(Music playing)
>> PATRICK COLLISON: Hi, Sam.
Thanks very much for joining us today.
>> SAM ALTMAN: Hi, Patrick.
Glad to be here, but I thought I was supposed to
be talking about John.
>> PATRICK COLLISON: Hmm, I think John has a bug.
>> JOHN COLLISON: Get out of here, Patrick.
You had your own talk this morning.
Sorry, Sam.
Patrick's always deepfaking his way into my talks.
Anyway, we should probably step out of these little
circles and get going.
(Music playing)
>> JOHN COLLISON: All righty, I am delighted to be
joined here in the flesh properly with Sam Altman.
And Sam is at this stage basically part of
Silicon Valley lore.
You founded Loopt when you were 19?
>> SAM ALTMAN: I think so.
>> JOHN COLLISON: Eighteen?
Yeah, and then you're on stage for the original iPhone launch
as, you know, one of the key partners.
You sold Loopt to Green Dot.
Sam ran Y Combinator for many, many years and most recently
founded OpenAI, which powered many of the demos, you know,
the GPT-powered natural language queries that you
saw earlier in the keynote.
So, Sam, thank you.
>> SAM ALTMAN: Thanks for having me.
>> JOHN COLLISON: You tweeted in 2014, long before you I saw this
reference recently, long before you started OpenAI: AI will
either be the best or worst thing ever, highest volatility
of any tech I can think of.
How are we doing?
(Laughter)
>> SAM ALTMAN: I think we're going to be great.
>> JOHN COLLISON: Okay.
>> SAM ALTMAN: I am much more confident than but I don't
remember tweeting that, but I'm much more confident than I felt
around then, at that time, about the ability to manage this.
I think we are on such a different technology tree than
my default would have been and the way things were going then.
I thought this idea that we're going to have, like, RL agents
that learn to play increasingly more complex games, where
they're really just trying to, like, beat humans, and
sort of we're going to, like, keep letting that go,
that didn't seem like a great trajectory to me.
But I think what we're doing now, where we have systems that
are really optimized to help us and work in a way that we can
sort of see what they're doing step by step, even if we
can't see exactly what's happening inside the neural
network, I feel way better.
>> JOHN COLLISON: Oh, okay, because you have been kind of
an AI doomer in the past, that this stuff is really dangerous.
So it sounds like you're meaningfully less so now?
>> SAM ALTMAN: Yeah, well, I still think we have to treat
this with, like, extreme seriousness and that there is
legitimate existential risk here, but I think through the
hard work of people at OpenAI and other AGI labs, we're making
significant progress and still have a very long way to go
towards, like, a trajectory we can all be pretty happy about.
>> JOHN COLLISON: There's probably a set of people here
who follow the Internet very closely and have read a lot of
less wrong posts and, you know, keep up with what, you know,
analyzer is saying and everything and guess why one
might be worried about AI.
And then I think there's probably a set of people here
who just, you know, have real lives, and they, you know, look
at the progress in AI, and they're, like, that's cool, and,
you know, autocorrect still doesn't work that well.
And just it doesn't seem like certainly the AI in my
autocorrect is, you know out -- is going to kill
me anytime soon.
What is if people are going to worry about AI, what
should they be worried about?
>> SAM ALTMAN: How many people are really worried about AI?
>> JOHN COLLISON: A decent number.
>> SAM ALTMAN: Not that much, that's great.
(Laughter)
>> SAM ALTMAN: Look, at some level I fundamentally
agree with you.
This is just, like, just software; right?
Other software has deterministic parts, nondeterministic parts.
It's you know, it's like fancy autocorrect.
So we could take that belief.
But I think from some very other way, like, truly, you know, no
tricks, no you know, no, like no parlor tricks, like, we
fundamentally have figured out how to create systems that can
learn, and that is something that I think is difficult to
overstate the importance of.
And when it moves from fancy autocorrect to something that is
much more complex than that, it may not be this, like, super
clear, super bright light; it may be a lot of the gray area,
but as these systems are capable of doing more and more
sophisticated tasks and figuring out more and more autonomously
increasingly complex problems, that certainly does shift the
way the world works.
At a minimum, things just go way faster in the world.
Like we have these AIs that are now kind of,
like, part of our society.
And, you know, that can go a lot of different ways, but it does
mean that what any individual is capable of doing and the speed
at which the world evolves is going to be very different.
>> JOHN COLLISON: Maybe a good analogy is, you know,
I heard one take on, you know, the recent financial
turbulence in bank runs.
Someone saying that just bank runs are different in
the age of Twitter.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: When you have much more virality in
the -- kind of the information cascades.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: Maybe that's a good example of, like, there
isn't anything inherently dangerous about, you know, a
bunch of tweets, but it does cause knock-on effects
to reckon with.
>> SAM ALTMAN: In the previous financial crisis that everybody
that I think that that was probably calibrated for,
it was, like, pre-iPhone and pre-Twitter pretty much.
You know, there were iPhones, but not everyone had them and
not everyone used everything mobile all the time.
And so that was like this example of where people were
totally miscalibrated because of two important technological
shifts, and both of those I would say will be smaller and
less revolutionary than what AI is going to be like.
>> JOHN COLLISON: Yep, so you guys have kind of two tigers
by the tail now with GPT4 or the GPTs, the
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: series of them and ChatGPT as well.
And everyone is saying ChatGPT is the fastest growing
consumer product in history.
Like what's the superlative for ChatGPT?
