WEBVTT

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So, how did OpenAI come about?

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>> I knew I wanted to do a startup
because I felt like that was something.

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>> But you were just in a startup.
Stripe was a startup.

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>> It's true. But I never I I felt like

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Stripe, the problem that we
were solving was not my problem,

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right? It wasn't the problem I had grown
up thinking about. It was an important

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problem that I I dedicated myself
to that mission for a number of years, but

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I felt like it was going to succeed with
or without me. And so then I had a first

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moment to really think about what is a
mission that I want to dedicate myself

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to where I would spend the rest of my life
working on this problem just to see

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it play out in a slightly better way.

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And it was very clear to me that top
of the list was AI, right? If you could

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actually make a difference in how
AI will play out in the world,

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like that would be a life well-lived.

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When you were thinking about leaving,
Patrick told you to go talk to Sam

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Alman. What happened in that conversation?

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>> Well, so Patrick
had said, "Sam has seen

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lots of young people in your situation."

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And Patrick, I think, really hoped
that Sam would convince me to stay. A few

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minutes of talking to Sam, he's like,
"Okay, you clearly have already decided.

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It is very obvious." And so he asked,
"Well, what are you planning on doing

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next?" And I said, "Well, I'm thinking
about doing an AI company." And he said,

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"I'm also thinking about doing something

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in AI. We should keep in touch." So, I

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had talked to Sam maybe one more time

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after I was leaving Stripe and he asked,
"Are you still thinking about doing

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something in AI?" I said, "Yes."
He said, "I'm also starting to get more

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details and putting together this dinner

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in

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July." And I flew out for the dinner.

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And the thing that I remember was a

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topic was, is it too late to start a lab

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with many of the best researchers?
Is it possible? And this is what year?

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>> 2015, right? Because you think about

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just the degree to which Deep Mind had

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all the researchers, all the capital,
all the data. It just felt like, is it

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even possible to get something off
the ground still? People came up with all

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sorts of reasons. It was hard.
No one could come up with a reason it was

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actually impossible. And so Sam and I
driving back to the city that night. I

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remember we looked at each other and we

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said, "We got to do this, right? Like,

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we just have to." And so next day I was
full-time on putting this together. And

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it was tough because it was very

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illdefined. We had a mission, a vision

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of saying we think that we can build
human level AI, make it be something

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positive for the world,
make the benefits be something that are

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distributed broadly. But how and how do
you get people to actually leave their

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jobs to come and join this thing?

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Initially, the set of people that I

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narrowed down to were actually Ilia,

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Dario, Amadai, Chrysola, and myself.

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that was going to be the team and we
spent a lot of time together. We spent a

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lot of time talking about potential
visions for the lab, potential ways that

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things would work. It didn't quite come
together and that there was just partly

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a question of will this have enough
momentum. You know, Daario felt like

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that he needed to go and establish
a name for himself and he wasn't sure if

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this was really going to be it. It was a
question of just how it was all going to

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work. In the meanwhile, I was trying
to get John Schulman interested. He said

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that he was going to do it. Dario and
Chris ended up deciding to go to Google

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Brain. And so it was really just,
you know, Ilia, me, and you know, John

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starting to be maybe a few others. And

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so I had a group of about 10 people that

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many of them were saying, "I'm interested,
but who else is in?" I asked

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Sam, "Okay, how do we break symmetry here?
How do we actually get everyone to

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say, all right, we're joining?" And Sam's
suggestion was, "Invite people out

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for an off-site." So, we set up a thing
in Napa and I actually made t-shirts.

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Uh, at the time we were >>
and this is before they had joined.

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>> There was no official offers. No one
had joined. We didn't have a structure. We

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had nothing. Right? We just had an idea.

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We had a vision. We had a mission and we

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flew people out. We drove up to to Napa

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together and it was an amazing day.

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Right? The ideas were flowing. We came

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up with what I would really say
is almost the technical plan that we have

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pursued for the past 10 years.
Number one, solve reinforcement learning.

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Number two, solve unsupervised learning.

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And number three was gradually learn

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more complicated in quotes. After that

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offsite, I sent offers to everyone and

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said, "Hey, we want to get started
in the next 2 to 3 weeks. Please let me

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know if you're in."
>> Why did you think that Deep Mind had

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such an insurmountable advantage?

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>> It was very much the case that Google
Deep Mind was the 10,000lb gorilla in

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the field. They just had lots of capital.
They had the track record. And

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this was before AlphaGo, right? AlphaGo
came out a couple months later, but it

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wasn't a surprise, right? It's like
very much the momentum was very clearly

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there. And so the question of is it

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really possible to build something

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independent and new? It wasn't obvious.

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>> At what point did you realize that like
this nonprofit thing just wasn't going

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to work? >> In 2017,
we started to think very hard

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about first of all, how do we really
achieve the mission? How do we actually

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build an AGI? What will that look like?

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And we started to do the math on compute.
And you start to realize that

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it's gonna take big computer. And we

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came across a company called Cerebras,

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which was building a unique piece
of computing hardware. And the kind of

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computer that they were promising, we
realized was going to be far advanced of

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where our compute calculations looked.

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As you start to realize if we could
buy a lot of those computers, we could

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actually probably succeed at building an
AGI. If we could get exclusive access to

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Cerebrris, that could give us
an overwhelming advantage. If we could buy

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very large data centers, that could be
something unique as well. And the thing

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about nonprofit [clears throat]
fundraising is I think that there is

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essentially a cap to what is possible

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there. And so Elon, Sam, Ilia, and I all

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agreed that the only path forward
for OpenAI, the only path to achieve the

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mission was to create a for-profit
entity associated with OpenAI of some

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form. And so we were committed to that
direction and that is something that we

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knew was the only way
to achieve the mission.

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>> When was the moment that you realized
everything was going to change for you?

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Was that Dota or was it before then or

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after? The way that opening eye works is

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it's a series of moments where you

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realize that it's real now and every

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time you think that you understand it,
that is really settled in for you, you

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realize that there is a new horizon you
had not yet appreciated. And so along

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the way, I think that there was the
initial launch. It was like, "Wow, we

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actually got a team together. Now we can
pursue this mission." But you show up at

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the office the next day and you're like,
"Well, what do we do?" Right? We didn't

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even have a whiteboard. You know,
I John wanted to write something on a

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whiteboard. I was like, "I will get
a whiteboard. That's something I can do."

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Dota, we had our first big result,
right? That really was like, "Wow, we

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can actually accomplish something when
we put our mind to it. You can actually

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see all this compute coming together.

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You scale up the compute, you scale up
the result." There were multiple moments

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with the GPT series and I remember
actually an early moment was the

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unsupervised sentiment neuron paper.

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Have you heard of that one?
>> I've heard of it but I haven't read it.

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>> Okay. Yes, that one's that one's
an interesting one cuz it's 2017 and it's

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really the first time that we saw
semantics arise from training on a

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language modeling objective.
So you train on learn the next character,

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predict the next character and then
suddenly you get a neural net that

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understands sentiment, understands if
something is positive or negative. It's

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harder than it sounds, but that was a
moment where you realize, wow, we are

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building machines that can learn
semantics, not just where the commas are

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and where the nouns and verbs are,
but it can really learn the meaning of

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sentences. You got to push that.
And then of course when you see something

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like GPD4 I remember we were playing

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with it and someone asked

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why is this thing not an AGI right it's
like actually really hard to put your

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finger on it because you can talk to it
fluently in anything you want it clearly

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wasn't an AGI it was lacking something

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but just if you'd described your
criteria for AGI 2 months prior it

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probably would have been compatible
with what GBD4 was and so there are many

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moments along the way where you feel
like it's real now. It's going to really

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happen. The economy is going
to transform into this compute powered

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world. And I think that those moments
are not yet at the end. I think that we

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have many more breakthrough moments
where you realize that the next stage is

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possible. I thought Dota was like
an incredible moment because it was it

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wasn't u chess like deep blue
and it wasn't alpha go which is like

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computationally intensive but very
defined rules. It was actually

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interactive against humans in a way that

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like the world is sort of structured but

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you have all this freedom. Yeah, that

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was that was something very compelling

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about it. And the ironic thing is we'd

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actually set out with Dota to develop

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new methods because the reinforcement

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learning at the time was clearly not

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going to scale. Right? The the algorithm
we use called PO you plan over every

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single time step. there's no hierarchy,
right? As a human, that's not how you

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plan your day. And so, we knew that this
algorithm was incredibly flawed, would

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never scale, and had all these problems,
but you got to start somewhere. You got

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to push your baselines to reach the wall
so you actually see the limits of what

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good looks like with what you have.
And then you can bring to bear a new

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algorithm. And we just kept scaling PO

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and we exceeded the performance of the
best humans. And that itself was the

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finding, right? That actually massive
compute with simple algorithms,

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>> right? that that is something where we
cannot just doesn't just work in theory,

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it works in practice. We can really make
it happen. And in this incredibly messy

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environment where you cannot program it,
you cannot look ahead. You cannot do a

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search. You just need this almost
humanlike intuition. And by the way, the

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neural net we used tiny tiny little
insect brain, similar number of synapses

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as to truly an insect brain. and you
realize like, wait, what if you had the

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same computational

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approach but scaled it up to something

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that's much more human brain scale? What
would that be like? Very, very evocative

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question. >> Is there
a difference between reasoning

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and predicting? You mentioned sort
of like predicting the next character,

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predicting the next word versus
actually reasoning in first principles.

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>> I think they are
connected in a deep way. So

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on the one hand, just predicting what
comes next sounds like a pedestrian

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task, but if you really can predict
the next word out of Einstein's mouth, you

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are at least as smart as Einstein.
And you can make arguments, oh well, like,

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you know, it's but I I think that those
arguments fall flat, that there's

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something there's something false there
because the point of prediction is not

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about being able to predict what is known.
The point is you put yourself in

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a new situation you've never seen before

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and predict what comes next. And I think

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that there's something deeply connected

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to intelligence and prediction
that there's a long story of academic

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literature and how you think about this
compression. They're all kind of part of

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the same thing. Now, these reasoning
models, the thing that I think is very

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interesting is that we train them with
reinforcement learning. And so there's

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really back to the original OpenAI plan,
there's two steps to it. The first is

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unsupervised learning. You train a model
just by having it predict what comes

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next. And there it's much more static
data. It's much more observational.

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Again, it's data it's never seen before,
situations never seen before, but it is

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a situation that has already happened.

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Then you do reinforcement learning,
which is you basically have the AI learn

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on its own data, right? You have it make
its own here's the action I'm going to

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take. You get an observation from the
world and you learn from that. And it's

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again the way you actually train it is
still predicting. It's trying to predict

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if I take this action, what's the thing
that's likely to happen? And you

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reinforce that depending on how good of
a job you did. And the beauty of that is

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that it now is an AI that has this
background knowledge and has real world

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experience. But fundamentally the
technology that we used to train during

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unsupervised stage and during
the reinforcing stage, they're exactly the

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same. You are just predicting but you've
changed the structure of the data.

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>> When did things start to get tense?
I think the thing about open AI is that if

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you truly believe in the mission,
if you truly believe in the possibility of

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creating machines that have the

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intelligence level of humans, it means
the stakes always feel very high. The

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question of who's making the decision,
the question of what are the values that

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go into those decisions, the question of
these things that are maybe mundane in a

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typical company that are much more like
office politics start to take on this

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existential weight. And I think that

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that has colored a lot of how open AI

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these more high-profile conflicts, you

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know, sometimes it's like you put it
in like even just the question of who gets

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credit for a particular thing
and suddenly takes on this existential

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weight. Well, that's where I was sort of
thinking about this because it's like at

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that point you probably realized this
technology is inevitable and it's going

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to change the world and that wasn't
broadly known to the world and then I

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would imagine there's people who like
I want to be front and center. I want to

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take credit for this.

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>> Yes, that that is the overwhelming

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dynamic that I have observed in this
field. It's not just about open AI

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actually. Like one observation I had
early on is that this technology is by

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nature very fragmentaryary right that it's
sometimes it you know like when you

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have a lot of pressure you can get a

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diamond or you can get cracks.
Often you'll see diamonds form in pockets,

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right? Teams of people that really work
together, that have a lot of high trust,

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that know how to operate, but sometimes
you can see that they that they splinter

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off and they kind of go their own way.

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And I think within AI, I think we've
gotten some real benefits out of

00:13:36.080 --> 00:13:38.877
diversity of approach and different
groups that are really pushing each

00:13:38.960 --> 00:13:41.757
other in order to both bring this

00:13:41.840 --> 00:13:45.197
technology in a more beneficial way

00:13:45.280 --> 00:13:49.277
sometimes how to think about all
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00:13:49.360 --> 00:13:52.797
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What does it mean to actually deploy this

00:13:52.880 --> 00:13:56.557
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00:13:56.640 --> 00:14:00.157
those benefits? And that's something
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00:14:00.240 --> 00:14:04.157
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00:14:04.240 --> 00:14:05.757
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00:15:37.360 --> 00:15:42.157
Take me back to the moment you found out
that Sam had been fired. Where were you?

