WEBVTT

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Our revenue has been going up 10x per
year. It's currently, I think it was

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reported this morning, which I'm happy

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to confirm is correct, is approaching

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approaching 7 billion run rate. Um,

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that's gone up 10x a year for the for

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the last 3 years. We are using Claude to

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help build [music] products on top of

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Claude and to help train the next

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Claude. I was uh originally interested

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in science, not in uh not in tech per

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se. So you know I did my did my uh uh

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graduate work in uh neuroscience. I was

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a posttock in computational biology at

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at Stanford and you know as I was working
hard on solving the problems of

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biology you know trying to understand

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the diseases we we we face like uh like
cancer and to understand biology one of

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the things I noticed is that the
complexity of biology felt like it was

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beyond human scale you know and I
was starting to despair a little bit that

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that we would be able to make progress
at any speed just because of you know if

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you've ever seen one of these diagrams
of human metabolis capitalism. It's just

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it's just beyond human scale. But almost
exactly at the time that I was wondering

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[clears throat] about this out of Google,
out of University of Toronto

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came the first work on deep neural
networks. Uh and I looked at it, you

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know, I I replicated some of the results
myself. I said, "My god, this stuff

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really works. This stuff's really going
to go there." So, I went into AI. I

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ended up working at um uh Google.
I joined Open AI in the early days. I led

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research there for for for several years.

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um uh and and and you know was one
of the people who kind of documented this

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trend of exponential scaling of the
technology um you know myself and the

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folks who eventually became
the anthropic co-founders were among the

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first to uh study the scaling of the

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technology and so you know around 2019

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or 2020 had really two realizations one

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was wow AI is is really going to you

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know it's going to it's going to scale
to the moon and it's really going to be

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you know this whole thing is going
to reshape the economy. It'll take a few

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years, but it's going to reshape the

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economy. And you know, they are almost

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there now. Um uh and and the second was

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look, there's, you know, these systems
are different from ordinary software.

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You don't write every line of code,
right? Building ordinary software, it's

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like building a skyscraper or something.

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You make the blueprint, you make
everything to design. This is ironically

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a bit more like biological. It's organic
or something. You're growing these

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models. You set a highle recipe. It's
like growing a plant or baking a cake or

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something like that. And that
unpredictability combined with the

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incredible economic impact means that
safety and responsibility are going to

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be very important to make sure that the
models behave predictably as well as as

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we deploy them and they have enormous
impact on the world. We you know we we

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need to make sure that we deploy them
responsibly. And so those were kind of

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the two pillars on which myself and the

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seven co-founders um who left OpenAI to

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to found Anthropic um you know generally

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generally based our generally based our

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view uh and you know the the the rest is

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history. Enthropic has been um you know

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around for 4 and 1/2 years um we're

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growing very quickly. um you know I
think we're we're at this point um by

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revenue have at least a plurality
perhaps the majority of uh um you know

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enterprise enterprise customers
and enterprise revenue while most of the

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other players are are focused on the
consumer side and we find that you know

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as as you know I think you would know
from running an enterprise focused

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company these these values
of trust and responsibility

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you know are really are synergistic
with serving the enterprises right these

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values help us to serve the enterprise
and Having a business model that serves

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the enterprise helps
to reinforce our values. [snorts]

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>> One of the one of the things um and you

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went pretty fast into kind of starting

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the company and kind of shot out of uh

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uh uh shot through uh Google and API to

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anthropic, but you didn't mention
your sister and that was like one of the

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things that first really shocked me when
we started to get to know you was you

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and your sister together are working on
this company and can you kind of tell us

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the origin story? >> Yeah.
Yeah. So, you know, there are

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seven co-founders and and me
and Dianiela are are two of them. As I

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mentioned, we both, you know, we both
grew up in San Francisco. We went to to

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LOL High School and you know, even even
very early on, we really figured that we

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wanted to do >> My mom went
to LOL and she's sitting

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right there. So, she's going
to be very happy about that.

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>> Amazing. Um, uh, no, no, it was it
was a great place. I actually didn't over

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Daniela was four years younger than me,
so we were never there at the same year,

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but I was there for four and then,
you know, the next four years she was she

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was there. So um uh yeah you know very

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early on you know we we we had these you
know these ambitions to you know to to

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do something to do something good
for the world to have some kind of grand

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project and and to work together.
I think I think many many people you know

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kind of or many siblings I guess have
these have these ambitions but you know

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some somehow things found their way to

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us actually actually being able to do

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it. So um uh you know she joined me at

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at she joined me at OpenAI in uh uh 2018

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um and then the two of us along with
some folks we worked with many of whom

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had been longtime uh uh friends decided

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to start Antropics. So me and Daniela
essentially essentially run the company

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together. You know she's responsible
for the dayto-day running of the company.

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you know, kind of operate, you know, how
how the company is put put together, how

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the org runs, what its processes are,
does, you know, kind of kind of a fair

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amount of the the the, you know, kind
of the business on the on the the business

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on the structural side. Um, uh, you know,
I I I I I just, you know, couldn't

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couldn't run the company without her and
without her without her doing all those

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things. Um you know I tend to think

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about the strategic side where the where

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the research is going what our kind of

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you know plans are as a company
strategically how we think about safety

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how we think about the company's uh uh
role in the world and and the vision and

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and I find that's a very good um that's

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a very good split that's a very good

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separation um because both of us can do

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both pieces but it's it's really hard
to switch between a kind of operational

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mode and a vision mode and I think both
people can be more effective if you if

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you split. >> Well,
thanks for allowing me to come

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back to that because I think it's such
a great part of the company and the

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culture and how you're working. And last
year she was here and now this year

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you're here. Um, and we've known you all

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from the beginning. I mean, I think
Salesforce owns like a some little point

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or something of anthropic. So,
we're very happy to be an investor.

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>> Very early investor. Very >>
very early investor. Believe in you.

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>> I think you I think you invested
when the valuation was 4 billion. Is that

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correct? >> I I don't know,
but it was too high.

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whatever it was. I haven't >> Before
you say it's too high, you should

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calculate how many times
it's gone up since then.

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>> Okay. We made money [laughter] so far.

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>> Well, um, no, I I'm thrilled obviously
that we're part of your journey and, uh,

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you know, we love working with you.
We love working with Daniela um, so much.

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And, you know, I think kind of getting
back to this story of you and your

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sister and your friends, you know,
starting this company here in San

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Francisco and then you came to us. We
were starting to consolidate some of our

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real estate here. And then you,
you know, we had a conversation about you

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taking over the Slack building. We had
just bought Slack, >> which we have done.

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>> And now you're in the Slack building.