>> SAM ALTMAN: I mean, I don't know exactly what are those
things, but I will tell you, as John mentioned, I used to run Y
Combinator, and people would, like, ask me for advice.
And if you had told me as a startup you were going to, like,
release a product that had no viral loop, no sharing features,
no social anything, no network effect, like, no, like, you
know, nothing, like, no reason that you using it should make
anyone else want to sign up for it, also, that you weren't going
to, like, have the ability to use it without registering it in
the first place, like, I would say, like, you are in bad shape.
And I feel now like I've given bad advice for a long time.
(Laughter)
>> JOHN COLLISON: Well, ChatGPT is viral, just
not within the product.
>> SAM ALTMAN: Right.
>> JOHN COLLISON: People post tweet screenshots.
People, you know, are
>> SAM ALTMAN: Right, but that's, I mean, I would have
said in a vacuum that viral loop is, like, too difficult.
Like, you know, it worked for Wordle, I guess.
>> JOHN COLLISON: Well, just so you know, don't listen too much
>> SAM ALTMAN: But not for that long.
>> JOHN COLLISON: Don't listen too much to investors.
>> SAM ALTMAN: Clearly.
>> JOHN COLLISON: for product advice.
I feel like we knew that -- we knew that already.
What is your favorite like, again, we had in the keynote,
and we're very excited about just natural language queries.
Like SQL has always been in the interface.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: And so human language is a
much nicer interface.
We've been using it a lot inside Stripe for query optimization
that works really well.
What are your favorite GPT use cases?
Because you must have the best catalog of anyone.
>> SAM ALTMAN: You touched on this.
But before I talk about specific use cases, my favorite thing of
all is we have finally and I think legitimately gotten to a
new computer interface.
And there has been this arc for a long time of computers getting
easier and more natural to use.
So you're all holding up iPhones recording this.
Those are, like, way easier than a computer.
You get to, like, you know, just use your hands instead
of having to use a mouse.
It's, like, a little bit more direct.
But language is even more natural and even simpler, and
we're used to conveying very complex concepts to each other
via language, and then things just happen and that's it.
So the fact that we now have this new way to interact with
computers and just sort of new idea of software I think is
going to go super far, and one of the surprises will be just
how many places that turns out to be the right way to do it.
>> JOHN COLLISON: I think kind of maybe a funny restatement of
that is: Do you remember in this was during your Y
Combinator days, so I presume you saw this a lot.
There was this hype cycle in I want to say 2016 or 2017 in
Silicon Valley around conversational bots.
And people thought conversational bots were
the be-all and end-all.
And, you know, you would not go on to the
1-800-Flowers.com website.
You would instead, like, be interfacing with, like, a flower
bot through, like, a natural language interface, and that's
how you would buy flowers.
I just remember there's some demo of that at some point.
And it always seemed a bit off to me because, like,
the bots were so clunky.
>> SAM ALTMAN: So dumb, yeah.
>> JOHN COLLISON: Whereas now we just maybe need to bring back
that vision because maybe it was exactly right.
>> SAM ALTMAN: It's definitely happening now.
That's I think like many other tech hype cycles, it was right
in terms of what people wanted; the technology just
couldn't deliver it.
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: And to the degree that technology can deliver it,
I still think it's a way people want to use software.
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: And we're seeing that.
You asked about, like, favorite use cases.
We're seeing that from, like, how people want to learn things
and these new sort of, like, AI tutors or medical advisors to
just how they want to control a computer.
And, yeah, so I think that the user desire was right.
The technology just wasn't there.
>> JOHN COLLISON: Yeah, one of the ways we conceived this
inside of Stripe is anywhere someone is doing just manual
work or working withyou know, on a series of tasks, having
a team of research assistants with them, you know, cueing
up all of the relevant data and having everything they need at
their fingertips, you know, having done any, you know, mise
en place, you know, any work that they can do, you know, to
get things ready for them is probably just
a huge productivity boost.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: So that's one of the ways we're looking at it.
Okay, let's talk a bit about how OpenAI actually works.
How do you produce things like well, actually, before I get to
that, what's next after can you give us the inside scoop?
You know, you have GPT4, ChatGPT.
>> SAM ALTMAN: Probably five.
I mean maybe.
>> JOHN COLLISON: Okay, that's but I mean or 4.5 judging
based on your numbering system.
Anything else we should be excited about?
Not, obviously, the new products, but just
directionally, like, there's a group of people here who are
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: very excited about technology developments,
so let's prime them.
>> SAM ALTMAN: Multimodality is definitely something we're very
excited about, so you want these
>> JOHN COLLISON: Explain that to the non-AI.
>> SAM ALTMAN: Yeah.
You want a system that can't only interact with text but
can do text and audio and images and video and, you
know, who knows what else.
Obviously, you want actually, I think one of the areas that
people most want is just better reliability.
So if you could have the like the best one out of 10,000
responses to every GPT4 query, you'd probably be pretty happy
most of the time.
There's a lot of intelligence in there.
It's just not very evenly distributed to
the average query.
So if we can make that much better, if we can make the
system way, way more reliable, that's a big deal.
And then eventually you want a system that can, like, help do a
better job of generating new knowledge, but that's
going to be a little bit.
>> JOHN COLLISON: The reliability is a good point.
So, you know, people in AI I notice you love using
this term "hallucinate."
And in general people in AI love making up words for stuff.
And so, you know, it's inference rather than how long it takes to
run, and it's hallucinate rather than just making shit up.
(Laughter)
>> SAM ALTMAN: Yeah, that's a weird term.