00:15:42.240 --> 00:15:44.397
>> I was at home. >> And what happened?

00:15:44.480 --> 00:15:47.517
>> I got a text saying, "Can we hop on a

00:15:47.600 --> 00:15:49.437
video call?"

00:15:49.520 --> 00:15:52.477
So, I hopped on the video call. I

00:15:52.560 --> 00:15:58.317
noticed that it was the board
minus Sam who were on there.

00:15:58.400 --> 00:16:02.637
>> No, at that point. >> No,
I mean I inferred something was up, but

00:16:02.720 --> 00:16:05.277
>> cuz you're on the board.
>> I am on the board or was on the board

00:16:05.360 --> 00:16:08.077
>> at this point? >> Yes.
>> And then what happened?

00:16:08.160 --> 00:16:12.237
>> I was told that the board has decided

00:16:12.320 --> 00:16:15.917
that Sam would be removed and

00:16:16.000 --> 00:16:19.677
effectively the message that I got
was the same messaging that was in the

00:16:19.760 --> 00:16:21.517
public post.

00:16:21.600 --> 00:16:24.557
And I asked if I could have any more

00:16:24.640 --> 00:16:27.437
information. I was told no, not right

00:16:27.520 --> 00:16:31.437
now. And I pressed on that maybe another

00:16:31.520 --> 00:16:36.477
time and again was told nothing more
to share. And then was told wait there's

00:16:36.560 --> 00:16:39.837
more. Also that I had
been removed from the

00:16:39.920 --> 00:16:42.797
board but would be staying with the

00:16:42.880 --> 00:16:47.037
company because I was very critical to

00:16:47.120 --> 00:16:49.597
accompanying the mission. I said again I

00:16:49.680 --> 00:16:52.477
asked if I get any reasons

00:16:52.560 --> 00:16:56.397
any feedback was told no towards the end

00:16:56.480 --> 00:16:59.437
was told that hey like in this new setup

00:16:59.520 --> 00:17:02.237
that you will start to get hopefully you

00:17:02.320 --> 00:17:07.117
can get feedback in this new
configuration and so that was the

00:17:07.200 --> 00:17:10.317
conversation.
>> What went through your mind?

00:17:10.400 --> 00:17:14.397
>> It just wasn't right. >> Was it anger?

00:17:14.480 --> 00:17:16.957
>> No. I felt like I understood what had

00:17:17.040 --> 00:17:21.677
happened. How long before you knew what
had actually transpired to sort of cause

00:17:21.760 --> 00:17:24.717
this? >> Well, there's
two parts to the answer.

00:17:24.800 --> 00:17:29.117
One is I feel like I still am learning
some additional fact, some additional

00:17:29.200 --> 00:17:32.237
thing that was in someone's head.
To some extent, it comes down to a lack of

00:17:32.320 --> 00:17:36.157
communication, right? That you realize
that there are all these different

00:17:36.240 --> 00:17:39.357
things that I buffered. And to some
extent, you know, approximately I kind

00:17:39.440 --> 00:17:43.917
of knew I was like, I understand for
every person here. I have a pretty good

00:17:44.000 --> 00:17:47.997
model of why they acted the way that
they did. But it wasn't what was most

00:17:48.080 --> 00:17:51.197
important to me in the moment. I just
knew that this wasn't right. Right after

00:17:51.280 --> 00:17:53.757
I hung up the call, I talked to my wife

00:17:53.840 --> 00:17:59.277
and I said, "Got to quit."
And she said, "I agree."

00:17:59.360 --> 00:18:01.837
>> And you quit that day. >> Yes.

00:18:01.920 --> 00:18:05.917
>> And then what happened >> that day
when I quit? Started to get all

00:18:06.000 --> 00:18:08.877
these messages, people saying, "I don't
know what you and Sam are doing next,

00:18:08.960 --> 00:18:12.877
but I'm with you. I want to go start
something with you." Like just that was

00:18:12.960 --> 00:18:16.717
that was a real honest surprise.
I didn't really expect to get that kind of

00:18:16.800 --> 00:18:18.637
support, that kind of outpouring. There

00:18:18.720 --> 00:18:21.037
were a few of my close collaborators who

00:18:21.120 --> 00:18:23.757
quit that day as well. That's Jakob,

00:18:23.840 --> 00:18:27.677
Shimone, Alexander, and the five of us.

00:18:27.760 --> 00:18:31.997
So, those people plus Sam, we all got
together and we started to chart out

00:18:32.080 --> 00:18:36.477
what a new company could look like.
I remember feeling that first day like,

00:18:36.560 --> 00:18:38.637
okay, there's a 10% chance that we

00:18:38.720 --> 00:18:43.677
actually get the company back. 10%.
The next day, we set up a meeting at Sam's

00:18:43.760 --> 00:18:48.477
house. a bunch of people from the company
came by and we showed the vision

00:18:48.560 --> 00:18:51.677
that we'd been sketching out. So, it
was really one day in, you know, this this

00:18:51.760 --> 00:18:53.837
fresh picture of how we'd run the

00:18:53.920 --> 00:18:57.517
project. And we spent that a bunch of

00:18:57.600 --> 00:19:01.597
time over that weekend also negotiating
with the board and the company and

00:19:01.680 --> 00:19:04.557
trying to figure out is there a path
back together that makes sense. That

00:19:04.640 --> 00:19:08.317
Sunday night, the board replaced me as

00:19:08.400 --> 00:19:11.917
interim CEO with a new person and the

00:19:12.000 --> 00:19:15.597
company just rebelled.
like we'd actually been at the office.

00:19:15.680 --> 00:19:19.437
We thought we were close
to a deal and >> to come back

00:19:19.520 --> 00:19:21.037
>> to come back. Yeah. We thought that we

00:19:21.120 --> 00:19:24.637
had a path and then the board made that

00:19:24.720 --> 00:19:28.877
change and then suddenly it was everyone

00:19:28.960 --> 00:19:30.957
streaming out of the building and it was

00:19:31.040 --> 00:19:33.837
just like real chaos. I was on video

00:19:33.920 --> 00:19:37.997
calls with many of the people who had
been interested in coming to this new

00:19:38.080 --> 00:19:42.557
this new company saying it's going to be
okay. we have a plan and we expanded you

00:19:42.640 --> 00:19:45.677
know we'd been building this little life
raft right for the small set of people

00:19:45.760 --> 00:19:49.677
we expected to want to come and suddenly
it was like no one wanted to be

00:19:49.760 --> 00:19:52.397
associated with this this entity right

00:19:52.480 --> 00:19:54.637
people wanted to you know stand up for

00:19:54.720 --> 00:19:59.757
what they viewed as right Sam talked
to Satia who we'd been talking about hey

00:19:59.840 --> 00:20:02.877
could you be a funder could you you know
help help support this new endeavor I

00:20:02.960 --> 00:20:07.357
was like hey actually can we expand
from the small life raft to like

00:20:07.440 --> 00:20:11.357
yes we take everyone and we're like all
right we'll figure it out somehow and a

00:20:11.440 --> 00:20:13.597
lot of people this was right before
Thanksgiving a lot of people were

00:20:13.680 --> 00:20:17.597
supposed to be flying to home wherever
that is and instead they canceled their

00:20:17.680 --> 00:20:21.517
flights and the office was packed. It's
like everyone was at the office just to

00:20:21.600 --> 00:20:24.477
be there, be part of it and just,
you know, even if they couldn't contribute

00:20:24.560 --> 00:20:26.637
to any of these conversations that they

00:20:26.720 --> 00:20:31.757
just wanted to be there as this history
was made. Then this petition starts to

00:20:31.840 --> 00:20:34.877
circulate. So many people were trying
to sign the petition at once. It actually

00:20:34.960 --> 00:20:38.157
crashed Google Docs. And so that you
had to have certain people who are

00:20:38.240 --> 00:20:41.597
designated as the person you go to to
actually put your name on the document

00:20:41.680 --> 00:20:44.397
so you don't don't have too many
editors at once. I think that that was a

00:20:44.480 --> 00:20:49.917
statement that that was really
heard loudly. And I remember

00:20:50.000 --> 00:20:52.557
You know, I probably got home around
like 5:00 a.m. or something, went to

00:20:52.640 --> 00:20:54.237
sleep, and I woke up like 45 minutes

00:20:54.320 --> 00:20:57.917
later, and I checked Twitter, and I saw

00:20:58.000 --> 00:21:02.797
that Ilia had posted and had
signed the petition, and it said

00:21:02.880 --> 00:21:06.317
that he wanted the company to come back
together. And that was this real moment

00:21:06.400 --> 00:21:09.677
of relief. I felt so much gratitude that

00:21:09.760 --> 00:21:14.877
it just felt like, okay, like, we can
put this back together. We can get back

00:21:14.960 --> 00:21:18.717
to a good track. >> You and I
had built this together. What

00:21:18.800 --> 00:21:23.117
was it like trying to find your way
back to that relationship after?

00:21:23.200 --> 00:21:27.757
>> Look, it was tough. It was definitely
that was definitely a very close

00:21:27.840 --> 00:21:30.077
relationship. You'd been the officient

00:21:30.160 --> 00:21:33.517
at my civil ceremony, right? We'd been

00:21:33.600 --> 00:21:37.117
through extremely tough times together
and like any relationship, you always

00:21:37.200 --> 00:21:39.597
have your ups and downs and we spent a

00:21:39.680 --> 00:21:41.677
lot of time afterwards really talking

00:21:41.760 --> 00:21:44.317
things through and really trying to

00:21:44.400 --> 00:21:49.357
understand and just articulate some
of the things that we had let build up or

00:21:49.440 --> 00:21:53.677
had left unsaid between us and I think
that we got to through that process I

00:21:53.760 --> 00:21:57.517
think we had gotten to a really good
place and for me it was I felt like we

00:21:57.600 --> 00:22:01.917
got to closure on on everything that had
happened. How did you feel about all the

00:22:02.000 --> 00:22:06.237
loyalty you inspired? >> Deeply grateful.

00:22:06.320 --> 00:22:09.677
Truly, it was never something that I
would have asked for, something I never

00:22:09.760 --> 00:22:14.157
would have expected. I think the way
that I operate is I'm very much a in the

00:22:14.240 --> 00:22:18.317
trenches kind of leader. Try to lead
from the front. And sometimes when I do

00:22:18.400 --> 00:22:22.477
that, I don't always, sorry, I'm going
to get a little emotional. Um, but I

00:22:22.560 --> 00:22:24.237
don't always look back to see if

00:22:24.320 --> 00:22:25.997
everyone's following. I just like run

00:22:26.080 --> 00:22:29.517
right in. And when people do when when

00:22:29.600 --> 00:22:31.437
people do come and really help to build

00:22:31.520 --> 00:22:35.117
the thing, I just it it makes me feel so

00:22:35.200 --> 00:22:39.677
grateful for them and to feel like they
have exceeded my expectations in every

00:22:39.760 --> 00:22:43.437
way. >> And so eventually
everybody comes back.

00:22:43.520 --> 00:22:47.837
>> I'll tell you it was not guaranteed
because throughout that weekend all the

00:22:47.920 --> 00:22:52.157
competitors were circling. Just imagine
this like feeding frenzy that people

00:22:52.240 --> 00:22:56.397
were shaping up to do. people are
getting offers and we actually did not

00:22:56.480 --> 00:22:59.277
lose a single person through that weekend.
No one accepted a competing

00:22:59.360 --> 00:23:02.477
offer. I think that's incredible. It

00:23:02.560 --> 00:23:04.717
really was. That's more, you know, Coach

00:23:04.800 --> 00:23:09.917
Belich told me this actually uh when we
were talking about the best teams and he

00:23:10.000 --> 00:23:12.957
said they're not playing for money,
they're playing for the person beside

00:23:13.040 --> 00:23:15.677
them. And when you were saying that all

00:23:15.760 --> 00:23:19.997
these people quit, it makes me think
of that like and none of them left for

00:23:20.080 --> 00:23:22.237
presumably more money, better offers.

00:23:22.320 --> 00:23:24.477
Everybody was trying
to circle and poach and

00:23:24.560 --> 00:23:28.477
>> yeah, it was a very
that was a diamond moment.

