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123123. We're going
to do all that personal

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drama in 90 minutes. Oh yeah,
oh don't you worry. Don't you worry.

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Is this part all going
to be in the podcast?

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Bear with me through
my slightly silly intro, but uh

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it's going to be okay. >> [music]

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[music]

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>> Welcome to the fortress of finance, the

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capital of capital.
This is not that podcast. This is core

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memory. I am Ashley Vance.
>> And I'm Kylie Robison. And I think we

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have a hell of an episode for you.

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Normally we do an introduction
of people at this point, but

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that's perhaps unnecessary today.
We have Sam Altman

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and Greg Brockman, the co-founders
of OpenAI. You may have heard of it. Thank

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you guys for being here.
Thank you for having us.

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>> [laughter] >> Thank you so much.

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I think this is the first time you guys
have ever done a podcast together. That

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is amazing, but I think that's true.

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Certainly in a long time. In a long time.
Maybe the first one. And I will

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not fight you for doing it on our show,
but you did buy a podcast. Is there We

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just lucked out. You lucked out.

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>> Yeah. I'll take it. I'll take it. I
was curious why you guys bought a podcast.

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It's not really You don't
have to go deep on it, but did

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you have quick thoughts on that?

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I think that the people who who do TBPN
are incredible. I think they're just

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very creative thinkers, and I think
that in this world that we're moving to of

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building these AI systems that are so
useful for people and helping people

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understand why why that's valuable
for them in their personal lives and work

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lives. Like these are the kinds of people
that I think could help tell that

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message. I've seen you on it. Have you
been on TBPN? I have. Yeah, okay. Okay.

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Um I don't watch all
the episodes, but It's

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a fun pod. Um well I thought, you know
since this is you guys haven't done this

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together at least in a while, we would I
was going to go down nostalgia lane just

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for a little bit at the beginning.

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Um, Kylie and I have
gotten to know both of

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you over the years. You know,
I was just I we were reflecting as we were

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preparing for this.
We're a little bit past the 10-year

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anniversary. You guys are two of the
remaining co-founders. I think Wojciech

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is the third. So you you know, you're
this constant line that's been running

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through the company. >> company
you started as an underdog. You

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ended up as the the top dog. It's all

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through a lot of drama, undulations.

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Um, I was I was just I
was genuinely curious of

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how your relationship through
all this has changed and how you

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guys have played off each other
and if it's morphed over time.

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>> It is extremely nice in Look,
we always wish there were less

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drama. We wish we just
got to focus on the tech.

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But in a world of so much chaos and

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drama and you know, tension and fighting

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and power struggles, it has been
unbelievably nice to have a relationship

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with someone that's got the full context.
We have all this history and to

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really depend on each other
in amazing times, very tough times.

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Uh, it has been one of the nicest things
about OpenAI. You know, in many ways the

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very first

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moment of OpenAI was right after this

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dinner that we did in July of 2015 and

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Sam and I were driving
back to the city together

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and we looked at each other and we were
like, we have to do this. Right? There'd

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been all this conversation of is it too
late to start a lab that could go after

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AGI and have have a positive impact.

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>> so ridiculous now that we were so
worried about that. Yeah, it was too late.

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>> Yeah, that's the You
missed it. You missed it.

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>> feeling like that when you guys started
though. I was like, no. DeepMind's going

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to run away with this, you know, yeah.

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Yeah. And you know, the conclusion
of dinner was it wasn't obviously

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impossible. And I think both of us
just felt like, okay, this is just so

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important. We just have to do it. Yeah.

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And I think that spirit continues. And a
lot of how we operate in the early days,

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like I remember I was unemployed at the
time, so I was full-time on it the next

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day. Sam actually had a day job, um
but we were constantly on the phone, like

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probably like five times a day.
Yeah, exactly.

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>> Were you Were you guys already Were you
like close friends already at that point

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or or not? We had known each other for
a super long time. Or I felt like a super

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I don't actually Yeah. What What year
was that? 2010? 2011? Whenever you

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started at Stripe. That's right.
Yes. So we met through the Collisons.

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>> Yeah. And so we'd been
kind of casual social friends.

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>> it wasn't as long as I thought. Yeah,
maybe it was 2010 and this was now 2015.

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So five years. Time compresses. Yeah,

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and and and obviously
I mean the being in the

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pressure cooker of all this doing this
work, I mean I would imagine it only you

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guys have only got closer over time.

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Yeah, you you know, people use the word
trauma bonding. I hate that. I've I've

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like I like the other things about like
the people you're in the fox holes with

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the fox hole with, but the the one
of the nice things about hard work,

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no matter what,
but certainly hard work in um

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stressful times is you really
like forge these relationships that

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I at least have not seen get formed any

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other way. Yeah. And I do I do think the

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way that Sam and I work and relate is

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maybe different from what you'd expect
from a typical co-founder relationship.

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Like I think that we are just in constant
contact, that five calls a day,

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two minutes, five minutes each,
that kind of spirit remains. Like I think

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we're just in constant sync. And we
don't always agree on everything, right?

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It's not like we come at the world
from exactly the same point of view, but

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that's why we're so strong
together, right? Is I

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think we have very complementary
approaches, that Sam will say, "Here's

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an idea." I'll think about, "Well,
maybe we could do this other way." Or what

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about if we approached it with this angle?
Or how does this relate to this

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other thing that we're thinking about?

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And one thing I deeply appreciate about
Sam is that I think he always sees these

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connections between different ideas,
or just like keeps focused on here's the

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big picture that we need to get to and
then together we figure out well how do

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we actually do it and I think connecting
the grand ambition with the execution

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like that is what has always
distinguished Open AI. Yeah, what are

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some of the points in these 10 years
where you felt like it was really

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important that you guys diverged? Do you
remember any key moments for you guys?

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I I think one of the things
that Greg has done

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the best which is not my instinct is uh

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really just pushed to

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focus on the most important thing in his
own work and also in what the company is

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going to do. Um so there have been times

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where I have wanted to do more things
uh and Greg has just said, you know, is

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this the most important thing?
Let's really just do this. Let's get the

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company focused. Uh and we've
diverged on that and that's

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been like a very helpful spirit
of Greg's throughout the company.

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Yeah, and I would also add I think even
for example thinking about compute and

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just constantly raising the ambition.

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And sometimes I feel like okay, I kind
of logically know that yes, like we're

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moving to this compute powered economy
and yes, that demand is always going to

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outstrip supply, but like we've got all
this like hard work to do and we already

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have all these big computers and we're
operationalizing them and you know, you

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still have all of this like just
physical infrastructure to build and you

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feel already swamped in it and swamped
in it and Sam is like no, we need even

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more. And I think that that actually has

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been a very important thing to really
not like sometimes it's easy to lose

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sight of the higher order bit of just
the fact of this is going to be so

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important for not just
the next 6 months, but this is

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what's important for the next 2 years,
the next 5 years, 10 years and I think

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that the keep like you need
this balance of sometimes

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swimming in the details, but you can't
be swamped in the details and I think

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that that balance is something
that I think again is what really has

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contributed to what Open AI
is and where we are going to go.

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Is there There must be one product or

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strategy you guys have What's the one
that you've disagreed about the most

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vehemently? >> I was just thinking when
Greg was talking about this. This is not a

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a product, but it was this thing that
came to mind is that I was going to say

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it before you asked that. Uh

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we used to talk a lot about how to talk

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about safety. We never disagreed on

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the extreme importance of safety

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and what it will mean to get this right

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or get this wrong, but the the field
has had a strange relationship with how

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we've talked about safety, how we've
used safety, and how much that becomes

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about power versus actually keeping things

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safe. And I I think earlier in our
history, I got swept up more in the we

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got to really talk about
this in a particular frame,

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and Greg was very disciplined about
we're not going to fall into the

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traditional frame. We can't talk about
that way. Now, even then, I think we

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have because this is so important,

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we I would ever have fallen into the trap
of still talking more in the wrong

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frame than we should, but I think one of

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Open AI's greatest
contributions to date has been

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finding a different way to talk about
safety, not just in how we build the

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products, and how we talk
about society needs to

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do, but like what how we
deploy them, the idea whole idea

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of iterative deployment,
and actually getting to

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maybe not the actually
getting to a world where we're

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figuring out how to deploy products that
get increasingly safe as the stakes go

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up, and Greg really held a line there

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that I think has been quite important to

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the company against extreme
pressure not to do that.

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And I think it's been like quite quite
important to our whole strategy, not

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just how we talk about things, but how
we ship and build products. Yeah, and if

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you look at for example
the OpenAI Foundation

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which is the nonprofit that governs

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OpenAI and has a very large chunk of of

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equity one of its pillars
is AI resilience. And

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what that really means is thinking about
how do we make AI be something positive

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for the world? And the answer is not
any one intervention, right? It's not you

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have chain of thought monitoring and now
you've achieved the mission. It's really

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a whole deep sequence
of different ways that

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society should orient around this
technology. And I think that this

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perspective of you're not going to solve

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AGI going well for the world in a paper.

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It has to be a worldwide effort from

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contributions from society, from many
different people, from many different

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ways of really understanding what
this technology is, how it will affect

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people, how it will affect the world.

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And this this is something that was not,
I think, at all appreciated or

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understood when we were starting out 10
years ago because it's very easy just to

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fix it on, you know, we're technologists.
We're, you know, building

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technology. That's the only problem
we need to solve. I'm not saying anyone

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explicitly said it that way, but I think
sometimes you can fall into a mental

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trap of thinking about it that way.
And so a lot of what I think we have spent

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time on is being first principles
thinkers and really thinking about how

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do you operationally deliver
transformative technology to the

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world in a way that is going to actually
help people in their daily lives. And

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one thing you realize is, well if you
have a very powerful piece of technology

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that will change things, probably it's
going to go better if you've had a less

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powerful piece of technology you've
already helped change things in a

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positive way. And so if you just think
about it that way, you start to really

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be pulled down this road of thinking
about resilience, thinking about

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iterative deployment. And again, I
think this is a lot of the dynamic within

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OpenAI with between the two of us
that we're always thinking about these

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questions of how do we actually achieve
this mission and make it go better.

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Yeah, I I feels like just yesterday I

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saw you at South by 2022 2023 and it was

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a completely different discussion about
AI than what we hear today and it feels

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like that's something that's changed
in 10 years of like how you discuss safety

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and alignment and I'm wondering how you

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reflect on that now like what would you
have changed in those panels and those

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news hits and like what have you
learned about talking about safety?

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Even before we get to safety, uh I
think we have fallen in the frame as tech

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nerds of talking about we're going
to build superintelligence and

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dot dot dot it's going to be great
for you. And we've not filled in enough of

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the dot dot dot. Like we talk about
we're building this amazing technology,

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it's going to do all
these wonderful things and

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there is like a sense
in the world now of okay

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looks like you were right, you are
going to build this thing. Um why like

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why do we want that? What's that going
to do for us? And the thing that a lot

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of the field has said of oh it's going
to you know cure cancer and you'll be so

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happy or like that's clearly
not quite resonating. A

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lot of people are like sure cure cancer

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that'd be wonderful. Um

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I think what people
really want is prosperity

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agency that they're going to continue
to have meaningful work to do. Um there I

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saw an incredible post the other day
that really stuck with me which was like

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a a right to adversity.
People people actually you want some

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challenges in life. You don't want every
day to be perfect and everything done

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for you and there there's a fear with AI
that like mm let's say you're right,

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let's say you build it, let's say it
like makes all this money and does all

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the work and whatever like what do I do?

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What's my kid going to do? What's life
going to be like? Where's the growth

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going to come from? What's the what
are people going to strive for? Um and

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I think we've talked as a field and as

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OpenAI and as Greg and I we've talked
a lot about the amazing technology and

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what it can do and what it
like the technological marvel

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and we have not connected
the dots enough on

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here's what the future's
going to be like. Here

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is, you know, when when

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when I talk to parents with kind of
school-age kids the most common question

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is like what what should my kids study?

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What's the future going to be like? What
what will still have economic value and

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I I and I realize that that's not quite
the question they're they're really trying

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to ask. It's like how is my kid going
to have a fulfilling life in this new

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world? You know, you can answer
it economically if you want, but

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that doesn't actually settle people
and I think it's because there's there's a

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deeper thing here.
I was just going to say and I think I

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think we actually have a lot
of perspective on this and I think that

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for example

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within ChatGPT, we have so many people

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who say that my life or the life
of a love loved one was saved through

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information I got through ChatGPT,
right? Whether it's there's someone who

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had a kid who had

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blinding headaches. They were denied
an MRI. They used ChatGPT to try to

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research the symptoms and used it
to argue to be able to get insurance for

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the MRI. Turned out there was a brain
tumor. They were able to intervene and

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save his life. And just navigating that

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experience that that family says,
"We have no idea how we would do this

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without ChatGPT." And that's just one
story. There are so many of those and I

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think people are really understanding
that this technology can help not just

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abstractly society, but can help them,
right? It can help them make money.

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Right now we're starting to see this wave
of entrepreneurship. I think that's

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going to be a huge theme throughout
this year. And I think we have a lot of

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perspective on well we're going
to build these AIs that are

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able to make rather than you contort
yourself to computer, right? You think

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about the way we do work. Yeah. It's not
natural, right? It's not It's not kind

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of what we were designed to do. Um

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and instead the computer's going to do
work for you. And the question of well,

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what is the work? What is good?
Like, what is the thing that you

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actually want to be done? That's going
to be something very deeply human,

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right? This agency, this empowerment.

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And so, I think there is this very
optimistic, and not blindly optimistic,

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but I think this very positive change
that this technology is bringing right

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now, but it's so much easier to just
notice what's going to go away. Like,

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what is the thing that you thought was
solid and stable that's going to change?

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But, it's much harder to see well,
what's coming? Like, what's the new

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thing that you get? What is the the
benefit that that comes as a part of

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this transformation? And I think
that's something that we're increasingly

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realizing that we also have to really
spell out in addition to spelling out

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the other sides of it. All right.
What do we do at Core Memory? We cover

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innovative, fast-moving,

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forward-thinking companies, which
is why Core Memory is sponsored by Brex,

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because Brex is the intelligent
finance platform for

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many of these companies. 30,000
companies from startups to the world's

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largest corporations rely
on Brex's technology for their

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finances. They've got smart corporate

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cards, high-yield business banking, and

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expense automation tools that are

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fantastic. I hate doing my expenses, and

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Brex's AIs and software run right
through those expenses, figure out where

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we're spending money, and take care of

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so much stuff for you, so you don't have

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00:16:50,240 --> 00:16:53,797
to waste your time on it yourself. Go to

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brex.com/corememory to learn
more, and just, you know, get

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00:16:58,160 --> 00:16:59,837
with the program. Let's get going. Let's

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get out of this archaic finance software

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and move toward the future.
Core Memory and Brex.

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I have a couple questions based
off what both of you were saying. Um

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So, I just went to Oberlin College
with my son, and we were in this room

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um with I don't know, 600-700 people and
it was fascinating to me. I mean it's in

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Ohio and people had come
from all over the country and

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the president was up there giving a talk
and then opened it up to Q&A and so many

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of the questions were
about AI but they were

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it reinforced for me that

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how much of a bubble we live in here
because they were what I would consider

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I mean it's not trying to like put
anyone down or they they were pretty

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basic question I I just my my
overwhelming sense of the room was like

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wow you guys kind of like don't know
what's unfolding and about to to come

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across all of you and I don't know it was
I don't know it was like alarming it was

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just it was eye-opening for me you know

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and and they were asking um They
were obviously asking stuff about

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like well how are you going to use AI
in class how are you going to stop kids

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from just relying on it but then it
was like you know they kind of advanced a

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little bit beyond there
but I felt like this room

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of pretty smart people were not not
really in touch with what's going on. So

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I mean if this is a problem that people
don't know these examples of of what

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could be done and we don't even know

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what shape this could take exactly you
just I don't see how we solve this in

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sort of um kind of prepare
people in some way.