>> JOHN COLLISON: Sometimes I know, it sounds like, you know
anyway, but this is one of the main problems and certainly, you
know, in, say, our use cases, like, we do need
the things to be true.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: And, you know, there's been historically an
area of mixed performance, sort of the Will's mustache
of GPT performance.
And so it seems like we're on a path to fixing that?
>> SAM ALTMAN: We're on a path to improving it a lot.
We have been improving it every week, but one of the things that
I think is just, like, deeply true about how people use this
is humans are fairly forgiving somewhat forgiving of other
humans making mistakes, extremely little forgiveness for
computers making mistakes.
And so the threshold for what it means to solve this is not what
it would take to have your human colleague solve it;
it's much, much higher.
And so that it is going to take a while, and I think
it'll be a very high bar.
>> JOHN COLLISON: GPT already makes up far less stuff than
your average dinner party guest?
>> SAM ALTMAN: For sure, but we've got to go much further.
>> JOHN COLLISON: And you said it's improving week over week.
I'm actually curious, how do you guys operationally track model
performance inside of OpenAI?
Like what will what is the
>> SAM ALTMAN: Yeah, we have a suite of evals.
And every time we, you know, do a different fine tune or make a
new model, we're looking at it constantly and saying
how's it doing on this whole stack of stuff.
>> JOHN COLLISON: But is it kind of like the CPI basket, where
you have to constantly keep changing what's in the eval?
>> SAM ALTMAN: You just keep making them harder.
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: You, likeyou know, we got a ways to go on
any kind of robustness
>> JOHN COLLISON: Yeah, yeah.
>> SAM ALTMAN: eval.
>> JOHN COLLISON: And let's talk about how OpenAI works.
And, again, I'm sure there's lots of people here
who are super curious.
Like you guys have, for example, the research versus
product I think split or maybe
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: you know, that's probably interesting for
people to hear about and just in general your philosophies
as you run OpenAI.
>> SAM ALTMAN: Well, the first thing I'd say is how it works
changes, like, every year or even less than that.
We're still in such a steep part of the technology
discovery curve that, you know, everyone you make
these, like, great plans.
What's that quote, like, everyone's got a plan until they
get punched in the face or something like that?
>> JOHN COLLISON: Yeah, Mike Tyson.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: And then you get, like, punched in the face and
you realize the org structure has got to change or what you
thought you were going to do or the product you wanted to make
is not possible or there's something much better or, like,
you know, who knows what.
So our the thing that we try to have is like our add variant is
that we are a very truth-seeking research culture.
We want to get the things to work.
We don't care if our ideas are the ones that turn out
not to be right.
And one of the things that I think really held the field back
is that a lot of the kind of research heavyweights in the
field for a long time, the whole culture of, like, academic AI
research, I think it wanted certain approaches to work more
than it wanted the technology to work.
And so we have this we'll do whatever it takes.
We will, like, follow the truth wherever it leads.
If the answer is we just have to, like, go scale up these,
like, ridiculously big systems, rather which is way less
intellectually satisfying than discovering this new brilliant
algorithm, but if that former is the one that works, that's what
we're going to do.
And that was really a culture that we built from
the beginning, but it means that, like, things
are difficult to predict.
>> JOHN COLLISON: Is it accurate to say there's kind of a
cyclicality to AI in discovering new techniques and then throwing
a whole pile of compute at it and then kind of going back and
discovering new techniques and then throwing the whole pile of
compute, and maybe part of your success is, as you
say, not being precious about just throwing a whole
pile of compute at it?
>> SAM ALTMAN: That idea you could sort of say there's a
cyclicality to that, but that whole idea of, like, throw a lot
of compute didn't really exist at all until a few years ago.
>> JOHN COLLISON: Really?
>> SAM ALTMAN: Yeah, this is not like an old idea.
>> JOHN COLLISON: Hmm.
>> SAM ALTMAN: Or it was sort of maybe known in some abstract
sense, but people other than us weren't really just saying,
like, let's just make that language model bigger.
Let's do the hard engineering.
>> JOHN COLLISON: Because it kind of seems
inelegant to an engineer.
>> SAM ALTMAN: It does.
>> JOHN COLLISON: that we're just going to brute force this.
>> SAM ALTMAN: It does.
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: So I don't like and, you know, the next thing we
dolike big computers are going to be important for sure but
I think we've now squeezed a lot of the easy juice there.
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: And so now it's back to, like, let's figure out
these very new ideas.
>> JOHN COLLISON: You're like a lock-picking expert
who uses a crowbar.
>> SAM ALTMAN: But we have this new tool.
We have, like, the GPT system that can help us
with all sorts of things.
>> JOHN COLLISON: Yeah, how do you create a truth-seeking
research culture?
>> SAM ALTMAN: I think it's basically all about the people.
Like with any company, it is all about the people and
particularly the initial people you hire, and we tried
to just be very diligent about who those people were
and who they weren't.
So there's, like, a lot of people who were just,
like, quite prestigious people that we tried hard to hire or
did hire in some cases in the early days that didn't
fit how we thought things were going to go.
>> JOHN COLLISON: And it sounds like it is quite important to
you that people are not they don't have a particular team
that they support in terms of techniques.
You just have to be open-minded.
>> SAM ALTMAN: Yeah, we have a very high internal flux of
movement between teams and new teams starting and stopping and
people changing to go work on something else, and I think
that's I think that is really important to how we work and
also different than I think how a lot of -- how a
lot of other people work.
>> JOHN COLLISON: How do you know if research
is worth funding?
