of thought that
I guess I didn't think about that hard, but like my retro on this is I must have assumed
that the overhang was not going to be that massive, that if the models had a lot of value in them,
the world was pretty quickly going to figure out how to deploy that.
But it looks to me now like the overhang is going to be massive in most of the world.
You'll have these areas like some set of coders that'll get massively more productive by adopting
these tools.
But on the whole, you have this crazy smart model that, to be perfectly honest, most people
are still asking similar questions they did in the GPT-4 realm.
Scientists, different coders, different.
Maybe knowledge work is going to get different.
But there is a huge overhang.
And that has a bunch of very strange consequences for the world.
We have not wrapped our head around all the ways that's going to play out yet.
But it's very much not what I would have expected a few years ago.
I have a question for you about this capability overhang.
Basically, the models can do a lot more than they've been doing.
I'm trying to figure out how the models can be that much better than they're being used for.
But a lot of businesses, when they try to implement them, they're not getting a return on their investment.
Or at least that's what they tell MIT.
I'm not sure quite how to think about that because we hear all these businesses saying, you know, if you 10x the price of GPT 5.2, we would still pay for it.
You're hugely underpricing this.
We're getting all this value out of it.
So I don't, that doesn't seem right to me.
Certainly, if you talk about what coders say, they're like, this is, you know, I'd pay 100 times the price or whatever.
Just be bureaucracy that's messing things up.
Let's say you believe the GDP value numbers.
And maybe you don't for good reason.
Maybe they're wrong.
But let's say it were true.
And for kind of these well-specified, not super long duration knowledge work tasks, seven out of ten times, you would be as happy or happier with the 5.2 output.
You should then be using that a lot.
And yet it takes people so long to change their workflow.
They're so used to asking the junior analyst to make a deck or whatever that they're going to like.
It just that's stickier than I thought it was.
You know, I still kind of run my workflow in very much the same way, although I know that I could be using much more than I am.
Yeah. All right. We got 10 minutes left.
I got that was quick. I got four questions.
Let's see if we can lightning round through them.
so uh the device that you're working on we'll be back with open ai ceo sam altman right after this
um what i've heard phone size no screen um why couldn't it be an app if it's the phone
if it's the phone without a screen first we're gonna do a fam a small family of devices it will
not be a single device uh there will be over time a this is this is not speculation so i'm
may turn out to be totally wrong but i think there will be a shift over time to the way people use
computers where they go from a sort of dumb reactive thing to a very smart proactive thing
that is understanding your whole life your context everything going on around you very aware of
the people around you physically or close to you via a computer that you're working with
and I don't think current devices are well suited to that kind of world and I am a big believer that
we like we work at the limit of our devices you know you have that computer and it has a bunch of
design choices like it could be open or closed but it can't be you know there's not like a okay
pay attention to this interview but be closed and like whisper in my ear if I forget to ask Sam a
question or whatever um maybe that would be helpful and there's like you know there's like
a screen and that like limits you to the kind of same way we've had graphical use interfaces
working for many decades and there's you know a keyboard that was built to like slow down how fast
you could get information into it and these have just been unquestioned assumptions for a long time
but they worked and then this totally new thing came along and it opens up a possibility space but
i don't think the current form factor of devices is the optimal fit to be very odd if it were
for this like incredible new affordance we have oh man we could talk for an hour about this but let move on to the next one cloud You talked about building a cloud Here an email we got from a listener
At my company, we're moving off Azure and directly integrating with OpenAI to power our AI experiences in the product.
The focus is to insert a stream of trillions of tokens powering AI experiences through the stack.
is that the plan to build a big big cloud business in that in that way first of all
joins of tokens a lot of tokens and if you know you asked about the need for compute and
our enterprise strategy like our enterprises have been clear with us about how many tokens
they'd like to buy from us and we are going to again fail in 2026 to meet demand but the strategy
is companies most companies seem to want to come to a company like us and say i'd like to aim my
company with AI. I need an API customized for my company. I need ChatGPT Enterprise customized for
my company. I need a platform that can run all these agents that I can trust my data on. I need
the ability to get trillions of tokens into my product. I need the ability to have all my internal
processes be more efficient. And we don't currently have a great all-in-one offering for them.
And we'd like to make that. Is your ambition to put it up there with the AWS and Azure's of the
world ah i think it's i think it's a different kind of thing than those like i don't i don't
really have an ambition to go offer whatever all the services you have to offer to host a website
or i don't even know but uh but i i think the concept
yeah my guess is that people will continue to have their
call it web cloud and then i think there will be this other thing where like a company will be like
I need an AI platform for everything that I want to do internally that service I want to offer,
whatever. And, you know, like it does kind of live on the physical hardware in some sense, but
I think it'll be a fairly different product offering. Let's talk about discovery quickly.
