WEBVTT

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We're now in the AGI era. Astra has
really hit something that I'm like,

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"Okay, I think this is pretty reasonable
to call it AGI. We've seen it run

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coherently [clears throat] for 24 hours
to go accomplish tasks that I think are

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quite amazing. >> The models
will be plenty powerful, but

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it'll be hard to get to everybody given
that we won't have enough compute to

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serve it all. >> You don't
win the Super Bowl by saying,

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"I want to [music] win the Super Bowl."

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You win it by blocking, tackling.
You really have to make sure that safety,

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security, alignment, those are all
standards that you're constantly

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upleveling. I think that's going
[music] to be a huge challenge people are

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underestimating. You've made two very
big bets in your career. Helping build

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Stripe early and helping
of course co-ound open AAI.

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>> I throughout OpenAI have always
focused on [music] whatever is the most

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important problem. For the past 2
years it's been the data centers, the

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infrastructure, the machine learning.

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>> Do you know what the next
year we'll focus on or

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>> I think this is going to become
the most important conversation.

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>> Greg, welcome to the A&Z podcast.

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>> Thank you for having me. So, Greg,
you've made two very big bets in your

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career, uh, helping build Stripe early
and helping, of course, co-found uh,

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Open AI. If we were talking 10 years ago
and you were predicting what would the

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world look like in 2026 as it relates to
AI, would you be able to predict that we

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would be making the breakthroughs
that you've uh, you know, ma made today or

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what would you tell them about what
you would expect? Well, so Ilie and I

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actually spent a lot of time trying
to predict what it would look like, what

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the timelines would be. And I remember

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we did some math on compute in around

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2016, 2017. And we kind of came to the

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conclusion that if you look at Mo's law
progress, that kind of thing, 15 years

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felt like about the timeline to AGI.
And if you really squinted at it, you're

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willing to scale up and build massive
supercomputers, spend the hundreds of

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billions of dollars, that kind of thing,

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that maybe it' be 10 And so I actually

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feel like in some ways obviously what's
happening it's remarkable. It's this

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amazing sort of moment for everyone
to be a part of and to be able to help

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shape collectively. But it also feels
a little bit like maybe it's kind of the

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conclusion of like a lot of forces that
are all coming together for this moment.

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You if you step back and really take
that sort of macro view it kind of makes

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sense it's happening now. And do you

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think um we're currently because you

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guys slightly underestimated
the timeline or I I guess it was basically

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on on point. Um do you think we're still

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on the timeline given we're now starting
to drive real shortages on the supply

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chain?

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>> Well, look, I do think that we are in a

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world where it is hard for compute
to keep up with the demand that we're

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already seeing in the market, right? And
just in terms of how people are going to

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use this [clears throat] technology,
benefit from it. I do think it's going

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to be very hard for us to scale the raw
potential and capability of these models

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to everyone and that's part of what we
try to do and so I think that the the

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progress I do see like we have line
of sight to continue to make the models

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much more capable, safe and aligned but

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also really distributing that power
and the benefits and the empowerment to to

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everyone. I think that's going to be a
huge challenge people are underestimating.

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>> Right. Ah interesting. So we'll we'll

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have the mo the models will be plenty

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powerful um or or they'll continue a

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pace uh but they'll be it'll be hard to

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get to everybody is certainly in an
affordable way given that we won't have

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enough compute to serve it all. I think
that's true and I do think we're at a

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point now where we have to really start
thinking about what we call pacing the

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frontier. And so thinking about as we
move to more capable models, you really

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have to make sure that safety, security,
alignment, those are all standards that

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you're constantly upleveling.
And those actually become almost the the

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bottleneck to progress or sort of the
the sort of you know the the part that

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you have to uh spend a lot of your effort
to make sure you've gotten right.

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And so I think in my mind it's more
those constraints and the compute. I

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think we can make it happen. And then
on the flip side, I think yeah, this

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bringing it to everyone, which
is ultimately about our mission, right?

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It's empower everyone, ensure it
benefits everyone. That's something that

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I think deserves a lot more
airtime than it's gotten.

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>> Yeah. Actually, let's get a little
deeper on the safety thing because it's

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been very interesting to me in that it

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felt like in the beginning, safety was

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like, okay, let's make these things not

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say nasty stuff that people like don't

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like. And so the approach that was taken

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was kind of a surface around the edges.

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Okay, we'll um you know put some filters

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on this guy and we'll RHF

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you know around the edges. But like if
you get deep into the thing you'll be

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able to get the bad words out. But if
somebody wants to go through that to

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hear bad words themselves, who cares? Uh

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but then you know when you get into okay

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now these things are really good at uh

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cyber hacking and other kinds of ideas.

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Um now you need kind of a more
architectural idea where the model

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itself knows not to reward
hack in a way that's

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going to be dangerous and so forth. And

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how do you feel like yes we can make

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progress against that fast or is that like
a really hard different category of

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problem or how how are you thinking
about that? Well, I absolutely think we

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can and are making very rapid progress

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on this problem. I think that there's
>> a lot of both great ideas and research

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that we've been investing in for many
years. Actually, if you rewind to 2017,

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uh that I think people underappreciate

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some of the most key results that came
out of OpenAI in the field at the time.

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So, both kind of the first inklings
of modern language models. You can find a

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paper paper from 2017 that kind of laid
that out with LCMS and you know it's

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like kind of this very baby result but

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also reward uh learning uh reinforcement

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learning from human preferences that was

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also created in 2017 to start thinking

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about how can you align a model to match
what humans want right by providing

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feedback from people.
>> Yeah. Just for usability.

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>> Exactly. In 2017 2018 we had ideas for
if you have something that's very smart

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and capable how can you actually
supervise what it's doing how can you

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provide feedback and ensure that it's

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staying aligned with you and we had

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ideas such as debate or uh iterative

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amplification. So these are ideas that
were really sort of at this phase before

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these systems existed and you can start
to see the sort of trickle down of those

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ideas into modern systems and and
investment. And so it's in some ways

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that I think that there was this early
phase when open started where we really

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were thinking about AGI safety things
like that and that was very front and

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center in even the comms. And then I
think that as things like chatb took off

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then people started to see okay well

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we're not at this point yet and so
the is the AI politically neutral and

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questions like that start to become
to the front and center >> right

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>> and now that we're here all these other
ideas that we've been talking about for

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a long time they're taking the main
stage again and I think we've been sort

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of thinking about this moment for a long
time. That's really good news. And and

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when you think about and we don't have

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like a really great community yet

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amongst the uh soda models, but it seems

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like those kinds of ideas

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you and Google and Anthropic and uh

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SpaceX would want to share and Meta now

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um as opposed to like okay this is a

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proprietary idea that's a way to keep

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these models safe since you're all on

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related architectures. um or h how do
you see that unfolding or is everybody

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going to do it independently?
>> Well, I think there's nuance here and I

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do think the coordination is going to be
a very important theme, right? To really

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think about within the frontier labs and

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really just thinking broadly about what

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has to happen for humanity as a whole

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>> to sort of navigate this technology
in the best way. I think that we we're

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going to have to really think hard
about those kinds of questions. And we

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published a lot of our thoughts. And
again, some of this is about pacing the

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frontier. Some of this is about
unilateral actions that we can take and

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how we think about how do you make
safety cases for even training and

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developing and evaluating these kinds
of models. All that's new. No one's ever

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really had to operationalize this before.
And I think it is not at all

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unique to OpenAI. Like there's a whole
world that is basically developing this

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technology. And I think one thing it's

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easy to miss is that what we're building
is almost a a sort of thing that falls

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out of compute progress. And in some ways,
compute progress is something that

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falls out of technological progress. And
so there's this this massive wave that's

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been building for a very long time. And
we're starting to see the leading edges

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of this technology. And companies like
OpenAI can lead by a bit in order to

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kind of peer into this future and really
understand what is possible. How do we

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shape this technology? But we can't
do that alone. And I think that having

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coordination and especially the more
that we can talk about safety techniques

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and share what we're seeing, alignment
failures, those kinds of things, all of

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that is going to again take a very
front seat for this next phase.

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>> Right. Very interesting.
>> You've called the OpenAI hugging face

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recent incident a watershed moment and
talked about how the defender window is

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now open. Can you explain that statement
and the significance behind it?

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>> So I think hugging face shows two

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things. one is call it a something for

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us in terms of how we monitor sandbox

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and control the models during evaluation
and that's something we've really risen

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to that occasion our team has totally
changed so much of our internal

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standards and and really implemented
a lot of controls that I think are very

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important and very critical as we look
to to future more capable models but

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there's a second thing that I think is

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also very valuable for the world that

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came out of this which is a insight into

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what future capabilities will be like
when they are broadly diffused and in

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the hands of threat actors and that will
happen right that there are so many

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people who are building these models
and again there's something very important

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and good about the diffusion broadly
of AI capabilities because there's a risk

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of concentration of power if one or a few
entities people huge risk right it's

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something not not to not to at all write

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off but you also have to prepare for if

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if everyone is empowered with tools that

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are cyber capable. And in the case of

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Hugging Face, you saw both an AI that

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was able to hack out of a secure
environment and hack into a company's

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production environment. And I think
that the takeaway >> very cleverly,

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>> very cleverly, right?
And it's like that

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the things that it found
were were quite sophisticated.