Because, again, you know, on a product you can kind of see ifs
anyone is using it or not.
You get a lot of real-time feedback.
One of the defining aspects of research is maybe you're working
away for five years and it looks like it's a dead end and
then suddenly it takes off.
>> SAM ALTMAN: Yeah, I think you just nothing deep beyond that.
You just have to accept the much longer time horizon, and you
need oh, this is another thing about kind of the
culture of how you get to this.
You need people who are not that dependent
on external validation.
Everybody is like a little bit, and I think it does no
good to pretend otherwise, but you want people who are,
like, relatively not.
And if you know, if we're going that's some approach that we
think is working but a lot of experts in the field say, like,
that's really not going to work - they actually say much meaner
versions of it than that, you need people who will keep doing
that in the same way that if you don't get the normal dopamine
hits of more frequent contact with reality while you're
trying to research an idea you believe in but isn't yet proven,
you need you need people who are, like, going to be
okay through that many-year journey through the wilderness.
>> JOHN COLLISON: Hmm, and it sounds like you tried to
create a lot of connectedness with the organization, where
it's not, you know, I join a team, and then I work on that
team for five years.
People will get bounced around somewhat deliberately.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: And this is your third startup if you count
kind of YC as a startup?
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: And how do you lead OpenAI differently
or do you try to run things differently from
your previous companies?
>> SAM ALTMAN: Running a research group is very different
than anything else and certainly like like an investment
firm is different again.
My sense is you can learn some of the standard
things, like, you know, how to deal with people.
That transfers pretty well.
But I think in general people try to apply way too many
lessons from stuff they've previously done to what they're
doing now, and I think you really want to take you really
want to try to come in with a fresh pair of eyes and meet the
shape of whatever is in front of you.
And so I think, like, OpenAI, we didn't it, like, went against
basically all of the YC advice.
You know, we took us four and a half years to launch a product.
We're going to be the most capital-intensive startup in
Silicon Valley history.
We kind of didn't we were, like, building a technology without
any idea of who our customer was going to be or what they were
going to use it for.
>> JOHN COLLISON: Jokes aside, did you have investors wagging
their fingers at you and, you know, telling you that you're
doing it wrong in some way?
>> SAM ALTMAN: Yeah, and I was just sort of, like, I don't
really care; don't invest.
You know, you can, like, sell your stock to somebody else.
(Laughter)
>> JOHN COLLISON: That sounds different from previous
startups, where, you know, maybe you're more self-actualized.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: Maybe you don't have to care so much.
>> SAM ALTMAN: Yeah, but we did.
>> JOHN COLLISON: But it sounds like you did a deep learning.
>> SAM ALTMAN: We did change things like that.
>> JOHN COLLISON: Yeah, now, OpenAI, again, currently,
you know, ChatGPT is on everyone's lips.
The products are working incredibly well.
GPT4 is delivering a huge amount of real value to people.
I presume it did not feel that way all along the journey.
Maybe do you have any fun war stories or just
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: what was it like?
>> SAM ALTMAN: I do, but I will say, like, no one is as honest
about how bad the bad early days are as they should be
because it's so embarrassing in retrospect, but --
(Laughter)
>> SAM ALTMAN: Nervous laughter out there.
But the beginning of OpenAI was really tough, like, stuff was
not working that well.
We were this, like, extremely rag-tag group of people.
We were, as I mentioned earlier, like, mocked by
everyone serious in the field.
There was this like kind of, like, pretty famous guy that
runs a lab another AI lab that is not doing as well as OpenAI
anymore that would, like, go around to the media when they
would write something nice about OpenAI and tell them, like, you
know, I don't know why you're saying stuff.
This is just not a good group of people.
>> JOHN COLLISON: You had a kerfuffle with Elon Musk?
>> SAM ALTMAN: We had a kerfuffle with Elon Musk.
But more than that, like, we just didn't have
working technical progress.
Like we had, like, some little things.
You know, we did some projects that kind of worked.
We even, like, did Doda, which was great, and it was what let
us raise the money from Microsoft that let us do the GPT
series, but it was, like, it was not it was deeply unclear how we
were going to make AGI, and we were, like, unbelievably
outgunned by DeepMind at the time; and a lot of people
were, like, why are you even doing this, like,
DeepMind is untouchable.
No one had figured out the idea of language models.
Certainly no one had figured out the idea of putting in API
around one or releasing ChatGPT.
So, yeah, it was, like it was pretty bad.
It was pretty hard.
And we just, like, kept on the one foot in front of another,
but we were, like, constantly not able to find enough money
or compute or people, and you just keep going, and
eventually something works.
>> JOHN COLLISON: When you say you just keep going, so if I
think back to the because, again, I think every company
that, you know, gets to some scale definitely goes
through the period, you know, the dark times.
And if I think back to Stripe's dark times, like, you know,
there's a point early on where, like, just after launch and, you
know, the endorphins have worn off, and we had, like, a bunch
of early people leave all at once at one point, and, you
know, it's very draining, and, you know, you lose some of your
own confidence, and you think about what keeps you going.
And, one, you know, it helps if you have an idea that you're
really excited about, but at least in our case,
also, there's a momentum from serving customers.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: Where, I don't know, it's, like, just, well, we
got to stay going because, you know, we have all these requests
coming into the API and we have all these customers to serve.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: So we kind of don't have a choice.
It sounds, like, you know, you guys were doing
more foundational research.
You did not have that customer momentum to keep you going, and
so were you not tempted to just pack it all up and quit?