00:23:28.560 --> 00:23:31.917
>> After all of this happened,
you took some time off. What was going on

00:23:32.000 --> 00:23:36.397
internally with you?
>> That was an intense experience to go

00:23:36.480 --> 00:23:38.317
through, an intense experience to come

00:23:38.400 --> 00:23:41.597
back to. And

00:23:41.680 --> 00:23:43.997
honestly, just

00:23:44.080 --> 00:23:45.357
one of the hardest moments for me at

00:23:45.440 --> 00:23:50.557
OpenAI was when Ilia left. And it was

00:23:50.640 --> 00:23:53.357
maybe the only moment in open eyes
history where I felt like I didn't want

00:23:53.440 --> 00:23:55.517
to do it anymore.

00:23:55.600 --> 00:23:58.077
Um,

00:23:58.160 --> 00:24:01.117
I think I needed some time to kind of

00:24:01.200 --> 00:24:05.277
find my way back to remembering like
why I was doing this and why it was so

00:24:05.360 --> 00:24:07.197
important and why it was worth the pain.

00:24:07.280 --> 00:24:09.437
>> What did you do during the time?

00:24:09.520 --> 00:24:13.357
>> I trained language models on >>
That's when you learned how to do it,

00:24:13.440 --> 00:24:16.237
right? Like you did the self-study thing.
I read on your blog.

00:24:16.320 --> 00:24:18.317
>> Well, no. So, I actually done that.
I actually had done that throughout the

00:24:18.400 --> 00:24:21.677
course of OpenAI. Um, so I trained
language models on DNA sequences.

00:24:21.760 --> 00:24:24.077
>> Oh wow. >> Yeah.
So I basically got to take for ARC

00:24:24.160 --> 00:24:29.197
>> for ARC Institute. Yeah. And it was it
was a very great experience. I took my

00:24:29.280 --> 00:24:32.397
skills that I had and applied them as
a very different domain. A domain that's

00:24:32.480 --> 00:24:36.157
very personally meaningful to both me
and and to my wife. You know, she has a

00:24:36.240 --> 00:24:40.877
lot of health conditions and that we
think about what AI can do for her

00:24:40.960 --> 00:24:44.877
health, what it can do for the health
of animals. who we're both very passionate

00:24:44.960 --> 00:24:46.797
about just it's like this application

00:24:46.880 --> 00:24:50.157
area that felt like maybe we could help

00:24:50.240 --> 00:24:53.997
in this very different way from how I've

00:24:54.080 --> 00:24:57.277
been pursuing this technology. So
that was that was that was a very you know

00:24:57.360 --> 00:24:59.357
positive part of the of the experience.

00:24:59.440 --> 00:25:03.117
If I were to say like open a Google
document, write out sort of what you

00:25:03.200 --> 00:25:05.517
learned about yourself on one page from

00:25:05.600 --> 00:25:09.037
this whole starting to Sam getting

00:25:09.120 --> 00:25:12.877
ousted to you quitting to inspiring all
this loyalty to the time off and then

00:25:12.960 --> 00:25:16.157
coming back. What would you write?
I think I've just learned to just keep

00:25:16.240 --> 00:25:17.917
going for something that's worth it.

00:25:18.000 --> 00:25:19.357
Right? If you have a mission that

00:25:19.440 --> 00:25:23.757
matters, then the fact of you keep going

00:25:23.840 --> 00:25:27.997
through the ups and the downs.
There going to be moments where it's

00:25:28.080 --> 00:25:32.077
it's all over. There's moments where
it's we're so back. And you just can't

00:25:32.160 --> 00:25:34.077
let those moments pull you off course.

00:25:34.160 --> 00:25:36.877
And I think that the degree of just

00:25:36.960 --> 00:25:40.317
personal resilience that you have to

00:25:40.400 --> 00:25:42.477
grow during these times because if

00:25:42.560 --> 00:25:45.517
you're leading, people look to you for

00:25:45.600 --> 00:25:50.237
that steadiness, for that support, for
the direction that the whole thing will

00:25:50.320 --> 00:25:54.157
go. And I think that a lot
of what I've tried to

00:25:54.240 --> 00:25:55.997
grow

00:25:56.080 --> 00:25:58.637
with is to really

00:25:58.720 --> 00:26:02.717
be able to both understand the details,

00:26:02.800 --> 00:26:06.317
right, of what we're doing, what
the implications will be of a choice, but

00:26:06.400 --> 00:26:09.677
also be decisive. I think that that
there have been moments where I think

00:26:09.760 --> 00:26:14.877
I've been very much approaching OpenAI
through a lens of uncertainty of feeling

00:26:14.960 --> 00:26:18.797
like I don't know what the right answer
is. I don't know what the right way to

00:26:18.880 --> 00:26:23.197
build this technology is or how do you
answer these very thorny questions, but

00:26:23.280 --> 00:26:26.157
there's lots of people here who are very
smart who have very strong opinions and

00:26:26.240 --> 00:26:29.037
so really try to understand all those
opinions and figure out how to put them

00:26:29.120 --> 00:26:33.117
together. And sometimes that's the right
thing. And sometimes that you realize

00:26:33.200 --> 00:26:36.077
that the opinions are mutually
contradictory. They can't all be true at

00:26:36.160 --> 00:26:38.957
once. And sometimes you do just have
to pick and you know that that means that

00:26:39.040 --> 00:26:41.117
there's going to be someone who's going
to be upset, someone who's going to

00:26:41.200 --> 00:26:45.437
quit, someone who's going to feel
slighted. And I think that a lot of what

00:26:45.520 --> 00:26:46.877
I've tried to do is have a stronger

00:26:46.960 --> 00:26:50.637
sense of self and a stronger sense of

00:26:50.720 --> 00:26:53.277
when there is conviction that we need to

00:26:53.360 --> 00:26:55.117
act on it. And I think of things that we

00:26:55.200 --> 00:27:00.317
have done over the course of OpenAI
where I feel like I wish that we had

00:27:00.400 --> 00:27:04.557
done it differently. I think usually
that's of the form we dragged our feet

00:27:04.640 --> 00:27:09.037
on something we knew. We knew it wasn't
quite the right person in the role. We

00:27:09.120 --> 00:27:11.837
didn't think this was like quite the right
technical direction. and we didn't

00:27:11.920 --> 00:27:15.037
think that this way of letting
the projects run was going to quite work,

00:27:15.120 --> 00:27:18.477
but we just waited too long. And so

00:27:18.560 --> 00:27:21.677
that's something I try to learn from and

00:27:21.760 --> 00:27:24.237
an ax in which I try to grow truly every

00:27:24.320 --> 00:27:26.957
day. When I reflect on both the course

00:27:27.040 --> 00:27:31.597
of OpenAI and Stripe and even rewinding

00:27:31.680 --> 00:27:34.317
to college and the projects I worked on

00:27:34.400 --> 00:27:36.797
in the past, I think that the way I tend

00:27:36.880 --> 00:27:40.557
to operate is that I both really love

00:27:40.640 --> 00:27:44.877
the day-to-day activity. I love
the individual contribution. I love the

00:27:44.960 --> 00:27:48.477
software. I love the thinking through
the problem. But I also really care

00:27:48.560 --> 00:27:53.117
about the environment in which these
things are done. And I actually am

00:27:53.200 --> 00:27:57.997
willing to give up on that, you know,
type one fun of just the quick hit, like

00:27:58.080 --> 00:27:59.197
you get to build the thing. It's it's

00:27:59.280 --> 00:28:01.997
always cool for something that's more

00:28:02.080 --> 00:28:07.277
like type two fun of it's painful in
the moment, but it's worthwhile. But that

00:28:07.360 --> 00:28:12.157
what you do is you create an environment
where everyone else can get that

00:28:12.240 --> 00:28:16.637
do the icy work, do the great the great
thing. And so really trying to build an

00:28:16.720 --> 00:28:19.597
environment is something I just
gravitate towards. It's not always the

00:28:19.680 --> 00:28:22.717
easiest, right? that you really do have
to be willing to take on great personal

00:28:22.800 --> 00:28:27.277
pain. Like in the words of Ilia,
Ilia always says that you have to suffer,

00:28:27.360 --> 00:28:30.877
right? If you're not suffering, like
you're not building value. And I think

00:28:30.960 --> 00:28:33.117
there's deep truth to it.
>> Double click on that.

00:28:33.200 --> 00:28:38.157
>> The Ilia perspective I think on it's
it's funny because he has a particular

00:28:38.240 --> 00:28:39.437
way of talking that I think is very

00:28:39.520 --> 00:28:42.877
unique to him and that there's always

00:28:42.960 --> 00:28:46.077
deep inspiration in the words that he

00:28:46.160 --> 00:28:50.157
chooses. And this picture of suffering
was something that that we thought about

00:28:50.240 --> 00:28:52.877
throughout the course of OpenAI where
it's like we had so much uncertainty

00:28:52.960 --> 00:28:57.757
from the beginning. Is this thing going
to work? And there's many reasons why it

00:28:57.840 --> 00:29:01.517
might not work, why it should not work,
why you could even say it cannot work,

00:29:01.600 --> 00:29:05.037
right? Whether it's how do you get
the people? How do you pursue the

00:29:05.120 --> 00:29:08.877
technology? Like how do you get
enough capital? How do you keep people

00:29:08.960 --> 00:29:11.517
motivated? Like how do you make
the right decisions? Like each of these

00:29:11.600 --> 00:29:15.517
things is extremely hard, extremely
uncertain. And it's easy to just sweep

00:29:15.600 --> 00:29:19.757
the problems under the rug and just
blindly say go. And I think that is the

00:29:19.840 --> 00:29:22.237
negative side of like Silicon Valley

00:29:22.320 --> 00:29:26.877
culture, right? Certainly Silicon
Valley perception, right? It's just the

00:29:26.960 --> 00:29:29.997
you just kind of blindly do the thing
and you kind of do a reality distortion,

00:29:30.080 --> 00:29:34.077
whatever it is. But I don't think that
works in AI and I don't think that works

00:29:34.160 --> 00:29:37.197
for open AI. I don't think that's how
we've operated ever. I think the way

00:29:37.280 --> 00:29:39.437
that we have always operated is to say

00:29:39.520 --> 00:29:42.637
encounter the hard truth understand

00:29:42.720 --> 00:29:45.357
science the reality as it is and that is

00:29:45.440 --> 00:29:46.877
I think something that has contributed

00:29:46.960 --> 00:29:50.717
to the successes we have had of thinking

00:29:50.800 --> 00:29:55.117
about the problems differently of not
being happy with even in the early days

00:29:55.200 --> 00:29:56.797
we're thinking about okay if we just

00:29:56.880 --> 00:30:00.317
write some papers and publish them it'll

00:30:00.400 --> 00:30:03.997
be great we'll get citations we'll get
you know be the coolest people at these

00:30:04.080 --> 00:30:06.877
conferences but will
we achieve the mission

00:30:06.960 --> 00:30:11.277
like how is it that you do that activity
and then AGI goes better for the world?

00:30:11.360 --> 00:30:14.157
They're not connected, right? It's not
enough. Maybe it's a foundation, maybe

00:30:14.240 --> 00:30:16.317
it's a step, but it is not sufficient.

00:30:16.400 --> 00:30:19.677
And so then you start really thinking
about these bigger picture questions of

00:30:19.760 --> 00:30:23.917
what it would what would it take
to build an AGI and not pleasant, right?

00:30:24.000 --> 00:30:27.917
Because you realize there's no path. You
realize you need dollars. You don't have

00:30:28.000 --> 00:30:30.877
any mechanism that's going to allow you
to raise dollars. And you can try hard.

00:30:30.960 --> 00:30:34.717
We did try hard. We tried extremely hard.
But, you know, maybe you're

00:30:34.800 --> 00:30:37.037
raising $100 million, you could do $500

00:30:37.120 --> 00:30:41.757
million, great. But a billion, pretty
hard. And you look at what OpenAI has

00:30:41.840 --> 00:30:45.917
been able to accomplish with the
resources we have been able to raise to

00:30:46.000 --> 00:30:50.957
further that mission, there truly would
be no other way to do it besides having

00:30:51.040 --> 00:30:53.917
leaned into the suffering and trying to
understand the truth of what it is we're

00:30:54.000 --> 00:30:57.917
trying to accomplish. >> What's a lesson
you've had to learn more than once?

00:30:58.000 --> 00:31:02.157
>> Make the hard decision. Have the hard
conversation. What's the best advice

00:31:02.240 --> 00:31:03.517
you've ever been given?

00:31:03.600 --> 00:31:05.197
>> I would actually say it was from my

00:31:05.280 --> 00:31:09.517
Harvard freshman uh writing class of

00:31:09.600 --> 00:31:14.157
just keep cutting words in order
to be clear and communicate well.

00:31:14.240 --> 00:31:15.997
>> How do you filter information?