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>> Yes. >> And this capability
broadly diffused, I

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think, is something that will really

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empower threat actors in new ways. And I

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think that defenders need to use this

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time before that technology is broadly
available to secure themselves. And the

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nice thing about it is it's a dual use,
right? It's something where if you can

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find vulnerabilities, if you're
an attacker, you can use it for no good.

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But if you're a defender, you can patch,
right? If you're a defender, you control

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the battleground, right? You control the
setup of your systems. And so our belief

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right now is that there's this window of
you have frontier capabilities. You have

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the broadly diffused capabilities and

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you as a defender by default you know
your security is probably pretty static

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been static for the past 5 10 years
that kind of thing. you need to move use

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these frontier capabilities that you
you'll have differential access to right

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where we have trusted access programs
things like that to bring these

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capabilities to defenders and you can
use that to move yourself up so that as

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the frontier capabilities get better you
get pulled along too right okay so I've

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got a comment and a question on it I

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would say there's a third thing that we

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learn which is like these things have

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capabilities that I don't know that we

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all understood before which on the good
side like oh I can deploy 10,000 agents

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and they can talk to each other and
organize themselves and do stuff for me.

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Like that's uh pretty amazing. So that

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that was on the good side. On the other

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side, so I agree that we've got a kind

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of defense window. Um however,

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we have like 50 years of code and

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architectural ideas um and deployment

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ideas that weren't built for this world.

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And so yes, the AI can help us like

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okay, find a bug, patch a bug, and so

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forth. But it seems like there's, you

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know, maybe a bigger issue, which is we

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have these huge, you know, massive

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honeypotss of consumer data and all

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these things lying all over the

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internet. Uh, and you know, from a

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consumer standpoint, it's like, okay,

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I can't protect my stuff. uh these all

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these companies have to get their act

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together which um seems a bit worrisome

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and do you think kind of in the future

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we need a do we need a decentralized

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consumer architecture like will this
kind of current world that we live in

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with all these centralized
data repositories

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be viable in a world of AI >> so
several pieces to the answer and

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first to your point on what you can get

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out of 10,000 agents we actually use
10,000 agents to solve the Navier Stokes

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problem. [laughter] >> Yeah,
that was pretty pretty awesome by

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the way. Congratulations on that.

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>> Thank you. Thank you. And it's both
an important problem for what it is has

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significant implications
and applications to fluid dynamics to how

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you think about ocean currents,
all these things, but for what it

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represents, right, of new knowledge
created by AI and it unlocking a whole

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wave of scientific discovery, medicines,
all those things, they're on the table

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now. So I think there's something really
amazing to think about what can happen

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through the power of AI that is able to

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really help solve problems. And in the

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case of of cyber security, how I think

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about it, we at OpenAI took our models

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and applied them to finding
vulnerabilities. We took 25% of our

00:13:39.280 --> 00:13:43.117
production engineers and said, "Sorry,
all your projects are on hold. You are

00:13:43.200 --> 00:13:46.717
now defending. You are now upleveling
our security architecture. you're going

00:13:46.800 --> 00:13:48.717
to use the models to find all the holes

00:13:48.800 --> 00:13:53.837
and we found a number of of of serious
issues and we fixed them and I've talked

00:13:53.920 --> 00:13:57.517
to a number of CISOs over the past
couple couple weeks and months and there

00:13:57.600 --> 00:14:00.557
are many companies who are also telling
me that yeah that they've applied these

00:14:00.640 --> 00:14:03.917
models they found some very significant
issues but that they're able to fix them

00:14:04.000 --> 00:14:08.397
and one positive sort of part of the story
is that when we took Astra pointed

00:14:08.480 --> 00:14:13.277
out our our systems we found some new
new problems but eventually it saturated

00:14:13.360 --> 00:14:17.037
we basically found to our knowledge
all of the P zeros, all of the critical

00:14:17.120 --> 00:14:19.517
problems that Astra
is smart enough to find.

00:14:19.600 --> 00:14:22.077
>> And of course, there will be a new
model. There will be a new round.

00:14:22.160 --> 00:14:24.557
>> Even smarter. >> Exactly.
But I think that that's the

00:14:24.640 --> 00:14:27.437
world that we'll be in is that you'll be
in a world where you want to be in this

00:14:27.520 --> 00:14:32.397
tight loop of new cyber capability drops.
You deploy it against your

00:14:32.480 --> 00:14:36.717
systems. You find the new holes and
ideally you've managed to automate this

00:14:36.800 --> 00:14:39.757
what we call defense factory. And that's
what we're building internally. this end

00:14:39.840 --> 00:14:42.397
to end of both find vulnerability,

00:14:42.480 --> 00:14:46.797
triage it, >> remediate,
deploy, validate, right? That

00:14:46.880 --> 00:14:50.397
end to end. And if you can do that at
machine speed, I think the defenders

00:14:50.480 --> 00:14:53.997
will be advantaged in deeply
significant ways. And there are

00:14:54.080 --> 00:14:58.717
>> ideas, for example, formerly verifying
all of software that are possible with AI.

00:14:58.800 --> 00:15:02.077
>> Yeah, we never that's always been
a dream. We've had these ver formal

00:15:02.160 --> 00:15:06.157
languages and all these kinds of things,
but they never kind of took off.

00:15:06.240 --> 00:15:08.957
>> That's right. Because just it's just
intractable for people. It's just so

00:15:09.040 --> 00:15:12.077
hard. But we have these
that are solving these crazy

00:15:12.160 --> 00:15:16.877
>> impossible math problems. And so one
application of that that proving power

00:15:16.960 --> 00:15:19.837
right actually one of the things to know
about the navier stokes problem is that

00:15:19.920 --> 00:15:22.717
we formalized it right.
We formalized into lean and so

00:15:22.800 --> 00:15:25.837
>> so the AIs can write
verifiable code >> they can.

00:15:25.920 --> 00:15:28.397
>> Yeah. Very nice. Yeah that's a great

00:15:28.480 --> 00:15:31.757
idea. So I think I think there's real

00:15:31.840 --> 00:15:36.397
hope but I think that our view is that the
world needs to act with urgency because

00:15:36.480 --> 00:15:38.797
>> we're in a very dangerous
window right now. Yeah.

00:15:38.880 --> 00:15:41.357
>> We just see it coming.
>> Closing the loop on on on this in

00:15:41.440 --> 00:15:45.997
incident. Is there anything you felt
that the the narrative um gotten wrong

00:15:46.080 --> 00:15:49.837
in an important way or or or is there
any preferred way of of talking about

00:15:49.920 --> 00:15:52.557
what what happened or or or this window
that that's important to get across when

00:15:52.640 --> 00:15:56.157
you think about the public narrative?
or or is there anything just inside the

00:15:56.240 --> 00:16:00.237
company that uh else that that changed
in terms of how you're approaching the

00:16:00.320 --> 00:16:04.077
the set of issues? >> Well,
two things. I think that one big

00:16:04.160 --> 00:16:07.437
theme that people should take away from
it is a question of access, right? That

00:16:07.520 --> 00:16:12.397
these these capabilities exist right now
in the world, but that they are in small

00:16:12.480 --> 00:16:15.437
number of frontier companies and the
frontier companies have a trusted access

00:16:15.520 --> 00:16:19.677
program, which means that anyone who's
not in the trusted access program is not

00:16:19.760 --> 00:16:22.637
really able to benefit from the fact
that there's this differential. the

00:16:22.720 --> 00:16:26.717
people we're in, they get a benefit if
they use it. And so I think that there's

00:16:26.800 --> 00:16:31.437
something we need to do as a field
and as a society to really scale up the

00:16:31.520 --> 00:16:35.357
number of defenders that have access
to these technologies because that's it's

00:16:35.440 --> 00:16:40.157
like every day matters and to use those
days you need access to these tools. And

00:16:40.240 --> 00:16:43.197
one thing that was actually interesting
about the hugging face response was that

00:16:43.280 --> 00:16:47.437
they said that they used frontier models
to try to look over the logs of what had

00:16:47.520 --> 00:16:51.037
happened because that's the only way
to actually analyze an attack like this.

00:16:51.120 --> 00:16:54.477
and that they said the frontier models
refuse, but they didn't actually try our

00:16:54.560 --> 00:16:56.717
frontier models and they actually
believe that ours would have permitted

00:16:56.800 --> 00:17:00.317
it. And so there is something too about
the default stance of providers. I think

00:17:00.400 --> 00:17:02.877
there's something here about using these

00:17:02.960 --> 00:17:06.157
capabilities for good with this urgency

00:17:06.240 --> 00:17:10.717
and and this sort of sort of I yeah
this this this real sense that it has to

00:17:10.800 --> 00:17:14.477
happen. I'll tell a quick story by the
way which is unrelated but maybe also

00:17:14.560 --> 00:17:18.077
shows a little bit about how I think
about this. I remember when we trained

00:17:18.160 --> 00:17:20.877
GPD3. It was beginning of December 2019.