>> SAM ALTMAN: You know, there were all of these so I think
Silicon Valley used to be really good at research labs and then
it wasn't, and so we didn't have like like there's good canonical
advice now about what to do as a sort of product
startup, and it's totally true.
And this is one of the reasons YC pushes for it so much.
Once you have customers that you have to serve and, like, you
really feel that and they pull it out of you, but one of the
things that we had to sort of, like, rediscover about how to
run a research lab was how you keep that internal momentum
going when you don't have customers or you don't have,
like, contact with reality to know if you're doing the right
thing, and it is really hard.
A good set of colleagues matters.
We would try to, like, figure out how to set up, like, enough
external touch points that kind of like for our own, like, mine
included, like, sanity would, like, fake the equivalent of
having customer feedback, but there were, like, a lot of
things we had to rediscover how to do this.
>> JOHN COLLISON: Hmm, you know, you described the intense
competition and, you know, some of the other folks in
this space like DeepMind.
One thing I just observed with your saying this is the AI space
feels very competitive in spirit, where, like, people
really want to, you know, outpace the competition and,
like, there's a lot of competitive juices flowing, you
know, like, in a, you know, a sports game or something like
that, and, I don't know, it sort of reminds you of the early
days of computing where like you know, Microsoft versus Apple,
like, I mean, that was a real competitive battle; whereas, I
don't know, you know, there's other folks in our space, and
they're all grand, and, you know -- yeah, just it's a
bit more chill somehow.
And so is there something about these superfast moving
>> SAM ALTMAN: Actually, I think there's a way in which the AI
field is very competitive, as you said, but I think there's
this other way in which it's the least competitive, because I
think everyone working on it on this sort of frontier realizes
what's at stake, and we really want it to go well.
And so although, you know, people want to, like, be faster
and there's this, like, island, like, there is I think a really
deep appreciation for the need to collaborate and get this
right, and that is different than any space I've seen before.
>> JOHN COLLISON: What form does the collaboration take?
>> SAM ALTMAN: I mean, it's mostly just, like, the
leaders of the labs talking to each other.
>> JOHN COLLISON: Mhmm.
>> SAM ALTMAN: But increasingly I think it'll be about
governments doing it.
>> JOHN COLLISON: What will happen with the governments?
You know, there's a lot of talk now of AI regulation.
What should I be
>> SAM ALTMAN: I'll tell you what I want.
I don't know what they'll do.
What I want is some I think that computing systems above a
certain threshold, as we make more and more algorithmic
progress, are kind of as powerful for all the good and
also as dangerous for the bad as, like, any other human
technology we've had yet.
And what I would like is something like the IAEA,
the International Atomic Energy Group Agency that
does it for AI.
And so if you were going to create a giant AI training
system, and if you're going to train models that are above a
certain capacity, like, there should be global regulatory
authority over that that should say, you know, here's the test
you have to pass before you deploy something.
Here's, like, the standard for audits.
>> JOHN COLLISON: Well, wait, as we, you know, shut down nuclear
plants in Europe and, you know, fire up the new coal plants,
and, you know, maybe it feels like we're running slightly
backwards from a climate point of view, is the global
nuclear regulatory apparatus really the model we want?
>> SAM ALTMAN: We haven't had a bomb go off in war since 1945,
and I think most people in 1945 would have given a very low
probability of that happening.
It's not been perfect.
It's definitely been screwed up on the power side.
I'm honestly not sure that's the IAEA's fault.
Like the NRC in the US has never, as far as I know,
approved a plant that has then gone on to be built in the 45
years its existed, which is, like, incredibly shameful.
But, yeah, we haven't had a bomb go off.
>> JOHN COLLISON: And you credit at least to some degree
>> SAM ALTMAN: At least some of that.
>> JOHN COLLISON: -- to the IAEA.
Sorry, you just reminded me with nuclear, I forgot in Sam's
introso, you know, Sam has done all these very impressive
things, and just while doing that, in his spare time, he has
also done things like been the primary backer of Helion,
which is the leading I think nuclear fusion.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: startup.
And should we be excited about
>> SAM ALTMAN: You should be excited about that.
>> JOHN COLLISON: having fusion in the future?
>> SAM ALTMAN: I so fusion is going to work.
What I think is still unclear
>> JOHN COLLISON: I'm sorry, fusion will work?
>> SAM ALTMAN: Fusion will work.
>> JOHN COLLISON: Okay, you heard it here.
>> SAM ALTMAN: But, but
>> JOHN COLLISON: Sorry, I already tweeted it out; yeah.
>> SAM ALTMAN: The thing that I think matters now is how cheap
we can make the energy out of the system and how
quickly we can deploy it to the world, and these are
both tremendous challenges.
Maybe not quite as hard as getting the actual physics
to work, but up there.
And so whether there's like it's totally possible we get to a
world where fusion works, but it is more expensive
than solar plus storage.
That'd be really sad.
And it's totally possible that fusion works and it's cheap, but
we just can't manufacture it at the, like, thousands of
gigawatts of generative capacity we need for Earth.
And if both things happen, if the cost, like, falls by a
factor of 10 or 100, then the demand really goes crazy, and
it's really, really hard to make enough.
But I think probably we will figure it out, and probably we
will get to a world where in addition to the cost of
intelligence falling dramatically, the cost of energy
falls dramatically too.
And if both of those things happen in the same time -- I
would argue that they are currently the two most
important inputs to the whole economy -- we get to a
super different place.