00:31:16.080 --> 00:31:18.797
>> I read a lot triage aggressively.

00:31:18.880 --> 00:31:21.117
>> Who are your role models and why? I

00:31:21.200 --> 00:31:26.077
would say Gaus and Decart as

00:31:26.160 --> 00:31:29.677
people who are incredibly thoughtful,
very much ahead of their time, very much

00:31:29.760 --> 00:31:33.757
visionaries who came up with real
breakthroughs that I think transform how

00:31:33.840 --> 00:31:37.597
we think and and how we live.
>> What do you want non- tech people to

00:31:37.680 --> 00:31:40.877
know about AI? >> That it's
going to be a force for good

00:31:40.960 --> 00:31:42.717
in their personal life that they'll

00:31:42.800 --> 00:31:46.397
benefit from and will help advance

00:31:46.480 --> 00:31:51.837
science, medicine, and really lift up
everyone. What does the world get wrong

00:31:51.920 --> 00:31:53.277
about Greg Brockman?

00:31:53.360 --> 00:31:56.317
>> I think people don't understand how

00:31:56.400 --> 00:32:01.117
focused I am on this mission in a way

00:32:01.200 --> 00:32:04.477
that I think has been

00:32:04.560 --> 00:32:08.717
very personally painful at many turns.

00:32:08.800 --> 00:32:10.477
But I just believe this technology can

00:32:10.560 --> 00:32:12.637
just help

00:32:12.720 --> 00:32:15.997
empower people and benefit everyone. And

00:32:16.080 --> 00:32:17.597
I really want to help make that happen.

00:32:17.680 --> 00:32:20.707
Why is OpenAI so bad at naming models?

00:32:20.790 --> 00:32:23.837
[sighs and gasps]
That one I can't tell you.

00:32:23.920 --> 00:32:27.037
>> Are we near the point where AI makes AI

00:32:27.120 --> 00:32:32.077
go parabolic? >> I would say
we are in this phase where

00:32:32.160 --> 00:32:33.757
you apply AI to its own development

00:32:33.840 --> 00:32:35.917
process and it's going to go faster and

00:32:36.000 --> 00:32:40.077
faster. And that is something that's

00:32:40.160 --> 00:32:44.477
been happening really I mean certainly
since chat GBT in many ways right we

00:32:44.560 --> 00:32:47.037
used chat GBT to make our development

00:32:47.120 --> 00:32:52.077
process 10% 20% faster
now we have these amazing

00:32:52.160 --> 00:32:56.237
coding tools which have truly
revolutionized how software engineering

00:32:56.320 --> 00:32:58.637
is done and most of what we do in the

00:32:58.720 --> 00:33:03.757
production of models is bottlenecked
by software. It's about implementing these

00:33:03.840 --> 00:33:05.437
systems. It's about scaling them up.

00:33:05.520 --> 00:33:08.477
It's about managing
these massive computers

00:33:08.560 --> 00:33:12.397
and we're going to be hitting a phase
soon where the AI will also come up with

00:33:12.480 --> 00:33:14.157
its own research ideas and test those

00:33:14.240 --> 00:33:16.637
out, run experiments. And so I think

00:33:16.720 --> 00:33:20.717
that the speed of iteration and

00:33:20.800 --> 00:33:25.677
innovation is going to continue
to increase as a result of what we're

00:33:25.760 --> 00:33:29.357
producing. >> What percentage
of the code is now written by AI?

00:33:29.440 --> 00:33:32.317
>> It's hard to know what percent
of the code is not written by AI. It's a

00:33:32.400 --> 00:33:35.037
vanishing fraction. the actual writing

00:33:35.120 --> 00:33:38.317
of code. Currently, the AI is much

00:33:38.400 --> 00:33:41.917
better than humans at writing code given

00:33:42.000 --> 00:33:44.477
the right context, given the right

00:33:44.560 --> 00:33:46.717
structure. Now, there's parts of the

00:33:46.800 --> 00:33:52.077
actual structure of the code that our
human experts still are much better at,

00:33:52.160 --> 00:33:55.517
right? that that's about thinking about
how the module should be laid out, how

00:33:55.600 --> 00:33:59.597
the pieces should work, maybe
the definition of certain kinds of of

00:33:59.680 --> 00:34:01.917
interfaces, but the actual writing of

00:34:02.000 --> 00:34:04.397
code is essentially all AI now.

00:34:04.480 --> 00:34:08.237
>> Is it coming up with novel ideas
that you wouldn't have thought of?

00:34:08.320 --> 00:34:12.797
>> I'd say that where we are is we're
getting close. So we've seen for example

00:34:12.880 --> 00:34:14.477
in chip design. So in the design of our

00:34:14.560 --> 00:34:17.517
own chip last year we applied our

00:34:17.600 --> 00:34:21.997
technology to trying to get a better fit

00:34:22.080 --> 00:34:24.797
to actually shrink the area used by the

00:34:24.880 --> 00:34:28.317
the circuits and there we found that the

00:34:28.400 --> 00:34:31.917
optimizations that the model produced
were actually on our list. So it didn't

00:34:32.000 --> 00:34:35.197
come with something novel and new
that no human ever would have but it

00:34:35.280 --> 00:34:37.037
implemented it faster in a way that we

00:34:37.120 --> 00:34:39.277
wouldn't have had time to accomplish. If

00:34:39.360 --> 00:34:42.637
you look at math and physics, we now are

00:34:42.720 --> 00:34:47.197
solving open math problems.
We're solving open physics problems and

00:34:47.280 --> 00:34:50.397
actually have resolved this particular
physics problem recently in quantum

00:34:50.480 --> 00:34:54.877
physics in the opposite way that the
community expected and with a you know

00:34:54.960 --> 00:34:56.877
beautiful elegant formula. It's like

00:34:56.960 --> 00:34:59.117
it's really happening. So new ideas from

00:34:59.200 --> 00:35:02.877
these models extremely doable. We're

00:35:02.960 --> 00:35:06.397
starting to see it in some of these
domains. now applying it in harder and

00:35:06.480 --> 00:35:11.117
harder domains or ones that require more
real world context and things like that.

00:35:11.200 --> 00:35:14.557
We're starting to see it. We have a line
of sight for how to accomplish it, but

00:35:14.640 --> 00:35:19.037
we have a lot of work to do. Why do
models feel like they have a political

00:35:19.120 --> 00:35:21.677
leaning to them,
like a political bias almost?

00:35:21.760 --> 00:35:24.957
>> So, we put a lot of effort into
neutrality for our models to have them

00:35:25.040 --> 00:35:28.557
represent truth. And you can see exactly

00:35:28.640 --> 00:35:32.237
the values that go into our models
on our website. We have a publicly

00:35:32.320 --> 00:35:35.997
published spec which defines and you can
give feedback on the different ways we

00:35:36.080 --> 00:35:38.237
want our model to behave. We've spent a

00:35:38.320 --> 00:35:43.037
lot of effort to really get to this
mutual point of view and trying to be

00:35:43.120 --> 00:35:46.157
fair and balanced. And I think that
sometimes when you see these screenshots

00:35:46.240 --> 00:35:49.277
on Twitter that they're not always fully

00:35:49.360 --> 00:35:51.437
honest themselves in terms of where they

00:35:51.520 --> 00:35:53.597
came from, either because there's some

00:35:53.680 --> 00:35:56.717
memories that are behind the scenes that

00:35:56.800 --> 00:36:00.717
tweak the answer in a certain way or
hidden instructions or previous parts of

00:36:00.800 --> 00:36:04.557
the conversation. And so sometimes it's
also there's just no right answer. And

00:36:04.640 --> 00:36:07.757
so you you know you can have like
a question you say answer in one word and

00:36:07.840 --> 00:36:11.197
no matter which one you say you're going
to get some sort of claim of bias. And

00:36:11.280 --> 00:36:13.437
so I think that to some extent the core

00:36:13.520 --> 00:36:17.197
of it in my mind is that we are yeah we

00:36:17.280 --> 00:36:19.437
care a lot about truth and about having

00:36:19.520 --> 00:36:21.917
an AI that really represents you.

00:36:22.000 --> 00:36:26.237
>> Do you think the models evolve to tell
us what we want to hear if they're based

00:36:26.320 --> 00:36:29.597
on reinforcement learning? So if I lean
left, it's going to tell me an answer

00:36:29.680 --> 00:36:33.117
that leans left or if I lean right,
it's going to give me an answer that leans

00:36:33.200 --> 00:36:34.397
right. Well, so we've actually gone

00:36:34.480 --> 00:36:38.317
through an evolution of how we train the

00:36:38.400 --> 00:36:42.317
models to user preferences and that

00:36:42.400 --> 00:36:46.717
we've seen that at one point like last
year that the models really did start to

00:36:46.800 --> 00:36:48.637
lean into telling you what you wanted to

00:36:48.720 --> 00:36:49.997
hear, saying, "Oh, that was such a great

00:36:50.080 --> 00:36:53.997
answer." And we reacted to that. We said

00:36:54.080 --> 00:36:57.917
that this is not how we want our models
to operate. And we made changes because

00:36:58.000 --> 00:37:01.197
the true thing we want the models to be
aligned to is helping you solve your

00:37:01.280 --> 00:37:05.357
goals, your long-term goals. Right? And
maybe in the moment it feels good to be

00:37:05.440 --> 00:37:09.517
told that was a great question, best
question anyone's ever asked, but that's

00:37:09.600 --> 00:37:13.917
not what you actually want. Maybe there
are some people, but uh it's not it's

00:37:14.000 --> 00:37:16.237
not what most people truly want. And so

00:37:16.320 --> 00:37:18.397
we've actually made great technological

00:37:18.480 --> 00:37:23.517
improvements to make sure that our AI
training does not result in what is

00:37:23.600 --> 00:37:26.157
called hacking the greater, right? That we
really want to make sure that there

00:37:26.240 --> 00:37:31.357
is a good signal there that is about
the goal, not just your short term. what's

00:37:31.440 --> 00:37:35.757
going to get you a quick hit.
And that to me is maybe the most important

00:37:35.840 --> 00:37:40.397
part of the vision for where
our personal AI, personal AGI is going to

00:37:40.480 --> 00:37:44.397
take us is to really make sure it's not
just about something that looks good in

00:37:44.480 --> 00:37:47.677
the moment. It's really about alignment
with your long-term well-being, your

00:37:47.760 --> 00:37:50.797
long-term goals, the thing that you
actually want. And that is what I think

00:37:50.880 --> 00:37:53.677
will most empower people, right?
Is really put you in the driver's seat

00:37:53.760 --> 00:37:57.997
because you will have this entity that
is there operating on your behalf 24/7,

00:37:58.080 --> 00:38:01.197
right? You're asleep. it's out there
trying to figure out what is it that

00:38:01.280 --> 00:38:04.477
Shane wants, how can I do it better and

00:38:04.560 --> 00:38:06.317
is actually able to accomplish it.

00:38:06.400 --> 00:38:08.477
>> Are we in a global AI race?

00:38:08.560 --> 00:38:11.837
>> I think we are certainly in a global
AI renaissance and I think that the

00:38:11.920 --> 00:38:14.077
dynamics between countries are not yet

00:38:14.160 --> 00:38:15.997
fully defined. We have this

00:38:16.080 --> 00:38:19.437
concentration of where the breakthrough

00:38:19.520 --> 00:38:23.917
algorithms come from in the US in

00:38:24.000 --> 00:38:26.637
Western companies. There's clearly a lot

00:38:26.720 --> 00:38:28.717
of innovation happening around the

00:38:28.800 --> 00:38:31.437
world, but I think exactly the balance

00:38:31.520 --> 00:38:35.757
of dynamics and how like which countries

00:38:35.840 --> 00:38:39.437
re rely on which providers, all of that
is something that I think is still being

00:38:39.520 --> 00:38:41.997
determined. >> Is there
a consequence do you think for

00:38:42.080 --> 00:38:44.637
the United States not being the first

00:38:44.720 --> 00:38:47.597
country to reach AGI? Well, I do think

00:38:47.680 --> 00:38:50.637
that leading an AI is very critical for

00:38:50.720 --> 00:38:53.757
America because I think that this is how

00:38:53.840 --> 00:38:55.757
you can ensure that democratic values

00:38:55.840 --> 00:38:59.357
are protected and preserved. And I think

00:38:59.440 --> 00:39:03.757
that every country is also starting
to realize that they need some sort of

00:39:03.840 --> 00:39:08.317
sovereign AI strategy. They need to if
this is becoming the basis of economic

00:39:08.400 --> 00:39:10.557
security, national security, they need

00:39:10.640 --> 00:39:14.077
to participate somehow. And

00:39:14.160 --> 00:39:16.557
if you look at a lot of the efforts by

00:39:16.640 --> 00:39:19.837
the United States to think about how to

00:39:19.920 --> 00:39:22.237
manage chip exports, how to think about

00:39:22.320 --> 00:39:25.277
technology exports, there's something

00:39:25.360 --> 00:39:28.157
where if you lean too far out, then

00:39:28.240 --> 00:39:32.877
everyone else has to develop
their own competitor or rely on

00:39:32.960 --> 00:39:35.117
someone else who's who's building this.