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So, you're right on the park with us
looking at the tower from your window.

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>> I I believe my office is what I I was

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told it was formerly Steuart's office.

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>> Formerly Stuart's office.
Very good. And

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I would just, you know, it just just
occurred to me like here you are. You're

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the San Franciscan. Um,
you're right on you're right on the

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park with us. You're right in the tower
with us. We're an investor in the

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company. Um, it's, you know, obviously
a critical part of Slack today, right? In

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terms of how you couldn't use Slack
without it, but you're at the very

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bleeding edge of these models. It's
really incredible and you've really been

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visionary uh, right from
the very beginning. Now,

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when I didn't know you when you were at
OpenAI, I only met you after you started

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the company. So, just now kind of take

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us through your expectation.
What has been your biggest surprise,

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you know, since it's only been what,
four and a half, five years since you

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kind of walked out the door, said

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goodbye to Sam, right? And then you
just said, "Okay, Daniela, we're going to

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we're going to get this going." What
What was your biggest shock right there?

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>> Yeah, I mean,
you know, there were there

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were so many moments cuz I think that,

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you know, the last four and a half years

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were were just so surprising overall. Um

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you know I think I think at a high level

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there's a dichotomy where on one hand
everything we've seen over the last four

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and a half years was in some sense
predicted by those straight lines on

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graphs that I made with the scaling
laws right these enormous data center

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buildouts the way that our revenue is

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going up you know 10x per year the the use
across the economy you could you you

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know you can see it all in these
scribbles on the page but actually

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seeing it play out is is wild and and a

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lot of the individual things that that I'm
seeing happening I just I just never

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would have I never would have expected.

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I guess if I were to pick one most

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surprising thing, it is, you know, we

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left one company and started another
company. And uh something you see over

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time is that is that as you build a

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company, you really do see through the

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people you hire, through the choices

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that the company makes, you kind
of see the paths diverge and you see

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differentiation, right? OpenAI has gone
very much in the consumer direction.

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We've gone very much in the enterprise
direction. we've taken, you know, just

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just the way we approach things
is is very different. And so you have this

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market structure where there are these
two companies that are like it's sort of

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a natural experiment. They're they're
just taking a very different approach.

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Both exist in the ecosystem, but but
the approach they take is very different.

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>> And then you have to reflect back
at your time at Google as well. They're

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also very much with Gemini and you know
very much a top model as well. How do

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you look at them? Do you look at them
as a blend then? Yeah, I mean, you know,

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Google's a partner to us. They're
an investor in us. Obviously, their models

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compete with us, compete with us at the
same time. But I think this co-optition

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dynamic is becoming more and more
common, right? As we have all these

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layers, we have like the chip layer, the

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cloud layer, the the kind of model

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layer, the the like the like go
to market and distribution layer, and

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various companies are involved in more
than one of these layers. and and so I

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think it's not uncommon for companies to
cooperate in one area and and compete in

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another area. It's fairly normal.
I think it's also been helped by you know

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I I I know quite well some of the folks
behind Google. I've been you know

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friends with with Deisabis for uh who
you know who leads uh who leads research

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at Google for something like 15 years,
right? I think he was maybe one of the

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only people who was earlier than
me in in seeing the potential of the

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technology. He also has a neuroscience
background like me. So I you know I

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think that helps us to get along even
as there are some competitive aspects

00:11:02.160 --> 00:11:05.197
between the companies.
>> What was your reaction when he won the

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Nobel Prize this year?
>> Uh I I was I was very happy for uh

00:11:09.680 --> 00:11:13.517
Demis. Uh uh actually you know I
was writing this essay about the potential

00:11:13.600 --> 00:11:18.077
of AI in biology called machines
of loving grace. And two days before I was

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about to publish it of course Demis and

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his colleagues Alphafold won the won the

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Nobel Prize. So um uh you know I I I
ended up dedicating the essay to the

00:11:27.200 --> 00:11:29.197
2024 you know w w w w w w w w w w w w w
w w w w w w w w w w w w w w w w w w w w

00:11:29.280 --> 00:11:29.677
w w w w w w w w w w w w w w w w w w w w
w w w w w w w w w w w w w w w w w w w w

00:11:29.760 --> 00:11:30.077
w w w w w w w w w w w w w w w w w w w w
w w w w w w w w w w w w w w w w w w w w

00:11:30.160 --> 00:11:33.277
w w w w w w w w of the Nobel Prize in
chemistry for for Alphold because it was

00:11:33.360 --> 00:11:36.077
an example of the future
that I was talking about.

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>> Very [snorts] exciting. So just
getting back to you and Daniela and you're

00:11:40.080 --> 00:11:43.997
working on and growing the company
and up it goes and you're doing it right

00:11:44.080 --> 00:11:47.597
here in San Francisco, you know, what
are some of the characteristics and the

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models that you're looking at now that
are most surprising? But what's really

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exciting about, you know, as you as you
see the next next generations of these

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models unfold? >> Yeah.
You know, I think broadly it's the

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jump to being able to do agentic
abilities. Being able to do things.

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>> What was it? I didn't hear the word.

00:12:04.320 --> 00:12:07.357
>> Oh, sorry. Agentic abilities. Um, uh,

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uh, uh, turning the models into agents

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that can Oh. Oh, I see. You've been I

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>> don't worry about it, Dario. Don't
worry. I I wasn't. I just wanted I'm a

00:12:17.760 --> 00:12:20.397
little weak in my right ear.
genuinely thought you didn't hear.

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>> I see. I understand. [laughter] >> Um,

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>> uh, yeah, >> it
was very funny, wasn't it?

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>> Definitely a buzz word and you've,
you know, perhaps contributed more more to

00:12:32.880 --> 00:12:36.157
mo more than most to to making it
to making it a buzz word. But I, you know,

00:12:36.240 --> 00:12:37.677
I think I think it's I think it's a real

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thing, which is being able to do tasks

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end to end in a way that was not

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possible before. And I think we see it

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more uh you know in code than uh you

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know we see it in in any other area. But

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I think code is just a leading indicator

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honestly in other tasks in tasks like

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financial services in tasks like you

00:12:59.920 --> 00:13:04.877
know health and medical in tasks like uh
you know insurance or manufacturing many

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of the regulated industries that we're
working together on our on our on our

00:13:08.880 --> 00:13:13.677
partnership agents are agents are just
as you know valuable. you just as much

00:13:13.760 --> 00:13:17.277
want to do things end to end, right?
You think of the you know the sales and go

00:13:17.360 --> 00:13:21.517
to market and you know customer
management workflows. You want to string

00:13:21.600 --> 00:13:24.397
a bunch of things together and you want
the models to pull in a bunch of things

00:13:24.480 --> 00:13:27.437
in the context and learn this, do that,

00:13:27.520 --> 00:13:31.677
access this information. Um so it's it's
just as valuable. It's just the coders

00:13:31.760 --> 00:13:35.037
are early adopters. So you know they've
they've kind of given us a glimpse of

00:13:35.120 --> 00:13:39.037
the future because they they adopt
things so quickly. But I think we're

00:13:39.120 --> 00:13:42.477
going to see a similar
spike in in other areas.