>> JOHN COLLISON: Yeah, I was going to say, are those two
things more coupled than people realize, where electricity
>> SAM ALTMAN: In all these ways
>> JOHN COLLISON: is one of the limiting factors
>> SAM ALTMAN: Yeah, in all these ways they are.
Like if you really want to make, like, the biggest, most capable
super intelligent system you can, you need eyewatering
amounts of energy.
And if you have an AI that can help you, like, move faster and
do better materials science, you can probably get the fusion a
little bit faster too.
>> JOHN COLLISON: Hmm, is there as you trace back the text tree
and look at something like Helion, you know, get all
these interesting questions about what's dependent on what.
You know, could the Romans have had their own Industrial
Revolution or something like that, or, you know, were there
technologies that were needed.
And could we have had what Helion is doing
in, say, the '60s?
>> SAM ALTMAN: No.
>> JOHN COLLISON: Okay.
>> SAM ALTMAN: Again, it's, like, it's always funny how
these things play out, but the whole semiconductor revolution
also means that we can do the kind of switches that
we need for Helion now.
10 years ago, 15 years ago certainly it just would have
been totally impossible.
The materials science, the progress that we've made for
many different parts of the machine, the computer modeling
stuff that we can get to, like, understand how the plasma is
going to work, none of these things would have
been possible before.
>> JOHN COLLISON: So it's the product of decades
of materials science
>> SAM ALTMAN: On many different
>> JOHN COLLISON: and computer modeling?
>> SAM ALTMAN: Yeah, it's the product of, like, major progress
in five different areas being multiplied together.
>> JOHN COLLISON: Mhmm.
>> SAM ALTMAN: That means you can do things that the
last time around people made serious fusion efforts
were totally impossible.
>> JOHN COLLISON: Okay, okay; sorry, I distracted us.
Going back to AI regulations, so you think capping
compute is just a sensible, pragmatic way to like
>> SAM ALTMAN: I wouldn't cap it.
I think that I mean, maybe you should at some points along the
way, but, like, long term that's clearly not the answer.
But I would just say above a certain above increasing
thresholds there are increasing regulatory demands.
So if you're going to make a system that is likely to have
some very powerful capabilities, you should have a different
regulatory regime than if you're, like, training a
model on your laptop.
>> JOHN COLLISON: Do you think we'll sadly just back into
regulation through essentially protectionism and kind of, you
know, humans protecting human turf, whereyou know, the
lawyers have figured this out, where, you know, various things
just require a lawyer.
Why, because we make the rules.
We're the lawyers.
>> SAM ALTMAN: I don't, you know, I used to
worry about that.
I still have some worry about that.
You can, like, see all these mistakes where people have
tried to protect a bunch of jobs that they just that's
not a winning strategy.
I think the tenor of the conversation is quickly shifting
towards this AGI thing is just different than
anything we've faced before, and let's focus on that.
>> JOHN COLLISON: Mhmm, so as we look at again, you have, you
know, around 3,000 people in the room working on, you know, in
every imaginable industry, and, you know, I'm sure many
of them are working actually on AI startups.
You know, if you were putting out a request for AI startups,
you know, you guys are, obviously, with OpenAI powering
through your API products, a huge number, maybe most all of
the AI startups, what do you want to see people develop?
Like what would be on your request for a startups list?
>> SAM ALTMAN: We touched on this earlier, but, like,
anything that's in the direction of how can you design a new
interface model, that just seems to keep working, you know, like
from this sort of chatbot interface to something that
before was more difficult to do to the sort of, like,
copilot for every different workflow, this seems to me
like an easy area to go after.
What's happening in education is amazing.
So, like, the AI tutors are surprising me to the
upside, and I suspect can go much, much further.
But, you know, the most fun part of this is you always
underestimate the creative energy of the world
when it really gets focused on something.
>> JOHN COLLISON: Yeah, we work with at Stripe an extraordinary
number of vertical SaaS companies, where they are, you
know, providing software for some very specific vertical, and
it goes, like, really niche.
I mean, there are, like, very popular, you know, software
platforms for managing a Boy Scout troop, because as it turns
out, there's a lot of Boy Scout troops out there, and I know
they're all running around.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: You know, you need a software
platform to manage them.
But, anyway, you know, we see this across every kind of single
industry, and I was thinking that it will probably be
software platforms that are the vehicle through which many of
the productivity gains of AI are distributed to kind of the
economy more broadly, because most companies are not tech
companies, but SaaS is probably how you can deliver a lot of
these productivity gains.
>> SAM ALTMAN: I think that's probably right, but
I would say my own intuitions are uncertain here.
Like, you know, maybe where most of the gains come is in another
few turns of the crank you can just say, like, hey, AI, please
cure all cancer for me, and then it goes "choo choo" and gives
you the answer, and, like, people get it that way.
>> JOHN COLLISON: That'd be fun.
Obviously, it's very exciting.
(Laughter)
>> JOHN COLLISON: It'd be very exciting, actually, when you say
education, just like the Bloom 2 sigma effect that tutoring kids
one on one with mastery learning techniques where you,
like, wait until the kid has actually understood it until you
go to the next level.
The reason they call it the Bloom 2 sigma effect is because
it produces two sigmas of outperformance versus the
baseline, and that is maybe the most exciting thing.
>> SAM ALTMAN: I think this is why the AI tutors are surprising
so much on the upside, because they can do exactly this so well
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: one on one.
>> JOHN COLLISON: There's two sigmas.
Like that's a lot of
>> SAM ALTMAN: It's a lot.