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>> Well I I do have one sort
of positive take on this which is

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that I think that if
you just read about AI

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it gives you one impression right it's
again it's like very it just feels like

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you're trying to wrap your mind around
this new technology what is it but when

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you use it it's so intuitive.
Right and that is the purpose of AI in

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many ways it's like
you think about back to

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how we design computers for the past
you know 70 years it's really about the

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machine doesn't quite get you right it's
like you have these goals in your head

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you have to break it down into
the machine's language whether that's

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writing assembly code or okay fine now
we get higher level languages now you

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can kind of talk to your computer,
but even if you think about how ChatGPT

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works, you still have to really
understand concepts of language models,

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right? The fact like why do you have to
create new conversations, right? New new

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new tabs. Like, why can't it just
be a thing you talk to that remembers

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everything, right? Why there's there's
these limitations that are techno

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technological limitations, but we're
improving them. And so, I think that

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what we're building is the most
intuitive technology. We are building

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something that can torch the machine
to you. And when people use it, they

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realize, "Wow, this is what I can do
with it." Like, some of the stories that

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I love hearing are someone
in the Midwest who I you know, one one

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of my friends is who his
sister was telling him

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about this app that she wanted to to see
someone she wished someone had created.

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She described it in a detailed way.

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While he was listening, he was like,
"Uh-huh. Uh-huh." typing into Codex

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exactly what she was saying. Pushed enter.

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Few hours later, he shows her the result.

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App that's exactly what she described.

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And she's like, "What the hell is this?

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Who built this? Like, this is exactly
what I wanted." And he said,

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"You built it." Right?
And that is the kind of aha

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moment that I think that everyone
is going to go through. And once you're

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once you experience what AI
can do for you and the fact that

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you are now empowered, like you have
a image in your head that you want to see

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in the world. If you look
at some of the upcoming

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some of the new image models we have,
absolutely incredible. Like, you can

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create in a way that was
never possible before.

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And the fact that if you are like
like another story that I think

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about. So, my grandmother had

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dementia Alzheimer's, and that

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it was extremely tough for for everyone.

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But, one thing that really stuck out
to me was she was actually able to use her

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Alexa to play music,
and she could still remember

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the lyrics and sing along to songs. And

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it was just a way that she was connected

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to who she was through technology. And

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that really stuck stuck out to me.
It's like if you can build interfaces that

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are intuitive that anyone can
connect to them in ways that

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right now it feels like we we have
pieces of technology that are targeted

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to specific individuals that you have
to build all the skill that's not really

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related to deeply what you want,
but it's just somehow specific to the tech

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itself. Three things came up for me during

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what Greg was saying,
um which I I agree with all of.

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One before we launched ChatGPT,
we used to try to talk to people

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and say, "This AI thing is coming.

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You've got to pay attention. It's going
to change everything. It's really It's

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It's really important." And it got
kind of no attention. We wrote these

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beautiful blog posts. We sort of did
all these incredibly impressive

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feats. We won, you know, video game
competitions. We had a robot that could

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do a Rubik's Cube with a hand. We like
did all this amazing stuff. And we kind

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of felt pretty cool and we
kind of said, "Oh, you know,

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the New York Times wrote about this.
We must be doing great." But really truly

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no one cared.

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It got no actual impact. And then we

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launched ChatGPT, which was by far not

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the most impressive technological
thing we had done. By far by far.

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And as soon as people
could feel it, they're

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like, "Okay, I understand it." I think
that was the moment probably most that

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the world has said maybe this AI thing
is real. Like collectively all at once.

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Because people can use it and they can
get value out of it. They develop their

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own sense. It's very different than
hearing about it. That's happened again,

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as Greg said, with coding models. Um
but those have really been the two kind of

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like large significant
moments where I think

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the world has updated and said, "Okay,
there's this thing happening." There

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will be more in the future.
But so far the fact that you can

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00:22:49,680 --> 00:22:54,917
ask a computer anything and get an answer
or have a computer do anything anything

385
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with code for you. Um Tho- those
have been really powerful and

386
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I I think that's how the world's going
to update. Us saying superintelligence

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00:23:02,440 --> 00:23:04,237
is coming is going to change everything.

388
00:23:04,320 --> 00:23:06,757
You know, maybe the people who listen
to this podcast will sort of say, "All

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00:23:06,840 --> 00:23:08,237
right, sounds sounds reasonable.

390
00:23:08,320 --> 00:23:09,837
Probably should pay attention to that."

391
00:23:09,920 --> 00:23:13,877
But it won't it won't have
that big impact on the world. Uh

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00:23:13,960 --> 00:23:17,157
So, I think the most important thing we
can do to help that audience of people

393
00:23:17,240 --> 00:23:20,517
think not just about like what does this
mean for people not cheating in class,

394
00:23:20,600 --> 00:23:24,237
but like what is this going to mean
for the world is to ship great delightful

395
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products that create a lot of value
and are easy for people to use.

396
00:23:27,960 --> 00:23:30,397
And we will continue to do that.

397
00:23:30,480 --> 00:23:35,397
The The second thing is we have seen
again and again in our history that when

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we put something out that's pretty good,
people say, "I can't really imagine what

399
00:23:39,800 --> 00:23:42,197
happens if this gets better.
It can't get much better."

400
00:23:42,280 --> 00:23:44,917
This image model that Greg was mentioning,
which we'll launch soon, was

401
00:23:45,000 --> 00:23:48,357
a real example of that for me. I kind
of roughly thought image generation was

402
00:23:48,440 --> 00:23:51,157
solved. I was like, "Yeah, it's really
good. Like, I don't need it to get any

403
00:23:51,240 --> 00:23:54,197
better. There's a lot of" And then
this new thing has been a real reminder of

404
00:23:54,280 --> 00:23:59,117
like, "Wow, this can go so much further
and there's so much more I can do." What

405
00:23:59,200 --> 00:24:02,717
does it do? It makes
ridiculously great images. Uh

406
00:24:02,800 --> 00:24:06,477
and Has it solved text? Because I tried
to make core memory merch mock-ups Try

407
00:24:06,560 --> 00:24:09,917
again very soon. >> [laughter] >> Uh
the the team really did a great job at

408
00:24:10,000 --> 00:24:14,397
this one. But even with ChatGPT, like
when we put GPT-4 in ChatGPT, I remember

409
00:24:14,480 --> 00:24:17,917
a lot of people, like sophisticated
friends of mine, saying, "This is it.

410
00:24:18,000 --> 00:24:20,877
This is AGI. Like, if the model got
smarter, I don't care. Just make it

411
00:24:20,960 --> 00:24:23,717
cheaper. Like, I can't This is amazing."

412
00:24:23,800 --> 00:24:27,677
Um and if you go back
and use that whatever

413
00:24:27,760 --> 00:24:32,037
it was, uh I guess like
March of 2023 version of

414
00:24:32,120 --> 00:24:35,437
GPT-4, you'll be like,
"This was terrible."

415
00:24:35,520 --> 00:24:38,357
But at the time people were like, "It's
solved. It's solved. I mean, it's beat

416
00:24:38,440 --> 00:24:40,797
the Turing test. It's done.
It can't get any, you know."

417
00:24:40,880 --> 00:24:44,957
And and then it gets better
and better and it like you can just

418
00:24:45,040 --> 00:24:48,637
keep raising the expectations
and possibility of what you can do, to say

419
00:24:48,720 --> 00:24:51,517
nothing of then you're going to do
reasoning models and do code. I'm just

420
00:24:51,600 --> 00:24:55,077
talking about how much better ChatGPT
got in that time period. So, I think we

421
00:24:55,160 --> 00:24:59,117
see this again and again, too, where
the world is like, "Okay, you made this

422
00:24:59,200 --> 00:25:03,117
amazing thing. That's as good as it's
going to get. That's as good as I need."

423
00:25:03,200 --> 00:25:06,837
And then, month by month, or at least
quarter by quarter, expectations and

424
00:25:06,920 --> 00:25:09,437
ability ratchet up in a huge way.

425
00:25:09,520 --> 00:25:13,840
And then, the third thing
that Greg was saying there is

426
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these models are still quite
dumb relative to what they will

427
00:25:18,400 --> 00:25:22,277
be, but more than that, they have
quite limited awareness of your life.

428
00:25:22,360 --> 00:25:27,357
You are still having
to like massage them and

429
00:25:27,440 --> 00:25:30,277
cajole them and try to get
the thing that you want.

430
00:25:30,360 --> 00:25:33,237
Uh we are not no longer that far away

431
00:25:33,320 --> 00:25:37,997
from a model that just knows
all of your context. It knows

432
00:25:38,080 --> 00:25:40,357
about you. It knows about your life.
It knows what you're doing. It knows what

433
00:25:40,440 --> 00:25:43,157
you care about. It knows about
the people in your life. It has access to

434
00:25:43,240 --> 00:25:46,437
your computer and your browser and,
if you want, of course, in the ways you

435
00:25:46,520 --> 00:25:49,157
want, and it has access, maybe
increasingly over time, to what's

436
00:25:49,240 --> 00:25:52,117
happening in in the real world around you.

437
00:25:52,200 --> 00:25:55,237
That is going to be
a complete change to what

438
00:25:55,320 --> 00:25:59,437
it feels like to use a computer
and what it feels like to use AI.

439
00:25:59,520 --> 00:26:04,757
And I am tremendously excited about that,
but I don't think even we have a good

440
00:26:04,840 --> 00:26:07,717
intuition yet for what that's really
going to feel like. Yeah. And to to that

441
00:26:07,800 --> 00:26:11,437
point, yeah, you think about how much
time you spend right now just explaining

442
00:26:11,520 --> 00:26:15,397
to chat or whatever tool you're using
what's going on. And if you think about

443
00:26:15,480 --> 00:26:18,477
how frustrating that is, like,
if it's like you got a coworker you're

444
00:26:18,560 --> 00:26:21,157
constantly trying to explain to them,
"No, this is kind of what I want. This

445
00:26:21,240 --> 00:26:24,377
is what's going on." I have projects
I come back to and it's like that.

446
00:26:24,460 --> 00:26:26,197
>> [laughter] >> Exactly, right?
Like, it's it's just not

447
00:26:26,280 --> 00:26:29,957
it's not really how you want to how you
want these systems to behave. And and

448
00:26:30,040 --> 00:26:33,637
and just to say one more thing on on what
Sam was saying is that I think that

449
00:26:33,720 --> 00:26:37,197
one of the biggest technical
challenges we have at OpenAI is too much

450
00:26:37,280 --> 00:26:41,597
opportunity. Like, AI is this like field
of boundless opportunity. No matter what

451
00:26:41,680 --> 00:26:44,717
dimension you expand on, there's going
to be something new and unprecedented

452
00:26:44,800 --> 00:26:49,597
and amazing. And so, the important
thing is having a vision for where can we

453
00:26:49,680 --> 00:26:51,717
really focus, where we're going to get

454
00:26:51,800 --> 00:26:56,917
the most returns and the most benefit
from this you know having multiple

455
00:26:57,000 --> 00:27:00,357
different efforts that all add up
to something. And I think that when it

456
00:27:00,440 --> 00:27:04,117
comes to we have these coding systems
now, but I think it's going to clearly

457
00:27:04,200 --> 00:27:08,157
expand to just all computer work. It's
and it's funny by the way that you know

458
00:27:08,240 --> 00:27:13,037
you think about just the degree to which
you have all of this like the work that

459
00:27:13,120 --> 00:27:16,157
you do it's like very seamlessly
interlinked between you're having

460
00:27:16,240 --> 00:27:19,997
in-person conversation, you're typing
this on your computer and trying to

461
00:27:20,080 --> 00:27:22,117
really figure out how to get the context
in there. It's going to be such an

462
00:27:22,200 --> 00:27:25,397
important problem. All these things
matter, but then also in your personal

463
00:27:25,480 --> 00:27:29,637
life you really want this and we're
starting to call where we want to go the

464
00:27:29,720 --> 00:27:34,277
personal AGI, right? This AI that really
knows you has the context that you it

465
00:27:34,360 --> 00:27:37,437
you can trust it, right? That you ask
it for you ask it questions related to

466
00:27:37,520 --> 00:27:40,997
finances just or health and it can give
you trustworthy information. All these

467
00:27:41,080 --> 00:27:43,917
things are important and it needs
that context as well. And so you start to

468
00:27:44,000 --> 00:27:48,357
really see that there's this blurry line
between an AI that's used for this deep

469
00:27:48,440 --> 00:27:52,277
computer work and an AI that's used
in your personal life at a technological

470
00:27:52,360 --> 00:27:55,757
level even if you want different systems
cuz you want one that's really just

471
00:27:55,840 --> 00:27:59,077
knows your work context, one that knows
your personal context. And so you can

472
00:27:59,160 --> 00:28:03,317
pursue those in parallel
and build on the same

473
00:28:03,400 --> 00:28:06,837
technological foundations. And the thing
that I find most amazing about the

474
00:28:06,920 --> 00:28:10,317
technology we're building
on is it's all at the core

475
00:28:10,400 --> 00:28:13,917
one neural net, right? It's still deep
learning. You're scaling it up. You're

476
00:28:14,000 --> 00:28:16,717
building one system and you're applying
it to all of these different amazing

477
00:28:16,800 --> 00:28:20,477
applications. I I Can I ask
a quick Uh I want to ask a

478
00:28:20,560 --> 00:28:23,757
question sort of along those lines.
I mean I promised myself I wouldn't do

479
00:28:23,840 --> 00:28:27,997
this to you. Um but I mean I was kind of
like I wasn't like a non-believer, but I

480
00:28:28,080 --> 00:28:30,557
like I could see AI doing incredible

481
00:28:30,640 --> 00:28:35,557
things, but I was I was kind of like
skeptical until relatively recently on

482
00:28:35,640 --> 00:28:37,877
on >> What what updated you?
Well, a couple

483
00:28:37,960 --> 00:28:42,077
things. I mean one was I started
playing with agents a lot more and really

484
00:28:42,160 --> 00:28:46,557
shaping them to do what I wanted
and kind of feeling what both of you were

485
00:28:46,640 --> 00:28:50,277
talking about. I was like, "Oh, this is
actually saving me a lot of time. It's

486
00:28:50,360 --> 00:28:53,637
doing my bidding. It's doing it quite
well." And the other is, you know, I

487
00:28:53,720 --> 00:28:58,437
cover biotech so much and just
seeing some of the results that are

488
00:28:58,520 --> 00:29:01,997
coming out is is It strikes
me that like coding and

489
00:29:02,080 --> 00:29:06,077
biotech seem like the most obvious
places right now where it's really

490
00:29:06,160 --> 00:29:08,597
going. But then, you know, I was reading
it I was talking to you the other day at

491
00:29:08,680 --> 00:29:12,437
dinner and reading some of the
things that you've written about,

492
00:29:12,520 --> 00:29:16,717
you know, I see this clear path
based off LLMs to going to what you

493
00:29:16,800 --> 00:29:21,197
describe. I still have this part of me
though. I think it's because I deal in

494
00:29:21,280 --> 00:29:25,357
language so much where
the writing is still

495
00:29:25,440 --> 00:29:27,997
not great where I

496
00:29:28,080 --> 00:29:32,877
This is nothing other than like
anecdotal and intuitive. It's just It

497
00:29:32,960 --> 00:29:34,717
strikes me that

498
00:29:34,800 --> 00:29:37,197
the feeling I get back still is like,

499
00:29:37,280 --> 00:29:40,637
"No, this is not a super intelligence."

500
00:29:40,720 --> 00:29:43,357
We we are not there yet on personality.