00:17:20.960 --> 00:17:23.837
So, everyone's about to head out
on vacation. Y's like, "Okay, we can train

00:17:23.920 --> 00:17:27.517
the model." And I just remember feeling
like this model is sitting on a shelf.

00:17:27.600 --> 00:17:30.957
No one is using it. It's this like
amazing technology, new to the world,

00:17:31.040 --> 00:17:34.317
new to humanity. It's like every day
that no one is exploring what it's

00:17:34.400 --> 00:17:37.437
capable of and trying to understand it,
figure out what to do with it. Um,

00:17:37.520 --> 00:17:39.117
that's a day that is lost to the world.

00:17:39.200 --> 00:17:42.157
And so, I was just like, I canceled
basically all my holiday plans. I spent

00:17:42.240 --> 00:17:45.517
the whole time just playing with the
model, building interfaces around it,

00:17:45.600 --> 00:17:48.557
trying to see what it was capable of.
I remember I was trying to teach it how to

00:17:48.640 --> 00:17:51.917
how to sort lists some of numbers.
It didn't work very well. Uh but it was

00:17:52.000 --> 00:17:55.037
just like this like really try to to to
probe it and see what's possible. And I

00:17:55.120 --> 00:17:59.197
think that that spirit and ethos
is something I think we should bring to

00:17:59.280 --> 00:18:02.957
what we're building today. It's it's
sort of obviously at much larger scale,

00:18:03.040 --> 00:18:07.597
much larger impact, but we as a world
have the opportunity to understand this

00:18:07.680 --> 00:18:11.997
technology in this moment, which then
helps us shape and steer where it will

00:18:12.080 --> 00:18:14.717
go next. >> And very good point.

00:18:14.800 --> 00:18:17.677
>> You you said you two things. What what
do you have another one? One was access

00:18:17.760 --> 00:18:20.317
or did you say both of them?
>> I think I said both of them. Yes. Yes.

00:18:20.400 --> 00:18:23.597
>> The um let's go back to Astro. Uh it's

00:18:23.680 --> 00:18:27.597
incredible to see all the excitement
on on X u all sorts of cases. People are

00:18:27.680 --> 00:18:29.437
excited about computer use. Um you you

00:18:29.520 --> 00:18:31.677
you've said that in some ways it is uh

00:18:31.760 --> 00:18:37.037
bring us closer to AGI. What do you talk
about what you find most compelling in

00:18:37.120 --> 00:18:39.917
Astra or what you think the the
breakthrough there in light of that

00:18:40.000 --> 00:18:42.717
statement and and uh where
we have still left to go?

00:18:42.800 --> 00:18:46.397
>> Actually wait sorry let me let me
actually revise my my answer and I can

00:18:46.480 --> 00:18:49.277
say I so both access and let me also

00:18:49.360 --> 00:18:51.517
tell another story about how I have used

00:18:51.600 --> 00:18:54.157
the models personally. So after hugging

00:18:54.240 --> 00:18:59.277
face, I was thinking about how
can I use these models in my

00:18:59.360 --> 00:19:02.317
personal life? What can I do to secure
myself? And I have a website. It's a

00:19:02.400 --> 00:19:06.717
very simple website, gregarin.com.
Not the most popular website. Got a good

00:19:06.800 --> 00:19:09.837
blog post on it. Exactly. You got some
blog post. It's a static site. It's very

00:19:09.920 --> 00:19:13.277
simple. Like what what kind
of vulnerabilities could be there? So I

00:19:13.360 --> 00:19:17.917
took my codeex and asked it go check
out gregman.com. Tell me if there's any

00:19:18.000 --> 00:19:22.397
vulnerabilities. So I did a pen test and
it came back with 13 findings. And these

00:19:22.480 --> 00:19:26.237
findings were things like I had set
my SPF record so that you would prevent

00:19:26.320 --> 00:19:31.197
people from spoofing emails, right?
That there was some it wasing over HTTP

00:19:31.280 --> 00:19:34.877
without forcing people to HTTPS,
things like that. And individually, these

00:19:34.960 --> 00:19:37.837
things are maybe not the biggest deal,
but if you think about with an AI that's

00:19:37.920 --> 00:19:40.957
able to chain together many small
vulnerabilities into a big one, I'm

00:19:41.040 --> 00:19:44.237
like, do I really want a hole where
someone can scoop emails for me?

00:19:44.320 --> 00:19:46.957
Probably not. So 15 minutes for it to

00:19:47.040 --> 00:19:49.597
find these 13 findings. But then I asked

00:19:49.680 --> 00:19:51.757
it, can you fix these? Right. Because

00:19:51.840 --> 00:19:54.397
fixing is so annoying. So painful and

00:19:54.480 --> 00:19:58.237
boring. Exactly. And so 45 minutes it

00:19:58.320 --> 00:20:01.837
opened up my cloud for control panel.
It clicked around, set all the headers. It

00:20:01.920 --> 00:20:04.957
migrated me to COD for pages. It's like
set everything correctly. It started the

00:20:05.040 --> 00:20:08.317
demarked process, which apparently
you have to do like a 48 hour window of

00:20:08.400 --> 00:20:13.997
whatever. Um, and that was 45 minutes
of of fixing. And I felt so protected. I

00:20:14.080 --> 00:20:17.357
felt like, wow. >> Did you ask
it to find the vulnerabilities?

00:20:17.440 --> 00:20:19.997
>> There you go. No, I added Yeah.
So it actually does do that automatically.

00:20:20.080 --> 00:20:22.877
This was by six soul. It said I just
checked that this one's fixed, this

00:20:22.960 --> 00:20:26.477
one's fixed, this one's fixed, and in 48
hours I'm going to have to run and set

00:20:26.560 --> 00:20:29.437
up a little automation. So in 48 hours,
it would check back in to complete the

00:20:29.520 --> 00:20:33.597
demark process. And I was like,
all right, this is we're in business now.

00:20:33.680 --> 00:20:37.517
>> All right. All right.
Craig Brockman.com.

00:20:37.600 --> 00:20:40.397
>> There we go.
You too can't be protected.

00:20:40.480 --> 00:20:43.517
>> Awesome. >> Let's um
let's transition to Astra. Uh

00:20:43.600 --> 00:20:46.717
it's incredible to see all
the excitement online and the use cases.

00:20:46.800 --> 00:20:50.557
people really excited about computer use
among other things. You you've said it's

00:20:50.640 --> 00:20:55.277
uh sort of closer to the way along to AGI.
I'm curious what you find most

00:20:55.360 --> 00:20:58.397
groundbreaking with it and where
do you think we still left to go?

00:20:58.480 --> 00:21:01.757
>> Well, I think that Astra is really a

00:21:01.840 --> 00:21:07.197
step function on so many axes and in
many ways it is the sum of a number of

00:21:07.280 --> 00:21:10.557
research bets that we've been making
for years and to see them come into one

00:21:10.640 --> 00:21:13.917
model at one time it's been absolutely
incredible. And so just one thing to

00:21:14.000 --> 00:21:16.797
know about how we do numbering is that

00:21:16.880 --> 00:21:21.677
we kind of have been wanting to have
GPD6 represent something that's worthy

00:21:21.760 --> 00:21:25.117
of it. And the problem we always
have is that our models are kind of

00:21:25.200 --> 00:21:28.237
incrementally getting better. And so
it's just never feels like it's a right

00:21:28.320 --> 00:21:29.837
moment to go for a major version bump.

00:21:29.920 --> 00:21:33.677
You always have be like it's 56 now
should be 57. And this one just happened

00:21:33.760 --> 00:21:37.117
to be because all these things came
together at once the first time that we

00:21:37.200 --> 00:21:40.397
actually had this almost discontinuous
step on in a way that we could have

00:21:40.480 --> 00:21:45.037
predicted, but it just was like all
these these these factors um happened to

00:21:45.120 --> 00:21:47.197
line up at once. And so I was a real

00:21:47.280 --> 00:21:52.557
positive moment. And to me the computer
use is the headline thing that we've

00:21:52.640 --> 00:21:53.997
talked about. And part of the reason

00:21:54.080 --> 00:21:56.317
computer use is so significant is that

00:21:56.400 --> 00:22:01.277
for agentic use cases, it really comes
down to tools. It's like is the model

00:22:01.360 --> 00:22:04.397
smart enough to use the tools and then
does it have access to the context that

00:22:04.480 --> 00:22:07.277
needs to through these tools?
And so people have been building these MCP

00:22:07.360 --> 00:22:11.917
servers and these CLIs and just really
sort of taking the world of software and

00:22:12.000 --> 00:22:16.237
making accessible in this almost stilted
way that is not really meant for humans,

00:22:16.320 --> 00:22:17.677
right? It's like we're kind of retooling

00:22:17.760 --> 00:22:21.117
the world, right? You made it. It's kind

00:22:21.200 --> 00:22:23.677
of like, oh, we'll build an API like

00:22:23.760 --> 00:22:27.837
it's a software, but like what if it's

00:22:27.920 --> 00:22:29.837
really more behaving like a human? Can

00:22:29.920 --> 00:22:32.637
it just use a computer and and it's kind

00:22:32.720 --> 00:22:35.917
of and the result of building that other

00:22:36.000 --> 00:22:40.797
layer you have now another layer of
security challenges this that the other.