You've said something that's been really interesting to me that you think that the
models or maybe it's people working with models or the models make small discoveries next year and
big ones within five is that the models is it people working alongside them and what makes you
confident that that's going to happen yeah people using the models like the the models that can like
figure out their own questions to ask that does feel further off but if the world is benefiting
from new knowledge like we should be very thrilled and you know like i think the whole course of
human progress has been that we build these better tools and then people use them to do more things
and then out of that process, they build more tools.
And it's this scaffolding that we climb
layer by layer, generation by generation,
discovery by discovery.
And the fact that a human's asking the question,
I think in no way diminishes the value of the tool.
So I think it's great.
I'm all happy.
At the beginning of this year,
I thought the small discoveries were going to start in 2026.
They started in 2025, in late 2025.
Again, these are very small.
I really don't want to overstate them.
but anything
feels qualitatively to me
very different than nothing
and certainly when we launched
ChatGPT three years ago
that model was not going to make
any new contribution
to the total of human knowledge
what it looks like
from here to five years from now
this journey to big discoveries
I suspect it just looks like
the normal hill climb of AI
it just gets a little bit better
every quarter
and then all of a sudden we're like, whoa,
humans augmented by these models
are doing things that humans five years ago
just absolutely couldn't do.
And whether we mostly attribute that
to smarter humans or smarter models,
as long as we get the scientific discoveries,
I'm very happy either way.
IPO next year?
I don't know.
Do you want to be a public company?
You seem like you can operate private for a long time.
when you go before you needed to
in terms of funding.
There's like a whole bunch of things at play here.
I do think it's cool
that public markets get to participate
in value creation.
And, you know, in some sense,
we will be very late to go public
if you look at any previous company.
It's wonderful to be a private company.
We need lots of capital.
we're gonna you know cross all of the sort of shareholder limits and stuff at some point
so am i excited to be a public company ceo zero percent um am i excited for open ad to be a public company in some ways i am and in some ways i think it be really annoying i listened to your theo von interview very closely uh great
interview he was really cool really knows what he's talking about he's exciting yashua benji oh
he's he did his homework you told him this was right before gpt5 came out that gpt5 is smarter
than us in almost every way. I thought that that was the definition of AGI. Isn't that AGI? And if
not, has the term become somewhat meaningless? These models are clearly extremely smart on a
sort of raw horsepower basis. You know, there's all this stuff on the last couple of days about
GPT 5.2 has an IQ of 147 or 144 or 151 or whatever it is. It's like, you know, depending on whose
test it's like it's some high number and you have like a lot of experts in their field saying
it can do these amazing things and it's like contributing it's making it more effective you
have the gdp value things we talked about one thing you don't have is the ability for the model
to not be able to do something today realize it can't go off and figure out how to learn to get
good at that thing learn to understand it and when you come back the next day it gets it right
and that kind of continuous learning like toddlers can do it it does seem to me like an important part
of what we need to build now can you have something that most people consider an agi without that i
would say clear i mean there's a lot of people that would say we're at agi with our current models
um i think almost everyone would agree that if we were at the current level of intelligence and
had that other thing, it would clearly be very AGI-like. But maybe most of the world will say,
okay, fine, even without that, like it's doing most knowledge tasks that matter,
smarter than us. And most of us in most ways, we're at AGI. You know, it's discovering small
pieces of new science, we're at AGI. What I think this means is that the term, although it's been
very hard for all of us to stop using is very underdefined right i i have a i have a a candidate
like one thing i would love it's we got it wrong with agi we never define that that you know the
new term everyone's focused about is when we get to super intelligence um so my proposal is that we
agree that you know agi kind of went whooshing by it was didn't change the world that much
or it will in the long term, but okay, fine. We built AGIs at some point, you know,
we're in this like fuzzy period where some people think we have and some people think we have and
more people think we have and then we'll say, okay, what's next? A candidate definition for
superintelligence is when a system can do a better job being president of the United States,
CEO of a major company, you know, running a very large scientific lab than any person can,
even with the assistance of AI. I think this was an interesting thing about what happened with chess,
where chess got, it could be humans. I remember this very vividly, the deep blue thing. And then
there was a period of time where a human and the AI together were better than an AI by itself.
and then the person was just making it worse
and the smartest thing was the unaided AI
that didn't have the human
not understanding its great intelligence.
I think something like that
is an interesting framework for super intelligence.
I think it's a long way off
but I would love to have a cleaner definition this time around.
Well, Sam, look, I have been in your products
using them daily for three years.
Thank you very much.
Definitely gotten a lot better.
I can't even imagine where they go from here.
We'll try to keep getting them better fast.
Okay.
And this is our second time speaking, and I appreciate how open you've been both times.
So thank you for your time.
Thank you, everybody, for listening and watching.
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This past year, we've had Google DeepMind CEO Demis Asabas on twice, including one with Google founder Sergey Brin.
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