501
00:29:43,440 --> 00:29:47,757
>> But like does LLM Right.
But LLMs will get us there.

502
00:29:47,840 --> 00:29:51,317
I was just thinking of it as it's very
jagged, right? And I mean, it's Okay,

503
00:29:51,400 --> 00:29:54,477
for example, just in the past couple days

504
00:29:54,560 --> 00:29:58,717
our AI solved this long-standing
British problem.

505
00:29:58,800 --> 00:30:02,157
So, this mathematical mystery that's
been, you know, of great interest for

506
00:30:02,240 --> 00:30:05,997
for a long time. A mathematician spent
a lot of time working on this, many years

507
00:30:06,080 --> 00:30:10,157
thinking about this problem, posted
about his his views of of what was what

508
00:30:10,240 --> 00:30:13,917
what the contribution was. Terence Tao
also saying that this looks like there's

509
00:30:14,000 --> 00:30:18,037
like maybe connection between different
fields of mathematics that this AI has

510
00:30:18,120 --> 00:30:21,997
discovered. And we're starting to see
real beauty come out of these machines.

511
00:30:22,080 --> 00:30:23,477
Now, that's a particular domain, right?

512
00:30:23,560 --> 00:30:26,917
Mathematics is very different from
creative writing. And I think that the

513
00:30:27,000 --> 00:30:30,877
fact of these AIs being able to have
interesting insights and being able to

514
00:30:30,960 --> 00:30:35,277
help like it's you know, there's so
much that these mathematicians are now

515
00:30:35,360 --> 00:30:39,717
saying, "Well, think about what more we
can do." And so, I I think that we have

516
00:30:39,800 --> 00:30:43,357
a jagged frontier in terms of what these
AIs are good at. We know how to keep

517
00:30:43,440 --> 00:30:46,797
pushing that frontier back, but I think
that the the thing that we're really

518
00:30:46,880 --> 00:30:50,837
looking for, if you think about AlphaGo
move 37, right, that was something that

519
00:30:50,920 --> 00:30:54,437
not just it was this deep insight, but it
really changed people's understanding

520
00:30:54,520 --> 00:30:58,157
of the game of Go, and more people
play Go now, right? So, that it really

521
00:30:58,240 --> 00:31:00,797
increased the meaning of what people
were doing. And so, I think that what

522
00:31:00,880 --> 00:31:03,437
we're going to do is we're going to make
something that is going to you're going

523
00:31:03,520 --> 00:31:06,797
to have fewer complaints about it, but I
think you're also going to be just like

524
00:31:06,880 --> 00:31:11,197
way more able to do the thing that you
want in writing than you can imagine

525
00:31:11,280 --> 00:31:15,117
today. I'm sorry to harp on this, but I
was told GPT-5 was going to be really

526
00:31:15,200 --> 00:31:17,597
good at writing. And my like update when

527
00:31:17,680 --> 00:31:22,797
I became less cynical was reasoning
models, and when Claude delivered like

528
00:31:22,880 --> 00:31:26,837
really good research, and then you guys
had a research feature, that was for me

529
00:31:26,920 --> 00:31:28,677
like, "Oh my god, this is amazing."

530
00:31:28,760 --> 00:31:32,597
Saved me a lot of time. But, I
was promised before that I would have good

531
00:31:32,680 --> 00:31:35,837
writing by now. >> The writing
still there's no there's no

532
00:31:35,920 --> 00:31:39,397
soul, you know? There's there's
something missing. Well, I'll I'll tell

533
00:31:39,480 --> 00:31:42,877
you. So, from a technical perspective, the

534
00:31:42,960 --> 00:31:46,597
technology we have
is you can train a model

535
00:31:46,680 --> 00:31:50,237
in this unsupervised way, right? So,
it really we look at all the publicly

536
00:31:50,320 --> 00:31:53,837
available data, and it learns to predict
what comes next, and it's really about

537
00:31:53,920 --> 00:31:56,277
putting it in new situation, trying
to figure out what's a reasonable thing to

538
00:31:56,360 --> 00:32:00,237
do. Then we do a reinforcement learning
step, where it actually tries out ideas,

539
00:32:00,320 --> 00:32:03,677
and it gets rewards and punishments
based on how well it did. You know, just

540
00:32:03,760 --> 00:32:07,277
positive signal, negative signal, not
not You're not beating it. Exactly. Not

541
00:32:07,360 --> 00:32:12,717
at all. >> [laughter] >> Yeah, just just
you know, this this signal. Um and then

542
00:32:12,800 --> 00:32:16,277
the hard part is, well, how do you judge?
How do you decide if something

543
00:32:16,360 --> 00:32:21,557
was yep, thumbs up or thumbs down?
And so, in math and science, much easier

544
00:32:21,640 --> 00:32:25,037
than in some of these more open-ended
fields. But, we also have AI's that are

545
00:32:25,120 --> 00:32:28,397
getting much smarter and much more able
to provide that kind of reward signal.

546
00:32:28,480 --> 00:32:32,277
And so, I think that the part of the
challenge has been how do you expand the

547
00:32:32,360 --> 00:32:35,557
task the set of tasks that can be
graded, and that's been a lot of the

548
00:32:35,640 --> 00:32:39,477
focus. And uh there's actually very
interesting like a lot of this stuff

549
00:32:39,560 --> 00:32:42,197
dates back to the very beginning of Open
AI. We kind of had a picture this is how

550
00:32:42,280 --> 00:32:44,877
it would go. But I would just say I
think we're getting there. I think we

551
00:32:44,960 --> 00:32:48,197
definitely have a lot more progress
to make. But yeah, hopefully you can keep

552
00:32:48,280 --> 00:32:51,957
giving us feedback and we'll we'll
be able to improve it for you.

553
00:32:52,040 --> 00:32:56,557
One embedded challenge in that is
that the the writing you want is very

554
00:32:56,640 --> 00:33:00,597
different than the writing that most
people want. And right now we have to

555
00:33:00,680 --> 00:33:04,477
you know make a model that about
a billion people use and all kind of kind

556
00:33:04,560 --> 00:33:08,517
of like. And what we'd like to do is to
get the model so good at personalization

557
00:33:08,600 --> 00:33:10,797
that you think it's a great writer.

558
00:33:10,880 --> 00:33:14,237
And some other person that has a very
different kind of life and set of needs

559
00:33:14,320 --> 00:33:17,317
than you also thinks it's a great writer
for what they want. And those are very

560
00:33:17,400 --> 00:33:19,277
different things. >> I think
that's what I'm kind of asking

561
00:33:19,360 --> 00:33:23,877
though. Like for the personal AGI you
would want it to have that sparkle,

562
00:33:23,960 --> 00:33:26,117
that soul, that sort of that magic,

563
00:33:26,200 --> 00:33:29,517
right? And and I just I

564
00:33:29,600 --> 00:33:33,277
if it's not And I saw you
know obviously Jan LeCun's

565
00:33:33,360 --> 00:33:36,557
been arguing against this for a long time.
I I caught this Dennis thing very

566
00:33:36,640 --> 00:33:40,237
quick where he seemed to be kind
of aligned with you guys but you know that

567
00:33:40,320 --> 00:33:43,677
LLMs will get us there but he seemed
to be saying we still need a couple more

568
00:33:43,760 --> 00:33:46,277
ticks maybe of something special to to

569
00:33:46,360 --> 00:33:48,437
but you guys seem so confident.

570
00:33:48,520 --> 00:33:53,157
>> can solve we haven't tried to make it
be you know a what Ashley Vance will think

571
00:33:53,240 --> 00:33:56,877
is a great writer. We have tried to make
it you know solve open math problems

572
00:33:56,960 --> 00:34:00,157
that the smartest mathematicians in the
world can't solve. And I don't want to

573
00:34:00,240 --> 00:34:03,557
say anything about your intelligence
relative to these mathematicians but I I

574
00:34:03,640 --> 00:34:07,477
think >> Writers Writers are smarter
But I was going to say like you know

575
00:34:07,560 --> 00:34:10,917
solving math is also hard.
So if we can do that

576
00:34:11,000 --> 00:34:14,997
I'm pretty confident this approach
can also learn what you think is great

577
00:34:15,080 --> 00:34:16,877
writing and figure out to do it for you.

578
00:34:16,960 --> 00:34:20,797
But but but by the way we also have
some new new models coming on on this

579
00:34:20,880 --> 00:34:23,637
dimension as well.
So after this podcast comes out

580
00:34:23,720 --> 00:34:27,277
>> wait to judge it. Let us know. Let us
know if it looks better. writing I think

581
00:34:27,360 --> 00:34:30,477
personality and I think we're
always improving like all the

582
00:34:30,560 --> 00:34:33,517
capabilities is moving up the jagged
frontier. and so I think in this field

583
00:34:33,600 --> 00:34:37,557
it's always really important to judge
the not the current position but what

584
00:34:37,640 --> 00:34:41,357
has that slope been and also fit it
to an exponential. And so if you think

585
00:34:41,440 --> 00:34:44,557
about how does the writing stay
compared to a year ago? Sorry if you're

586
00:34:44,640 --> 00:34:48,837
disappointed with 2.5. Don't worry we're
we're we're very motivated to you know

587
00:34:48,920 --> 00:34:52,997
to to to really deliver. But I think
that I think we really have a line of

588
00:34:53,080 --> 00:34:56,197
sight for how to improve it for for
every application that people want.

589
00:34:56,280 --> 00:34:58,677
Since day one the core memory podcast

590
00:34:58,760 --> 00:35:01,197
has been supported by the fine people at

591
00:35:01,280 --> 00:35:03,357
E1 Ventures. They are a young and

592
00:35:03,440 --> 00:35:06,157
ambitious VC firm in Silicon Valley

593
00:35:06,240 --> 00:35:08,437
investing in young and ambitious

594
00:35:08,520 --> 00:35:11,077
companies and people. Thank you so much

595
00:35:11,160 --> 00:35:13,717
to E1 Ventures for all your support. In

596
00:35:13,800 --> 00:35:18,157
the world that you guys have outlined um

597
00:35:18,240 --> 00:35:22,557
this technology okay, you know, say
everything is working great and this you

598
00:35:22,640 --> 00:35:26,037
know, this is uh it's
a quite optimistic scenario

599
00:35:26,120 --> 00:35:30,077
um diseases are being cured um resources

600
00:35:30,160 --> 00:35:33,957
are becoming more abundant, problems are
being solved. So so humanity as a whole

601
00:35:34,040 --> 00:35:38,357
is being lifted up. It's I still don't
see I mean it seems like very very some

602
00:35:38,440 --> 00:35:42,117
of the world's smartest people are
developing this technology. It strikes

603
00:35:42,200 --> 00:35:44,797
me it will disproportionately benefit

604
00:35:44,880 --> 00:35:46,477
them

605
00:35:46,560 --> 00:35:50,237
in almost all scenarios even though like

606
00:35:50,320 --> 00:35:53,717
everyone might get lifted up a bit that

607
00:35:53,800 --> 00:35:58,837
um you know, things really get
more extreme because every time um

608
00:35:58,920 --> 00:36:01,517
what you guys are talking about about
how you would manipulate these tools use

609
00:36:01,600 --> 00:36:05,797
these I I just feel like I feel like this

610
00:36:05,880 --> 00:36:09,037
things will go even more extreme
and the have the haves will be

611
00:36:09,120 --> 00:36:12,517
>> the permanent underclass and that
sentiment of like seeing these really

612
00:36:12,600 --> 00:36:16,037
powerful tools and feeling like oh
my gosh, what what am I even here for then

613
00:36:16,120 --> 00:36:17,917
and feeling very disenfranchised.

614
00:36:18,000 --> 00:36:20,797
>> you get have all this time to do fun
creative things. I just feel like other

615
00:36:20,880 --> 00:36:22,997
people will be manipulating this in such

616
00:36:23,080 --> 00:36:27,837
extreme fantastic ways that >>
This This is at the core of the OpenAI

617
00:36:27,920 --> 00:36:31,517
mission. Like this is really
why we started this place.

618
00:36:31,600 --> 00:36:34,597
Because you see this powerful technology
coming. It's going to be the most

619
00:36:34,680 --> 00:36:39,157
important technology ever created. How
do you ensure it benefits everyone? Like

620
00:36:39,240 --> 00:36:42,837
truly all of humanity. Like that is in
our mission. Ensure AGI benefits all of

621
00:36:42,920 --> 00:36:47,077
humanity. And we really mean it. And if
you look at our corporate structure,

622
00:36:47,160 --> 00:36:51,197
we've tried multiple different
iterations at trying to in the structure

623
00:36:51,280 --> 00:36:54,397
codify some of our values of how do
we ensure that this is something that

624
00:36:54,480 --> 00:36:58,797
really does benefit people. You see it
in the product choices we make, right?

625
00:36:58,880 --> 00:37:03,077
We decided to launch ChatGPT because we
really believe this is technology that

626
00:37:03,160 --> 00:37:06,477
we need to be able to put into people's
hands. And that was very controversial

627
00:37:06,560 --> 00:37:09,477
by the way, that there was a different
school of thought saying that the way to

628
00:37:09,560 --> 00:37:13,517
do it is you have to build it in secret,
that you can't get people access, that

629
00:37:13,600 --> 00:37:17,757
you need to do it this other way. And I
think that when we look at the how do we

630
00:37:17,840 --> 00:37:20,837
actually make society resilient, how do
we actually benefit people, it all kind

631
00:37:20,920 --> 00:37:23,597
of points to the direction that we we
have been going. And there's a lot of

632
00:37:23,680 --> 00:37:25,757
nuance to it. Um but I think that the

633
00:37:25,840 --> 00:37:30,757
way that we're we're headed is very much
that I think the floor for everyone is

634
00:37:30,840 --> 00:37:33,597
not going to go up just a bit. Like I
think we are going to head to this world

635
00:37:33,680 --> 00:37:36,197
where I mean you think about even having

636
00:37:36,280 --> 00:37:38,237
a doctor in your pocket that's better

637
00:37:38,320 --> 00:37:43,317
than the best medical team that anyone
in the world could get today,

638
00:37:43,400 --> 00:37:47,677
accessible to anyone who has a smartphone.
Like that is coming and that'll be free.

639
00:37:47,760 --> 00:37:50,957
Right? That is a wild wild fact. That's
not a small It's going to be a little

640
00:37:51,040 --> 00:37:55,597
bit better. That is fundamentally
raising the floor in this massive way.

641
00:37:55,680 --> 00:37:58,557
Now I think the question of also raising
the ceiling, I think that's fair too,

642
00:37:58,640 --> 00:38:01,877
right? I think that the question
of exactly how the distribution goes. And

643
00:38:01,960 --> 00:38:05,717
we think about this a lot. You look
at the OpenAI Foundation. That's That has,

644
00:38:05,800 --> 00:38:09,397
you know, somewhere between, you know,
25%, 30%, somewhere around there of

645
00:38:09,480 --> 00:38:13,837
OpenAI equity, right? That's like more
than 150 billion dollars. If it is the

646
00:38:13,920 --> 00:38:16,837
case that OpenAI becomes very
successful, that all of that value

647
00:38:16,920 --> 00:38:20,357
locked up in the nonprofit, like
that is something that will be for really

648
00:38:20,440 --> 00:38:23,877
benefiting the world and we think that's
a good thing. Well, we were at dinner

649
00:38:23,960 --> 00:38:27,197
the other day, It's real quick, and I
was listening to you talk about what you

650
00:38:27,280 --> 00:38:30,557
do with agents, and I've known
you I mean, look, you're smart as

651
00:38:30,640 --> 00:38:35,317
and I was just like,
"Jesus, man, Greg, with this

652
00:38:35,400 --> 00:38:38,877
machine I like even though
I feel like I'm doing

653
00:38:38,960 --> 00:38:43,437
interesting things that help me out
when I heard you describing your life and

654
00:38:43,520 --> 00:38:46,117
what you were doing, I was just like,
"Oh my god, man. I mean, there's just

655
00:38:46,200 --> 00:38:49,157
going to be super people running around."