00:39:35.200 --> 00:39:39.357
If you lean too far in, then maybe you
lose your advantage. And the question

00:39:39.440 --> 00:39:43.357
is, how do you balance those? How do you
maintain your leadership? But leadership

00:39:43.440 --> 00:39:45.357
is not just about being ahead.

00:39:45.440 --> 00:39:49.197
Leadership is about also
bringing along the world with you.

00:39:49.280 --> 00:39:52.477
>> Are other countries stealing
advancements? I've been reading a lot

00:39:52.560 --> 00:39:55.837
about distillation. >> There's
certainly a lot of attempts to

00:39:55.920 --> 00:40:00.477
distill models and that comes from
companies in the US. It comes from uh

00:40:00.560 --> 00:40:04.797
from all over the world. But I think
that it misses the core point which is

00:40:04.880 --> 00:40:09.597
that the way this technology
is developing is it is on an exponential

00:40:09.680 --> 00:40:13.437
and anytime we have a model we've
already moved on to the next one. We're

00:40:13.520 --> 00:40:17.437
already moving to the next level.
So we put in a lot of effort to protect

00:40:17.520 --> 00:40:21.117
against distillation make it harder
to do especially with things like chain of

00:40:21.200 --> 00:40:25.277
thought and other parts of the model
that are not really necessary to get the

00:40:25.360 --> 00:40:26.797
benefits to someone to get the outputs

00:40:26.880 --> 00:40:30.957
to someone. But that the core advantage

00:40:31.040 --> 00:40:32.877
that we have, the strength that we're

00:40:32.960 --> 00:40:38.077
building up over time is really about
not just any one model. It's about the

00:40:38.160 --> 00:40:39.437
machine that makes the models.

00:40:39.520 --> 00:40:41.997
>> Oh, is that why you guys
stopped showing reasoning?

00:40:42.080 --> 00:40:45.757
>> That is part of it. So there's
two reasons. One is to think about

00:40:45.840 --> 00:40:50.717
distillation. But the second in some
ways more important is that we had this

00:40:50.800 --> 00:40:52.397
insight when we first developed the

00:40:52.480 --> 00:40:57.517
reasoning paradigm that it gives us a

00:40:57.600 --> 00:41:01.277
interpretability mechanism we had not
been anticipating because you can really

00:41:01.360 --> 00:41:03.917
read the model's thoughts. You can see

00:41:04.000 --> 00:41:05.757
exactly how it got to an answer. So you

00:41:05.840 --> 00:41:08.957
can interpret how like what was actually

00:41:09.040 --> 00:41:11.037
motivating that answer. Now the problem

00:41:11.120 --> 00:41:13.757
is if you train the model to have a

00:41:13.840 --> 00:41:18.717
chain of thought that looks good then
you lose all the faithfulness right it's

00:41:18.800 --> 00:41:21.997
just going to be like the model knows
that part of the answer that that is

00:41:22.080 --> 00:41:26.317
desired is for the chain of thought
to look a certain way and so it may not be

00:41:26.400 --> 00:41:29.357
representative of how it actually
arrived at that answer anymore and so we

00:41:29.440 --> 00:41:33.517
were we made an early decision to say
we want to avoid any temptation to train

00:41:33.600 --> 00:41:37.597
these chain of thoughts to look
favorable to look like something you

00:41:37.680 --> 00:41:41.197
could present to a user and so that
really made us lean out for multiple

00:41:41.280 --> 00:41:45.677
reasons, for competitive reasons, for
safety reasons from the idea of showing

00:41:45.760 --> 00:41:48.797
these intermediate thoughts.
>> It seems like the current trend right

00:41:48.880 --> 00:41:52.797
now is to release preview models. Is
that because we're computer constrained?

00:41:52.880 --> 00:41:53.597
Do you think?

00:41:53.680 --> 00:41:57.037
>> I would say that we in general are

00:41:57.120 --> 00:42:00.077
heading to a computed world. Like if you

00:42:00.160 --> 00:42:04.237
think about the amount of value that
these models can produce for someone,

00:42:04.320 --> 00:42:07.997
it's extreme, right? It's not just
answering a quick question anymore. It's

00:42:08.080 --> 00:42:12.077
not just even answering, you know,
giving you access to health information.

00:42:12.160 --> 00:42:16.797
It's really going deep and spending
a lot of tokens to put together a bunch of

00:42:16.880 --> 00:42:20.237
different data sources, search through
your enterprise knowledge base to

00:42:20.320 --> 00:42:24.077
actually be able to solve this hard
problem to write that software that

00:42:24.160 --> 00:42:27.757
that's better than a human would be
able to. All of that is something that is

00:42:27.840 --> 00:42:29.997
like hard. And if you look at the

00:42:30.080 --> 00:42:33.037
progress that we made between GBD5 to 51

00:42:33.120 --> 00:42:36.877
to 52 to 53 codecs to 54, it's been

00:42:36.960 --> 00:42:40.157
extreme. And these models
are getting extremely

00:42:40.240 --> 00:42:44.157
better at understanding your intent
molding to what you want to accomplish.

00:42:44.240 --> 00:42:47.917
And we also put them in these surfaces
like codecs that make them very usable

00:42:48.000 --> 00:42:51.517
so that you as a developer, you can
really fly, right? That you can achieve

00:42:51.600 --> 00:42:56.077
more than you would have dreamed
otherwise. And all this is powered by

00:42:56.160 --> 00:43:01.037
compute fundamentally. And there's not
enough comput. that if you just wanted

00:43:01.120 --> 00:43:04.877
enough compute for you know you wanted
one GPU for every person in the world

00:43:04.960 --> 00:43:08.797
you're talking like 8 billion GPUs
we are not on a trajectory to build

00:43:08.880 --> 00:43:12.637
anywhere near that level of compute
right it's like you know hundreds of

00:43:12.720 --> 00:43:16.317
thousands of GPUs like that's a pretty
big fleet these days millions of GPUs

00:43:16.400 --> 00:43:19.357
coming up it's not surprising that there
is too little compute in the world and

00:43:19.440 --> 00:43:22.077
that we're going to need much more
in order to really be able to bring this

00:43:22.160 --> 00:43:26.157
technology to everyone and then in terms
of the training I'd say that the way

00:43:26.240 --> 00:43:29.997
that we tend to launch things so we have
put in a lot of effort to make sure we

00:43:30.080 --> 00:43:33.997
are building compute in anticipation of
what we see coming. And so I think we're

00:43:34.080 --> 00:43:37.357
going to be very focused on our mission
of bringing these models to everyone,

00:43:37.440 --> 00:43:39.197
making them widely available.

00:43:39.280 --> 00:43:41.677
>> You guys were teased
for putting so much

00:43:41.760 --> 00:43:47.117
effort, money into data centers. How do
you think that's playing out now? Well,

00:43:47.200 --> 00:43:50.557
I think it's going to give us
an advantage and I think it's going to be

00:43:50.640 --> 00:43:53.437
something that's an advantage not
just for the business, but for actually

00:43:53.520 --> 00:43:57.197
delivering on the mission of bringing
this technology to everyone cuz you guys

00:43:57.280 --> 00:44:01.437
like you saw that way in advance. You
get teased for it by almost all of your

00:44:01.520 --> 00:44:04.557
competitors. >> Mhm. Who's laughing now?

00:44:04.640 --> 00:44:08.237
>> Yeah. [snorts] I mean,
I I think our competitors are

00:44:08.320 --> 00:44:10.317
not having a good time on compute,
let me put it that way.

00:44:10.400 --> 00:44:11.917
>> But you must have seen something that

00:44:12.000 --> 00:44:17.357
they didn't see. Like that's the I mean
everybody was in a very similar or it

00:44:17.440 --> 00:44:19.837
seems at least from the outside
everybody was in a very similar

00:44:19.920 --> 00:44:22.317
technological space. They all knew this

00:44:22.400 --> 00:44:25.517
was coming and yet you guys had the

00:44:25.600 --> 00:44:29.037
boldness to make that bet with a hundred

00:44:29.120 --> 00:44:30.877
billion dollars like but that is the

00:44:30.960 --> 00:44:34.637
core of open AI is really encountering

00:44:34.720 --> 00:44:38.957
reality as it is really thinking about
what is the implication of what it is

00:44:39.040 --> 00:44:43.277
we'll accomplish in the next 6 months
the next 12 months the next 10 years.

00:44:43.360 --> 00:44:45.757
And that is true for the grand mission.

00:44:45.840 --> 00:44:49.037
It's true for day-to-day how we design

00:44:49.120 --> 00:44:51.197
different pieces of our software. And

00:44:51.280 --> 00:44:53.517
it's true for things like scaling up

00:44:53.600 --> 00:44:58.637
compute. And I think that we are deeply
motivated by bringing this technology to

00:44:58.720 --> 00:45:01.517
everyone. And we think about lots
of different mechanisms for how to do that

00:45:01.600 --> 00:45:06.317
well and safely.
Smart operators look for leverage. Video

00:45:06.400 --> 00:45:08.317
is the most effective way to

00:45:08.400 --> 00:45:10.237
communicate, but it's always been too

00:45:10.320 --> 00:45:13.437
slow and expensive to produce. Hey Genen

00:45:13.520 --> 00:45:16.077
eliminates that. You go from idea to

00:45:16.160 --> 00:45:18.237
professional video in minutes. No

00:45:18.320 --> 00:45:21.117
camera, no crew. Their avatars are rated

00:45:21.200 --> 00:45:24.157
the most realistic on G2 and over 30

00:45:24.240 --> 00:45:26.317
million people are already using Hey

00:45:26.400 --> 00:45:29.987
Gen, including 85% of the Fortune 100.

00:45:30.070 --> 00:45:31.837
[snorts] You can even reach an audience

00:45:31.920 --> 00:45:35.757
in over 175 languages with AI lips sync

00:45:35.840 --> 00:45:39.837
and translation. Same voice perfectly
matched. If you're someone who

00:45:39.920 --> 00:45:42.397
communicates at scale, take a look. Your

00:45:42.480 --> 00:45:48.680
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Hey Jen.com hyge.com.

00:45:49.200 --> 00:45:53.757
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00:46:32.560 --> 00:46:36.477
Do you think data centers eventually get
dedicated towards a problem like you'll

00:46:36.560 --> 00:46:40.077
have a huge data center in North Dakota
and it's just on solving cancer and

00:46:40.160 --> 00:46:42.237
that's all it's doing? >> Yes.

00:46:42.320 --> 00:46:46.477
>> How far away are we from that? I think
that this kind of thing happening this

00:46:46.560 --> 00:46:50.237
year is not out of the question and it's
really amazing if you think about it

00:46:50.320 --> 00:46:54.957
having this giant machine right and have
you have you been to any of these data

00:46:55.040 --> 00:46:57.837
centers? >> No, I've seen them
online but never in person.

00:46:57.920 --> 00:47:00.957
>> It is a very different experience to

00:47:01.040 --> 00:47:04.077
walk amongst these racks, right? walk

00:47:04.160 --> 00:47:08.077
down the the rows and you look at the
cables, they're all perfectly exactly

00:47:08.160 --> 00:47:09.757
the right the right length and you just

00:47:09.840 --> 00:47:14.717
realize that what a data center is. It's

00:47:14.800 --> 00:47:18.717
a massive machine. These are maybe
the biggest machines that humanity creates

00:47:18.800 --> 00:47:20.157
and then you ask the question of why.

00:47:20.240 --> 00:47:25.597
Why do we build these machines? Why
is it worthwhile? And it is because they

00:47:25.680 --> 00:47:29.597
have the potential to solve problems
that matter for people, right? to solve,

00:47:29.680 --> 00:47:34.877
you know, come up with cures for cancer,
to help people run businesses, to, you

00:47:34.960 --> 00:47:36.077
know, sometimes maybe it's mundane

00:47:36.160 --> 00:47:38.557
queries. The purpose in my mind is

00:47:38.640 --> 00:47:42.157
really about how do you deliver value?

00:47:42.240 --> 00:47:44.637
How do you deliver on people's goals?