00:13:42.560 --> 00:13:46.637
So I want to ask you I have a few a few
questions in that area. So the f you

00:13:46.720 --> 00:13:49.997
know when we first started getting
in probably I think one of our very first

00:13:50.080 --> 00:13:54.637
conversations we were starting
to talk about this theory and also AGI

00:13:54.720 --> 00:13:56.877
especially and what the potential in the

00:13:56.960 --> 00:13:59.597
future and AGI at that time and this is

00:13:59.680 --> 00:14:04.157
kind of now we go back several years was
kind of being defined as yeah the model

00:14:04.240 --> 00:14:07.837
is almost kind of recursive.
It's building itself. It's growing. It's

00:14:07.920 --> 00:14:10.557
evolving. It knows it is coding itself.

00:14:10.640 --> 00:14:14.877
it can code and that it's it's enhancing
and it's off and going off and you know

00:14:14.960 --> 00:14:19.117
we had Brett Adcock on the stage earlier
today and he was talking about how kind

00:14:19.200 --> 00:14:23.277
of like had a scene out of you know we
are Bob we are Legion that the robot had

00:14:23.360 --> 00:14:24.957
taken off and was now you know

00:14:25.040 --> 00:14:27.437
self-replicating into the future and you

00:14:27.520 --> 00:14:30.877
know an infinite number of uh you know

00:14:30.960 --> 00:14:35.037
uh robots it was it was incredibly

00:14:35.120 --> 00:14:40.477
compelling would go off and that was
that you know to this to that point a

00:14:40.560 --> 00:14:44.077
few years ago when we were talking it
was like well now the model is kind of

00:14:44.160 --> 00:14:47.837
building itself it's growing itself it's
develop it didn't turn out exactly like

00:14:47.920 --> 00:14:51.837
that though right or it's not
we're not at that point yet are we

00:14:51.920 --> 00:14:55.277
>> yeah so you know I would separate two
things there there's I think inevitably

00:14:55.360 --> 00:14:59.437
as we you know talk about AI in the
future this kind of sci-fi sheen on on

00:14:59.520 --> 00:15:03.357
on on you know the on the whole thing
and that's you know in a way it's

00:15:03.440 --> 00:15:07.597
helpful as a metaphor but also it can be
distracting because you know it just it

00:15:07.680 --> 00:15:09.277
just kind of conjures everyone's mind

00:15:09.360 --> 00:15:12.077
into into this kind of uh you know into

00:15:12.160 --> 00:15:14.797
this kind of uh you know future dreamy

00:15:14.880 --> 00:15:19.197
mode. Um uh but you know I think there
are some things that are real. One thing

00:15:19.280 --> 00:15:22.557
that's real is that you know every 3
months we make a model that's smarter

00:15:22.640 --> 00:15:26.317
than the month than the model we made
before. And indeed the models are slowly

00:15:26.400 --> 00:15:30.237
getting to where they're you know better
than most humans at most things. Maybe

00:15:30.320 --> 00:15:34.477
at some point better all humans than all
things. The other thing I think is real

00:15:34.560 --> 00:15:37.037
is you know the model is not building

00:15:37.120 --> 00:15:41.597
itself in the sense that like I have
a specific copy of the model that's like

00:15:41.680 --> 00:15:45.437
making a specific copy of the the you
know the the next model you know we

00:15:45.520 --> 00:15:47.597
don't have our self-replicating you know

00:15:47.680 --> 00:15:50.237
nanobots or robots or or whatever but

00:15:50.320 --> 00:15:52.637
but what absolutely is happening is that

00:15:52.720 --> 00:15:55.117
within anthropic and elsewhere we are

00:15:55.200 --> 00:15:58.637
using claude to help build products on

00:15:58.720 --> 00:16:01.357
top of claude and to help train the next

00:16:01.440 --> 00:16:06.237
claude to help serve serve claude faster.
Um, you know, 6 months ago I I

00:16:06.320 --> 00:16:10.557
made this prediction that, you know, in
in 6 months 90% of code would be written

00:16:10.640 --> 00:16:14.877
by by by AI models. Some people think
that prediction is wrong, but within

00:16:14.960 --> 00:16:18.557
Enthropic and within a number
of companies that we work with. That is

00:16:18.640 --> 00:16:23.117
absolutely true. Um now 90 you know >>
so you're saying that 90% of all code

00:16:23.200 --> 00:16:26.237
adenthropics being written
by the by the model today

00:16:26.320 --> 00:16:29.437
>> on on many teams you
know not uniformly every

00:16:29.520 --> 00:16:33.117
>> is that something that you've
kind of provided something kind of

00:16:33.200 --> 00:16:37.037
>> something auditable around that or like
shown how that's happened. Yeah, the

00:16:37.120 --> 00:16:42.317
teams play an editing and supervisory
role, but you know Claude can both you

00:16:42.400 --> 00:16:45.757
can say write this feature and Claude
will do it and then the human kind of

00:16:45.840 --> 00:16:48.157
looks over the code or asks for help

00:16:48.240 --> 00:16:50.397
from another another model, another copy

00:16:50.480 --> 00:16:55.757
of Claude. Um uh the other thing is
sometimes we have these longunning bugs.