656
00:38:49,240 --> 00:38:51,877
I don't know, it's like kind
of intimidating, really. I mean, it was

657
00:38:51,960 --> 00:38:55,317
cool, but it was also yeah, quite
intimidating. I'm like, "I don't know

658
00:38:55,400 --> 00:38:58,677
how to use this like you do at all." Can

659
00:38:58,760 --> 00:39:03,760
I Can I try a less sanitized
version of Yes, please.

660
00:39:04,000 --> 00:39:05,317
I hope I don't get in trouble for this.

661
00:39:05,400 --> 00:39:08,677
Uh I I can see three

662
00:39:08,760 --> 00:39:11,157
futures of the world. Um

663
00:39:11,240 --> 00:39:14,357
I can see one where, as Greg was saying,

664
00:39:14,440 --> 00:39:18,757
the floor comes way up. You know,
everybody gets subjectively like 10

665
00:39:18,840 --> 00:39:21,277
times richer, just the sort of materially

666
00:39:21,360 --> 00:39:25,997
material abundance, the prosperity
is crazy huge. People are like, "Man,

667
00:39:26,080 --> 00:39:30,597
relative to my life a decade ago,
I'm doing great." But also in that world,

668
00:39:30,680 --> 00:39:33,677
these people who really learn how to use
agents and get a lot of compute together

669
00:39:33,760 --> 00:39:36,237
and whatever, we have some
trillionaires, you know, maybe 10

670
00:39:36,320 --> 00:39:41,157
trillionaires, whatever. So,
like, the floor comes way up,

671
00:39:41,240 --> 00:39:45,877
like dramatically up, but because this
is a lever that people can really use,

672
00:39:45,960 --> 00:39:49,317
uh the uh the the sort
of like most capable,

673
00:39:49,400 --> 00:39:52,077
most ambitious that people who already
started rich and have access to a lot of

674
00:39:52,160 --> 00:39:54,557
compute, inequality gets worse.

675
00:39:54,640 --> 00:39:56,957
So, that's that's one world I can see.

676
00:39:57,040 --> 00:40:01,597
Um I can see another
world where we, through

677
00:40:01,680 --> 00:40:05,997
many different kinds of things that
could happen, um the the floor doesn't

678
00:40:06,080 --> 00:40:09,437
come up as much, we don't generate
as much total prosperity, but also there's

679
00:40:09,520 --> 00:40:13,437
like less inequality. So,
maybe people in a decade feel like

680
00:40:13,520 --> 00:40:16,757
twice as rich,
but inequality has come down.

681
00:40:16,840 --> 00:40:22,437
Um and actually,
I'll just like stop and talk

682
00:40:22,520 --> 00:40:25,317
to those two because I think this
is like the real crux of the issue.

683
00:40:25,400 --> 00:40:28,277
>> Is the third much scarier? >> No,
but it it's like a distraction from

684
00:40:28,360 --> 00:40:32,837
the point I want to make.
I think a lot of people in the world

685
00:40:32,920 --> 00:40:35,717
uh like we think it's obvious. Greg and I
think it's obvious that people should

686
00:40:35,800 --> 00:40:39,557
prefer the first.
I assume you also agree with that.

687
00:40:39,640 --> 00:40:43,277
Um but emotionally, it's not
where a lot of people are.

688
00:40:43,360 --> 00:40:47,117
Wait. Well, I was going to say I don't I
don't want to claim what what is obvious

689
00:40:47,200 --> 00:40:52,037
or not. I but I think it's I think
for me personally, I just see so much

690
00:40:52,120 --> 00:40:56,037
potential in this technology and I think
it's just so important from a societal

691
00:40:56,120 --> 00:40:59,437
level, just thinking about
even American competitiveness.

692
00:40:59,520 --> 00:41:02,317
Like you look at robotics. Like I don't
think that we are ahead in robotics at

693
00:41:02,400 --> 00:41:04,517
all. We are not. But you're not.

694
00:41:04,600 --> 00:41:08,757
With the software side, we do have
a lead we do have this opportunity and I

695
00:41:08,840 --> 00:41:12,437
think that is something all of these
factors together in my mind are are

696
00:41:12,520 --> 00:41:16,397
worth considering at once.
Y- yeah. I mean, clearly I mean,

697
00:41:16,480 --> 00:41:18,837
actually, people who disagree on a lot
of other things I think do agree that

698
00:41:18,920 --> 00:41:22,957
America is needs to be competitive
on chips and robots and AI and everything

699
00:41:23,040 --> 00:41:26,677
else. But but there's going to be
this huge question about how we organize

700
00:41:26,760 --> 00:41:28,957
society and the economy and do we push

701
00:41:29,040 --> 00:41:32,397
for maximum prosperity and accept the

702
00:41:32,480 --> 00:41:37,437
inequality that will come with that or
do we say like, "Actually, we're going

703
00:41:37,520 --> 00:41:41,077
to constrain it because
we're going to be more focused

704
00:41:41,160 --> 00:41:46,117
on the relative nature and the fear
that well, if we don't do something, yeah,

705
00:41:46,200 --> 00:41:48,237
maybe the world will get more
prosperous, but the people who are

706
00:41:48,320 --> 00:41:51,717
really good at using this are
going to have all this power." And

707
00:41:51,800 --> 00:41:55,117
you know, emotionally, I think
intellectually, it's kind of seems clear

708
00:41:55,200 --> 00:41:56,597
to me. Emotionally,

709
00:41:56,680 --> 00:41:58,357
I really get why it's not clear. And I

710
00:41:58,440 --> 00:42:02,757
really get the the fear that if we let

711
00:42:02,840 --> 00:42:05,757
inequality run here, the compounding

712
00:42:05,840 --> 00:42:10,637
nature of this tool is something that we
don't yet understand. Now, the last

713
00:42:10,720 --> 00:42:13,437
thing I'll say on this is

714
00:42:13,520 --> 00:42:17,997
kind of no matter what I think
everyone should want much more

715
00:42:18,080 --> 00:42:21,757
compute, much more infrastructure uh
and the cheapest possible access to

716
00:42:21,840 --> 00:42:26,157
AI because otherwise I think you really
exacerbate inequality if there's a

717
00:42:26,240 --> 00:42:29,157
limited amount of this and the price
goes up because of supply and demand and

718
00:42:29,240 --> 00:42:32,397
only the rich people have it. Yeah,
I want I wanted to to just build on that

719
00:42:32,480 --> 00:42:35,877
because I think that to some extent

720
00:42:35,960 --> 00:42:39,597
and this also plays back to to earlier
just how Sam and I work together, how we

721
00:42:39,680 --> 00:42:42,757
think, right? Because I think that laying
out these two options I think is

722
00:42:42,840 --> 00:42:46,077
actually really good point where it's
like, well, are those the only two

723
00:42:46,160 --> 00:42:48,997
options, right? Is there something
that we're not seeing in the space? And I

724
00:42:49,080 --> 00:42:52,477
think that the last thing that Sam said
to me is an unlock in terms of thinking

725
00:42:52,560 --> 00:42:57,637
about so AI is really opportunity.
AI is opportunity for everyone if you have

726
00:42:57,720 --> 00:43:00,877
access, right? If you have compute.
If you don't have compute, you can't,

727
00:43:00,960 --> 00:43:03,557
right? No matter how good you are with
agents, if you don't have the computer

728
00:43:03,640 --> 00:43:07,277
to run them, you're not going to be able
to do much. And so I think a world where

729
00:43:07,360 --> 00:43:10,637
if everyone does have
access to compute and

730
00:43:10,720 --> 00:43:13,717
you know, I think for for my generation
growing up, we were much better at using

731
00:43:13,800 --> 00:43:17,637
computers than our parents, right?
We grew up with it. We were, you know,

732
00:43:17,720 --> 00:43:22,157
native. And I think that the generation
growing up now are going to be as good

733
00:43:22,240 --> 00:43:25,277
as I am at using agents, I think they're
going to be 10 times better. It's wild

734
00:43:25,360 --> 00:43:28,677
to watch that. It is crazy. I mean,
I wrote this is like the first massive

735
00:43:28,760 --> 00:43:33,317
story on core memory was that dude
who built the nuclear fuser with with

736
00:43:33,400 --> 00:43:36,557
Claude. I just remember it was the the
fuser didn't even sort of matter as much

737
00:43:36,640 --> 00:43:40,037
as like what I saw him doing with his
computer. I'm like, you just use this

738
00:43:40,120 --> 00:43:41,637
thing different. And you know, he's like

739
00:43:41,720 --> 00:43:44,477
22 and he has eight AI applications up

740
00:43:44,560 --> 00:43:48,957
and Yeah. And if you can get compute
to that kid, if you can get compute to

741
00:43:49,040 --> 00:43:53,357
every kid then it is really
going to be the most

742
00:43:53,440 --> 00:43:57,037
extreme version of the American dream,
right? Where it's really anyone who has

743
00:43:57,120 --> 00:44:01,157
the desire, the willpower to really play
with this technology, to really lean in,

744
00:44:01,240 --> 00:44:04,477
to build with it, to get the most out
of it, I think you're going to see this

745
00:44:04,560 --> 00:44:07,197
outperformance and I think you're
going to see people have all sorts of

746
00:44:07,280 --> 00:44:11,677
mobility. And so, I think that to some
extent the right way of answering this

747
00:44:11,760 --> 00:44:15,077
question of option one
versus option two is to say

748
00:44:15,160 --> 00:44:19,197
Neither Exactly. And something else. And I
think it's possible. I feel like we're

749
00:44:19,280 --> 00:44:22,517
doing some broad stuff and and we have
a bunch of specific questions that I'm

750
00:44:22,600 --> 00:44:26,917
going to I'm going to be bad and ask one
more broad one. It's okay. Just based on

751
00:44:27,000 --> 00:44:29,757
what we're talking I mean, you guys
probably know, maybe you don't. You

752
00:44:29,840 --> 00:44:35,077
know, I just cover hardware so
deeply and um I think I'm pretty

753
00:44:35,160 --> 00:44:37,877
I would venture to say I've probably
been to more factories and hardware

754
00:44:37,960 --> 00:44:41,717
startups in the US than just about
anyone and keep an eye on China. I mean,

755
00:44:41,800 --> 00:44:44,837
the thing that always plays through
my head is that yeah, we're ahead on

756
00:44:44,920 --> 00:44:48,717
software. We're ahead on AI.
Software's been this

757
00:44:48,800 --> 00:44:53,077
the story of the US for the last 40
or 50 years of our our strength. But, you

758
00:44:53,160 --> 00:44:55,757
know, when I think about how

759
00:44:55,840 --> 00:44:58,077
this technology manifests itself in the

760
00:44:58,160 --> 00:45:00,717
physical world, I do not see a future

761
00:45:00,800 --> 00:45:05,517
where the US is competitive in any way,
shape, or form. Not just in robotics,

762
00:45:05,600 --> 00:45:08,957
but you know, just all of the
componentry that goes in to these

763
00:45:09,040 --> 00:45:11,877
hardware systems. I go to El Segundo.

764
00:45:11,960 --> 00:45:15,677
I see 30 startups. It's great.
There's like this flourishing of ideas and

765
00:45:15,760 --> 00:45:19,637
people trying things, but it's like such
small potatoes compared to what I see in

766
00:45:19,720 --> 00:45:23,797
China. Um it just it just strikes me that

767
00:45:23,880 --> 00:45:28,317
there obviously will be a physical
manifestation of all this in the world

768
00:45:28,400 --> 00:45:32,877
and and the US just seems like
extraordinarily disadvantaged to to win

769
00:45:32,960 --> 00:45:36,037
that to me. Well, I think
of people who are working

770
00:45:36,120 --> 00:45:37,477
incredibly hard to try to change that.

771
00:45:37,560 --> 00:45:42,397
I'd say Sam is Like
on robotics and well, I know

772
00:45:42,480 --> 00:45:43,597
through through all the investments in

773
00:45:43,680 --> 00:45:49,720
different hardware companies or No,
no, we're trying to figure out how

774
00:45:50,800 --> 00:45:53,597
Robots are an obvious part of this and
we're trying to figure out how to be

775
00:45:53,680 --> 00:45:56,397
very successful at robotics. The I think

776
00:45:56,480 --> 00:46:00,637
if you could pick one thing to make the
US competitive at manufacturing and the

777
00:46:00,720 --> 00:46:03,997
world of atoms in general, you would say
we need a lot of robots that can build a

778
00:46:04,080 --> 00:46:06,277
lot lot more robots. But like we can't

779
00:46:06,360 --> 00:46:11,077
even make a actuator. You know,
like going to figure that out. We we will

780
00:46:11,160 --> 00:46:16,157
Open AI. Okay. Like through your robotics?
It would have to. We have to.

781
00:46:16,240 --> 00:46:19,477
Uh the But there's
other parts of this that

782
00:46:19,560 --> 00:46:23,317
really matter. If you think about like
the number of gigawatts of power that

783
00:46:23,400 --> 00:46:26,317
the world is going to need just
to support AI workloads, the number of

784
00:46:26,400 --> 00:46:30,677
chips we're going to have to fabricate,
the the boring stuff about like how

785
00:46:30,760 --> 00:46:34,117
we're going to just get enough racks
assembled and enough network cables

786
00:46:34,200 --> 00:46:39,277
strung and made in the first place. Uh
the US is extremely behind here. I think

787
00:46:39,360 --> 00:46:42,837
robotics will be the solution if you
just look at how fast this has to happen

788
00:46:42,920 --> 00:46:46,117
and what the US is currently good at and
how much infrastructure we have to build

789
00:46:46,200 --> 00:46:52,157
up and how long that would
take to do by hand. Um but

790
00:46:52,240 --> 00:46:54,797
you are right in the diagnosis and the

791
00:46:54,880 --> 00:47:00,397
criticality of it and I think
luckily we have like a new

792
00:47:00,480 --> 00:47:01,557
uh

793
00:47:01,640 --> 00:47:04,197
like a new piece on the chessboard.

794
00:47:04,280 --> 00:47:08,157
Through Open AI or the United States?

795
00:47:08,240 --> 00:47:10,517
>> Okay, tell me I mean what do you mean?

796
00:47:10,600 --> 00:47:14,837
I I I have a different feeling.
I I I feel like we're play acting

797
00:47:14,920 --> 00:47:18,877
hardware and we're going to get
completely If host. Well, on the current

798
00:47:18,960 --> 00:47:21,157
trajectory, yes. No,
I we totally agree with that.

799
00:47:21,240 --> 00:47:24,677
Uh the If we can make true general purpose

800
00:47:24,760 --> 00:47:27,557
robots, if we can have a

801
00:47:27,640 --> 00:47:32,797
Codex equivalent power thing for robots
that you can say like go configure a

802
00:47:32,880 --> 00:47:35,997
factory this way and make me more
of this kind of robot or go figure out a

803
00:47:36,080 --> 00:47:38,397
mine and refine this kind of thing.

804
00:47:38,480 --> 00:47:42,797
>> then the playing field changes.
Okay, but you said the US and not Open AI

805
00:47:42,880 --> 00:47:45,397
specifically. But yeah, I feel like
you're pointing at something that Did

806
00:47:45,480 --> 00:47:49,597
you know though? Well, I don't think
the US has a credible plan other than this

807
00:47:49,680 --> 00:47:53,037
kind of AI plus robotics to catch up
fast enough. I didn't know what the

808
00:47:53,120 --> 00:47:55,917
chess piece was. Did something
You saw something recently.