00:47:44.720 --> 00:47:47.277
And I think the opportunity presented by

00:47:47.360 --> 00:47:51.757
these massive machines targeting one
problem is something we have not yet

00:47:51.840 --> 00:47:54.717
really internalized. >> But if
we're computer constraint, like

00:47:54.800 --> 00:47:58.237
how do you decide who to serve? like why
are you serving me when I'm like trying

00:47:58.320 --> 00:48:01.037
to make an image over like solving cancer?

00:48:01.120 --> 00:48:04.557
>> Well, this is going to be the most
important question for society to

00:48:04.640 --> 00:48:09.757
answer. Where does the compute go? What
problems are worthy? And there's lots of

00:48:09.840 --> 00:48:12.477
worthy problems, but you need to
prioritize them because you only have so

00:48:12.560 --> 00:48:16.077
much compute. And so, one thing we
really believe in is that everyone is

00:48:16.160 --> 00:48:19.677
going to need access to compute.
And so, that's why we have a free tier of

00:48:19.760 --> 00:48:24.397
chatbt. We've really put effort into
making sure that people are able to use

00:48:24.480 --> 00:48:27.597
this technology that it's widely
available because we believe that is

00:48:27.680 --> 00:48:31.437
core to what we're doing here. We think
that putting this technology in people's

00:48:31.520 --> 00:48:34.637
hands that empowers them that lets
them achieve goals. It helps them also

00:48:34.720 --> 00:48:37.837
understand the technology, right? It's
something that helps them then shape how

00:48:37.920 --> 00:48:41.517
does this technology slot in. You could
take a very different approach and say,

00:48:41.600 --> 00:48:43.197
well, it's all about the ivory tower.

00:48:43.280 --> 00:48:47.117
It's all about the just solve the problem
and we will then distribute the

00:48:47.200 --> 00:48:49.117
technology breakthroughs in some way.

00:48:49.200 --> 00:48:51.997
And I think there's merit to

00:48:52.080 --> 00:48:54.797
that as well, but that's not where I'd

00:48:54.880 --> 00:48:59.517
put the the balance of of what we do.
I think that that is very much a like we

00:48:59.600 --> 00:49:03.757
do want to make great strides on specific
problems, but I think that that

00:49:03.840 --> 00:49:07.517
should be in service again of the we
want the benefits of this technology to

00:49:07.600 --> 00:49:10.637
be broadly distributed.
>> How do you think about that internally

00:49:10.720 --> 00:49:15.357
just at OpenAI between consumer
and enterprise? Well, a lot of what I've

00:49:15.440 --> 00:49:17.277
been thinking about recently has been

00:49:17.360 --> 00:49:20.797
focus because this field, it is

00:49:20.880 --> 00:49:25.597
opportunity incarnate, right? It's
like you can take AI and apply it to any

00:49:25.680 --> 00:49:30.077
problem, any sort of thing you want
to build. It's now on the table. And the

00:49:30.160 --> 00:49:33.437
problem that we have is that there's
only so much compute. Where do you want

00:49:33.520 --> 00:49:37.037
to put it? And so, you need to have
synergies. You need to have return to

00:49:37.120 --> 00:49:38.397
the fact that you have multiple things

00:49:38.480 --> 00:49:41.357
happening that they all add up. 1 plus 1

00:49:41.440 --> 00:49:46.477
equals 10. Like that's where that's the
dream that's the goal and a lot of what

00:49:46.560 --> 00:49:48.157
I think is important for this next phase

00:49:48.240 --> 00:49:51.917
of open AI very clearly enterprise

00:49:52.000 --> 00:49:55.757
because the economy is becoming this
compute powered economy before our very

00:49:55.840 --> 00:49:58.717
eyes. It's happening right now
like we've seen this with software

00:49:58.800 --> 00:50:03.517
engineering and it's going to happen
with every single field of work people

00:50:03.600 --> 00:50:08.237
do with a computer. everyone's computer
work like is going to be something where

00:50:08.320 --> 00:50:11.037
rather than you doing work with your
computer, your computer's going to do

00:50:11.120 --> 00:50:13.677
work for you. It's like truly going to

00:50:13.760 --> 00:50:18.557
be amazing. And so we need to be there
to help people deploy these models,

00:50:18.640 --> 00:50:21.997
figure out how to utilize them, figure
how to get the most benefit out of them.

00:50:22.080 --> 00:50:25.677
And by the way, there's also going to be
a blurring of the line between what is

00:50:25.760 --> 00:50:29.917
enterprise and what is consumer because
entrepreneurship is going to become far

00:50:30.000 --> 00:50:33.437
easier than ever before. Like we're
seeing this already. And even for

00:50:33.520 --> 00:50:37.437
example, one of my friends
was describing that his sister was

00:50:37.520 --> 00:50:40.877
describing this app that she really wish
someone had created that she had this

00:50:40.960 --> 00:50:42.557
picture of like exactly what she wanted.

00:50:42.640 --> 00:50:46.957
And he in the meanwhile was typing into
to Codex. Uh-huh. Uh-huh. And then push

00:50:47.040 --> 00:50:50.957
enter. And a few hours later, he shows
her this app and she's like, "Wait, what

00:50:51.040 --> 00:50:52.317
what what is this? Like where did this

00:50:52.400 --> 00:50:54.877
thing come from? Who built this?" And he

00:50:54.960 --> 00:50:59.997
said, "You did." And that is I think
just an amazing thing where you realize

00:51:00.080 --> 00:51:03.517
anyone can be a builder. Like these
tools like codeex is for everyone. It's

00:51:03.600 --> 00:51:07.197
not just for software engineers.
It's like everyone now can be a software

00:51:07.280 --> 00:51:10.717
engineer if they have a vision, if they
have this agency that they have a thing

00:51:10.800 --> 00:51:12.637
that they want to accomplish. Like you

00:51:12.720 --> 00:51:15.677
now have this magic tool that can do it.

00:51:15.760 --> 00:51:19.357
And then on the consumer side, the thing
about consumers, it's too broad of a

00:51:19.440 --> 00:51:21.997
term, right? That there's lots
of different things. There's like

00:51:22.080 --> 00:51:23.757
entertainment, there's a bunch of things

00:51:23.840 --> 00:51:26.957
in self-expression, and there's also

00:51:27.040 --> 00:51:31.197
solving goals. And the aspect
of consumer that we're really dialed in on

00:51:31.280 --> 00:51:35.437
is solving goals. Like we believe
that this technology, you look at

00:51:35.520 --> 00:51:39.677
smartphones, that's like four billion
people use them. All of those people

00:51:39.760 --> 00:51:44.397
should have a personal AI, you know,
a personal AGI that's out there that knows

00:51:44.480 --> 00:51:48.477
them well, that has their personal
context, that is trustworthy, that they

00:51:48.560 --> 00:51:52.397
can ask for advice, but that also knows
them so well that, you know, if your

00:51:52.480 --> 00:51:56.157
favorite musician is in town, it just
goes and proactively purchases tickets

00:51:56.240 --> 00:51:59.117
and maybe it knows like, oh, I should
check in before doing this. Or maybe it

00:51:59.200 --> 00:52:01.517
knows, yep, that just like I got to get

00:52:01.600 --> 00:52:03.597
do this and I I have prior approval.

00:52:03.680 --> 00:52:09.037
like that level of having an AI
that knows you and can help you achieve

00:52:09.120 --> 00:52:11.837
whatever it is you want to achieve and
help you flesh out what are your goals.

00:52:11.920 --> 00:52:15.037
You should still set those goals. They
should be your goals, right? You should

00:52:15.120 --> 00:52:18.477
be in charge, but that is something we
want to create. And that is something I

00:52:18.560 --> 00:52:21.677
think that not just 4 billion people
are going to want to need. I think it's

00:52:21.760 --> 00:52:22.957
going to be 8 billion people. I think

00:52:23.040 --> 00:52:25.357
that the whole planet is going to really

00:52:25.440 --> 00:52:28.717
benefit from and need access to personal

00:52:28.800 --> 00:52:31.677
AI, personal AGI. And so you look at

00:52:31.760 --> 00:52:33.917
those two dimensions of deep knowledge

00:52:34.000 --> 00:52:37.757
work and broad distribution of access to

00:52:37.840 --> 00:52:43.437
an agentic system. And we want to build
those two aspects and they come together

00:52:43.520 --> 00:52:47.517
because ultimately they're the same
technology. ultimately want an AI that

00:52:47.600 --> 00:52:49.437
is there in the cloud that has access to

00:52:49.520 --> 00:52:52.637
information that is trustworthy that is

00:52:52.720 --> 00:52:55.277
able to give good answers and able to

00:52:55.360 --> 00:53:00.077
take actions on your behalf whether it's
building whether it's in your personal

00:53:00.160 --> 00:53:03.837
life and maybe you have multiple
instances of it but fundamentally it is

00:53:03.920 --> 00:53:08.157
one technological system >> do you
think we'll have data centers in space

00:53:08.240 --> 00:53:10.157
>> I think we're going
to have data centers everywhere

00:53:10.240 --> 00:53:12.237
>> how far away do you
think we are from that

00:53:12.320 --> 00:53:15.917
>> well data centers in space has a lot
of has many technical problems associated

00:53:16.000 --> 00:53:18.077
with it. Even for example, the data

00:53:18.160 --> 00:53:21.997
centers we build today are very finicky,

00:53:22.080 --> 00:53:26.637
right? They're these massive machines
with very breakable, very expensive

00:53:26.720 --> 00:53:31.037
components. We've had many issues
in the past where the cables were just too

00:53:31.120 --> 00:53:35.277
taut, just literally like too too
tight of cables and then you get signal

00:53:35.360 --> 00:53:39.117
integrity issues and the computer
doesn't work. And so figuring out how do

00:53:39.200 --> 00:53:41.837
you maintain systems today? it's people

00:53:41.920 --> 00:53:46.237
go in physically pull them probably will
move to robotics. So I think figuring

00:53:46.320 --> 00:53:49.837
out how to solve some of these technical
problems are going to be very important

00:53:49.920 --> 00:53:52.397
dependencies as we think about putting

00:53:52.480 --> 00:53:54.637
them in you know people talk about

00:53:54.720 --> 00:53:59.517
putting data center in you know various
difficult locations. Um space feels like

00:53:59.600 --> 00:54:03.837
a like a grand challenge but I think
that we are going to have such need for

00:54:03.920 --> 00:54:05.837
compute that we need to be thinking

00:54:05.920 --> 00:54:09.037
about all options. What is iterative

00:54:09.120 --> 00:54:11.117
deployment and why do you do it?

00:54:11.200 --> 00:54:14.317
>> Well, iterative
deployment is one of the

00:54:14.400 --> 00:54:18.637
core pillars of how OpenAI has
approached how to get this technology to

00:54:18.720 --> 00:54:23.117
benefit people and to achieve our mission.
And this is something that I

00:54:23.200 --> 00:54:27.597
think I think I was probably the person
who articulated those two words, but

00:54:27.680 --> 00:54:30.717
this spirit is something that really
emerged as we thought about our first

00:54:30.800 --> 00:54:33.837
product deployment and really thinking
about how does that connect to what

00:54:33.920 --> 00:54:37.677
we're trying to do. and you realized
that there were two different routes

00:54:37.760 --> 00:54:39.757
that you could take in terms of thinking

00:54:39.840 --> 00:54:42.957
about the you want to build an AGI it's

00:54:43.040 --> 00:54:45.037
going to benefit people how do you do it

00:54:45.120 --> 00:54:47.517
and one is you go for kind of build it

00:54:47.600 --> 00:54:52.317
in secret you don't deploy anything
you have a lot of time to just kind of

00:54:52.400 --> 00:54:55.437
polish it get it right but then at some
point you push a button and you say

00:54:55.520 --> 00:54:58.557
deploy and I remember thinking about

00:54:58.640 --> 00:55:03.117
could I sign up for that strategy could
I be accountable for that strategy do

00:55:03.200 --> 00:55:06.077
you want to sitting in a
room thinking about, okay,

00:55:06.160 --> 00:55:09.597
we ran all our tests. Are we ready
to deploy? And you've never deployed

00:55:09.680 --> 00:55:14.717
anything ever before, right?
That's your first contact with reality

00:55:14.800 --> 00:55:18.397
and it's a very powerful system that's
going to really change the world. Like

00:55:18.480 --> 00:55:21.437
that is a very tough problem set.