>> JOHN COLLISON: sigmas.
>> SAM ALTMAN: It is a lot of sigmas.
>> JOHN COLLISON: The companies here that are not AI
companies, any advice for them as they look to adopt AI
within their business; so.
>> SAM ALTMAN: Yeah, internal productivity.
We're seeing a huge divergence between people at companies --
like individual people at companies and companies that
embrace versus companies that don't embrace AI tools.
Whether your business has anything to do with AI or not,
you can certainly, like, figure out how to dramatically impact
your, you know, productivity per unit time, and I think it'd be
very dangerous not to -- not to learn how to do that.
>> JOHN COLLISON: When you talk about AGI,
three years, five years?
What are you thinking?
>> SAM ALTMAN: Honestly, the, like, goalposts on that one are
moving so much that I think at this point we're kind of we'd
have to get into, like, a very precise talk agreement on what
the definitions are, and then I could give you some range.
>> JOHN COLLISON: What is your working definition?
>> SAM ALTMAN: Well, my own one is moving, too,
as we get closer.
Like a thing that I would say is definitely AGI is a system that
can dramatically increase the rate of scientific progress.
And I don't think this has to mean, like, we have this
one system that is, like, going off and discovering
all the science itself.
That's actually not what I expect will happen, but that
somehow working in collaboration with us and sort of, like,
becoming part of society in all these ways, like, our collective
rate of scientific progress dramatically changes shape, and
I would expect that by the end of the decade.
>> JOHN COLLISON: Yeah, I mean, it feels like if that is your
your criterion, we can see that actually quite soon.
>> SAM ALTMAN: Yeah, but, again, it's, like, not a
fully autonomous system.
>> JOHN COLLISON: Yes.
>> SAM ALTMAN: Like do you some people wouldn't count that.
>> JOHN COLLISON: Yeah, yeah, yeah, yeah.
Well, that's something that's kind of the moving definition.
Many people probably don't know this.
You were, along with Paul Graham, Stripe's
first angel investor.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: Why did you invest in Stripe?
>> SAM ALTMAN: I thought and continue to believe in addition
to the fact that this is just, like, one of the largest markets
that if you can take friction out of economic transactions,
which is part of why the Internet is exciting as a whole
but only one part, that that would just, like, enable a
tremendous amount of growth in the economy and sort of
better stuff happening faster, cheaper, better, more
accessible, all of that.
And that has become even more.
Like let's say Stripe has certainly outperformed anyone's
expectation, but that fundamental belief I felt then
and I feel very strongly now.
I also thought you and Patrick were awesome.
And that's kind of, like, all I look for in a startup, like,
big-market idea that I personally want to support
great founders, something that would have, like,
real long-term defensibility.
It was so long ago, I actually wrote a check, a physical check,
one of the last times probably in my life.
But, you know, when you say it
>> JOHN COLLISON: Kind of ironic for Stripe when you
think about it.
>> SAM ALTMAN: Well, I mean, it makes sense,
(Laughter)
>> SAM ALTMAN: It makes sense that it would be one
of the last ones.
>> JOHN COLLISON: Yeah, we can digitize that.
>> SAM ALTMAN: Like, you know, when people say, like, I wrote a
check into this startup, like, I actually wrote a
check into Stripe.
>> JOHN COLLISON: Yeah, we have our new RFAs.
We can digitize all that for you now.
>> SAM ALTMAN: Great.
>> JOHN COLLISON: But you guys are, obviously, now monetizing
ChatGPT with Stripe.
Any product feedback for us, sir?
>> SAM ALTMAN: We're very happy.
And I'll actually tell you one thing that I that the
team is probably tired of hearing about this.
Like Stripe does such a good job with customer support,
documentation, that whole experience, like, so much
better than basically any other startup.
And OpenAI, we're going to get better at this.
I apologize to everyone.
It's clearly the bottom of the pack.
But it's very inspiring, and we appreciate it.
>> JOHN COLLISON: When you say documentation, actually, one
thing I'm quite excited about and, again, this is, like, a
very narrow, I don't know, thing to be excited about with GPT4,
but I am, is, like, technical documentation.
Like, Stripe has produced a lot of quite technical, quite deep
documentation, but everyone's use case is kind of different,
where it's, like, well, I'm building a platform in the
Netherlands to accept point-of-sale payments with the
new S700 reader, which has the longest battery life
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: of any reader we've ever shipped, and I just
want to, you know, figure out what my implementation plan is.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: That is the kind of thing GPT4
is excellent for.
>> SAM ALTMAN: Yeah, this seems like clearly the future.
>> JOHN COLLISON: Yeah.
>> SAM ALTMAN: Where you'll just go to GPT4 or some system like
it and ask it any question you have, and it'll give
you a perfect custom answer.
>> JOHN COLLISON: Actually, it may be a
narrow product question.
Will you, like, go to the Stripe integration builder, and the
integration builder will come up with a thing for you, or is
everyone kind of overthinking it, and actually you'll just go
to GPT4 and say, hey, GPT4, how should I build
my Stripe integration?
>> SAM ALTMAN: We're still trying to figure out the
answer to that question.
You know, do people want to use one centralized
chatbot for their entire workflow, or do they want
the chat interface everywhere?
So we're like you know, we're trying versions where it all
comes together with plugins.
We're trying versions where we make it easy to embed
chatlike experiences elsewhere.
And I think the world hasn't figured this out yet.
>> JOHN COLLISON: Yeah, and you guys you should
pitch your API product.