809
00:47:56,000 --> 00:47:59,357
>> Oh, I just I mean I meant general
purpose artificial intelligence.

810
00:47:59,440 --> 00:48:02,157
>> I feel like there's there's a real
chicken and egg that's existed in

811
00:48:02,240 --> 00:48:06,117
robotics where if you don't
have the robotic hardware,

812
00:48:06,200 --> 00:48:08,557
it's kind of hard to develop the software.
And if you don't have the

813
00:48:08,640 --> 00:48:11,437
great brain, it's kind of hard to be
motivated to develop the hardware. And

814
00:48:11,520 --> 00:48:14,717
we really saw this. We had a robotics
project back in 2018. Do you remember

815
00:48:14,800 --> 00:48:17,317
the robotic hand? Yeah, yeah. Super cool.

816
00:48:17,400 --> 00:48:19,477
The thing about that hand to know,
so we trained it through reinforcement

817
00:48:19,560 --> 00:48:22,557
learning. Actually, the exact same
algorithm we used to solve competitive

818
00:48:22,640 --> 00:48:26,277
video games, right? Wild. Single
piece of technology doing both. The one

819
00:48:26,360 --> 00:48:30,637
difference is that this hand would run
for 20 hours before it had these strings

820
00:48:30,720 --> 00:48:34,197
as its tendons, and they would snap.
And then you'd have downtime, mechanical

821
00:48:34,280 --> 00:48:37,157
engineer would come in and fix it,
you know, maybe be wake them up, have them

822
00:48:37,240 --> 00:48:40,717
come into the office.
And you realize that you cannot

823
00:48:40,800 --> 00:48:44,677
do ML that way. You just cannot. So,
actually, we ended up canceling that

824
00:48:44,760 --> 00:48:48,037
project. That team went on to go work on
what became GitHub Copilot, you know, to

825
00:48:48,120 --> 00:48:51,037
contribute to that effort. And so,
you just realize how much faster software

826
00:48:51,120 --> 00:48:54,917
world has been moving. But I think
we're at a point now where we have these

827
00:48:55,000 --> 00:48:57,677
amazing general-purpose algorithms
that can be used in all sorts of different

828
00:48:57,760 --> 00:49:02,077
ways. And we see how if you start to be
able to apply those to physical world,

829
00:49:02,160 --> 00:49:05,877
then I think you are going to have
a very different story for how the

830
00:49:05,960 --> 00:49:10,197
hardware development and how
the hardware tuning to the co-design is

831
00:49:10,280 --> 00:49:13,477
going to happen. So, I think that there's
like real potential for change,

832
00:49:13,560 --> 00:49:16,997
but we as a nation need to have the
willpower to really do this. But I think

833
00:49:17,080 --> 00:49:20,877
we really agree with you that without
something like that, the current

834
00:49:20,960 --> 00:49:25,077
trajectory looks terrible. We're so
He's convinced that it's really bad.

835
00:49:25,160 --> 00:49:27,317
It's pretty bad. It's really bad.

836
00:49:27,400 --> 00:49:31,197
>> Taking a huge step back, you know,
robotics, models, these are things that

837
00:49:31,280 --> 00:49:34,557
you guys are interested in building
and really care about, but you guys have

838
00:49:34,640 --> 00:49:38,517
recently, it's been reported that you
consolidated and focused, and I'm really

839
00:49:38,600 --> 00:49:41,997
curious, I'm sure like the audience
wants to know, like, what's on the table

840
00:49:42,080 --> 00:49:45,797
now? What got cut? What do you guys care
about? And why did you make those cuts?

841
00:49:45,880 --> 00:49:49,237
Why was it important? So, before I let
Greg answer, before he does, Greg has

842
00:49:49,320 --> 00:49:53,477
taken over really figuring out what
our cohesive product offering and the

843
00:49:53,560 --> 00:49:55,277
research to support that is going to be.

844
00:49:55,360 --> 00:49:59,117
Um and it's been amazingly uh joyful
inside of the company. Like I it's going

845
00:49:59,200 --> 00:50:01,277
to take a little bit longer for all
the stuff to ship. He's only been in the

846
00:50:01,360 --> 00:50:06,477
role for a few weeks, maybe, something
like that. But uh it the the energy and

847
00:50:06,560 --> 00:50:09,597
excitement and the sort of like
enthusiasm about what Greg is doing here

848
00:50:09,680 --> 00:50:12,557
is unbelievable. So, you can say what
it's going to be. Dude, like can you

849
00:50:12,640 --> 00:50:16,397
expand on so like a few weeks ago you
came in and because some of these cuts

850
00:50:16,480 --> 00:50:19,357
were already taking place, right?
I mean, so but you've come in and like

851
00:50:19,440 --> 00:50:23,477
assessed everything. Uh so I've I've
always been very involved behind the

852
00:50:23,560 --> 00:50:27,317
scenes with, you know, many parts
of OpenAI and so I think that kind of

853
00:50:27,400 --> 00:50:29,957
taking a a foreground uh role is

854
00:50:30,040 --> 00:50:34,837
relatively recent uh in this particular
area. Although, fun fact is I actually

855
00:50:34,920 --> 00:50:36,757
built the very first version of the API.

856
00:50:36,840 --> 00:50:40,357
So, like I've been doing products since
product existed at OpenAI and always

857
00:50:40,440 --> 00:50:43,717
deeply cared about it.
And there's a bunch of things I could say

858
00:50:43,800 --> 00:50:46,677
there. Like I think that how core it
is to our mission uh was something we

859
00:50:46,760 --> 00:50:49,197
didn't appreciate before we started
building products and afterwards you

860
00:50:49,280 --> 00:50:53,717
realize how important it is. So, that's
why I've always been so close to it.

861
00:50:53,800 --> 00:50:57,517
So, the place that we're
at now is that we are

862
00:50:57,600 --> 00:51:02,237
clearly at a moment
of transition to agents.

863
00:51:02,320 --> 00:51:05,157
No question, right? People in software
engineering you've been feeling this

864
00:51:05,240 --> 00:51:07,557
for, let's say, the past 6 months.

865
00:51:07,640 --> 00:51:11,477
And the you know, at the course of 2025
I think there was a transition from

866
00:51:11,560 --> 00:51:14,277
yeah, it's like kind of autocomplete
to okay, yeah, you have kind of a sidebar

867
00:51:14,360 --> 00:51:17,477
in your editor and then you'll start
talking mostly over there, but you're

868
00:51:17,560 --> 00:51:20,437
still mostly doing the same kind
of software development you were doing

869
00:51:20,520 --> 00:51:24,957
before to now it's like actually you
want a tool like Codex that is really an

870
00:51:25,040 --> 00:51:27,757
agent management platform. And the agents
are going to take care of all the

871
00:51:27,840 --> 00:51:31,277
details. The agents are going to do all
the the nitty-gritty work. And there's

872
00:51:31,360 --> 00:51:35,237
still probably 20% of how you put things
together and the the way that you will

873
00:51:35,320 --> 00:51:39,237
structure your code and exactly some of
these these higher-level things that you

874
00:51:39,320 --> 00:51:41,877
would normally put in an architecture
document that the human still really

875
00:51:41,960 --> 00:51:45,837
cares about and wants to manage, but the
details of exactly the code, like nope,

876
00:51:45,920 --> 00:51:50,757
that's what agents are for. And so,
the question that we had is, first of all,

877
00:51:50,840 --> 00:51:52,277
how do we really rise to this moment?

878
00:51:52,360 --> 00:51:54,037
Cuz it's not just software, right? We

879
00:51:54,120 --> 00:51:56,357
see line of sight for every single

880
00:51:56,440 --> 00:52:01,077
vertical, right? For law, for finance.

881
00:52:01,160 --> 00:52:03,797
Some of the mechanical skills there of
writing, you know, creating spreadsheets

882
00:52:03,880 --> 00:52:06,917
and presentations. How do we make
our models extremely good at those, right?

883
00:52:07,000 --> 00:52:10,317
It's like you work with domain experts,
you produce evaluations, you produce

884
00:52:10,400 --> 00:52:14,837
training data, you have the AI actually
take its great domain knowledge and

885
00:52:14,920 --> 00:52:18,117
apply it in these verticals and gets
experience and get those, you know, yep,

886
00:52:18,200 --> 00:52:19,677
you did a good job in order to figure

887
00:52:19,760 --> 00:52:22,557
out what what good looks like. So, we

888
00:52:22,640 --> 00:52:27,077
have the mechanical the the vision
of exactly how to do this, but we need to

889
00:52:27,160 --> 00:52:31,277
make sure that we're building the right
product surface to unlock all of it. And

890
00:52:31,360 --> 00:52:35,077
one thing that we have found is that
the models have shifted from being the

891
00:52:35,160 --> 00:52:38,837
product to being a part of the product,
right? They used to have these very thin

892
00:52:38,920 --> 00:52:41,757
layers of software on top of them, and
you didn't have to like think that hard

893
00:52:41,840 --> 00:52:45,317
about how it was architected. But now,
it's a very fat layer, right? That you

894
00:52:45,400 --> 00:52:49,917
have things like skills, connectors, you
have exactly how you hook up to computer

895
00:52:50,000 --> 00:52:53,237
use, how you manage context and memory,
and all of these things. And so, there's

896
00:52:53,320 --> 00:52:57,277
just this deep layer of software, which
is almost you could think of it as the

897
00:52:57,360 --> 00:53:00,157
AI. It's kind of like we have this brain
in the form of the model, and now we're

898
00:53:00,240 --> 00:53:03,397
building the body. Both are hard. They
have to be co-designed together. And so,

899
00:53:03,480 --> 00:53:06,877
a lot of what we're focusing on is,
number one, getting together an amazing

900
00:53:06,960 --> 00:53:10,837
agentic platform. Like, that is the
number one focus that we are delivering

901
00:53:10,920 --> 00:53:14,397
on. We have teams that are executing
extremely well on this. I'm super

902
00:53:14,480 --> 00:53:18,637
excited about what we'll be releasing
over upcoming weeks. Um the second is,

903
00:53:18,720 --> 00:53:21,677
where do you actually want to apply
these agents? And that our priority

904
00:53:21,760 --> 00:53:24,677
there is really towards computer work,
right? So, that And I I use that term

905
00:53:24,760 --> 00:53:27,677
very deliberately, by the way. People
like to talk about knowledge work, but

906
00:53:27,760 --> 00:53:29,877
no one thinks of themselves as a
knowledge worker, right? Like, that's

907
00:53:29,960 --> 00:53:33,357
not a thing that people do. It's kind
of a term that's like almost removed from

908
00:53:33,440 --> 00:53:36,757
what the actual thing is. But the thing
I like about computer work is it's like,

909
00:53:36,840 --> 00:53:37,997
I don't really want to do computer work.

910
00:53:38,080 --> 00:53:40,237
That doesn't sound like the thing that I
want. But you realize how much of your

911
00:53:40,320 --> 00:53:44,637
time you do spend doing it, like chain
behind your desk, you know, typing away,

912
00:53:44,720 --> 00:53:47,797
hunching over your shoulders, getting
your carpal tunnel, all of those things.

913
00:53:47,880 --> 00:53:52,997
And so, we are focusing on that, bringing
Codex that exists today, not just for

914
00:53:53,080 --> 00:53:56,397
software engineers, but really making
Codex be for everyone. And that's

915
00:53:56,480 --> 00:53:57,957
something that's coming very quickly.

916
00:53:58,040 --> 00:54:02,477
We'll have some updates even coming
today as of this podcast filming. And

917
00:54:02,560 --> 00:54:04,717
there's a lot of exciting things
that are still in the pipeline for that

918
00:54:04,800 --> 00:54:08,597
direction. And then the third thing
is really thinking about personal AGI,

919
00:54:08,680 --> 00:54:13,237
which is about Thinking about ChatGPT
right now, used by a billion users. And

920
00:54:13,320 --> 00:54:16,957
every single person on the planet
is going to want an AI that represents

921
00:54:17,040 --> 00:54:21,277
them, has their context, that they have
built trust with, that it's not just

922
00:54:21,360 --> 00:54:24,437
something that you build,
or it's not just something that

923
00:54:24,520 --> 00:54:27,317
you talk to one-on-one, but it can
be out there doing things for you. For

924
00:54:27,400 --> 00:54:30,557
example, maybe it knows that you like
a specific musician, and that musician's

925
00:54:30,640 --> 00:54:33,957
in town, and it knows it proactively,
and tickets have just become available.

926
00:54:34,040 --> 00:54:36,797
There's some great tickets that are very
cheap that you you get for you. It just

927
00:54:36,880 --> 00:54:39,997
goes and buys them, right? And maybe it
knows it's built trust with you, so it

928
00:54:40,080 --> 00:54:42,557
kind of knows you're allowed to to do
this without getting approval, or maybe

929
00:54:42,640 --> 00:54:46,837
it realizes, "I should probably I'm
not quite sure. I should check." And so,

930
00:54:46,920 --> 00:54:50,997
we're building that as well.
And if you think about these things,

931
00:54:51,080 --> 00:54:54,557
they all are kind of expressions
of something that fits together into

932
00:54:54,640 --> 00:54:58,677
cohesive whole. Like in the end,
you fast forward to where we're going, you

933
00:54:58,760 --> 00:55:02,037
really just want an AGI, right?
You don't want a language

934
00:55:02,120 --> 00:55:05,397
model, you don't want threads, you don't
want any of these like details. You just

935
00:55:05,480 --> 00:55:09,157
want something that is helping you,
that is operating on your behalf, that is

936
00:55:09,240 --> 00:55:11,997
able to help you solve problems,
that knows what your goals are and achieve

937
00:55:12,080 --> 00:55:14,717
those in work context, personal context.

938
00:55:14,800 --> 00:55:18,877
And so, this is what we are prioritizing
and building. Yeah. And so, you know, to

939
00:55:18,960 --> 00:55:21,957
me one of the important questions,
you know, people are asking, "How do we

940
00:55:22,040 --> 00:55:25,717
think about consumer? How do we think
about enterprise?" And the answer is,

941
00:55:25,800 --> 00:55:29,277
if you take the definitions of these
words as they exist today, we care a lot

942
00:55:29,360 --> 00:55:32,437
about consumer, we care a lot about
enterprise. But I think that the meaning

943
00:55:32,520 --> 00:55:37,077
of these words will change and blur,
because what we're doing is we are going

944
00:55:37,160 --> 00:55:40,237
to So, we're going to unlock this wave
of entrepreneurship. Again, I I think

945
00:55:40,320 --> 00:55:41,757
we're seeing the leading edges of it.

946
00:55:41,840 --> 00:55:44,597
Small companies be able to get tons
and tons of revenue that was not possible

947
00:55:44,680 --> 00:55:47,317
before. And that's been
a trend for a while.

948
00:55:47,400 --> 00:55:50,517
It's just going to really accelerate.
Is that enterprise?

949
00:55:50,600 --> 00:55:54,837
Is that consumer? It's kind of neither,
right? And so, I think we are really

950
00:55:54,920 --> 00:55:59,237
focused on solving goals across all
context. And that is the lens that we

951
00:55:59,320 --> 00:56:03,717
that we look at things through. And so,
that has mean that has that has meant we

952
00:56:03,800 --> 00:56:07,477
need to deprioritize other things
that are also amazing on their own.