00:16:55.840 --> 00:16:59.517
you know, when we were training our our
the most recently released Claude, it

00:16:59.600 --> 00:17:03.357
was like, you know, the cluster
was broken. There was this bug, engineers

00:17:03.440 --> 00:17:07.517
were spending a few days on it, and you
know, we just we just went to Claude and

00:17:07.600 --> 00:17:10.317
we said, I don't know, just kind of play
around with the cluster and see if you

00:17:10.400 --> 00:17:13.757
can figure out what's wrong. And,
you know, Claude found this just super

00:17:13.840 --> 00:17:17.277
obscure bug that the engineers had missed.
So, this this was one area where

00:17:17.360 --> 00:17:19.197
it was like Claude is like having a very

00:17:19.280 --> 00:17:21.357
talented teammate. So if claude is now

00:17:21.440 --> 00:17:24.477
running writing 90% of all of the code

00:17:24.560 --> 00:17:29.277
in enthropic do you need less engineers
if 90% of all the code when will it be

00:17:29.360 --> 00:17:33.597
95% or 99%. >> Yeah.
So you know I you know I think

00:17:33.680 --> 00:17:38.077
people can misinterpret it as you know
you know like Claude writing 90% of the

00:17:38.160 --> 00:17:41.677
code means we're firing 90% of the
software engineers. But you know if

00:17:41.760 --> 00:17:44.877
you're familiar with the principle of >>
well you have made some very aggressive

00:17:44.960 --> 00:17:51.650
comments around firing executives
>> that that so [laughter]

00:17:51.920 --> 00:17:53.677
um >> I just want to put
two and two together

00:17:53.760 --> 00:17:55.357
to get four. Okay. [laughter]

00:17:55.440 --> 00:18:00.717
>> Um uh so uh um you know if >>
one thing I've learned in knowing you

00:18:00.800 --> 00:18:03.757
for so long is I have to come back
and kind of like get that qualifying

00:18:03.840 --> 00:18:08.157
question again. Yes. Because you're go
you know you you're you're going fast.

00:18:08.240 --> 00:18:12.557
So trying to I'm trying to keep up
with you and it's amazing but I wanted to

00:18:12.640 --> 00:18:14.477
just make sure I understand
exactly what you're saying.

00:18:14.560 --> 00:18:16.077
>> Yes, absolutely. Let me let me let me

00:18:16.160 --> 00:18:19.437
take them one by one. So um uh you know

00:18:19.520 --> 00:18:22.717
under comparative advantage if Claude is

00:18:22.800 --> 00:18:26.157
writing 90% of the code what that means

00:18:26.240 --> 00:18:30.957
usually is you need just as many
software engineers you might need more

00:18:31.040 --> 00:18:35.277
because they can they can then be more
leveraged. they can focus on the 10%

00:18:35.360 --> 00:18:37.277
that's editing the code or writing the

00:18:37.360 --> 00:18:40.157
10% that's the hardest or supervising a

00:18:40.240 --> 00:18:44.557
group of a of AI models. And so what
happens is, you know, you can just end

00:18:44.640 --> 00:18:46.957
up being 10 times more productive

00:18:47.040 --> 00:18:50.237
because the remaining 10% is is now a

00:18:50.320 --> 00:18:53.997
complement to the to the AI system
that's that's getting much better at at

00:18:54.080 --> 00:18:56.477
getting much better at what
it's producing. So So

00:18:56.560 --> 00:18:59.597
>> it's a rebalancing of the work
is what you're really saying.

00:18:59.680 --> 00:19:00.877
>> It's it's not a replacement.

00:19:00.960 --> 00:19:02.077
>> It's not a replacement. It's a

00:19:02.160 --> 00:19:05.277
rebalancing. Okay. Now, as that 90% goes

00:19:05.360 --> 00:19:10.317
to 95 99 totally automated end to end,
there may be some point at which all

00:19:10.400 --> 00:19:12.637
bets are off. >> What's
your time frame for that? You

00:19:12.720 --> 00:19:16.637
It's a pretty big jump there. >> It's
very hard to say. Um, but you know,

00:19:16.720 --> 00:19:20.397
we're looking at it. We're looking at it
closely. We're always looking for ways

00:19:20.480 --> 00:19:24.397
to encourage complimentarity. >> You see
that in the short term or the long term?

00:19:24.480 --> 00:19:27.117
>> Um, I you know, >> is that a
fiveyear goal or a one-year goal?

00:19:27.200 --> 00:19:29.277
>> I'm optimistic about 10ear goal. I'm

00:19:29.360 --> 00:19:31.357
optimistic about complimentarity in the

00:19:31.440 --> 00:19:34.317
short run. I you know I I I I do worry

00:19:34.400 --> 00:19:37.437
as we get out to two years 5 years that

00:19:37.520 --> 00:19:39.997
across the economy not that there won't

00:19:40.080 --> 00:19:42.317
be anything for humans to do but that

00:19:42.400 --> 00:19:44.637
there will be enough disruption that

00:19:44.720 --> 00:19:47.677
that will have a you know a kind of

00:19:47.760 --> 00:19:49.837
labor disruption that is you know not

00:19:49.920 --> 00:19:52.397
fundamentally different from what we've

00:19:52.480 --> 00:19:54.717
seen with previous technologies but that

00:19:54.800 --> 00:20:00.157
operates faster that is broader across
the economy because it relates to all. I

00:20:00.240 --> 00:20:01.917
think that's in the next
two to five years.

00:20:02.000 --> 00:20:03.357
>> Two to five years I would say.

00:20:03.440 --> 00:20:05.117
>> Okay. And at what
point just coming back

00:20:05.200 --> 00:20:10.557
to my previous question, at what point
is Enthropic the model writing 100% of

00:20:10.640 --> 00:20:14.397
the code? >> Yeah. Again,
it's it's a tricky question

00:20:14.480 --> 00:20:18.237
because maybe even if the model's
writing 100% of the code, the humans are

00:20:18.320 --> 00:20:23.800
still supervising the model
or prompting it or clarif

00:20:26.480 --> 00:20:28.557
it's it's it's it's a surprisingly

00:20:28.640 --> 00:20:33.437
clever thing. I don't, you know, I I'm
I I I am concerned particularly about

00:20:33.520 --> 00:20:37.517
people's ability to adapt as the jobs
are changing fast. The jobs are changing

00:20:37.600 --> 00:20:41.037
so fast, but comparative comparative
advantage is powerful. That's going to

00:20:41.120 --> 00:20:43.277
be one of our tools in this.
>> Well, you've mentioned healthcare

00:20:43.360 --> 00:20:47.357
several times, but we've seen these kind
of very conflicting studies recently in

00:20:47.440 --> 00:20:51.357
healthcare around accuracy, especially
because these are word models. There

00:20:51.440 --> 00:20:53.357
seems to be some level of accuracy

00:20:53.440 --> 00:20:58.877
that's not 100%. and were, you know,
you know, and then when applied to some of

00:20:58.960 --> 00:21:02.317
these kind of other things like
radiology is one where we saw these very

00:21:02.400 --> 00:21:04.637
interesting, you know, studies where we

00:21:04.720 --> 00:21:09.437
just didn't see the accuracy of a
radiologist reflected in what the model

00:21:09.520 --> 00:21:13.517
was doing. Is that how you see it going
forward or how did you reflect back on

00:21:13.600 --> 00:21:17.357
that? >> Yeah, I I would
say where we are now is

00:21:17.440 --> 00:21:20.957
I think of the models and I think a lot
of people who use the models in practice,

00:21:21.040 --> 00:21:22.717
>> you know where I'm going, right?