00:55:21.520 --> 00:55:25.437
But instead, what about if you take
an approach where this is your hundth

00:55:25.520 --> 00:55:29.597
system? You've had to solve this problem
99 times before with systems of

00:55:29.680 --> 00:55:33.357
increasing power. and the world has
also had a chance to adapt to them to

00:55:33.440 --> 00:55:38.237
reconfigure around them. And we learned
very early on with GPD3. We got to see

00:55:38.320 --> 00:55:40.877
this very concretely what it's like to
deploy something where we spent a lot of

00:55:40.960 --> 00:55:43.917
time thinking about what are all
the misuses of GPD3. What are the ways it

00:55:44.000 --> 00:55:46.477
could go wrong? We thought about
misinformation. We thought about these

00:55:46.560 --> 00:55:49.677
kinds of, you know, grand pictures.
And you know what the number one misuse of

00:55:49.760 --> 00:55:53.357
GPD3 was? >> What?
>> It was medical spam. Like advertising

00:55:53.440 --> 00:55:56.717
different drugs to people, right?
It's like not something we ever would have

00:55:56.800 --> 00:56:00.957
thought of as a problem, [clears throat]
but we see it in front of our eyes and

00:56:01.040 --> 00:56:04.717
we get a chance to react and learn from
it. And so iterative deployment is the

00:56:04.800 --> 00:56:07.917
idea that we will bring intermediate
intermediate versions of this

00:56:08.000 --> 00:56:11.517
technology. Now, it's not an excuse
to just blindly deploy, right? You still

00:56:11.600 --> 00:56:16.157
need to think at every step about what's
our best view on all the ways this might

00:56:16.240 --> 00:56:20.477
be misused, what are the downsides, what
are the risks, let's mitigate those, but

00:56:20.560 --> 00:56:23.917
you get to see it. You get to see if
you're right, learn from reality, and do

00:56:24.000 --> 00:56:27.037
better the next time.
>> I think people don't understand the

00:56:27.120 --> 00:56:29.117
extent of which like this is all new.

00:56:29.200 --> 00:56:30.477
There's no playbook. Like you're

00:56:30.560 --> 00:56:32.797
figuring this out as you go as well on

00:56:32.880 --> 00:56:36.877
the most rapidly deployed technology in

00:56:36.960 --> 00:56:39.677
the world perhaps that's so powerful.

00:56:39.760 --> 00:56:44.557
>> It is true that at various points
in OpenAI's history, we've had some hope

00:56:44.640 --> 00:56:47.597
that hey, there are people who have
deployed transformative technologies

00:56:47.680 --> 00:56:51.437
before, maybe they can tell us the
answers. And it's never been so simple.

00:56:51.520 --> 00:56:54.317
They do have wisdom and insights
and that's something that I think we've

00:56:54.400 --> 00:56:58.237
really incorporated. But we realized
that we're the closest ones to this

00:56:58.320 --> 00:57:00.637
technology that by virtue of creating

00:57:00.720 --> 00:57:04.077
it, we have an understanding of the ways

00:57:04.160 --> 00:57:05.917
in which we could shape it that is hard

00:57:06.000 --> 00:57:08.877
for someone who isn't so close to it to

00:57:08.960 --> 00:57:11.437
appine on to advise on. And I think that

00:57:11.520 --> 00:57:13.997
one observation I have is that the right

00:57:14.080 --> 00:57:18.717
choices are extremely specific
to the facts of the technology or there's

00:57:18.800 --> 00:57:20.477
different pressures that are exerted by

00:57:20.560 --> 00:57:23.277
cell phones versus, you know, mainframe

00:57:23.360 --> 00:57:26.077
computers versus AI versus electricity.

00:57:26.160 --> 00:57:30.077
Each one of these has its own unique
proclivities and problem sets, ways that

00:57:30.160 --> 00:57:33.917
they're being developed. The individuals
doing it matter too, right? the dynamics

00:57:34.000 --> 00:57:38.077
between different humans and these human
factors have been hugely impactful for

00:57:38.160 --> 00:57:43.037
how AI is playing out today in front
of our eyes. I think that a lot of what we

00:57:43.120 --> 00:57:46.717
spend our time doing from the beginning
of OpenAI and really even before is you

00:57:46.800 --> 00:57:49.917
spend a lot of time dreaming. We spend
a time a lot of time really thinking about

00:57:50.000 --> 00:57:53.757
all the implications of what you might do.
And I think that one thing I've

00:57:53.840 --> 00:57:55.837
observed is that we haven't really been

00:57:55.920 --> 00:57:58.797
surprised by some moments along the way,

00:57:58.880 --> 00:58:01.117
but we have been surprised by when they

00:58:01.200 --> 00:58:03.757
arrive, how hard they are to accomplish,

00:58:03.840 --> 00:58:07.277
exactly the order in which we see them.

00:58:07.360 --> 00:58:09.037
And that the world that we are moving

00:58:09.120 --> 00:58:11.437
towards is I think one that is in many

00:58:11.520 --> 00:58:16.477
ways more wonderful and all inspiring

00:58:16.560 --> 00:58:19.197
than many of the ones we anticipated. If

00:58:19.280 --> 00:58:22.797
one frontier model puts safety as a

00:58:22.880 --> 00:58:25.677
primary concern and another frontier

00:58:25.760 --> 00:58:27.597
model doesn't, how do you view that

00:58:27.680 --> 00:58:30.397
competition playing out over time?

00:58:30.480 --> 00:58:33.757
>> Well, I think we have found that safety

00:58:33.840 --> 00:58:38.557
is actually a core product feature. Like
no one wants a model that is not aligned

00:58:38.640 --> 00:58:40.397
with them, right? You want a model you

00:58:40.480 --> 00:58:44.397
can trust, that does the right things in

00:58:44.480 --> 00:58:47.677
any circumstance you give it. And so we

00:58:47.760 --> 00:58:52.317
have invested and I think we've
actually invested possibly far more than

00:58:52.400 --> 00:58:53.837
certainly people perceive and possibly

00:58:53.920 --> 00:58:57.037
more than any other lab in safety right

00:58:57.120 --> 00:58:59.357
that we have in JBT the broadest

00:58:59.440 --> 00:59:02.717
deployment of AI these language models

00:59:02.800 --> 00:59:05.357
in the world used by the most people we

00:59:05.440 --> 00:59:09.757
have to care we've always cared but you
really see it in terms of us being able

00:59:09.840 --> 00:59:13.517
to bring this technology to so many
people so I don't think that there's a

00:59:13.600 --> 00:59:18.077
sustainable state where the people who
are building this technology and having

00:59:18.160 --> 00:59:20.397
successful products are not also

00:59:20.480 --> 00:59:25.277
investing super hard in safety. And I
think that actually the challenge is a

00:59:25.360 --> 00:59:26.957
little bit about if you step back

00:59:27.040 --> 00:59:29.437
because there are some aspects of what

00:59:29.520 --> 00:59:34.397
it means to deliver safety that are
not necessarily short- term. You have to

00:59:34.480 --> 00:59:37.997
think long term for not just your
business but for what it is that you're

00:59:38.080 --> 00:59:41.197
creating. And some of this is about how
you train the models. Some of this is

00:59:41.280 --> 00:59:43.357
about how do you get your feedback loop.

00:59:43.440 --> 00:59:46.877
But I would just say that we are
committed to safety as part of our

00:59:46.960 --> 00:59:51.917
mission and that's something where I
think it has played out in our products

00:59:52.000 --> 00:59:56.397
and in the world. One thing that people
also miss is that it's not just about

00:59:56.480 --> 01:00:00.797
the safety of the model. It's about
the resilience of society. If you look at

01:00:00.880 --> 01:00:03.197
how transformative technologies enter

01:00:03.280 --> 01:00:06.237
the world that society builds around

01:00:06.320 --> 01:00:10.797
them about their strengths and their
risks, you think about engines, right?

01:00:10.880 --> 01:00:15.357
You build cars, but you also need seat
belts. You also need to have roads. And

01:00:15.440 --> 01:00:20.317
you reorient cities around the fact that
this is how this technology works. You

01:00:20.400 --> 01:00:24.237
think about electricity. You have
various safety standards. You have

01:00:24.320 --> 01:00:28.877
different kinds of where you're allowed
to put the electric poles, right? And

01:00:28.960 --> 01:00:31.197
high voltage lines and all these things.

01:00:31.280 --> 01:00:36.077
And I think the same will be true for AI
that it's not just about the technology

01:00:36.160 --> 01:00:38.077
itself. It's not about the model itself.

01:00:38.160 --> 01:00:43.277
It's really about how do they integrate
into the world with a society that is

01:00:43.360 --> 01:00:47.677
resilient and that is something we're
investing in very significantly. The

01:00:47.760 --> 01:00:49.917
OpenAI Foundation has this as one of its

01:00:50.000 --> 01:00:55.597
key focuses of trying to help society
invest in and build a resilience layer

01:00:55.680 --> 01:00:59.517
for AI. >> What do you think
regulation for AI should look like?

01:00:59.600 --> 01:01:01.037
>> Well, I think that there's a number of

01:01:01.120 --> 01:01:06.557
different pieces to what regulation for AI
needs to accomplish. One that I think

01:01:06.640 --> 01:01:09.277
is very important is we need
to ultimately ensure this technology

01:01:09.360 --> 01:01:12.477
benefits people and you think about

01:01:12.560 --> 01:01:15.197
questions like like it is very clear

01:01:15.280 --> 01:01:19.517
that institutions, jobs, just life paths

01:01:19.600 --> 01:01:23.757
that people thought would be stable.

01:01:23.840 --> 01:01:25.997
Those assumptions may not hold anymore.

01:01:26.080 --> 01:01:28.477
And we need to make sure that we provide

01:01:28.560 --> 01:01:33.437
support that we're all there to support
each other as this technology rolls out.

01:01:33.520 --> 01:01:36.157
And so what does that mean from a
regulatory perspective? I think there's

01:01:36.240 --> 01:01:39.757
a lot of ideas whether it's things like
everyone should have access to compute.

01:01:39.840 --> 01:01:44.157
How do we ensure that that's true?
How do we ensure that as this technology

01:01:44.240 --> 01:01:48.397
starts to generate more economic value,
it doesn't acrude to just one place,

01:01:48.480 --> 01:01:52.077
right? This should be something that
actually everyone is benefiting from.

01:01:52.160 --> 01:01:55.197
This technology shouldn't just
abstractly benefit the economy. It's

01:01:55.280 --> 01:01:59.037
very clearly going to do. It should
directly be something that people feel

01:01:59.120 --> 01:02:02.717
in their daily lives that they
themselves their life is better because

01:02:02.800 --> 01:02:04.797
this technology exists because they're

01:02:04.880 --> 01:02:06.557
using it because they're able to

01:02:06.640 --> 01:02:10.237
accomplish more. And I think that the

01:02:10.320 --> 01:02:13.437
ways in which I see this playing out

01:02:13.520 --> 01:02:15.997
it's very important to ground it in what

01:02:16.080 --> 01:02:20.957
are we really seeing like a good example
is the number of people whose who say

01:02:21.040 --> 01:02:24.477
that their life their life was saved
or life of a loved one was saved through

01:02:24.560 --> 01:02:27.277
the use of chat GBT and you realize that

01:02:27.360 --> 01:02:31.677
that's something that should be
supported and protected and so a good

01:02:31.760 --> 01:02:35.917
example of how you can do that through
regulation is thinking about privacy and

01:02:36.000 --> 01:02:39.917
privilege. You talk to a doctor,
you talk to a lawyer, those are privileged

01:02:40.000 --> 01:02:43.117
conversations, right? You feel
comfortable sharing them. There's

01:02:43.200 --> 01:02:46.637
certain guard rails on when the health
care provider would have to, you know,

01:02:46.720 --> 01:02:49.997
provide that information to law
enforcement or alert someone. It's well

01:02:50.080 --> 01:02:53.517
defined in the law. We don't have
anything like that for AI right now, but

01:02:53.600 --> 01:02:55.437
people are using these tools and they

01:02:55.520 --> 01:03:00.637
should use these tools because they're
so important for giving people access to

01:03:00.720 --> 01:03:03.837
information that they wouldn't be able
to get otherwise and that they should

01:03:03.920 --> 01:03:07.197
have the appropriate kind
of understanding of protections there too.

01:03:07.280 --> 01:03:10.397
And so I think that there's a lot
of just really leaning into thinking about

01:03:10.480 --> 01:03:15.037
how do these models insert into people's
lives? How do we make sure that we can

01:03:15.120 --> 01:03:19.757
continue to innovate while at the same
time also making sure that the benefits

01:03:19.840 --> 01:03:24.157
flow broadly? How do we ensure that
America remains a leader? Right? That

01:03:24.240 --> 01:03:25.597
you think about robotics where I think

01:03:25.680 --> 01:03:27.917
we are not the leader. I think that for

01:03:28.000 --> 01:03:31.837
AI we have to make sure that we continue

01:03:31.920 --> 01:03:36.397
with this remarkable position that we

01:03:36.480 --> 01:03:40.397
have been able to achieve. And you
think about things like data centers that

01:03:40.480 --> 01:03:43.837
those are something where there's
clearly been a lot of concern about

01:03:43.920 --> 01:03:47.197
questions like do they drive up
electricity prices and we have a

01:03:47.280 --> 01:03:51.357
commitment to ensure that they do not
and I think that each of these things

01:03:51.440 --> 01:03:54.237
can be achieved through many different
mechanisms sometimes it's through

01:03:54.320 --> 01:03:57.117
regulation sometimes it's through
commitments from the company sometimes

01:03:57.200 --> 01:04:01.437
it's just through people understanding
the facts like a good example is data

01:04:01.520 --> 01:04:05.037
centers and water usage like that that's
something that people talk about a lot

01:04:05.120 --> 01:04:07.997
but actually our data centers use
incredibly little water right It's

01:04:08.080 --> 01:04:09.677
actually misinformation
that they use a lot.