953
00:56:07,560 --> 00:56:10,757
>> So, what got cut? >> Yeah.
>> Well, So, Sora is the most Sora is the

954
00:56:10,840 --> 00:56:14,037
most obvious one. >> And why?
Well, because it's a different

955
00:56:14,120 --> 00:56:16,957
branch of the tech tree, right? So,
if you look at the models that actually

956
00:56:17,040 --> 00:56:19,077
power Sora, that they're not unified

957
00:56:19,160 --> 00:56:22,037
with the core GPT series. And secondly,

958
00:56:22,120 --> 00:56:25,277
the use case is not quite unified

959
00:56:25,360 --> 00:56:29,397
either, right? It doesn't fall as far
under this goal like there's creative

960
00:56:29,480 --> 00:56:32,357
expression. There's something very
important there. I think it was like an

961
00:56:32,440 --> 00:56:34,917
incredible model. The team does
incredible work. And I think that

962
00:56:35,000 --> 00:56:38,757
technology will live on for other
applications. But what we were really

963
00:56:38,840 --> 00:56:42,877
focusing on was this the product suite
that we want to be delivering. What do

964
00:56:42,960 --> 00:56:46,637
we want to be doing over the next 3
months, 6 months, 12 months? And the

965
00:56:46,720 --> 00:56:50,557
thing I described, by the way, is just
step one. Because we also see line of

966
00:56:50,640 --> 00:56:52,597
sight to much more powerful models.

967
00:56:52,680 --> 00:56:55,797
Like, you look at what we're doing
right now in mathematics. It's Okay, it's

968
00:56:55,880 --> 00:57:00,157
actually kind of mind-blowing.
Where this result that I just mentioned of

969
00:57:00,240 --> 00:57:04,117
solving the new Erdős problem that
seems actually really significant.

970
00:57:04,200 --> 00:57:07,157
That was just someone using GPT-4 Pro.

971
00:57:07,240 --> 00:57:09,997
Like, 2 years ago >> Yeah,
we used to like train our model We

972
00:57:10,080 --> 00:57:12,157
had a team of 20 people to try to train

973
00:57:12,240 --> 00:57:17,117
our models to go solve a computing
Olympiad, and we got a bronze medal. A

974
00:57:17,200 --> 00:57:20,037
team of 20 people for like 2 weeks and
lots of compute. And now it's just this

975
00:57:20,120 --> 00:57:21,797
model that we trained very casually.

976
00:57:21,880 --> 00:57:24,837
Someone is able to just point it
at problems and get this kind of result.

977
00:57:24,920 --> 00:57:28,917
What if you point that at drug discovery?
What if you do take that team

978
00:57:29,000 --> 00:57:32,237
of 20 people and all that compute and
And really try to push it for scientific

979
00:57:32,320 --> 00:57:36,077
discovery. And that's something no one
is pricing in right now. So, I think

980
00:57:36,160 --> 00:57:40,037
it's rising to the moment of agents
really trying to make sure that the

981
00:57:40,120 --> 00:57:44,557
product investments we're making are
sort of well-structured, that we think

982
00:57:44,640 --> 00:57:47,517
about how all these pieces fit together,
that we have connectors that work really

983
00:57:47,600 --> 00:57:50,957
well, and each of these pieces can
be composed, and really also build an

984
00:57:51,040 --> 00:57:54,477
ecosystem because it's not just about
what we built, right? That we want to

985
00:57:54,560 --> 00:57:58,557
build some example agents. You think of
Codex almost as an example agent, but it

986
00:57:58,640 --> 00:58:01,917
should be that if you're a developer, if
you're someone with a creative idea, you

987
00:58:02,000 --> 00:58:04,997
can build your own agent, right? That
you can build it for your application,

988
00:58:05,080 --> 00:58:07,837
for your purpose. You care about
the specific math problem, you should be

989
00:58:07,920 --> 00:58:10,477
able to apply the agent to your that
math problem. And so, we're we're

990
00:58:10,560 --> 00:58:14,557
enabling all of that. But you I mean,
okay, just bear with me for 1 second

991
00:58:14,640 --> 00:58:18,117
just to go quick through a couple things
around this. I mean, so we I mean, part

992
00:58:18,200 --> 00:58:22,317
of this was like you had to probably
cut Sora for compute to get

993
00:58:22,400 --> 00:58:24,517
compute, right? So, that compute.

994
00:58:24,600 --> 00:58:27,997
>> reported that it was taking up a lot
of compute. I mean, every everything in

995
00:58:28,080 --> 00:58:31,197
this field that is successful is going
to take a compute. I mean, the two of

996
00:58:31,280 --> 00:58:34,757
you in particular, and I think Ilya
and some others, you know, kind of famous

997
00:58:34,840 --> 00:58:39,077
for going all in on where you were going
to direct the compute in the early days,

998
00:58:39,160 --> 00:58:43,077
and now you have to make these very
difficult decisions. You have to serve

999
00:58:43,160 --> 00:58:46,157
all these customers that you have.
You got to make some money cuz you're

1000
00:58:46,240 --> 00:58:48,477
spending a lot of money, and you know,

1001
00:58:48,560 --> 00:58:51,797
so you two are both wired to take the

1002
00:58:51,880 --> 00:58:54,637
very biggest bet possible, but you're

1003
00:58:54,720 --> 00:58:59,677
constrained now by the business
into some degree. So,

1004
00:58:59,760 --> 00:59:02,157
that just seems very difficult.
I feel like it's not in

1005
00:59:02,240 --> 00:59:05,037
your nature.
Does it You're looking skeptical.

1006
00:59:05,120 --> 00:59:09,037
It's a It's a strange phrasing because
I don't feel constrained by the business.

1007
00:59:09,120 --> 00:59:12,837
I feel enabled by the business because
the business is what has really allowed

1008
00:59:12,920 --> 00:59:17,797
us to say we can scale compute, right?
I remember when we launched ChatGPT, and I

1009
00:59:17,880 --> 00:59:20,037
remember we were talking right
afterwards, and we're trying to figure

1010
00:59:20,120 --> 00:59:24,037
out how much compute to buy.
And I was just like, we got to buy it all.

1011
00:59:24,120 --> 00:59:27,157
We just got to we got to do it cuz it's
just so clear there's so much demand.

1012
00:59:27,240 --> 00:59:30,797
And think that that has been a huge
unlock in our ability to get lots of

1013
00:59:30,880 --> 00:59:34,717
compute for mission critical >>
this incredible revenue machine, we

1014
00:59:34,800 --> 00:59:38,597
would not be able to convince anyone
that we should get all this compute.

1015
00:59:38,680 --> 00:59:41,597
>> Get get this. But then the impression
obviously when you see the stories about

1016
00:59:41,680 --> 00:59:45,357
Stargate and things like that is that
you guys are somehow pulling back on

1017
00:59:45,440 --> 00:59:48,877
infrastructure. I don't know where
that's coming from. Like there you know,

1018
00:59:48,960 --> 00:59:51,317
there will be like a site here
and there we say okay, you know what this

1019
00:59:51,400 --> 00:59:54,197
particular site maybe it only has air
cooling and so this is not as valuable

1020
00:59:54,280 --> 00:59:57,677
to us as this other site and that's like
there's specifics and people really want

1021
00:59:57,760 --> 00:59:59,477
to write the story of like pulling back.

1022
00:59:59,560 --> 01:00:02,797
But very soon it will be again like
open AI is so reckless. How can they be

1023
01:00:02,880 --> 01:00:06,477
spending this crazy amount?
So like the media will go

1024
01:00:06,560 --> 01:00:09,477
will flip out either way just cuz you
all need something to write about I

1025
01:00:09,560 --> 01:00:14,277
guess. Um but we will keep building out
as much compute as we possibly can. One

1026
01:00:14,360 --> 01:00:19,477
one one one thing that I think is also
worth thinking about is that compute for

1027
01:00:19,560 --> 01:00:24,397
us is not a cost center. It's a profit
center, right? When you deploy it in the

1028
01:00:24,480 --> 01:00:28,277
product. And so in many ways our business
is extremely simple, right? We

1029
01:00:28,360 --> 01:00:32,517
rent or buy compute and then
we resell it at a margin.

1030
01:00:32,600 --> 01:00:36,357
And as long as we have some positive
margin on it, then it's scalable, right?

1031
01:00:36,440 --> 01:00:41,317
Because the demand is just unlimited.
So okay, so data center hardware still

1032
01:00:41,400 --> 01:00:44,637
all systems go? Or you mean
like our own chip? Yeah, own

1033
01:00:44,720 --> 01:00:49,557
chip, networking Yeah,
very very excited about our our chip.

1034
01:00:49,640 --> 01:00:51,117
We have an incredible team there.

1035
01:00:51,200 --> 01:00:55,997
>> One Titan one? Uh no, we're not talking
about timelines of course, but I'll just

1036
01:00:56,080 --> 01:01:00,317
say that like I spend a lot of time with
that team and I think it's so So they're

1037
01:01:00,400 --> 01:01:04,157
doing great. So that's our all systems go.
Robotics sounds like it's all systems

1038
01:01:04,240 --> 01:01:07,197
>> to be a while till we have something
to where you're like ah this is the chat

1039
01:01:07,280 --> 01:01:10,277
should be team moment, but But but you
haven't pulled back on that program.

1040
01:01:10,360 --> 01:01:12,757
Social net social network

1041
01:01:12,840 --> 01:01:17,437
Robots are not the current thing, but are
going to be so clearly important in

1042
01:01:17,520 --> 01:01:20,357
the future. >> Okay.
And social network? Not doing that.

1043
01:01:20,440 --> 01:01:21,877
>> Not not doing that right now.

1044
01:01:21,960 --> 01:01:25,757
And then clearly like the super app, the
browser, all that stuff still still going.

1045
01:01:25,840 --> 01:01:28,517
>> Yeah, and one thing to realize about
super app because I feel like it's one

1046
01:01:28,600 --> 01:01:32,117
of these things where it's like a catchy
word, but I thought you guys like came

1047
01:01:32,200 --> 01:01:35,997
up with this. I mean but You guys used
this like internal short Exactly. This

1048
01:01:36,080 --> 01:01:38,997
is the thing to realize is that like
sometimes we're communicating to to our

1049
01:01:39,080 --> 01:01:42,157
team and then of course it ends up being
communications the world and these are

1050
01:01:42,240 --> 01:01:45,957
not intended to be the same thing. Super
app in many ways is an iceberg, right?

1051
01:01:46,040 --> 01:01:49,477
It's like yes, we're going to have a an
app and you'll see updates to what the

1052
01:01:49,560 --> 01:01:53,037
Codex app is today to make it so it's
Codex is for everyone and I think that

1053
01:01:53,120 --> 01:01:56,597
ultimately like what that becomes that we
have we have a lot of steps to get to

1054
01:01:56,680 --> 01:02:00,077
where we want to be, but it's really
about saying we're building this unified

1055
01:02:00,160 --> 01:02:04,197
identity infrastructure that I described
and that is I think going to be a huge

1056
01:02:04,280 --> 01:02:08,237
unlock for every single thing
that people want to do. Okay. Um

1057
01:02:08,320 --> 01:02:11,317
You and I have talked
a little bit about yeah,

1058
01:02:11,400 --> 01:02:12,797
you guys have had lots of drama. It's

1059
01:02:12,880 --> 01:02:16,157
slowed you down at times. You still have

1060
01:02:16,240 --> 01:02:18,597
drama sadly. Um there's a lawsuit

1061
01:02:18,680 --> 01:02:24,317
coming. Uh It'll be fun. Yes, yes. Well,
let's talk about that in a second. Um

1062
01:02:24,400 --> 01:02:25,557
Like which company do you think has

1063
01:02:25,640 --> 01:02:28,997
actually executed better over the last

1064
01:02:29,080 --> 01:02:33,637
two years? Open AI or Anthropic?

1065
01:02:33,720 --> 01:02:36,197
Look, I think that this is a hard thing

1066
01:02:36,280 --> 01:02:39,757
to say from just the current moment,

1067
01:02:39,840 --> 01:02:44,117
right? Because I think that the view
in my mind is you got to step back

1068
01:02:44,200 --> 01:02:47,997
and really think about what is it that
we're all here to do, right? And I think

1069
01:02:48,080 --> 01:02:51,357
that from the Open AI perspective, the
things we've been talking about in some

1070
01:02:51,440 --> 01:02:55,437
ways are about it's very clear that yes,
selling to enterprises like figuring out

1071
01:02:55,520 --> 01:03:00,237
how to really deliver these coding tools
and it's been really it's been actually

1072
01:03:00,320 --> 01:03:04,037
I think competition can really help
elevate your own thinking and realize

1073
01:03:04,120 --> 01:03:07,717
that hey, we need to focus on this.
And one example in coding was that I think

1074
01:03:07,800 --> 01:03:11,357
we got late to the game of not just
building models that were good in the

1075
01:03:11,440 --> 01:03:14,117
abstract of coding. Like we always
had the best numbers on programming

1076
01:03:14,200 --> 01:03:18,437
competitions, but you also need to apply
them to messy repos, real-world data,

1077
01:03:18,520 --> 01:03:20,717
those kinds of things.
And that was something that I think we had

1078
01:03:20,800 --> 01:03:22,757
appreciated later than Anthropic did.

1079
01:03:22,840 --> 01:03:26,877
So, I think that's That's kudos to them,
but also something that's helped elevate

1080
01:03:26,960 --> 01:03:30,117
our own execution. And now,
head-to-head, Codex versus Claude, I

1081
01:03:30,200 --> 01:03:32,997
think that that that we
get very favorable results.

1082
01:03:33,080 --> 01:03:36,157
Um and I think we've done an incredible
Our teams have done an incredible job

1083
01:03:36,240 --> 01:03:39,157
across the whole company to really build
a product that's not just competitive,

1084
01:03:39,240 --> 01:03:41,437
but actually ahead in many, many ways.

1085
01:03:41,520 --> 01:03:44,917
Um but, I think that the the core it's

1086
01:03:45,000 --> 01:03:47,397
never about the ups and downs of the

1087
01:03:47,480 --> 01:03:52,997
news cycle. It's about progress towards
AGI, towards it benefiting everyone. And

1088
01:03:53,080 --> 01:03:55,597
that is something that I think we've
been extremely focused on. The team's

1089
01:03:55,680 --> 01:04:00,237
executing extremely well. And so, I just
want to say that you can't always tell

1090
01:04:00,320 --> 01:04:03,357
from the outside, but if
you think about the things we

1091
01:04:03,440 --> 01:04:07,437
talked about, that there is this focus,
but on many timescales that all add up

1092
01:04:07,520 --> 01:04:11,437
to where we need to go. Yeah, there's
a million questions I want to ask with the

1093
01:04:11,520 --> 01:04:15,757
time we have left. But, one of them we
we touched on this, like making sure a

1094
01:04:15,840 --> 01:04:18,517
bunch of models are in the hands
of a bunch of people and they have that

1095
01:04:18,600 --> 01:04:21,757
powerful technology. But, we've reached
a point where now some of these models

1096
01:04:21,840 --> 01:04:25,197
are too powerful, we're being told,
and they're they're gated for only certain

1097
01:04:25,280 --> 01:04:29,637
companies. Claude and Mythos
have really made a lot

1098
01:04:29,720 --> 01:04:33,957
of headlines and I think a lot of a lot
more fear. And I'm wondering what you

1099
01:04:34,040 --> 01:04:37,157
guys think of this new moment. Are we
getting to a point where like we need to

1100
01:04:37,240 --> 01:04:40,797
start keeping these powerful models
behind the scenes rather than in the

1101
01:04:40,880 --> 01:04:44,717
hands of everybody?
There are people in the world who

1102
01:04:44,800 --> 01:04:48,037
for a long time have wanted to keep
AI in the hands of a smaller group of

1103
01:04:48,120 --> 01:04:53,077
people. Um you can justify that in a lot
of different ways. And some of it's real,

1104
01:04:53,160 --> 01:04:56,157
like there are going to be
legitimate safety concerns.