00:21:22.800 --> 00:21:26.797
I'm trying to like understand where are
we really in the evolution of the world.

00:21:26.880 --> 00:21:30.877
>> Where's the arc? So, so I think a lot
of I I think and a lot of people who use

00:21:30.960 --> 00:21:33.357
the models in practice, I think think of

00:21:33.440 --> 00:21:36.077
the models as maybe a bit like a second

00:21:36.160 --> 00:21:40.717
opinion, right? Where you don't
necessarily trust them alone. Just like

00:21:40.800 --> 00:21:44.557
if a doctor says something to me, I you
know, I don't I don't necessarily take

00:21:44.640 --> 00:21:47.917
that as fact. They're an expert, but
and they're often right, but they're not

00:21:48.000 --> 00:21:50.957
always right. Um uh so
you know I'll tell you

00:21:51.040 --> 00:21:54.957
>> especially in this one category
radiology it seems strange like it just

00:21:55.040 --> 00:21:57.917
can't get its >> I was going
to say can't get its head

00:21:58.000 --> 00:22:01.277
around it but that's not really
the right metaphor but doesn't have

00:22:01.360 --> 00:22:05.197
>> it does there's something about either
it doesn't have enough data or it's not

00:22:05.280 --> 00:22:07.917
getting it's too nuanced. What is it?

00:22:08.000 --> 00:22:11.437
>> Yeah. So let me let me actually tell
you a story about about one case where it

00:22:11.520 --> 00:22:15.277
where it where it did work. Um so
you know we're talking about you know

00:22:15.360 --> 00:22:18.557
founding the company with with my sister.
She recently had her second

00:22:18.640 --> 00:22:22.397
child while she was pregnant. Um, you
know, pregnancy can suppress your immune

00:22:22.480 --> 00:22:26.557
system. Uh, was was viral. For some
reason, it didn't occur to to any of

00:22:26.640 --> 00:22:29.917
them that it could be a bacterial
infection. She took all her data. She

00:22:30.000 --> 00:22:34.637
uploaded it to Claude and, you know,
Claude, you know, systematically listed

00:22:34.720 --> 00:22:37.837
the possibilities in the way
that the doctors had somehow missed.

00:22:37.920 --> 00:22:41.517
>> That is amazing, right? How many of us
have already used the models in some way

00:22:41.600 --> 00:22:43.677
to look at our healthcare?
Raise your hand.

00:22:43.760 --> 00:22:45.837
>> Yeah, I think we're
all having exactly that.

00:22:45.920 --> 00:22:48.717
>> That's that's a lot of people.
It's amazing, right? Where you can kind of

00:22:48.800 --> 00:22:52.397
say, especially how many of you are the
hypochondrics that are doing that? Raise

00:22:52.480 --> 00:22:55.677
your hand. Yeah, [laughter] I got a few.

00:22:55.760 --> 00:22:59.117
Okay, I know I'm >> But but
but here's the point, which is

00:22:59.200 --> 00:23:02.397
Claude listed possibilities. If she had

00:23:02.480 --> 00:23:05.757
just blindly taken the advice of Claude,

00:23:05.840 --> 00:23:09.757
that would not have gone well. Um Claude
was good for saying, "Hey, have you

00:23:09.840 --> 00:23:14.317
considered this? Have you considered
that?" So, so it has this assistive

00:23:14.400 --> 00:23:17.197
augmentative ability now >>
especially in regards to words, right?

00:23:17.280 --> 00:23:20.237
It's very good on words. >> Words.
Yeah. Yeah. I mean, you know,

00:23:20.320 --> 00:23:23.997
this specialized stuff of reading
images like I think that's more about

00:23:24.080 --> 00:23:27.757
specialized models like if you don't
have the right data maybe it's not doing

00:23:27.840 --> 00:23:31.597
well. It's less reflective of the overall
>> is that the crux of the issue like in

00:23:31.680 --> 00:23:33.517
radiology cuz like when we're saying

00:23:33.600 --> 00:23:35.917
it's going to improve healthare but we

00:23:36.000 --> 00:23:39.197
also saw this weird study where doctors

00:23:39.280 --> 00:23:44.157
who were using the models some in some
cases were relying too much of the

00:23:44.240 --> 00:23:48.477
models and then ending up with bad
outcomes. On the other side then we saw

00:23:48.560 --> 00:23:52.797
models that were like trying to read
radiology but then were not able to

00:23:52.880 --> 00:23:56.957
achieve accuracy. there was like this
kind of strange bifurcation between the

00:23:57.040 --> 00:24:00.317
two things. >> Yeah.
You know, I think there's a lot

00:24:00.400 --> 00:24:04.877
still left to work out in what the role
of the human is and what the role of the

00:24:04.960 --> 00:24:09.357
model is. And of course, that's changing
as the models improve rapidly. But, you

00:24:09.440 --> 00:24:12.477
know, I think it's something you often
see in new technologies where there's

00:24:12.560 --> 00:24:16.797
this great technology and then these
practical issues of exactly how do you

00:24:16.880 --> 00:24:21.197
use it, exactly how do you interact with
it, they take surprisingly long to work

00:24:21.280 --> 00:24:23.437
out. And you know if you look back at

00:24:23.520 --> 00:24:25.997
you know the early days when people you

00:24:26.080 --> 00:24:30.557
know kind of you know deployed deployed
new technologies I don't know it's it's

00:24:30.640 --> 00:24:33.037
just it's often >> you think
this is going to be about

00:24:33.120 --> 00:24:34.877
humans and agents working together.

00:24:34.960 --> 00:24:36.957
>> Yeah. No no no exactly just just the

00:24:37.040 --> 00:24:40.077
collaboration model needs to be worked

00:24:40.160 --> 00:24:43.997
out a bit more than it has in these
practical areas. code is good to point

00:24:44.080 --> 00:24:46.557
to like code happens so fast, right?