00:22:40.880 --> 00:22:42.957
>> Exactly. >> Really so
it's kind of Yeah. They've

00:22:43.040 --> 00:22:45.757
always felt like very
weird and suboptimal.

00:22:45.840 --> 00:22:49.677
>> Yes. And from the very beginning
of OpenAI I remember in November 2015 we

00:22:49.760 --> 00:22:53.197
did this offsite in Napa and we talked
about our plans. We actually laid out

00:22:53.280 --> 00:22:55.997
this three-step plan that basically
is what we ended up following for the next

00:22:56.080 --> 00:22:57.997
10 years. But we also talked about what

00:22:58.080 --> 00:23:00.637
if we could do reinforcement learning

00:23:00.720 --> 00:23:05.037
where the environment is screen pixels,
keyboard, mouse, right? Same interface

00:23:05.120 --> 00:23:08.717
as a human. Suddenly any sort of task
you could do with a computer is in

00:23:08.800 --> 00:23:13.277
there. It's in it's in distribution
as long as set aside sound, whatever. But

00:23:13.360 --> 00:23:17.357
you basically have the full power of a
computer there. And we had some aborted

00:23:17.440 --> 00:23:20.637
attempts early on to try to build agents
that could do that. And so it really

00:23:20.720 --> 00:23:25.677
took us until now, but you're seeing the
power immediately. And it's just been so

00:23:25.760 --> 00:23:29.357
cool to see people take the Blender
capabilities and, you know, take a

00:23:29.440 --> 00:23:32.877
screenshot of something and they have
to make a 3D model and you can actually,

00:23:32.960 --> 00:23:36.077
you know, lots of people are now
designing houses or trying to redesign

00:23:36.160 --> 00:23:40.237
uh their living room, all those things
by just utilizing this capability. And

00:23:40.320 --> 00:23:41.917
to me that the the thing that really

00:23:42.000 --> 00:23:45.597
stands out is that you can now

00:23:45.680 --> 00:23:50.077
move forward on AI that can do things
for you without you having to build all

00:23:50.160 --> 00:23:53.437
these specific connectors. And I think
there's so much software they don't even

00:23:53.520 --> 00:23:56.957
think about that you have to orchestrate
every day. And like how much of your

00:23:57.040 --> 00:24:00.317
life is like clicking around menus
and like you know typing things into a

00:24:00.400 --> 00:24:04.157
spreadsheet and things like that. Like
none of that is what we should be doing

00:24:04.240 --> 00:24:07.517
100 years ago. No one is doing any
of these things. And so it's not crazy to

00:24:07.600 --> 00:24:11.837
think that in 5 years 10 years no one
will be doing any of this stuff anymore.

00:24:11.920 --> 00:24:14.477
That we will get our time back. We're
not going to have to be get our carpal

00:24:14.560 --> 00:24:18.717
tunnel or hunch shoulders or you know
all of those all those physical problems

00:24:18.800 --> 00:24:20.397
that are us contorting to the machine.

00:24:20.480 --> 00:24:22.397
It's now the machine is there to help us

00:24:22.480 --> 00:24:25.037
to empower us to to really serve us.

00:24:25.120 --> 00:24:29.117
>> Yeah. And you know that's a really
good point because I think you know one of

00:24:29.200 --> 00:24:31.197
the things that that that you have been

00:24:31.280 --> 00:24:34.237
um I would say more sober on as a

00:24:34.320 --> 00:24:37.197
company is just okay what happens with

00:24:37.280 --> 00:24:41.517
employment and uh I think that I think

00:24:41.600 --> 00:24:44.397
that that's exactly right that there's
all these things that we do because we

00:24:44.480 --> 00:24:48.237
have to do and like they became valuable
but we shouldn't be doing it. All they

00:24:48.320 --> 00:24:50.317
do is wreck our health and and wreck our

00:24:50.400 --> 00:24:53.197
personalities. And

00:24:53.280 --> 00:24:57.677
the idea that humans are going to just
run out of ideas of cool things to do or

00:24:57.760 --> 00:24:59.677
how to make the world better or problems

00:24:59.760 --> 00:25:03.917
to solve um seems a little absurd to me.

00:25:04.000 --> 00:25:06.797
And so far, at least in the numbers, the

00:25:06.880 --> 00:25:11.437
better AI gets, the higher employment
goes, not the lower. And so I wonder

00:25:11.520 --> 00:25:14.637
your and of course it's unknowable.
You know, we've never had this technology

00:25:14.720 --> 00:25:17.517
before. It's getting better and so forth.
So, how do you kind of think

00:25:17.600 --> 00:25:22.077
about the future of employment as it
relates to these models and AI as it

00:25:22.160 --> 00:25:24.877
progresses? >> Well,
I do have a fundamental belief

00:25:24.960 --> 00:25:29.357
that AI is surprising. And I think we
even put this in the OpenAI launch post

00:25:29.440 --> 00:25:33.117
back in the day in 2015, just saying
that the history so far has been somehow

00:25:33.200 --> 00:25:35.597
it just doesn't play out the way
that you think it does, even when there's

00:25:35.680 --> 00:25:38.797
this like logical conclusion it should
be a certain way. And I think the same

00:25:38.880 --> 00:25:41.677
will be true, right? I think that
there's something that we've learned

00:25:41.760 --> 00:25:44.237
about that humans and I think like for

00:25:44.320 --> 00:25:47.037
any job that we almost it's easy to not

00:25:47.120 --> 00:25:51.437
give it as much credit for how deep
the field is and how much sort of

00:25:51.520 --> 00:25:54.877
sophistication building relationships
accountability is a good example of

00:25:54.960 --> 00:25:58.397
something where I think that people
setting goals and being accountable for

00:25:58.480 --> 00:26:01.597
outcomes like those feel fundamental
to me those feel like things that we

00:26:01.680 --> 00:26:05.037
actually should preserve for the long
term right that's something that feels

00:26:05.120 --> 00:26:08.317
like deeply human like people are not
valuable just because we can do tasks

00:26:08.400 --> 00:26:11.917
right we're valuable because of her
people and I think that it's important

00:26:12.000 --> 00:26:15.037
not to lose sight of that in some
of these narratives and I think that the

00:26:15.120 --> 00:26:19.517
way that things will change and how
what we do with our time evolves and we

00:26:19.600 --> 00:26:22.557
clearly will be in a world of abundance
and how do we ensure that that abundance

00:26:22.640 --> 00:26:25.917
is broadly distributed um but also
at the same time I think that we should be

00:26:26.000 --> 00:26:29.917
in a world where the ceiling of ambition
is higher than ever before and I think

00:26:30.000 --> 00:26:32.957
we're going to see a wave
of entrepreneurship where it's actually

00:26:33.040 --> 00:26:37.277
already starting I've heard um from
someone in you know particular industry

00:26:37.360 --> 00:26:41.197
was saying that a bunch of people in in
his world are now making the leap to go

00:26:41.280 --> 00:26:44.717
quit and start their own firms and that
they're doing it because they have these

00:26:44.800 --> 00:26:48.557
AI tools and they're just like I can do
so much more and so it's the barriers to

00:26:48.640 --> 00:26:53.117
entry and for entries and this should
be a wonderful renaissance. Yeah though

00:26:53.200 --> 00:26:55.357
well it's it's been a lot of fun for us

00:26:55.440 --> 00:26:58.957
in just going okay there are you know

00:26:59.040 --> 00:27:03.517
particularly for our young people
because you know they get by default the

00:27:03.600 --> 00:27:08.237
grunt work but like what if the AI does
to the grunt work then they can really

00:27:08.320 --> 00:27:10.397
develop much faster actually because

00:27:10.480 --> 00:27:16.237
they can um kind of get involved on the
you know really the the real part of our

00:27:16.320 --> 00:27:18.717
business which is what is the
relationship with the entrepreneur? How

00:27:18.800 --> 00:27:20.877
do we open up the world for them? How do

00:27:20.960 --> 00:27:25.677
we make um them feel like oh they can do

00:27:25.760 --> 00:27:29.997
anything and they're an important CEO
and they can go build things and as

00:27:30.080 --> 00:27:32.317
opposed to you know spend the whole

00:27:32.400 --> 00:27:34.397
weekend writing an investment memo which

00:27:34.480 --> 00:27:39.357
by the way uh I have to say Astra very
good at writing investment it's awesome

00:27:39.440 --> 00:27:42.477
>> I love hearing that >> yeah
and again I do think it's going to

00:27:42.560 --> 00:27:45.197
be a nuance story right I don't think
that we should paint that everything's

00:27:45.280 --> 00:27:48.077
going to be rosy and it's all going
to be just easy I think it's going to be

00:27:48.160 --> 00:27:51.757
hard I think there's going to be change
but I think that it can be a much better

00:27:51.840 --> 00:27:55.677
world. I think the future could be much
better for than the past for everyone.