1105
01:04:56,240 --> 01:05:01,077
Um but, I expect But, if what you want
is like, we need control of AI just us

1106
01:05:01,160 --> 01:05:04,237
cuz we're the trustworthy people,
I think the the fear-based marketing is

1107
01:05:04,320 --> 01:05:05,677
probably the most effective way to

1108
01:05:05,760 --> 01:05:08,717
justify that. Um that doesn't mean it's

1109
01:05:08,800 --> 01:05:13,757
not legitimate in some cases.
Uh but, it is

1110
01:05:13,840 --> 01:05:17,317
you know, clearly incredible marketing
to say, "We have built a bomb. We are

1111
01:05:17,400 --> 01:05:19,877
about to drop it on your head.
We will sell you a bomb shelter for $100

1112
01:05:19,960 --> 01:05:22,637
million. You need to like run across all
your stuff, but only if we like pick you

1113
01:05:22,720 --> 01:05:25,637
as a customer." And it

1114
01:05:25,720 --> 01:05:29,317
the way that we view balancing these new

1115
01:05:29,400 --> 01:05:33,157
capabilities that are
going to come with uh

1116
01:05:33,240 --> 01:05:36,917
still our our belief
that the world needs to get

1117
01:05:37,000 --> 01:05:41,117
and use and understand and come up
with new ideas for this technology is not

1118
01:05:41,200 --> 01:05:45,077
always easy. We We have had um
cybersecurity in our preparedness

1119
01:05:45,160 --> 01:05:48,797
framework for a long time, and we have
been building mitigations to figure out

1120
01:05:48,880 --> 01:05:51,877
how we release this, um how we
put these models in the hands

1121
01:05:51,960 --> 01:05:55,517
of a trusted access program, how how we

1122
01:05:55,600 --> 01:05:57,717
then put more capable models in the

1123
01:05:57,800 --> 01:06:02,597
hands of everybody. Um but,
there will be a lot more rhetoric

1124
01:06:02,680 --> 01:06:05,237
about models that are too dangerous
to release. There will also be very

1125
01:06:05,320 --> 01:06:08,197
dangerous models that will have
to be released in different ways.

1126
01:06:08,280 --> 01:06:12,077
But, to the point Greg was making about

1127
01:06:12,160 --> 01:06:16,077
the goal here is to benefit everybody.

1128
01:06:16,160 --> 01:06:20,317
And also to I don't want
to say market this in a

1129
01:06:20,400 --> 01:06:23,997
way, but but like get the world to come
along on this journey with us where

1130
01:06:24,080 --> 01:06:27,397
where it's like we are going to give you
more powerful technology. There's going

1131
01:06:27,480 --> 01:06:29,877
to be responsibility that goes along
with that. We are going to help set up

1132
01:06:29,960 --> 01:06:32,317
the world for success as much as we can.

1133
01:06:32,400 --> 01:06:36,477
Um but, we will try
to avoid the fear-based

1134
01:06:36,560 --> 01:06:39,677
marketing as much as we can. So, I'll
I'll just ask you directly. Do you think

1135
01:06:39,760 --> 01:06:42,917
Mythos is just a lot of marketing and not
I'm sure it's a great cybersecurity

1136
01:06:43,000 --> 01:06:46,197
like I We've been talking about this for
a long time. Like this has been in our

1137
01:06:46,280 --> 01:06:50,157
in our model, but there's there's a way
of saying like like our version of this is

1138
01:06:50,240 --> 01:06:53,717
to say these models are going to get
much better at cyber. We have this

1139
01:06:53,800 --> 01:06:56,837
preparedness framework category. Here's
our plan for how we deploy this into the

1140
01:06:56,920 --> 01:06:59,237
world. Here's how this trusted
access program looks like. Here's the

1141
01:06:59,320 --> 01:07:01,517
mitigations we put on the models.

1142
01:07:01,600 --> 01:07:04,957
Um I think I'm talking about how it is not
how it is one of the categories in their

1143
01:07:05,040 --> 01:07:08,477
preparedness framework. Uh so,
I'm sure Mythos is a great model

1144
01:07:08,560 --> 01:07:12,357
uh for cybersecurity. But,
I think we have a plan we feel good

1145
01:07:12,440 --> 01:07:16,437
about for how we put this kind
of capability out into the world. When the

1146
01:07:16,520 --> 01:07:19,957
Anthropic stuff was going down with
the Department of War, I mean, from my

1147
01:07:20,040 --> 01:07:22,757
perspective, it looked like

1148
01:07:22,840 --> 01:07:25,437
you had sort of David Sacks and people

1149
01:07:25,520 --> 01:07:29,837
with long ties to Elon. I could see
putting pressure to make some of these

1150
01:07:29,920 --> 01:07:33,797
things happen and have
the government focus it on that.

1151
01:07:33,880 --> 01:07:37,357
You guys kind of came in pretty quick
after and and had an announcement of

1152
01:07:37,440 --> 01:07:39,877
your own that was more favorable.

1153
01:07:39,960 --> 01:07:40,957
Um

1154
01:07:41,040 --> 01:07:44,037
I mean, you know, Elon's

1155
01:07:44,120 --> 01:07:47,437
aggressive against you guys as well.

1156
01:07:47,520 --> 01:07:50,637
And some of this is funny to me that I
mean, there is a world where like you

1157
01:07:50,720 --> 01:07:55,397
guys and Anthropic are actually on this
this other side and and it feels like

1158
01:07:55,480 --> 01:07:59,477
Elon and even Zach to some
degree, my understanding,

1159
01:07:59,560 --> 01:08:03,117
kind of pressuring things.
As a side, yeah, I

1160
01:08:03,200 --> 01:08:06,997
mean, did you did you feel like
Anthropic was treated fairly in that? Do

1161
01:08:07,080 --> 01:08:13,160
you feel um No. >> [snorts] >> Um I don't

1162
01:08:13,360 --> 01:08:15,197
Well, I think there was like a lot
of bad behavior to go around there, but I

1163
01:08:15,280 --> 01:08:17,157
don't think Anthropic was treated well.

1164
01:08:17,240 --> 01:08:18,837
Can you explain more? Like what was

1165
01:08:18,920 --> 01:08:24,360
standing out to you about
what was particularly wrong? Um

1166
01:08:25,760 --> 01:08:30,797
I don't like look with the things
that have happened since with,

1167
01:08:30,880 --> 01:08:34,477
you know, these models reaching this
cybersecurity threshold that is clearly

1168
01:08:34,560 --> 01:08:36,997
in the national security interest.
I I think it all has a little bit of a

1169
01:08:37,080 --> 01:08:40,157
different flavor to it, but

1170
01:08:40,240 --> 01:08:43,397
you know, like threats of

1171
01:08:43,480 --> 01:08:46,597
using the DPA and actually using supply

1172
01:08:46,680 --> 01:08:49,197
chain risk designation, like this is

1173
01:08:49,280 --> 01:08:50,877
this is not the relationship that I

1174
01:08:50,960 --> 01:08:53,757
think our government and our AI efforts

1175
01:08:53,840 --> 01:08:58,237
need to have. Uh We really
care about supporting the US

1176
01:08:58,320 --> 01:09:00,917
government. I think that's going
to become increasingly important as these

1177
01:09:01,000 --> 01:09:04,957
models get more capable.
And I certainly don't think it's like a

1178
01:09:05,040 --> 01:09:08,797
good stance for uh labs to say,
you know, we have the

1179
01:09:08,880 --> 01:09:11,557
super weapon by the way we're not going
to work with you to help help you defend

1180
01:09:11,640 --> 01:09:17,960
the country. Um but I also don't think
it's good for the government to be like

1181
01:09:18,400 --> 01:09:21,197
fighting this stuff out in the press and
using the big hammer that the government

1182
01:09:21,280 --> 01:09:23,077
has that needs to be used very rarely.

1183
01:09:23,160 --> 01:09:27,837
So, I A thing about
OpenAI is we generally try

1184
01:09:27,920 --> 01:09:32,077
to be like moderate and centrist and
reasonable. And that is what we've tried

1185
01:09:32,160 --> 01:09:34,837
to do here with the US government, but I

1186
01:09:34,920 --> 01:09:38,197
see no future no good future where

1187
01:09:38,280 --> 01:09:42,437
leading AI efforts don't assist the US
government. Uh if everything we say is

1188
01:09:42,520 --> 01:09:45,877
right, we believe the things we're
saying about where the models are going

1189
01:09:45,960 --> 01:09:50,997
to go, which I certainly do, then
the government needs our help and we are

1190
01:09:51,080 --> 01:09:54,637
honored to get to provide it. You know,
when I am old as a tech reporter, I

1191
01:09:54,720 --> 01:09:57,397
mean, when I was first writing
about tech, you know, the big

1192
01:09:57,480 --> 01:10:00,797
war was it was basically Microsoft
versus everybody else and it was they

1193
01:10:00,880 --> 01:10:05,397
were the big evil um proprietary
software company. You had the Yeah. open

1194
01:10:05,480 --> 01:10:08,757
source companies counter. You had Mark
Andreessen with Netscape as you know,

1195
01:10:08,840 --> 01:10:11,517
and and you know, like things would
play out in the press and it would be a

1196
01:10:11,600 --> 01:10:16,077
little vitriol here and there,
but you know, and then people had their

1197
01:10:16,160 --> 01:10:17,917
philosophical and religious camps.

1198
01:10:18,000 --> 01:10:20,357
Obviously, like this stuff where we are

1199
01:10:20,440 --> 01:10:23,677
in AI land is insane. Um

1200
01:10:23,760 --> 01:10:27,757
that so much of this seems
tied to the personalities of

1201
01:10:27,840 --> 01:10:32,317
you, Elon, um Zuck, Dario,
and and Demis and the

1202
01:10:32,400 --> 01:10:34,717
various animosities and world views.

1203
01:10:34,800 --> 01:10:37,997
Like you wrote about
this the Shakespearean

1204
01:10:38,080 --> 01:10:42,117
drama of it. Like how do we possibly get
past this? I mean, you guys are all so

1205
01:10:42,200 --> 01:10:46,597
um I feel I don't see
a path out and and obviously

1206
01:10:46,680 --> 01:10:49,437
like we mentioned there's a lawsuit
coming that will only Wouldn't all the

1207
01:10:49,520 --> 01:10:51,517
hands on stage? I'm so sorry to say.

1208
01:10:51,600 --> 01:10:56,637
>> Some some of the people involved only

1209
01:10:56,720 --> 01:11:01,077
trust themselves to get it right.
And because they think the stakes are

1210
01:11:01,160 --> 01:11:04,837
infinite and because they don't,
you know, for different reasons, uh they

1211
01:11:04,920 --> 01:11:07,717
don't think anybody else can

1212
01:11:07,800 --> 01:11:09,117
do it right or they don't want anybody

1213
01:11:09,200 --> 01:11:13,557
else to do it. Um that leads to

1214
01:11:13,640 --> 01:11:16,677
I think some very toxic behavior.
We can't control the behavior of other

1215
01:11:16,760 --> 01:11:19,597
people. Uh but we will continue to

1216
01:11:19,680 --> 01:11:22,557
advocate for doing this as a

1217
01:11:22,640 --> 01:11:26,837
collective project that humanity has
to get right together and not something

1218
01:11:26,920 --> 01:11:28,357
where it should be about

1219
01:11:28,440 --> 01:11:30,757
one person or one ideology winning or

1220
01:11:30,840 --> 01:11:34,157
losing or you know, having a

1221
01:11:34,240 --> 01:11:38,117
sole victory or something like that.
I mean, we'd be remiss and I know it's

1222
01:11:38,200 --> 01:11:41,837
super sensitive, but I mean,
it you know, if you look at what happened

1223
01:11:41,920 --> 01:11:46,357
to you personally over the last week,
there's tons of science fiction books

1224
01:11:46,440 --> 01:11:50,277
that have been written
about what happens when um

1225
01:11:50,360 --> 01:11:51,837
the factions that really don't want to

1226
01:11:51,920 --> 01:11:54,877
see technological progress happen get um

1227
01:11:54,960 --> 01:11:57,997
you know, things get more extreme. Um

1228
01:11:58,080 --> 01:12:02,117
I don't know. Like there's an argument
to be made that this this switch is now

1229
01:12:02,200 --> 01:12:05,277
flipping. Along those lines.
With people that want

1230
01:12:05,360 --> 01:12:09,077
to like stop. Yeah, I mean, you know,
I've I've read Richard Clark's book

1231
01:12:09,160 --> 01:12:13,437
years ago where a Bill Joy-like figure
decides AI can't happen and he's running

1232
01:12:13,520 --> 01:12:16,357
around destroying data center. You know,
this stuff has been out there forever.

1233
01:12:16,440 --> 01:12:19,317
It just feels like, God, I mean,
I don't see how things could get like more

1234
01:12:19,400 --> 01:12:23,157
heightened and so um it doesn't
seem like it's getting better.

1235
01:12:23,240 --> 01:12:27,317
I I assume it will go up and down,
but directionally more heightened.

1236
01:12:27,400 --> 01:12:30,797
This must be like you wrote about.
I mean, clearly this must be horrifying.

1237
01:12:30,880 --> 01:12:33,077
Horrifying. Yeah, yeah, I mean, I don't
really have anything like deep to say

1238
01:12:33,160 --> 01:12:35,520
here.

1239
01:12:36,280 --> 01:12:40,120
That was a crazy way to wake up. Um

1240
01:12:40,720 --> 01:12:44,437
The the first day I was sort of in this
light kind of adrenaline shock about it

1241
01:12:44,520 --> 01:12:46,957
and just trying to like
figure out logistics.

1242
01:12:47,040 --> 01:12:50,557
And then the day after I
was just like, you know,

1243
01:12:50,640 --> 01:12:53,357
there's going to be more stuff like this
and it's incredibly disheartening and I

1244
01:12:53,440 --> 01:12:54,757
went through a real depressive cycle

1245
01:12:54,840 --> 01:13:00,720
about it. Um, but it's very scary and I

1246
01:13:04,480 --> 01:13:07,077
Yeah, I don't think I have anything
super deep to say. I think the doomerism

1247
01:13:07,160 --> 01:13:10,477
talk hasn't helped. Uh,

1248
01:13:10,560 --> 01:13:14,197
I think the way certain other labs talk
about us hasn't helped. I actually don't

1249
01:13:14,280 --> 01:13:16,437
want to make like a a side statement.
I think the way Anthropic talks about

1250
01:13:16,520 --> 01:13:21,720
OpenAI doesn't help. Um, and

1251
01:13:22,880 --> 01:13:24,837
you know, I I I like

1252
01:13:24,920 --> 01:13:26,837
I hope that cooler times will prevail.

1253
01:13:26,920 --> 01:13:31,437
One One thing I do want to say, just
to you know, the point of we can't control

1254
01:13:31,520 --> 01:13:32,717
other people, we can control ourselves.

1255
01:13:32,800 --> 01:13:37,077
Like one thing I I've been just really
impressed by and astounded by with Sam

1256
01:13:37,160 --> 01:13:39,637
is just how throughout this time, like

1257
01:13:39,720 --> 01:13:43,197
that very day, he was there doing things

1258
01:13:43,280 --> 01:13:47,917
that absolutely required him. Like he
just like keeps pushing on the mission.

1259
01:13:48,000 --> 01:13:52,397
And I think that is something that I
don't take for granted at all. Like I just

1260
01:13:52,480 --> 01:13:56,597
think that the degree of resilience that
is represented by Sam is is extreme and

1261
01:13:56,680 --> 01:13:58,637
I think very underappreciated.

1262
01:13:58,720 --> 01:14:03,157
The I mean I've seen
the same thing. I don't know

1263
01:14:03,240 --> 01:14:05,677
what people will be like, oh, you're
taking it easy or something like that. I

1264
01:14:05,760 --> 01:14:09,077
don't know. I I am It's hard
to think of a like another

1265
01:14:09,160 --> 01:14:14,317
individual over the last 3 years who's
been through more dramatic business and

1266
01:14:14,400 --> 01:14:16,957
and personal good >> The narrative
has been quite dramatic.