00:24:46.640 --> 00:24:49.757
Developers are early adopters and so

00:24:49.840 --> 00:24:53.677
that cycle we've gone through it
in like months, right? You can see people

00:24:53.760 --> 00:24:58.077
talking in blog posts about, oh,
you know, what's the best way to use cloud

00:24:58.160 --> 00:25:02.717
code? Do I want it to suggest and I make
edits or I write code and it makes edits

00:25:02.800 --> 00:25:06.397
and these people riff off each other
and you know, over the course of three or

00:25:06.480 --> 00:25:10.557
four months they work out what the best
way to use quad code is. I think that

00:25:10.640 --> 00:25:14.077
same thing is happening in healthcare
and radiology, but because there are a

00:25:14.160 --> 00:25:18.237
lot of barriers in these industries
because they're high stakes, that that

00:25:18.320 --> 00:25:23.197
process is working itself out much slower.
So that's really kind of getting

00:25:23.280 --> 00:25:26.717
down to it, right? That it's
a collaborative thing like here's the best

00:25:26.800 --> 00:25:29.197
way to use cloud code. Cloud code is

00:25:29.280 --> 00:25:31.517
writing the code, but here's really the

00:25:31.600 --> 00:25:34.477
best way as a developer now to work on

00:25:34.560 --> 00:25:38.797
cloud code. It's not writing a 100%
of the code. It's writing code now. I'm

00:25:38.880 --> 00:25:42.877
like checking here's here's and now now
I'm going to have to maintain the code.

00:25:42.960 --> 00:25:46.237
I'm going to have to provide all
the capabilities the whole infrastructure

00:25:46.320 --> 00:25:48.877
around the code.
Is this the right way to think about it?

00:25:48.960 --> 00:25:50.397
>> I I think that's the right way to think

00:25:50.480 --> 00:25:53.277
about it right now. Um uh you know as as

00:25:53.360 --> 00:25:56.157
the models get stronger then the

00:25:56.240 --> 00:26:01.037
collaboration may change to more distant
supervision by the human. Um so it's

00:26:01.120 --> 00:26:05.117
it's very hard to say what will happen
even six months from now. But but I

00:26:05.200 --> 00:26:08.317
think what you've said is a very
accurate description of where we are now

00:26:08.400 --> 00:26:13.437
as of you know our our you know
as of set 4.5 which was the latest model

00:26:13.520 --> 00:26:16.797
released at >> which is probably one
of the best coders that we've seen right

00:26:16.880 --> 00:26:21.117
>> it it you know every every we you know
we have these benchmarks that are like

00:26:21.200 --> 00:26:25.917
you know what fraction of you know
a random sample of GitHub PRs can this

00:26:26.000 --> 00:26:29.597
thing write from a description
and that's over the last year and a half

00:26:29.680 --> 00:26:32.317
that's gone from like 5% which was

00:26:32.400 --> 00:26:37.357
basically the thing can't code
at all to something like you know 77%.

00:26:37.440 --> 00:26:41.517
Uh so it's it's it's really >>
and it's also yielding also this whole

00:26:41.600 --> 00:26:46.077
other ecosystem of things on top of it
as well like even we showed agent force

00:26:46.160 --> 00:26:50.317
fives yesterday that I think our customers
will benefit from or other you

00:26:50.400 --> 00:26:53.597
know other tools lovable these things
have been built on these things right

00:26:53.680 --> 00:26:57.197
has that been a surprise to you to see
a whole ecosystem on top of the model

00:26:57.280 --> 00:27:00.477
>> I mean that's been that's been great
to see there are there are many different

00:27:00.560 --> 00:27:04.237
models right if you look at the GitHub
co-pilots of the world you know that's

00:27:04.320 --> 00:27:07.677
kind of complimentary to GitHub if you
look at the cursors of the world. That's

00:27:07.760 --> 00:27:12.637
kind of an IDE model. If you look at the
lovables and replets, that's more of a

00:27:12.720 --> 00:27:16.877
consumer model. It allows people who are
not traditionally software developers to

00:27:16.960 --> 00:27:20.797
write code. Um, you know, there's
like a, you know, Verscell is like a

00:27:20.880 --> 00:27:25.597
different a different way of looking
at it. Augment code, wind surf, you know,

00:27:25.680 --> 00:27:28.957
there's a whole ecosystem. And then
there's the command line tools like like

00:27:29.040 --> 00:27:31.837
claude code. Uh, so I think it's too

00:27:31.920 --> 00:27:36.077
early to say, you know, what's going
to be the winner there or or even if there

00:27:36.160 --> 00:27:39.037
is one winner, it's just possible
there's just different things that work

00:27:39.120 --> 00:27:41.997
for different people or or different
>> there's a whole civilization.

00:27:42.080 --> 00:27:43.917
>> There's a whole eos capability, right?

00:27:44.000 --> 00:27:48.157
It's a whole civilization like it's
an EO it's even it's broader like it's

00:27:48.240 --> 00:27:50.797
really coming across the whole span. It

00:27:50.880 --> 00:27:53.277
it's really impressive, you know? It's

00:27:53.360 --> 00:27:55.917
it's it's so fundamental that it's able

00:27:56.000 --> 00:28:00.477
to yield all this incredible innovation
from others. Yeah. No, this I mean

00:28:00.560 --> 00:28:03.917
>> which one of those is the most
like the one you did not expect?

00:28:04.000 --> 00:28:06.157
>> Which one is the one
that I most did not

00:28:06.240 --> 00:28:11.037
expect? Um I think the model of kind of

00:28:11.120 --> 00:28:13.597
allowing people to write code end to end

00:28:13.680 --> 00:28:16.237
like allowing non-coders to write code.

00:28:16.320 --> 00:28:19.917
It totally makes sense once I see it
and it's doing really well as a business

00:28:20.000 --> 00:28:23.437
model. But but I I I just when
I was conceptualizing all the

00:28:23.520 --> 00:28:27.037
>> which product do you love the best?
Uh yeah, this is this is one of those

00:28:27.120 --> 00:28:31.117
questions that you know you know it's
c your customers are like your children.

00:28:31.200 --> 00:28:34.157
You you you you really shouldn't give
an answer. So I'm not going to give an

00:28:34.240 --> 00:28:36.830
answer. [laughter]

00:28:41.360 --> 00:28:43.380
[snorts]

00:28:46.600 --> 00:28:50.157
[applause] All right.
So are you getting a sense

00:28:50.240 --> 00:28:54.237
for Daario, by the way? [laughter]
Are you getting a sense for Daario?

00:28:54.320 --> 00:28:58.877
Okay. So that's I'm trying to like you
know help them to understand you and how

00:28:58.960 --> 00:29:00.237
you're thinking about this and where

00:29:00.320 --> 00:29:03.357
we're going. So now here you are you

00:29:03.440 --> 00:29:05.997
know you're entrepreneur you've left

00:29:06.080 --> 00:29:08.877
open AI this thing is taking off. It's

00:29:08.960 --> 00:29:12.877
you and Daniela you know um you're in

00:29:12.960 --> 00:29:17.837
the Slack building you're get where
is it going to be in five years?