00:27:55.760 --> 00:27:57.837
>> Yeah, that and that's
it feels like what

00:27:57.920 --> 00:28:02.797
we should expect, you know,
like before the plow,

00:28:02.880 --> 00:28:07.277
you know, the world was a lot worse like
it was just a worse life even though it

00:28:07.360 --> 00:28:11.837
did put like a lot of human labor out
of business, you know, and and created the

00:28:11.920 --> 00:28:15.437
whole lite movement and all those kinds
of things. You know, nobody here wants

00:28:15.520 --> 00:28:17.997
to go back to 1870.

00:28:18.080 --> 00:28:20.877
uh and so the idea that no we don't want

00:28:20.960 --> 00:28:23.357
to go into the future now um seems a

00:28:23.440 --> 00:28:26.637
little shortsighted but uh I think the

00:28:26.720 --> 00:28:30.557
speed at which things are moving
is is very very scary for people

00:28:30.640 --> 00:28:34.557
>> and we we really recognize the fact
of how things are moving and that we spend

00:28:34.640 --> 00:28:37.757
a lot of time really trying
to understand as well as we can how people

00:28:37.840 --> 00:28:41.597
are feeling how we can be showing up
better and I think that two things like

00:28:41.680 --> 00:28:44.877
one is that when we think about
development the pace of progress we're

00:28:44.960 --> 00:28:48.957
being very deliberate about it safety
is our foremost priority. We think about

00:28:49.040 --> 00:28:52.477
how do we build this technology in a
safe secure way and what should those

00:28:52.560 --> 00:28:56.397
standards be and you can see that showing
up in a lot of our comms inside

00:28:56.480 --> 00:29:00.237
the building. It is absolutely what
people are thinking about and what we

00:29:00.320 --> 00:29:03.917
care about is that we really want this
technology to empower everyone broadly.

00:29:04.000 --> 00:29:06.397
And I think that for us as a world to

00:29:06.480 --> 00:29:11.597
really think about how do we get
the most out of this technology? How do we

00:29:11.680 --> 00:29:14.637
get the benefits? How do we mitigate
the risks? I think this is going to become

00:29:14.720 --> 00:29:18.317
the most important conversation that we
have and I think that that will emerge

00:29:18.400 --> 00:29:21.277
over even maybe the next one to two years.
I think that this should be

00:29:21.360 --> 00:29:24.237
something that is front and center.
I think people sense it yet you can sense

00:29:24.320 --> 00:29:27.837
it in how people react right now
and even thinking about things like data

00:29:27.920 --> 00:29:30.957
centers and these kinds of questions
of do we want AI and how do we how do we

00:29:31.040 --> 00:29:34.077
think about where it's appropriate
and how do we ensure child safety all of

00:29:34.160 --> 00:29:38.637
these kinds of questions these are core
questions that we care so much about

00:29:38.720 --> 00:29:41.517
getting right >> to that end
why do we think sentiment

00:29:41.600 --> 00:29:44.317
and AI is higher certain Asian countries

00:29:44.400 --> 00:29:46.957
>> all Asian countries
well and actually in

00:29:47.040 --> 00:29:51.757
European countries everywhere but the US
has like got the lowest AI sentiment

00:29:51.840 --> 00:29:54.157
>> why is that or more
>> what's driving the

00:29:54.240 --> 00:29:56.717
>> what can we do about
like what can we learn from

00:29:56.800 --> 00:29:58.717
>> well one thing that I think about is

00:29:58.800 --> 00:30:01.757
that I think we as a field as a company

00:30:01.840 --> 00:30:03.837
need to do a much better job of

00:30:03.920 --> 00:30:06.717
articulating to people why they benefit

00:30:06.800 --> 00:30:11.277
why is this a good thing for them
and not just for the country right which I

00:30:11.360 --> 00:30:14.957
think that this technology is going
to be and is rapidly becoming the single

00:30:15.040 --> 00:30:18.957
most important strategic
priority and resource

00:30:19.040 --> 00:30:21.837
for the United States it's happening Yes,

00:30:21.920 --> 00:30:27.037
>> absolutely. You look at chatt 300
million health queries or 300 million

00:30:27.120 --> 00:30:30.797
people every single week using it
for help, right? That's a huge deal. And

00:30:30.880 --> 00:30:35.037
we're at a billion almost, you know,
1.1 billion weekly active users. I think

00:30:35.120 --> 00:30:38.077
within the US it's about 100 million
something like that. Like a third of the

00:30:38.160 --> 00:30:40.957
population, if I have that number
correct, right, is using chat every

00:30:41.040 --> 00:30:44.717
single week. So people are touching
this technology. But I think that that for

00:30:44.800 --> 00:30:48.397
many people there are some people who
have gone very deep and really gone

00:30:48.480 --> 00:30:52.477
through the health journey for example
that's been true for my family for my

00:30:52.560 --> 00:30:56.237
wife that she has a number of health
conditions that would be we don't even

00:30:56.320 --> 00:30:59.917
really know how we would have managed
these before chat and there's just so

00:31:00.000 --> 00:31:03.917
much toil and time and just getting
to the right answer and a doctor tells you

00:31:04.000 --> 00:31:06.957
something you don't know what the thing
is and how do you get that that sort of

00:31:07.040 --> 00:31:08.557
sanity check to really even understand

00:31:08.640 --> 00:31:13.357
it people who I that their life

00:31:13.440 --> 00:31:17.917
was saved through information delivered
by chatbt like I'll tell you a story for

00:31:18.000 --> 00:31:19.997
one one of my friends is that she was in

00:31:20.080 --> 00:31:25.197
the hospital and uh that the doctor
was about to inject a antibiotic and she's

00:31:25.280 --> 00:31:28.717
like give me a moment she typed into
chatbt and Chad said absolutely do not

00:31:28.800 --> 00:31:33.437
take that if you do you may die because
you have this thing that you had a year

00:31:33.520 --> 00:31:36.637
ago you have this condition like this
kind of thing your reaction showed the

00:31:36.720 --> 00:31:41.037
doctor I know right >> and the
doctor said oh my goodness no

00:31:41.120 --> 00:31:45.037
that's absolutely right I had no idea.
I only had five minutes to read your

00:31:45.120 --> 00:31:50.237
chart. >> Yeah. Many such cases, by the
way, telling me stories in their chart at

00:31:50.320 --> 00:31:55.037
least. >> Exactly. And so that these kinds
of stories I think don't get told nearly

00:31:55.120 --> 00:31:58.717
enough, but they're out there. I hear
them every day. And the people who run

00:31:58.800 --> 00:32:02.237
their small business on chat and would
be totally unable to do it otherwise.

00:32:02.320 --> 00:32:06.157
Like that kind of empowerment. Again,
people who are able to save money, make

00:32:06.240 --> 00:32:10.877
money, live a better life. Um those
kinds of stories I think need to be in

00:32:10.960 --> 00:32:14.957
the public consciousness as we approach
this this question. So it's painting the

00:32:15.040 --> 00:32:17.517
narrative that hey you've got a teacher
in your pocket, a doctor in your pocket,

00:32:17.600 --> 00:32:20.157
something you know lawyer in your pocket,
therapist in your pocket, you

00:32:20.240 --> 00:32:23.357
know all these utilities in your pocket
while also not threatening those the

00:32:23.440 --> 00:32:26.797
same well also telling the teachers and
doctors and lawyers therapists that hey

00:32:26.880 --> 00:32:29.517
you've now got this tool too and it's
going to make your your business better

00:32:29.600 --> 00:32:31.197
as well. >> Yes.
And it's not just the narrative,

00:32:31.280 --> 00:32:32.717
it's the reality, right? It's you need

00:32:32.800 --> 00:32:37.277
both. I think that many other countries

00:32:37.360 --> 00:32:41.597
are looking in. Yeah. >> Seeing
the position that the US is in,

00:32:41.680 --> 00:32:44.957
right, that seeing the potential of this
technology. And partly too, you think

00:32:45.040 --> 00:32:47.997
about demographics that I think in many
of these other countries, it's more

00:32:48.080 --> 00:32:52.557
keenly felt that there's an older gen
generation that's much larger than than

00:32:52.640 --> 00:32:55.837
the younger population that is going to
need to support them. These questions of

00:32:55.920 --> 00:32:59.437
how is that supposed to work? And so,
I think that there is something about

00:32:59.520 --> 00:33:02.957
really thinking to the future
and thinking about what's possible. how do

00:33:03.040 --> 00:33:05.757
you get the benefits out of this
technology and really wanting to lean

00:33:05.840 --> 00:33:09.197
into that that I think we're seeing
across the world. And so again, I think

00:33:09.280 --> 00:33:13.437
that there's something that we need
to do better as a field and as as a as a

00:33:13.520 --> 00:33:17.597
company in order to communicate this
domestically, but I think the potential

00:33:17.680 --> 00:33:21.837
is there and we're in such a privileged
position and leading this field in a way

00:33:21.920 --> 00:33:25.437
that I think was not guaranteed and it's
not guaranteed to remain true for the

00:33:25.520 --> 00:33:27.597
future either. >> Yeah.
Particularly if we ban data

00:33:27.680 --> 00:33:31.997
centers, I think that that'll be
a problem for us maintaining our lead. It

00:33:32.080 --> 00:33:35.837
will it will drive the data centers
overseas which is what happened with

00:33:35.920 --> 00:33:37.917
silicon back in you know the 80s or so.