1267
01:14:17,040 --> 01:14:19,077
>> be sympathetic cuz
they're like you're rich.

1268
01:14:19,160 --> 01:14:21,957
>> be sympathetic cuz they're
like And that's fine. Um,

1269
01:14:22,040 --> 01:14:25,437
I I I said this the night before

1270
01:14:25,520 --> 01:14:27,437
that this thing happened at my house.

1271
01:14:27,520 --> 01:14:31,877
Um, I had some people over for dinner
and we were talking like uh some work

1272
01:14:31,960 --> 01:14:33,957
colleagues and we were talking about
the next phase and they're like, uh, it's

1273
01:14:34,040 --> 01:14:36,757
been like a you know, a brutal time
for you in the press, but I I guess it's

1274
01:14:36,840 --> 01:14:39,437
like good in some sense and I was like,
uh, you know, at least no one tried to

1275
01:14:39,520 --> 01:14:40,477
kill me.

1276
01:14:40,560 --> 01:14:42,637
Um, You said that the night before?

1277
01:14:42,720 --> 01:14:46,560
Uh, and

1278
01:14:46,720 --> 01:14:50,557
and it but it really did put into
perspective that like, you know, people

1279
01:14:50,640 --> 01:14:53,557
can say all the mean things they want
and as long as they only say the mean

1280
01:14:53,640 --> 01:14:56,557
things, it's like it's really not
that big of a deal. You keep going.

1281
01:14:56,640 --> 01:14:58,917
>> You always told me
your dream is to like

1282
01:14:59,000 --> 01:15:03,597
retire out in that someday. So like why
not We got a lot of work left. But like

1283
01:15:03,680 --> 01:15:06,557
somebody's going to get It's clear
somebody's going to get to AGI now. We

1284
01:15:06,640 --> 01:15:09,997
got like five companies
that could reasonably do this.

1285
01:15:10,080 --> 01:15:12,917
>> Which company would you most like to
get there first? Is that Is that what it

1286
01:15:13,000 --> 01:15:15,997
comes down to? >> cuz look,
I think that the point cuz the

1287
01:15:16,080 --> 01:15:19,037
the missing thing is this point of how

1288
01:15:19,120 --> 01:15:23,477
do we help people really understand what
it is that this technology can do for

1289
01:15:23,560 --> 01:15:25,757
them. And I think that's
something that every

1290
01:15:25,840 --> 01:15:29,117
company can contribute to, right? That I
think is something we have a perspective

1291
01:15:29,200 --> 01:15:32,557
on. We've talked a lot about it in this
podcast. And fundamentally, I think that

1292
01:15:32,640 --> 01:15:35,957
that is something that is kind of the

1293
01:15:36,040 --> 01:15:39,997
responsibility and on all the people who
are trying to create this technology is

1294
01:15:40,080 --> 01:15:44,077
also to show its benefits and why people
should want it. Why should people be

1295
01:15:44,160 --> 01:15:48,397
protecting and defending the ability to
create it? Like why does America need to

1296
01:15:48,480 --> 01:15:52,277
have leadership here? Like why is all
this good for not just a country, but

1297
01:15:52,360 --> 01:15:55,677
you personally, your future, the future
of your kids? And that's something that

1298
01:15:55,760 --> 01:16:00,037
we wake up thinking about like like
I would say every day. Like it I don't

1299
01:16:00,120 --> 01:16:02,917
think it's an overstatement. Like this
is something we talk about constantly.

1300
01:16:03,000 --> 01:16:05,997
We think about whether it's
weird creative, you know,

1301
01:16:06,080 --> 01:16:09,237
legal structures for a company.
We've done that many times

1302
01:16:09,320 --> 01:16:12,957
because we're trying to solve for this
mission, for this thing that we think is

1303
01:16:13,040 --> 01:16:16,117
so important. And that's something where
if other people want to contribute to

1304
01:16:16,200 --> 01:16:18,717
that, then more power to them. Like
that's something we should all be doing,

1305
01:16:18,800 --> 01:16:23,077
but it's something that drives us
uniquely. We so believe that if we can

1306
01:16:23,160 --> 01:16:26,477
deliver technology that enables
prosperity for everybody, if we can give

1307
01:16:26,560 --> 01:16:30,717
people more agency over
the future, uh that will

1308
01:16:30,800 --> 01:16:35,037
through some fits and starts lead
to a better world. I don't think all

1309
01:16:35,120 --> 01:16:37,957
people working in the field believe
that, but anyone who does, we are

1310
01:16:38,040 --> 01:16:41,757
delighted to work with and that is the
the that, you know, we we want to move

1311
01:16:41,840 --> 01:16:45,797
the world towards. Go Going back Well,
I think we have time for like a couple

1312
01:16:45,880 --> 01:16:47,477
questions. >> Actually,
I have wish list questions.

1313
01:16:47,560 --> 01:16:50,517
>> I've got Can I'm going to I'm going
to give you two answers real fast.

1314
01:16:50,600 --> 01:16:53,637
>> I actually want to end on the one that
I'm about to ask. So, I'll do this one

1315
01:16:53,720 --> 01:16:56,837
and then [laughter] All right,
we're ready. We're ready. I

1316
01:16:56,920 --> 01:16:59,317
mean, how you

1317
01:16:59,400 --> 01:17:04,717
There is like a How existential
do you view this trial?

1318
01:17:04,800 --> 01:17:08,477
I I actually think it's a real
opportunity for us to tell our story.

1319
01:17:08,560 --> 01:17:13,637
Because if you look at the course
of OpenAI, we have really let

1320
01:17:13,720 --> 01:17:17,117
the other sides, when there's splits,
tell the story. And we really have done

1321
01:17:17,200 --> 01:17:19,597
our best not to sort of say, well, well,

1322
01:17:19,680 --> 01:17:24,717
that's not really what happened. Like,
let's talk about the truth. And the In

1323
01:17:24,800 --> 01:17:27,557
this case, we finally
have no choice, right?

1324
01:17:27,640 --> 01:17:30,397
Because we have to defend ourselves.
We have to tell the truth. We have to tell

1325
01:17:30,480 --> 01:17:32,837
what happened. And I'm extremely proud.

1326
01:17:32,920 --> 01:17:35,637
Like, I've spent a lot of time looking
back at the history at a bunch of

1327
01:17:35,720 --> 01:17:38,397
different messages. And of course,
there's always things that you can like

1328
01:17:38,480 --> 01:17:40,837
try to like cherry-pick, be like,
"Aha, you said this thing."

1329
01:17:40,920 --> 01:17:44,557
>> Your diaries are are famous. I know,
right? But the thing is, that they're

1330
01:17:44,640 --> 01:17:48,517
not because And first of all, incredibly
personal documents. Extremely painful to

1331
01:17:48,600 --> 01:17:52,597
have something so personal be sort
of taken from you and then, you know,

1332
01:17:52,680 --> 01:17:56,157
attempted to be weaponized. But those
particular, you know, sentences are kind

1333
01:17:56,240 --> 01:18:00,597
of the worst thing that the opposition
could find. You're like, "Really?" And

1334
01:18:00,680 --> 01:18:03,357
the thing is, they're all taken out
of context, right? The question of, you

1335
01:18:03,440 --> 01:18:07,277
know, "Hey, like, we're in the middle
of this negotiation, right? We've all

1336
01:18:07,360 --> 01:18:11,957
agreed the only path forward for OpenAI
is a for-profit." Sam, Ilya, Greg, Elon,

1337
01:18:12,040 --> 01:18:15,317
we all agreed on this. We all said this
is what we got to do. This is literally

1338
01:18:15,400 --> 01:18:18,837
the thing for the mission. And now
you're in this crazy negotiation, right?

1339
01:18:18,920 --> 01:18:20,677
Elon's like, "You need majority equity.

1340
01:18:20,760 --> 01:18:24,117
You need to to be CEO.
You need full control." And

1341
01:18:24,200 --> 01:18:26,597
we got so close. It's like, "Okay, fine.

1342
01:18:26,680 --> 01:18:29,597
Okay, we're not going to be equal
partners. You need this this massive

1343
01:18:29,680 --> 01:18:33,077
amount of equity. If that's
what you say you need, like,

1344
01:18:33,160 --> 01:18:36,797
we would like you to be involved. We
can get there. Okay, fine. You know, Sam

1345
01:18:36,880 --> 01:18:40,037
will be CEO. Elon will be CEO.
He needs it so that everyone knows he's in

1346
01:18:40,120 --> 01:18:43,957
charge. Fine. But absolute control.

1347
01:18:44,040 --> 01:18:47,557
Absolute control over OpenAI. Even if
you say, "Well, I'll dilute down. I'll

1348
01:18:47,640 --> 01:18:50,917
give it up in the future."
And you're like, "What is our mission?

1349
01:18:51,000 --> 01:18:54,957
Like, do we really believe in our mission?
Do we really care about this picture of

1350
01:18:55,040 --> 01:18:57,997
we want this technology to benefit
everyone, and there shouldn't be one

1351
01:18:58,080 --> 01:19:00,797
person person in charge
of the whole future.

1352
01:19:00,880 --> 01:19:04,077
Doesn't matter who that person is. Like,
that was the breaking point. That was

1353
01:19:04,160 --> 01:19:06,197
the thing that caused us to say no.

1354
01:19:06,280 --> 01:19:11,197
And so, we've never told that story
for years. But now we will. And so, I I

1355
01:19:11,280 --> 01:19:14,117
think that it is a real opportunity
for people to understand what truly

1356
01:19:14,200 --> 01:19:18,037
motivates us, what we truly stand for.
I think it's insane that he's doing this,

1357
01:19:18,120 --> 01:19:20,957
but I'm kind of my fear
at this point is he decides to

1358
01:19:21,040 --> 01:19:23,517
like drop the case right before the trial,
and we don't get to do all this.

1359
01:19:23,600 --> 01:19:26,317
But I am happy to like explain all
this to the world and have this chapter

1360
01:19:26,400 --> 01:19:30,317
behind us. Okay. So, my my question
is rounding back to the beginning of this

1361
01:19:30,400 --> 01:19:34,477
podcast. Like, I was talking about
this narrative I feel like I as an AI

1362
01:19:34,560 --> 01:19:37,557
reporter grew up with
hearing of, you know,

1363
01:19:37,640 --> 01:19:40,157
"We're all going to die, and we're all
going to lose our jobs." I feel like I

1364
01:19:40,240 --> 01:19:44,597
heard that time and time again, and now
portions of your personal notebook are

1365
01:19:44,680 --> 01:19:48,037
public. Um I'm curious,
again, how would you have

1366
01:19:48,120 --> 01:19:49,957
talked about this differently with

1367
01:19:50,040 --> 01:19:53,237
hindsight being 20/20?

1368
01:19:53,320 --> 01:19:57,357
Well, I think that the way
that we think about it today

1369
01:19:57,440 --> 01:20:01,117
is the way that I wish we had talked
about it in some sense. But I don't know

1370
01:20:01,200 --> 01:20:04,197
we could have with the knowledge we had
at the time. For example, even some of

1371
01:20:04,280 --> 01:20:06,437
it is related to the technology itself.

1372
01:20:06,520 --> 01:20:09,917
We had a picture in 2017, 2018, the way

1373
01:20:10,000 --> 01:20:13,877
we would build AGI was through
a competitive multi-agent simulation.

1374
01:20:13,960 --> 01:20:17,717
Like, imagine an island of like
a thousand agents that all are in a battle

1375
01:20:17,800 --> 01:20:21,197
to survive and replicate. And you could
see that, if you put a ton of compute

1376
01:20:21,280 --> 01:20:25,357
into it, maybe it would build something
very smart. But that smart thing

1377
01:20:25,440 --> 01:20:28,117
that would not be connected to human
values at all. You'd have to have a

1378
01:20:28,200 --> 01:20:31,277
separate step of figuring out,
how do you even talk to this thing, right?

1379
01:20:31,360 --> 01:20:34,277
Didn't grow up at all in the real world,
doesn't have any concept of language,

1380
01:20:34,360 --> 01:20:38,757
doesn't have any connection to our
reality. It's just smart and powerful.

1381
01:20:38,840 --> 01:20:42,797
That's a very scary system,
right? You start from a place of

1382
01:20:42,880 --> 01:20:46,037
trying to think about how could you
possibly align it. But instead we have

1383
01:20:46,120 --> 01:20:50,037
this language model route, which is rooted
in our values, which is rooted in

1384
01:20:50,120 --> 01:20:54,317
understanding humans, and we have
chain of thought that actually you can

1385
01:20:54,400 --> 01:20:57,637
monitor, that actually if you approach
things right in a technical sense, you

1386
01:20:57,720 --> 01:21:01,237
have a path to make that be faithful.
So it really represents what is truly

1387
01:21:01,320 --> 01:21:04,877
motivating the AI. And you just
realize we have a totally different

1388
01:21:04,960 --> 01:21:08,917
technological path to get to the outcome
we're talking about, and it's a much

1389
01:21:09,000 --> 01:21:13,157
more optimistic one. And so I think
that there was some technical learnings we

1390
01:21:13,240 --> 01:21:17,077
had to have for what is this technology
truly going to be, how will it be

1391
01:21:17,160 --> 01:21:20,477
created, what are the ways in which you
make it useful. And that is something I

1392
01:21:20,560 --> 01:21:23,677
think we could not have
appreciated, but we do today.

1393
01:21:23,760 --> 01:21:25,997
Um it's been a fascinating discussion.

1394
01:21:26,080 --> 01:21:28,397
It's um really kind of you guys to both

1395
01:21:28,480 --> 01:21:31,477
show up and do this together. Um

1396
01:21:31,560 --> 01:21:35,797
we we have been a humble podcast.
We're honored um for for you guys to spend

1397
01:21:35,880 --> 01:21:39,237
this time. And I guess I mean
the obviously we covered a lot of ground,

1398
01:21:39,320 --> 01:21:42,197
but the headline is in relatively short

1399
01:21:42,280 --> 01:21:46,837
order, new new models Yeah. are really
good new models. Yeah. Um well, thank

1400
01:21:46,920 --> 01:21:48,077
you guys. Thank you guys so much.

1401
01:21:48,160 --> 01:21:50,507
>> Thank you for having new
models useful for everyone.

1402
01:21:50,590 --> 01:21:55,360
>> [laughter] >> Thank you guys.
Thank you. Thank you.

1403
01:21:56,080 --> 01:21:58,397
The Core Memory podcast is hosted by me,

1404
01:21:58,480 --> 01:22:01,717
Ashley Vance, and or Kylie Robinson, or

1405
01:22:01,800 --> 01:22:04,717
both of us together. It is produced by

1406
01:22:04,800 --> 01:22:06,877
me and David Nicholson. [music]

1407
01:22:06,960 --> 01:22:09,277
Our theme song is by James Mercer and

1408
01:22:09,360 --> 01:22:12,757
John Sortland, and the show is edited,

1409
01:22:12,840 --> 01:22:16,797
always, by the John Sortland. Thank you

1410
01:22:16,880 --> 01:22:21,077
so much to Brex and Combinator Ventures
for all [music] your support, and thank

1411
01:22:21,160 --> 01:22:23,157
you most of all to everybody for

1412
01:22:23,240 --> 01:22:26,037
listening or watching. We love you.

1413
01:22:26,120 --> 01:22:28,277
Please leave us a like, a review, a

1414
01:22:28,360 --> 01:22:31,797
subscribe, all those tremendous things.

1415
01:22:31,880 --> 01:22:33,087
Thank you and we'll see you again.

1416
01:22:33,170 --> 01:22:35,190
[music]