00:29:17.920 --> 00:29:22.157
What does it look like? We've seen zero
to five years now. You don't have to

00:29:22.240 --> 00:29:24.957
tell me as an investor and there's
nobody hold going to hold you

00:29:25.040 --> 00:29:27.197
accountable for it. But just if you just

00:29:27.280 --> 00:29:30.637
fantasized out and now it's not 2025 but

00:29:30.720 --> 00:29:33.997
it's 2030 and you know where's

00:29:34.080 --> 00:29:36.237
Enthropic? What does it look like?

00:29:36.320 --> 00:29:40.157
Forget about all the other companies
and all the other competitors whatever.

00:29:40.240 --> 00:29:42.717
Nobody cares. Where's Enthropic 5 years

00:29:42.800 --> 00:29:47.677
from now? What's your dream big what's
your big dream? So, you know, I I think

00:29:47.760 --> 00:29:50.957
I think maybe maybe one of the best
ways to think about this is, you know, I

00:29:51.040 --> 00:29:54.637
wrote this essay called Machines of Loving
Grace, where where it's really it

00:29:54.720 --> 00:29:57.757
was written from a societal perspective,
but, you know, of course, the company is

00:29:57.840 --> 00:30:01.357
trying to enact that and and and
contribute to it, and so you can back

00:30:01.440 --> 00:30:05.677
out from that where we where we hope
the the company will be. So, you know, I I

00:30:05.760 --> 00:30:08.637
really thought of it as like the upside
of AI, right? There's lots of concerns

00:30:08.720 --> 00:30:10.397
with AI. I I you know I'm one of the

00:30:10.480 --> 00:30:13.037
loudest in voicing them but but I am you

00:30:13.120 --> 00:30:16.477
know I am I am I am incredible believer

00:30:16.560 --> 00:30:20.717
in the upside of AI you know I think
I think more so than people who code or

00:30:20.800 --> 00:30:23.117
describe themselves as more optimistic I

00:30:23.200 --> 00:30:25.917
have just a really expansive view I am

00:30:26.000 --> 00:30:28.077
perhaps most excited about what it can

00:30:28.160 --> 00:30:30.877
do for curing diseases um you know going

00:30:30.960 --> 00:30:33.117
back to my background in biology like

00:30:33.200 --> 00:30:35.357
you know we look at these tough diseases

00:30:35.440 --> 00:30:38.717
like cancer like Alzheimer's um you know

00:30:38.800 --> 00:30:43.197
like like you know various kinds
of metabolic disease like I I I just think

00:30:43.280 --> 00:30:47.117
AI is the right thing to cure them
and so I think of all the work we can do

00:30:47.200 --> 00:30:50.237
working with pharmaceutical companies
working with scientific research

00:30:50.320 --> 00:30:54.157
institutions and working with the health
care system to kind of enact it out and

00:30:54.240 --> 00:30:57.597
you know you again our partnership
is about regulated industries and one of

00:30:57.680 --> 00:31:02.077
the regulated industries I think
especially is is healthcare but you know

00:31:02.160 --> 00:31:05.997
that is one of the most positive
applications but I think that we will

00:31:06.080 --> 00:31:08.237
see applications across the industry I'm

00:31:08.320 --> 00:31:10.717
very excited about financial services.

00:31:10.800 --> 00:31:13.437
I'm very excited about insurance. I'm

00:31:13.520 --> 00:31:15.917
very excited about taking basically

00:31:16.000 --> 00:31:19.437
every enterprise um and learning to

00:31:19.520 --> 00:31:24.077
serve every enterprise with smarter and
smarter models. And the skill ceiling is

00:31:24.160 --> 00:31:27.917
very high. One way to say it is what
is an enterprise? It's kind of a super

00:31:28.000 --> 00:31:29.997
intelligence, right? It it is an entity

00:31:30.080 --> 00:31:31.917
that acts in the world in in in in in in

00:31:32.000 --> 00:31:35.517
in a way with a level of strategy and

00:31:35.600 --> 00:31:39.277
and knowledge and power in the world
that goes far beyond what any individual

00:31:39.360 --> 00:31:43.357
human can do. So, you know, even
as these models get smarter than than than

00:31:43.440 --> 00:31:47.597
any than any individual human, there's
a ceiling substantially higher than that

00:31:47.680 --> 00:31:51.917
in terms of doing the things that a
company tries to do in the world. Um,

00:31:52.000 --> 00:31:55.357
you know, most of which are good for
the world, most of which create economic

00:31:55.440 --> 00:32:00.157
value. Um and and and so then my my
vision of Enthropic is a company that

00:32:00.240 --> 00:32:01.837
serves the enterprise of the world that

00:32:01.920 --> 00:32:04.797
transforms every every enterprise puts

00:32:04.880 --> 00:32:07.357
AI at the center at the center of every

00:32:07.440 --> 00:32:09.837
enterprise. Um uh you know and and and

00:32:09.920 --> 00:32:12.077
and and you know I think I won't give

00:32:12.160 --> 00:32:14.557
specific numbers but like if you if you

00:32:14.640 --> 00:32:16.877
think about the actual market potential

00:32:16.960 --> 00:32:22.077
of that is very very high right our
revenue has been going up 10x per year.

00:32:22.160 --> 00:32:24.797
It's currently, I think it was reported
this morning, which I'm happy to confirm

00:32:24.880 --> 00:32:28.077
is correct, is approaching approaching 7

00:32:28.160 --> 00:32:32.797
billion run rate. Um, that's gone up
10x a year for the for the last 3 years.

00:32:32.880 --> 00:32:36.957
It's can't continue like that for much
longer, of course, but uh you know,

00:32:37.040 --> 00:32:40.797
>> as an investor, I'm looking forward
to the $70 billion year. You should not

00:32:40.880 --> 00:32:43.597
feel bad investing at 4 bill. You should

00:32:43.680 --> 00:32:47.837
not feel bad. Um uh uh uh so you know

00:32:47.920 --> 00:32:52.397
but I I I think the potential is is
is very large but but I think even even

00:32:52.480 --> 00:32:56.717
more important than that or perhaps even
necessary to that success. We want to be

00:32:56.800 --> 00:32:59.197
known for, you know, the company that is

00:32:59.280 --> 00:33:01.677
trustworthy, the company that behaves

00:33:01.760 --> 00:33:04.397
responsibly, the company that has a that

00:33:04.480 --> 00:33:07.517
has a reputation for, you know, doing

00:33:07.600 --> 00:33:11.917
what it says, saying what it does,
and building products that are trustworthy

00:33:12.000 --> 00:33:15.677
that, you know, if you if you if
you build on us, you won't get any

00:33:15.760 --> 00:33:18.237
surprises. You won't get anything crazy.