00:33:38.000 --> 00:33:39.837
>> Right. Right. And and there are so many

00:33:39.920 --> 00:33:41.197
you know one of the interesting things

00:33:41.280 --> 00:33:45.597
about data centers is it creates so many

00:33:45.680 --> 00:33:47.357
bluecollar

00:33:47.440 --> 00:33:50.317
manufacturing jobs. I think uh switch

00:33:50.400 --> 00:33:53.997
employs like 45,000

00:33:54.080 --> 00:33:58.637
>> uh people on kind of a union contract
basis to build data centers. That's just

00:33:58.720 --> 00:34:00.557
one of the data center providers in the

00:34:00.640 --> 00:34:03.917
US. and and and they're great jobs,

00:34:04.000 --> 00:34:07.997
they're highpaying, uh and then you know

00:34:08.080 --> 00:34:11.837
I think that while there have been bad

00:34:11.920 --> 00:34:14.557
actors in the data center space um most

00:34:14.640 --> 00:34:16.877
of them are very good actors and

00:34:16.960 --> 00:34:20.477
contribute uh to the power grid uh don't

00:34:20.560 --> 00:34:24.157
waste water and uh are not noisy and so

00:34:24.240 --> 00:34:28.637
like not that there was never an issue
but like we could just say hey you have

00:34:28.720 --> 00:34:30.957
to be a well-behaved data center as

00:34:31.040 --> 00:34:32.957
opposed to like we're going to ban them.

00:34:33.040 --> 00:34:38.077
Um or like we're going
to stop AI. Uh which I

00:34:38.160 --> 00:34:41.917
like we're not going to even as a country
we're not big enough to stop AI.

00:34:42.000 --> 00:34:46.797
Uh so the idea like AI will continue
without us. Uh and then we'll have zero

00:34:46.880 --> 00:34:51.037
say as opposed to we're the leaders
and then we have all the say. So it's a

00:34:51.120 --> 00:34:52.797
really really important cultural message.

00:34:52.880 --> 00:34:55.917
>> I think it's very important and and I
think on on data centers. So we've made

00:34:56.000 --> 00:34:58.797
commitments on not increasing people's

00:34:58.880 --> 00:35:03.197
electricity bills. Our data centers are
all closed loop water. So the amount of

00:35:03.280 --> 00:35:07.197
water used by Abalene which is the data
center that actually trained Astra that

00:35:07.280 --> 00:35:10.397
it uses about the same amount of water
as an office building. Right. So it's

00:35:10.480 --> 00:35:14.157
it's really it's really yeah that the
technology is quite quite advanced on

00:35:14.240 --> 00:35:17.917
that and that we have a number
of community commitments so that we can

00:35:18.000 --> 00:35:21.757
actually help um in Ohio and Georgia
where we have data centers. We've

00:35:21.840 --> 00:35:24.557
announced we've talked about how we're
providing credits to every college

00:35:24.640 --> 00:35:28.397
student for codeex access. So there's
this broad set of benefits that we are

00:35:28.480 --> 00:35:31.037
bringing to bear. But again,
I think that we need to do even more.

00:35:31.120 --> 00:35:33.517
>> Yeah. >> And you know,
like I think making those

00:35:33.600 --> 00:35:35.277
kinds of things a requirement to build a

00:35:35.360 --> 00:35:38.717
data center is very reasonable. Um, but

00:35:38.800 --> 00:35:41.757
like let's have positive some ideas as

00:35:41.840 --> 00:35:44.157
opposed to okay, we're going to jump out

00:35:44.240 --> 00:35:49.917
of the AI game as a as a country and let
uh China or whomever dictate what it's

00:35:50.000 --> 00:35:52.957
going to be. >> Speaking
of uh contributions, you guys

00:35:53.040 --> 00:35:56.077
made a billion dollar commitment
to frontline defenders. Why don't you talk

00:35:56.160 --> 00:36:00.957
about that? So we believe that every
organization, every company, every

00:36:01.040 --> 00:36:05.197
government, critical infrastructure such
as water service providers, hospitals

00:36:05.280 --> 00:36:09.357
should all be using this defender window
to secure themselves. But not every

00:36:09.440 --> 00:36:13.997
organization will have the capital
required to do it. So we have a billion

00:36:14.080 --> 00:36:18.557
dollar commitment to frontline defenders,
so to organizations that we

00:36:18.640 --> 00:36:23.277
all rely on every day in our communities
to access our models to secure

00:36:23.360 --> 00:36:26.797
themselves. We think this is the
beginning. This is not the end. We're

00:36:26.880 --> 00:36:29.837
working part closely with partners. For
example, CrowdStrike and we are working

00:36:29.920 --> 00:36:32.077
together to provide discounted access to

00:36:32.160 --> 00:36:33.757
defenders as well. And I think that

00:36:33.840 --> 00:36:36.877
there's there should be a global effort

00:36:36.960 --> 00:36:41.917
in order to bring these tools to bear
to really secure every single organization

00:36:42.000 --> 00:36:44.157
given what we see coming
and what's possible.

00:36:44.240 --> 00:36:46.477
>> Yeah. And that's a a super positive

00:36:46.560 --> 00:36:50.157
advance because this is our before AI

00:36:50.240 --> 00:36:52.317
hospitals were getting broken into, held

00:36:52.400 --> 00:36:55.917
hostages all the time. um our water

00:36:56.000 --> 00:36:59.837
supply has been hacked by uh foreign

00:36:59.920 --> 00:37:03.917
actors um state actors and so like we're

00:37:04.000 --> 00:37:08.637
already dealing with kind of our
critical infrastructure was not built

00:37:08.720 --> 00:37:12.397
for with cyber security in mind. It's
not been maintained with cyber security

00:37:12.480 --> 00:37:14.957
in mind and here's an opportunity to go

00:37:15.040 --> 00:37:19.837
from not even secure in a pre I AI world

00:37:19.920 --> 00:37:23.757
to completely secure. So to me this is

00:37:23.840 --> 00:37:26.237
like an incredibly important effort uh

00:37:26.320 --> 00:37:29.117
to you know not have our water supply

00:37:29.200 --> 00:37:33.437
and uh our hospitals at risk. I I really
agree with that perspective, right? That

00:37:33.520 --> 00:37:38.077
the point of I think that we as a society
have been lax, right? That we've

00:37:38.160 --> 00:37:43.037
allowed tech debt to pile up that every
cyber security organization I've never

00:37:43.120 --> 00:37:45.917
met a CISO who felt that they were
appropriately resourced, right? That

00:37:46.000 --> 00:37:47.597
they were appropriately prioritized.

00:37:47.680 --> 00:37:49.837
>> Never. >> And particularly
not in the public sector.

00:37:49.920 --> 00:37:53.357
>> That's right. And so I think that we
have to change that. And we should have

00:37:53.440 --> 00:37:57.037
changed this years ago, but now is a
moment where we actually have a real

00:37:57.120 --> 00:38:00.717
both motivation to do it and a real
ability to do it. And I think that

00:38:00.800 --> 00:38:03.917
delivering the secure world that we
all deserve so that we can we can really

00:38:04.000 --> 00:38:08.477
depend on it and be safe and secure
in our in our daily lives and online lives

00:38:08.560 --> 00:38:10.557
like that to me feels like table stakes.

00:38:10.640 --> 00:38:11.997
We absolutely need to do this.

00:38:12.080 --> 00:38:14.797
>> Yeah, definitely. No,
that's a that's a great effort.

00:38:14.880 --> 00:38:17.757
>> Closing the loop on Astra.
You've emphasized that capabilities still

00:38:17.840 --> 00:38:22.077
remain jagged. What do you think um
still still left to go or still needs to

00:38:22.160 --> 00:38:26.637
be fleshed out that gets approximates
most of your definition of AGI? Well, I

00:38:26.720 --> 00:38:28.637
think that AGI has turned out to be less

00:38:28.720 --> 00:38:30.797
of a point in time and more of this sort

00:38:30.880 --> 00:38:35.757
of fuzzy spectrum. And for me, Astra
has really hit something that I'm like,

00:38:35.840 --> 00:38:40.157
okay, I think this is pretty reasonable
to call it AGI in that with its computer

00:38:40.240 --> 00:38:44.317
use capabilities, you really can ask it
to do long live tasks and it'll just do

00:38:44.400 --> 00:38:46.557
it. That we've seen it it run coherently

00:38:46.640 --> 00:38:49.437
for 24 hours to go accomplish tasks that

00:38:49.520 --> 00:38:54.477
I think are quite quite amazing
and across a wide variety of domains. Now,

00:38:54.560 --> 00:38:58.477
it still is jagged and so that there
are still places where, for example, it's

00:38:58.560 --> 00:39:01.677
writing. It's pretty good writing.
It's the first time it's not sloping.