Because, again, one of the things that people are worried
about is, like, the data usage and, like, you know, you want to
be able to use data, but you don't necessarily want to go
back into feeding, you know, the models and everything
like that, but you guys have solved all that with your,
you know, API product.
>> SAM ALTMAN: Yeah.
>> JOHN COLLISON: Just people may not know that.
>> SAM ALTMAN: So we're trying to, like, clear
this misconception up.
We don't ever train on any data submitted to the API.
So you can, like, use it comfortably as a business.
That will never get into our training set.
And, you know, that's, like, really important to us,
because we realize how sensitive the data is.
We don't retain it long term.
We don't train on it.
It's, like, your data.
We totally get that.
We have plenty of other ways to improve our models.
We will continue to drive prices down a lot over time.
We will continue to make the models better.
Like we have this vision that we can make our APIs pretty simple
to use, and then as and pretty, like, long-term stable, and then
as we make the models smarter and smarter, it just
sort of, like, lifts up the whole Internet, the
whole technology industry, with more intelligence.
So that is our, like, we want to always, like, astonish people on
how much intelligence they get for how little money.
And I think we can keep doing that for a long time.
>> JOHN COLLISON: When you talk about data, I think
that one thing that maybe surprises people is how
small the models are.
Like I don't know if you have really too many thoughts
on just how small the various models are.
Will we see a lot of on device or, you know, on
prem or, like, on my individual device inference?
>> SAM ALTMAN: It will be a long time until you can have, like, a
GPT4 class model running on your iPhone, but, you know,
technology marches on, and some day.
>> JOHN COLLISON: Yeah, so maybe for specialized use cases
>> SAM ALTMAN: Yes.
>> JOHN COLLISON: but probably not kind of for broad use cases.
Okay, and last question, because we are running up against time.
But, again, you talk to a lot of startups.
You advise a lot of startups.
You have companies big and small, not just
startups, but including some startups in the room.
As you stare down 2023, I mean, we've spent a lot of time
talking about AI in particular, but, also, as you look at the
macroeconomy, as you look at the hiring market, as you look at,
you know, the return to office, you know, we were talking about
backstage, everything like that, just what are you advising your
startups on these days?
What mistakes do you see people making that you try to correct?
What advice would you leave everyone with?
>> SAM ALTMAN: I mean, look, I think definitely one of the tech
industries worst takes in a long time was that everybody
could go full remote forever and startups didn't need to
be together in person.
And, you know, there was going to be no loss of creativity.
I would say that the experiment on that is over, and the
technology is not yet good enough that people can be
full remote forever, particularly not startups.
I feel pretty strongly that startups need a lot of in-person
time, and the more fragile and nuanced and uncertain a set of
ideas are, the more time you need together in person.
Like that is the thing that we still have not translated on to,
like, Slack and Zoom and whatever else.
And I was talking to a lot of recent startups who have been
who were all remote the whole time and decided not to come
back versus the ones that either started in person
or came back together in person, and they're different.
Like there's a huge separation there.
So I think that's one big thing.
The sort of macroeconomy, I don't really know.
>> JOHN COLLISON: You've always had good predictions
on the macro.
>> SAM ALTMAN: I only think about AI now.
I used to have, like, all these interests.
(Laughter)
>> SAM ALTMAN: You know, I used to, like, have a lot of ideas,
and now I only have one.
I mean, presumably the economy recovers at some point.
(Laughter)
>> JOHN COLLISON: That's actually as specific as many
serious macro-prognosticators  or as specific as many
macro-prognosticators, I guess, but you're just
much more concise.
They use many more words to express roughly
the same sentiment.
>> SAM ALTMAN: Right, yeah.
(Laughter)
>> JOHN COLLISON: Okay.
So get back to the office, and presumably the economy
recovers at some point.
>> AUDIENCE: I have a question.
>> JOHN COLLISON: Well, we're not taking live questions,
but apparently we are with this person.
So we'll take one live question, then we'll close.
(Laughter)
>> JOHN COLLISON: This is a new way.
>> SAM ALTMAN: Sure.
>> AUDIENCE: So there was a research paper about the
emergent abilities to be more original in how we
measure progress than the inherent property of
language models itself.
>> SAM ALTMAN: The question was: There was a paper about emergent
properties being more something of how we measure progress than
an inherent property of language models itself.
I didn't see the paper.
I totally disagree.
But I think it's, like, a question of how you
define emergent properties.
The number of experts that sort of try to weigh in about why
this is just statistical prediction, there's no
intelligence, there's, you know, nothing useful here versus,
like, the experience of people who use these models, I'd say
the experts are clearly in the wrong in some important
sense, and you can safely just, like, ignore all of
their Twitter hyperventilation.
(Laughter)
>> JOHN COLLISON: Not speaking about anyone in particular?
>> SAM ALTMAN: So many people in particular.
(Laughter)
>> SAM ALTMAN: But in the same way, like, you know, you can
say, well, this really isn't emergent behavior;
it's a measurement error.
If it's doing something new and useful, that is emergent
behavior in a sense that I think like utility is the figure of
merit here, but more than that, I think now that we can just,
like, see a year or two into the future, there is definitely
going to be behavior that I would say just categorically
doesn't exist right now that is going to come in a way that I
would call and I think most normal people would
call emergent, and what matters is the utility it
eventually provides us.
>> JOHN COLLISON: Okay, Sam, thank you so much.
>> SAM ALTMAN: Thank you.
(Applause)
>> SAM ALTMAN: Thanks for having me.
(Applause)