00:33:18.320 --> 00:33:21.277
You won't get the model, you know,
saying it's Hitler. you won't you won't

00:33:21.360 --> 00:33:24.477
get, you know, all kinds of um you won't

00:33:24.560 --> 00:33:28.317
get all kinds of crazy stuff. Um and
and I think that's very important to us

00:33:28.400 --> 00:33:31.437
being good stewards of this.
>> Are there certain companies that you're

00:33:31.520 --> 00:33:33.117
trying to imply that that might be?

00:33:33.200 --> 00:33:36.317
>> None whatsoever. >> There's
an entrepreneur down in Palo Alto.

00:33:36.400 --> 00:33:38.717
>> None at all. I don't know
what you're talking about.

00:33:38.800 --> 00:33:42.637
>> Okay, I see. [laughter] That's
a certain letter in the alphabet.

00:33:42.720 --> 00:33:45.357
>> I don't I don't I I have no
idea what you're talking about. Um

00:33:45.440 --> 00:33:50.437
>> Okay, that's before the letter Y.
I sorry I still don't know what you mean.

00:33:50.520 --> 00:33:53.677
[laughter] >> Okay,
one more thing. I'm going to take

00:33:53.760 --> 00:33:56.397
a couple I'm going to use moderator
privilege here and take a couple extra

00:33:56.480 --> 00:34:00.157
minutes with you. So number one, we see

00:34:00.240 --> 00:34:03.117
right now a tremendous fever on building

00:34:03.200 --> 00:34:06.797
data centers and you know we've seen a

00:34:06.880 --> 00:34:09.917
tremendous fever around the chips, the

00:34:10.000 --> 00:34:14.077
financing, the size and scale of the
data centers, the energy of the data

00:34:14.160 --> 00:34:15.757
centers. you haven't really addressed it

00:34:15.840 --> 00:34:18.077
in your comments per se besides the

00:34:18.160 --> 00:34:23.677
scaling laws but let's talk about it
just in part and parcel to enthropic and

00:34:23.760 --> 00:34:27.037
when you think about obviously one
of your core partners has been Amazon it's

00:34:27.120 --> 00:34:29.517
been very important for your growth very

00:34:29.600 --> 00:34:34.077
important company um even though you
do have you obviously relationship with

00:34:34.160 --> 00:34:38.157
others as well but that one has
been especially important to you

00:34:38.240 --> 00:34:41.197
know just give us a vision of what's
happening with these data centers right

00:34:41.280 --> 00:34:45.437
now what are we going to see in the next
few years with these data centers and

00:34:45.520 --> 00:34:48.397
how do you think about it as an
entrepreneur who's going to need data

00:34:48.480 --> 00:34:52.797
centers to grow? Who will you go to? How
will you do it? Will you build your own?

00:34:52.880 --> 00:34:56.077
Will you just continue to partner?
What what what is your plan?

00:34:56.160 --> 00:34:59.597
>> Yeah. So, you know, my my my view is is

00:34:59.680 --> 00:35:03.837
that there is going to be an enormous
need for compute for for all the reasons

00:35:03.920 --> 00:35:07.597
that uh you know that that we've laid out,
right? The the scaling laws suggest

00:35:07.680 --> 00:35:10.397
that we're going to need more and more
compute to train smarter and smarter

00:35:10.480 --> 00:35:13.757
models. And then as the revenue goes up,
we're gonna we're, you know, we're going

00:35:13.840 --> 00:35:17.117
to need to serve more. We're going
to need to serve more of our models. So

00:35:17.200 --> 00:35:22.317
when that revenue is not seven billion,
but a hundred billion or more. Um uh and

00:35:22.400 --> 00:35:25.837
you know, when when model training
requires millions of chips, you know,

00:35:25.920 --> 00:35:29.837
we're we're going to need many many
gigawatts of power to to train the

00:35:29.920 --> 00:35:31.917
models. So we're trying to secure that

00:35:32.000 --> 00:35:36.877
power basically wherever we can. Um you
know, and I'm confident in the company's

00:35:36.960 --> 00:35:38.557
uh I'm confident in the company's

00:35:38.640 --> 00:35:41.597
ability to do so. I I I do get a bit

00:35:41.680 --> 00:35:44.397
worried when the press focuses too much

00:35:44.480 --> 00:35:47.357
on the building of the data centers and

00:35:47.440 --> 00:35:51.677
deals to build data centers. Some
of these deals seem a little bit fishy.

00:35:51.760 --> 00:35:55.517
They seem like they're maybe double
counting. And I I also worry again

00:35:55.600 --> 00:35:57.117
>> deals are you ask talking about?

00:35:57.200 --> 00:35:58.157
>> I don't know what you're talking about.

00:35:58.240 --> 00:36:03.437
I have no idea. Um uh uh uh >> who
is double counting? These are all

00:36:03.520 --> 00:36:07.037
public companies. I I >> We're going
to want to know who's double counting.

00:36:07.120 --> 00:36:09.517
>> I don't I don't I don't know.
I don't know anything more than you.

00:36:09.600 --> 00:36:11.037
>> What about the ones who
are doing triple counting?

00:36:11.120 --> 00:36:14.557
>> I I don't There might be some
of that, too. Wow. All right.

00:36:14.640 --> 00:36:17.917
>> I don't know. Um uh uh uh but but but

00:36:18.000 --> 00:36:20.557
look look look at the end of the day,

00:36:20.640 --> 00:36:22.797
all this data center stuff is spending

00:36:22.880 --> 00:36:25.197
money, right? Um I think the thing

00:36:25.280 --> 00:36:27.997
that's most important is making money.

00:36:28.080 --> 00:36:30.797
Um and and so this is why I focus on,

00:36:30.880 --> 00:36:33.197
you know, the revenue and the actual

00:36:33.280 --> 00:36:35.357
work that we're doing with businesses.

00:36:35.440 --> 00:36:38.317
Andropic is growing faster than any

00:36:38.400 --> 00:36:42.477
other company in the space. The revenue
is growing faster and you know I think I

00:36:42.560 --> 00:36:46.557
think basically if if you know if the if
the demand is there if customers want

00:36:46.640 --> 00:36:50.317
the money and they're willing to pay you
then you are willing to pay your compute

00:36:50.400 --> 00:36:54.477
providers and you'll get the compute
you need at the end of the day. Daria, we

00:36:54.560 --> 00:36:56.637
could not be more grateful for you to be

00:36:56.720 --> 00:37:01.677
here, to be in San Francisco, to be
a native son, and to Daniela as well, and

00:37:01.760 --> 00:37:03.117
for everything that you're doing with

00:37:03.200 --> 00:37:09.240
Enthropic. Congratulations.
Please give Dario a round of applause.

00:37:16.200 --> 00:37:18.220
>> [music]