00:39:01.760 --> 00:39:03.277
>> Yeah. >> But it's not great writing.

00:39:03.360 --> 00:39:06.797
>> Yeah. >> And I think
that there's a number of

00:39:06.880 --> 00:39:11.677
areas where I feel like we just need
to polish it a little bit and it would be

00:39:11.760 --> 00:39:15.597
fantastic. And it's just like not
quite there. So I I see this like I saw

00:39:15.680 --> 00:39:18.957
someone post a graph on on Twitter of like
you know this like kind of you know

00:39:19.040 --> 00:39:23.277
jagged frontier and that where we really
need to to be is a much more steady

00:39:23.360 --> 00:39:27.117
across the board um really hit on all
these categories. But I think that what

00:39:27.200 --> 00:39:32.397
people are finding is that it is so
capable across such a wide variety of

00:39:32.480 --> 00:39:35.997
tasks that it is accelerative.
It is empowering and it's something that I

00:39:36.080 --> 00:39:39.757
think we've never really seen a model
that that's been a jump like this.

00:39:39.840 --> 00:39:44.397
>> Yeah. One of the things that's been
interesting for me is that a as you

00:39:44.480 --> 00:39:48.637
solve problems sometimes the

00:39:48.720 --> 00:39:51.197
the world doesn't realize it like so I

00:39:51.280 --> 00:39:53.197
haven't seen a hallucination in quite

00:39:53.280 --> 00:39:56.717
some time. Um but nobody says oh the

00:39:56.800 --> 00:39:58.957
models don't hallucinate anymore. It's

00:39:59.040 --> 00:40:01.037
just kind of in the ethos that that's

00:40:01.120 --> 00:40:04.157
what AI does. Um, how do you do you

00:40:04.240 --> 00:40:06.157
think that'll just go away over time or

00:40:06.240 --> 00:40:09.517
is it you know does there need to be

00:40:09.600 --> 00:40:14.237
some like continual education for the

00:40:14.320 --> 00:40:17.917
for the non- like hardcore tech
people >> I this thing is moving

00:40:18.000 --> 00:40:20.957
>> I think one of the most important
problems we actually have is the

00:40:21.040 --> 00:40:25.357
continual education right the really how
do you people shouldn't have to extract

00:40:25.440 --> 00:40:28.557
from the AI what it's capable of it
should go the other way around the AI

00:40:28.640 --> 00:40:31.997
should say hey I can help you in this
new way so we have about you know we

00:40:32.080 --> 00:40:35.197
over a billion weekly active users
on chatbt, but I think we have something

00:40:35.280 --> 00:40:40.797
like another maybe 1.5 billion people who
have used chatbt and don't use it anymore.

00:40:40.880 --> 00:40:43.437
>> Oh wow. >> Right.
So think about that. That's a

00:40:43.520 --> 00:40:45.837
significant fraction of the of the planet.

00:40:45.920 --> 00:40:50.077
>> And those people exactly those people
we should really be able to go back to and

00:40:50.160 --> 00:40:52.717
say, "Hey, we have made so much

00:40:52.800 --> 00:40:57.197
progress. We think we can be useful
to you in these ways." And I think that

00:40:57.280 --> 00:41:01.597
that is just shows you the kind
of problem we have in front of us is that

00:41:01.680 --> 00:41:05.917
these AI like if you look at chatbt
and chatbt work, they're both text boxes,

00:41:06.000 --> 00:41:08.877
right? It's like this new text box
is way better than the old text box, but

00:41:08.960 --> 00:41:11.757
there's still some things that the old
text box is better at. So don't always

00:41:11.840 --> 00:41:15.917
use it. It's like that is not the AI we
were promised. The AI we were promised

00:41:16.000 --> 00:41:19.117
should be an AI that you talk to over
voice primarily. You can talk to it over

00:41:19.200 --> 00:41:22.557
text if you want to. uh that it has
persistence, that it has memory, it has

00:41:22.640 --> 00:41:26.877
context, it knows you. It's trustworthy,
that you have seen it be proactive and

00:41:26.960 --> 00:41:29.677
helps solve problems for you, that helps
in your personal life, in your work

00:41:29.760 --> 00:41:33.197
life. And that's how it should be. It
should be something that is able to that

00:41:33.280 --> 00:41:36.717
you can really sort of rely on for the
things that you care about that empowers

00:41:36.800 --> 00:41:39.437
you and and helps you solve your goals.

00:41:39.520 --> 00:41:42.317
And I think that being able to explain

00:41:42.400 --> 00:41:46.557
to you how it can help you is a core

00:41:46.640 --> 00:41:50.077
part of that. >> Yeah.
Interesting. and proactively do

00:41:50.160 --> 00:41:52.317
it. That that's such an interesting idea

00:41:52.400 --> 00:41:57.757
like we need more
helpfulness out of our AIs.

00:41:57.840 --> 00:42:01.437
>> Yes. >> Which is kind of a thing like
some humans aren't and probably the humans

00:42:01.520 --> 00:42:05.197
who develop AI are not very helpful
people. I would guess just [laughter]

00:42:05.280 --> 00:42:07.277
being around engineers and researchers.

00:42:07.360 --> 00:42:10.557
>> You'd be surprised. I think we have
very very uh helpful helpful engineers at

00:42:10.640 --> 00:42:13.997
OpenAI. But but there is something about
if you think about how do you work with

00:42:14.080 --> 00:42:16.637
another person, right?
A new co-orker you've never worked with.

00:42:16.720 --> 00:42:18.797
>> It takes you a little bit of time,
right? you kind of feel them out. You

00:42:18.880 --> 00:42:20.317
see how they respond in different areas.

00:42:20.400 --> 00:42:23.757
People do not come with an instruction
manual. And often actually sometimes

00:42:23.840 --> 00:42:26.957
it's it's interesting in areas like
consulting or something where they they

00:42:27.040 --> 00:42:30.557
really lean into like MyersBriggs
and that they do say like here's like a

00:42:30.640 --> 00:42:34.237
quick way to know who I am and how I
operate. So that there is some precedent

00:42:34.320 --> 00:42:37.837
for how humans can kind of present
a little bit more. You have a resume. You

00:42:37.920 --> 00:42:41.437
have sort of track record. People can
ask for back channels on you. So we have

00:42:41.520 --> 00:42:45.277
built up a way of how do you understand
how a human will work and what the best

00:42:45.360 --> 00:42:47.677
way is to to get the best out of that

00:42:47.760 --> 00:42:50.397
person and I think sort of figuring out

00:42:50.480 --> 00:42:52.637
what is the right analog for AI and

00:42:52.720 --> 00:42:57.597
especially as AI changes and we produce
new tools and product surface and new

00:42:57.680 --> 00:42:59.517
models and all these things. I think

00:42:59.600 --> 00:43:02.157
that this will be a very important

00:43:02.240 --> 00:43:07.437
society company interplay and again I
think that what we what our northstar

00:43:07.520 --> 00:43:08.957
should be is simplicity right that we

00:43:09.040 --> 00:43:12.077
really should be one AI that's unified

00:43:12.160 --> 00:43:17.437
that makes it so easy
and smooth for you to be less

00:43:17.520 --> 00:43:20.237
>> engaging with the computer and less
wrapping your yourself around the

00:43:20.320 --> 00:43:23.597
computer. The computer should be
there to empower you to help serve you.

00:43:23.680 --> 00:43:25.837
>> Right. Right.

00:43:25.920 --> 00:43:28.317
>> The the business is ripping. You guys

00:43:28.400 --> 00:43:33.197
have such broad surface area in terms
of what you cover. How do you decide in

00:43:33.280 --> 00:43:37.677
terms of prioritizing where to go deepest,
what not to build and then also

00:43:37.760 --> 00:43:40.957
your role has also evolved and changed
and you've you know encompassed so many

00:43:41.040 --> 00:43:44.957
things you know research product
commercialization or management etc. How

00:43:45.040 --> 00:43:47.517
are you also thinking
about prizing your time?

00:43:47.600 --> 00:43:49.277
>> Well, they go hand in hand. Yeah.

00:43:49.360 --> 00:43:52.717
>> So this year the theme was focus.