01:04:09.760 --> 01:04:11.117
>> It's less than a household, isn't it?

01:04:11.200 --> 01:04:13.437
>> It's It is because it's a closed loop.

01:04:13.520 --> 01:04:15.677
You basically fill up a giant like,
you know, think of it as like a swimming

01:04:15.760 --> 01:04:18.717
pool of water and you just circle it

01:04:18.800 --> 01:04:20.797
around. And so it's a fixed amount of

01:04:20.880 --> 01:04:25.757
water that's not very large, but I think
people really understanding the why. Why

01:04:25.840 --> 01:04:30.397
are we building these things? Why is it
worthwhile? How does it benefit me? and

01:04:30.480 --> 01:04:35.037
being able to give people that
empowerment whether it's helping them

01:04:35.120 --> 01:04:38.477
feel that they can be an entrepreneur
now that they can build a business that

01:04:38.560 --> 01:04:42.797
they can create something like all
of that we have to solve for we have to

01:04:42.880 --> 01:04:45.437
make sure that people feel
it in their daily lives

01:04:45.520 --> 01:04:49.037
>> when I told people I was doing this
interview one of the common reactions is

01:04:49.120 --> 01:04:52.797
that they're fearing for their job
and their uncertainty what would you tell

01:04:52.880 --> 01:04:56.877
them >> well I do think
that this technology it

01:04:56.960 --> 01:05:01.677
is uncertain exactly how it will play I
think it is surprising how it will play

01:05:01.760 --> 01:05:04.797
out as well. Like the AIS that we have
right now, the world that we have right

01:05:04.880 --> 01:05:08.397
now is not really something that was
anticipated by science fiction. It's

01:05:08.480 --> 01:05:10.397
just different and some inevitable

01:05:10.480 --> 01:05:13.277
conclusions I think actually turn out to

01:05:13.360 --> 01:05:15.677
not quite look the same way when they

01:05:15.760 --> 01:05:20.557
come to pass. So I believe it's always
easiest to see what you lose, right? And

01:05:20.640 --> 01:05:23.597
the change is coming. There is no
denying that. That is absolutely the

01:05:23.680 --> 01:05:26.157
case. But it's much harder to see a

01:05:26.240 --> 01:05:28.397
prairie what you gain. And as an

01:05:28.480 --> 01:05:31.277
example, just think about Uber being

01:05:31.360 --> 01:05:35.597
described to someone in 1950. You have
to think about computers. You have to

01:05:35.680 --> 01:05:37.837
think about mobile phones. You have to

01:05:37.920 --> 01:05:39.677
think about GPS. And it's all so that

01:05:39.760 --> 01:05:42.637
you can get a car to appear where you

01:05:42.720 --> 01:05:46.797
where you are in 3 minutes. And like
that's actually crazy if you think about

01:05:46.880 --> 01:05:51.277
that level of technological investment
for that kind of use case. But it really

01:05:51.360 --> 01:05:54.637
happened. And it didn't just happen
for that one use case. It happened for

01:05:54.720 --> 01:05:56.237
thousands, for tens of thousands, for

01:05:56.320 --> 01:05:58.397
millions of other use cases. And so I

01:05:58.480 --> 01:06:01.837
think that my view of AI is it is about

01:06:01.920 --> 01:06:05.437
empowerment. It is about human agency.

01:06:05.520 --> 01:06:06.797
And that that does mean that some of

01:06:06.880 --> 01:06:09.997
these institutions, jobs, these kinds of

01:06:10.080 --> 01:06:14.317
things that there will be things that we
thought we could rely on that turn out

01:06:14.400 --> 01:06:16.077
not to be as stable as we thought. And

01:06:16.160 --> 01:06:18.557
so it will affect people. But the

01:06:18.640 --> 01:06:21.357
question to lean into is what do you

01:06:21.440 --> 01:06:25.117
gain and how do you benefit from it. So

01:06:25.200 --> 01:06:29.357
now you can be a builder. You can create
anything. Anything you can imagine can

01:06:29.440 --> 01:06:33.117
become real. Well,
what do you imagine? How do you

01:06:33.200 --> 01:06:36.557
build that skill? Really leaning into
this technology. Like one thing that I

01:06:36.640 --> 01:06:39.837
have observed is across multiple
generations of this technology, the

01:06:39.920 --> 01:06:43.997
people who seem to be getting the most
benefit out of it are the people who did

01:06:44.080 --> 01:06:45.677
it for the previous one. Right? So the

01:06:45.760 --> 01:06:47.837
more that you build the skill at the

01:06:47.920 --> 01:06:51.277
core of it is agency is having a vision

01:06:51.360 --> 01:06:55.597
is having ideas because now the barrier
to entry to trying them out is lower

01:06:55.680 --> 01:06:59.597
than ever before. So I think there will
be new opportunity created. I think that

01:06:59.680 --> 01:07:03.917
the world does need to think about
how do we support everyone through this

01:07:04.000 --> 01:07:07.517
moment of uncertainty through whatever
transitions will come because the

01:07:07.600 --> 01:07:12.157
economy will be a computed economy.
It will be different, but I think that

01:07:12.240 --> 01:07:15.117
there will be a place
for everyone to contribute.

01:07:15.200 --> 01:07:18.477
>> Where should young people be investing
today? If you're in high school or

01:07:18.560 --> 01:07:20.477
university or just trying to start out

01:07:20.560 --> 01:07:22.717
in a job, what skills do you think will

01:07:22.800 --> 01:07:25.517
be more valuable in the future?

01:07:25.600 --> 01:07:27.597
>> Well, I really think leaning into this

01:07:27.680 --> 01:07:32.157
technology is going to be a critical

01:07:32.240 --> 01:07:36.477
skill. Just really understanding how
do you get the most out of AI? because

01:07:36.560 --> 01:07:40.957
we're all going to be heading to a world
where we're managers of agents and soon

01:07:41.040 --> 01:07:43.277
maybe the CEO of an autonomous AI

01:07:43.360 --> 01:07:45.277
corporation, right? Just imagine if you

01:07:45.360 --> 01:07:48.957
had the workforce of, you know, 100,000

01:07:49.040 --> 01:07:54.317
person company all at your disposal
operating on your behalf as long

01:07:54.400 --> 01:07:57.917
>> 24/7, right? And as long as you've
got the tokens, the compute to power it,

01:07:58.000 --> 01:08:01.277
which again I think everyone needs
access to compute, that's like so

01:08:01.360 --> 01:08:02.797
critical for the world to figure out and

01:08:02.880 --> 01:08:05.997
get right because then at that point,

01:08:06.080 --> 01:08:09.437
you can point that at any problem and

01:08:09.520 --> 01:08:11.917
the number of problems that humanity

01:08:12.000 --> 01:08:15.117
could want to solve are boundless. And

01:08:15.200 --> 01:08:19.357
so I think that the more that people
do lean into this technology, figure out

01:08:19.440 --> 01:08:22.797
how to take advantage of what's coming,
how to combine these technologies in new

01:08:22.880 --> 01:08:28.157
ways, how to interact with our agents
to really manage them to think about when

01:08:28.240 --> 01:08:31.757
what is it that I want, what is it that
is my sense of self, what is my purpose,

01:08:31.840 --> 01:08:35.757
what do I want to see in the world,
it is going to be easier than ever to

01:08:35.840 --> 01:08:39.917
accomplish that. And I think
that that world with what we

01:08:40.000 --> 01:08:45.117
gain, I think is going to be almost
unimaginable in its upside. That's the

01:08:45.200 --> 01:08:48.557
most positive sort of view of the future.
What's the most negative one you

01:08:48.640 --> 01:08:52.477
can imagine? One thing that's very
interesting about how technology has

01:08:52.560 --> 01:08:55.437
played out to date is that it's really

01:08:55.520 --> 01:08:57.997
been about contorting ourselves to the

01:08:58.080 --> 01:09:01.117
machine, right? You think about how many

01:09:01.200 --> 01:09:05.917
people work where you have this box
and you're typing away at it and you're

01:09:06.000 --> 01:09:09.117
getting your carpal tunnel and your
shoulders are hunched and all of those

01:09:09.200 --> 01:09:10.637
things that were not natural, right?

01:09:10.720 --> 01:09:14.717
That's not really what we're designed for.
And we're going to be moving to

01:09:14.800 --> 01:09:19.277
this world where it's not just that
you're doing work with your computers.

01:09:19.360 --> 01:09:20.717
Your computer actually does work for

01:09:20.800 --> 01:09:24.637
you. And that is something that presents

01:09:24.720 --> 01:09:28.557
opportunities. I think it presents risks.
I think we need to figure out how

01:09:28.640 --> 01:09:32.957
to mitigate those. Like one core
thing at the end of the day is that

01:09:33.040 --> 01:09:36.957
if you have machines that help people
actualize their goals, right, that's out

01:09:37.040 --> 01:09:41.437
there doing what you want.
Sometimes people have conflicting goals.

01:09:41.520 --> 01:09:45.037
How do you resolve that? How do you
decide what the bounds are on what an A

01:09:45.120 --> 01:09:47.517
will help you with and what they won't?

01:09:47.600 --> 01:09:50.717
Really trying to figure out how does
this slot into society? How do you make

01:09:50.800 --> 01:09:53.277
sure that the benefits don't just go to

01:09:53.360 --> 01:09:55.677
one corporation, one set of people, but

01:09:55.760 --> 01:09:58.397
that actually do lift up everyone? We

01:09:58.480 --> 01:10:02.397
need to raise the floor so that everyone

01:10:02.480 --> 01:10:06.957
has access to a great life,
this technology, and are able to do things

01:10:07.040 --> 01:10:09.997
with it. And I think
it'll correspondently also

01:10:10.080 --> 01:10:14.077
lift the ceiling. And so I think
we're going to be in a world where

01:10:14.160 --> 01:10:18.237
everyone is going to have new
opportunities that there will be more

01:10:18.320 --> 01:10:21.917
just I don't know if the right word
is safety net or just like that. There

01:10:22.000 --> 01:10:24.957
should be something that that really
is able to make sure that everyone gets

01:10:25.040 --> 01:10:27.837
brought along, but then
we're going to be able to

01:10:27.920 --> 01:10:31.997
accomplish so much more. And you think
about things like access to medical

01:10:32.080 --> 01:10:33.997
care. Like we should be in a world if we

01:10:34.080 --> 01:10:37.517
do our job right where everyone gets

01:10:37.600 --> 01:10:42.557
access has a doctor in their pocket
that is better than any team of doctors

01:10:42.640 --> 01:10:46.077
today. The world's best doctors. They're
there for you. They care about you.

01:10:46.160 --> 01:10:49.677
They're actually reading your charts
that they're thinking 24/7 about how can

01:10:49.760 --> 01:10:52.557
we actually help with this condition.

01:10:52.640 --> 01:10:57.117
It's disruptive, right? It's it's not
going to come for free in terms of how

01:10:57.200 --> 01:11:00.797
this technology will interact with
the world. And we've already seen the

01:11:00.880 --> 01:11:04.077
beginning eras of it. But I think that

01:11:04.160 --> 01:11:07.997
what we're going to see over just even
the next two years, I think it will be

01:11:08.080 --> 01:11:12.957
this force for good. But we have to also
acknowledge all the ways that it could

01:11:13.040 --> 01:11:16.637
go wrong or the risks of it
in order to achieve those upsides.

01:11:16.720 --> 01:11:20.387
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01:11:48.160 --> 01:11:52.077
same question which
is what is success for you?

01:11:52.160 --> 01:11:56.077
achieving the open eye mission
of ensuring that artificial general

01:11:56.160 --> 01:11:59.037
intelligence benefits all of humanity.

01:11:59.120 --> 01:12:01.197
>> Thank you very much. This was awesome.

01:12:01.280 --> 01:12:02.717
This is a great conversation, man.

01:12:02.800 --> 01:12:06.680
>> Thank you. I had a great time.