00:43:52.800 --> 00:43:56.637
>> I think that we really realized that we
can't do it all right. We need to pick

00:43:56.720 --> 00:44:00.157
and particularly there's one thing
we're trying to accomplish which is our

00:44:00.240 --> 00:44:02.957
mission right we want to ensure AGI
benefits all of humanity now how do you

00:44:03.040 --> 00:44:07.357
backsolve from that what are the areas
like deployment and productization is

00:44:07.440 --> 00:44:09.837
actually something that does reinforce
that right that we do want to bring this

00:44:09.920 --> 00:44:13.837
technology to bear and have it uplift
everyone and people deploying it in

00:44:13.920 --> 00:44:15.997
useful applications all of that personal

00:44:16.080 --> 00:44:17.997
life work life the whole thing um very

00:44:18.080 --> 00:44:21.117
core but how do you when you think about

00:44:21.200 --> 00:44:23.117
this moment we're in of this agentic

00:44:23.200 --> 00:44:25.917
coding takeoff that exponential

00:44:26.000 --> 00:44:29.437
what areas reinforced that and which

00:44:29.520 --> 00:44:32.957
ones were kind of just sort of you know
they got labeled a side quest in the

00:44:33.040 --> 00:44:35.997
media but just were not on track for it
even if they were individually something

00:44:36.080 --> 00:44:40.077
very exciting was a very core question
that we had to grapple with and so

00:44:40.160 --> 00:44:43.437
things like Sora that's maybe
the highest profile one of these projects

00:44:43.520 --> 00:44:46.557
that we decided to cancel
very very painful by the way

00:44:46.640 --> 00:44:50.157
>> not an easy thing to do
but it was so critical to

00:44:50.240 --> 00:44:53.997
>> unleash the business in many ways so we
could really focus bringing together the

00:44:54.080 --> 00:44:59.117
consumer and enterprise side of chats into
chat work. That's another area where we've

00:44:59.200 --> 00:45:03.117
really had to focus and really say
this is what we're doing. So, a lot of the

00:45:03.200 --> 00:45:06.237
way that we've thought about this to
unlock this moment is to really have

00:45:06.320 --> 00:45:09.597
vision about where we think the future's
going and how do we think that the new

00:45:09.680 --> 00:45:13.277
capabilities that are emerging can best
be brought to bear with a single unified

00:45:13.360 --> 00:45:16.957
stack that works across the different
areas, different walks of life,

00:45:17.040 --> 00:45:20.237
different areas that we're trying
to focus on. And it's been painful, right?

00:45:20.320 --> 00:45:22.877
that if you look for the first half,
I think that there was just a lot of

00:45:22.960 --> 00:45:26.397
metrics that were not looking the
direction that we wanted and that there

00:45:26.480 --> 00:45:29.357
was a lot of just sort of telling
the team we just need to focus on the

00:45:29.440 --> 00:45:32.397
basics. Like one of my one of my
favorite management books is the score

00:45:32.480 --> 00:45:34.477
takes care of itself.
Have you guys read that one?

00:45:34.560 --> 00:45:37.597
>> Yeah, Keith boy favorite.
>> Yeah, it's a it's a great one and it

00:45:37.680 --> 00:45:40.797
just is a very empowering book because
you just realize it's like you cannot

00:45:40.880 --> 00:45:45.277
affect the outcome. You can only affect
the inputs, right? You can only affect

00:45:45.360 --> 00:45:48.477
the like the basics. And so focus
on those basics, right? You don't win the

00:45:48.560 --> 00:45:51.597
Super Bowl by saying I want to win
the Super Bowl. You win it by blocking and

00:45:51.680 --> 00:45:53.597
tackling. Yeah. And so that's what we

00:45:53.680 --> 00:45:56.077
have done for this whole year. And for

00:45:56.160 --> 00:46:00.957
myself, I throughout OpenAI have
always focused on whatever is the most

00:46:01.040 --> 00:46:03.597
important problem that I think that I
can move the needle on that just isn't

00:46:03.680 --> 00:46:07.757
going to happen without me. And >>
for the past two years, it's been the

00:46:07.840 --> 00:46:12.157
data centers, the infrastructure, the
machine learning engineering. And that's

00:46:12.240 --> 00:46:15.197
an area where we really spent a lot
of effort to get our pre-training

00:46:15.280 --> 00:46:17.197
infrastructure into great great shape.

00:46:17.280 --> 00:46:20.077
This year it's really been about
the business. It's really been about the

00:46:20.160 --> 00:46:23.277
okay, we've figured out how to get the
research really humming. We figured out

00:46:23.360 --> 00:46:25.917
how to get the infrastructure really
humming, but how do we really bring this

00:46:26.000 --> 00:46:30.477
technology to the world? And I think
that that's where I've been really

00:46:30.560 --> 00:46:35.197
putting a lot of my efforts in trying to
bring together a bunch of functions that

00:46:35.280 --> 00:46:39.117
were otherwise kind of running
in parallel or crosswise. And that is

00:46:39.200 --> 00:46:43.117
something where I think as a founder,
as someone who has kind of touched every

00:46:43.200 --> 00:46:45.997
part of this business from the beginning,
I think I've been uniquely

00:46:46.080 --> 00:46:49.677
able to go in and make the changes,
the make the hard decisions and really

00:46:49.760 --> 00:46:54.237
figure out this is the direction. Let's
go. And a lot of my style is that I like

00:46:54.320 --> 00:46:57.677
to lead from the trenches. And so I get
very deep in the weeds on what the thing

00:46:57.760 --> 00:47:01.517
is and really try to keep asking a lot
of questions. Like that's actually a lot

00:47:01.600 --> 00:47:04.157
of my style is just asking like, hey,
does this make sense still? I don't

00:47:04.240 --> 00:47:07.597
quite get that. Um, sometimes when
things are confused, for example, over

00:47:07.680 --> 00:47:10.237
the past couple days, there have been
times when it's just like, we've got a

00:47:10.320 --> 00:47:12.957
thing, we got to figure out how to even
talk about it. How do we think about it?

00:47:13.040 --> 00:47:14.157
How should the world think about this?

00:47:14.240 --> 00:47:17.037
I'm just like, let's just get everyone
who can touch different parts of the

00:47:17.120 --> 00:47:20.877
elephant on a call. We're like going
through a Google doc on a hangout and

00:47:20.960 --> 00:47:23.677
just kind of like being like, does this
line make sense? Wait, what do we really

00:47:23.760 --> 00:47:27.277
mean by this? And so really trying
to uplevel execution sometimes in small

00:47:27.360 --> 00:47:30.317
ways and sometimes large.
>> Yeah. No, that's fantastic.

00:47:30.400 --> 00:47:32.397
>> By the way, exact right way to operate.

00:47:32.480 --> 00:47:35.917
>> Yeah. Do do you know what the next
year will focus on or prep for now?

00:47:36.000 --> 00:47:40.717
>> Well, look, I think that I think
that the business is is a huge area that I

00:47:40.800 --> 00:47:44.077
think we're not done yet with really
upleveling every part of execution. So,

00:47:44.160 --> 00:47:45.597
I think there's a lot more to do there.

00:47:45.680 --> 00:47:49.677
But I also think we are moving into
a new phase of AI development, right? I

00:47:49.760 --> 00:47:53.917
call this and we call this we're now
in the AGI era. And I think that that is

00:47:54.000 --> 00:47:56.877
something that you can debate. Is it
this model, previous model, next model?

00:47:56.960 --> 00:48:00.637
It doesn't matter. The point is that we
are in a new phase where safety,

00:48:00.720 --> 00:48:03.517
security, alignment, really thinking
about these things not just at

00:48:03.600 --> 00:48:07.357
deployment time, but all the way back
at development time evaluation. It's

00:48:07.440 --> 00:48:10.957
objective. This is critical. It must
happen. This is core to our mission.

00:48:11.040 --> 00:48:13.997
This is core to what we need to do.
And so a lot of what I spend my time

00:48:14.080 --> 00:48:17.837
thinking about is making sure do we have
all the right processes? Are we talking

00:48:17.920 --> 00:48:19.757
about the right things? Do we have plans

00:48:19.840 --> 00:48:22.717
that really at a operational level, at a

00:48:22.800 --> 00:48:25.117
practical level lead us to the kind of

00:48:25.200 --> 00:48:29.757
security and variance and the kinds
of safety guarantees that we view as core

00:48:29.840 --> 00:48:31.197
to our mission, what we need to do. And

00:48:31.280 --> 00:48:34.077
so I think that again the theme of open

00:48:34.160 --> 00:48:37.357
AI certainly for the past five years has

00:48:37.440 --> 00:48:40.797
been deeper codees, deeper intertwining

00:48:40.880 --> 00:48:42.717
across these functions that are maybe on

00:48:42.800 --> 00:48:44.637
the surface very disperate, right? all

00:48:44.720 --> 00:48:50.417
the way from go to market to long-term
research to chip design by building

00:48:50.500 --> 00:48:53.197
[clears throat] these in a coherent way
where everyone has context right they

00:48:53.280 --> 00:48:56.877
kind of understand how do I fit into the
overall picture what are we trying to do

00:48:56.960 --> 00:49:01.197
and what is the end outcome we want
to achieve like that is what has to happen

00:49:01.280 --> 00:49:03.837
so I think that the areas that I will

00:49:03.920 --> 00:49:06.957
focus on I think will be dictated by the

00:49:07.040 --> 00:49:10.237
areas that most need that intertwining

00:49:10.320 --> 00:49:14.557
and I think that I see us moving more
and more in concert in lock up as time

00:49:14.640 --> 00:49:17.837
goes on. >> Yeah.
I uh we could go all day, but we

00:49:17.920 --> 00:49:20.717
have a hard stop. Uh I think this is a
great place to wrap. Greg, thank you so

00:49:20.800 --> 00:49:21.837
much for coming on the podcast.

00:49:21.920 --> 00:49:25.880
>> Thank you. >> Great. Fantastic.