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you know, I would separate two things
there. There's I think inevitably as we,

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you know, talk about AI in the future,
this kind of sci-fi sheen on on on on,

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you know, the on the whole thing. And
that's, you know, in a way it's helpful

4
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as a metaphor, but also it can be
distracting because, you know, it just

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it just kind of conjures everyone's
mind into this into this kind of uh, you

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know, into this kind of uh, you know,
future dreamy mode. Um, uh, but, you

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know, I think there's some things that are
real. One thing that's real is that,

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you know, every three months we make
a model that's smarter than the month than

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the model we made before. And indeed,
the models are slowly getting to where

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they're, you know, better than most
humans at most things. Maybe at some

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point better all humans than all things.

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The other thing I think is real is,
you know, the model is not building itself

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in the sense that like I have a specific
copy of the model that's like making a

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specific copy of the the, you know, the
the next model. you know, we don't have

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our self-replicating, you know, nanobots

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or robots or or whatever, but but what

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absolutely is happening is that within

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Anthropic and elsewhere, we are using

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Claude to help build products on top of

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quad and to help train the next Quad to

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help serve cla um you know, six months

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ago I I made this prediction that, you

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know, in in six months 90% of code
would be written by by by AI models. Some

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people think that prediction is wrong
but within Enthropic and within a number

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of companies that we work with that
is absolutely true. Um now 90 you know

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>> so you're saying that 90% of all code
at anthropics being written by the by the

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model today >> on on many teams
you know not uniformly every

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>> something that you've kind
of provided something kind of

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>> something auditable around that or like
shown how that's happened. Yeah, the

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teams play an editing and supervisory
role, but you know Claude can both you

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can say write this feature and Claude
will do it and the human kind of looks

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over the code or asks for help from

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another another model or another copy
of Claude. Um uh the other thing is

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sometimes we have these long running bugs.
you know, when we were training

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our our the most recently released
Claude, it was like, you know, the

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cluster was broken. There was this bug,
engineers were spending a few days on

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it, and you know, we just we just went
to Claude and we said, I don't know,

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just kind of play around with the
cluster and see if you can figure out

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what's wrong. And, you know, Claude
found this just super obscure bug that

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the engineers had missed. So, this this
was one area where it was like Claude is

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like having a very talented teammate. So

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if quad is now running writing 90% of

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all of the code in entropic do you need

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less engineers if 90% of all the code

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when will it be 95% or 99%.

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>> Yeah. So you know I you know I think
people can misinterpret it as you know

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you know like claude writing 90%
of the code means we're firing 90% of the

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software engineers. But you know if
you're familiar with the principle of

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>> you have made some very aggressive

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comments around firing
executives >> that that so

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um >> I just want to put
two and two together

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to get four. Okay.
>> Um uh so uh um you know if

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>> one thing I've learned in knowing you
for so long is I have to come back and

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kind of like get that qualifying
question again. Yes. because you're go,

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you know, you you you're you're going
fast. So trying to I'm trying to keep up

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with you and it's amazing, but I want to
just make sure I understand exactly what

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you're doing. >> Yes, absolutely.
Let me let me let me

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take them one by one. So um uh you know,

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under comparative advantage, if Claude

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is writing 90% of the code, what that

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means usually is you need just as many

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software engineers, you might need more
because they can they can then be more

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leveraged. they can focus on the 10%

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that's editing the code or writing the

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10% that's the hardest or supervising a

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group of a of AI models. And so what
happens is, you know, you can just end

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up being 10 times more productive

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because the remaining 10% is is now a

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complement to the to the AI system
that's that's getting much better at at

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getting much better at what
it's producing. So So

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>> it's a rebalancing of the work
is what you're really saying.

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>> It's it's not a replacement.

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>> It's not a replacement. It's a

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rebalancing. Okay. Now, as that 90% goes

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to 95 99 totally automated end to end,
there may be some point at which all

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bets are off. >> What's your time frame
for that? It's a pretty big jump there.

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>> It's very hard to say.
Um, but you know,

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we're looking at it. We're looking at it
closely. We're always looking for ways

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to encourage complimentarity. >> You see
that in the short term or the long term?

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>> Um, I, you know, >> is that
a 5year goal or a one year goal?

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>> I'm optimistic about 10ear goal.

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>> I'm optimistic about complimentarity in

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the short run. I you know I I I I I do

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worry as we get out to two years 5 years

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that across the economy not that there

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won't be anything for humans to do but

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that there will be enough disruption

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that that we'll have a you know a kind

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of labor disruption that is you know not

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fundamentally different from what we've

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seen with previous technologies but that

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operates faster that is broader across
the economy because it relates to all

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>> you think that's
in the next 2 to 5 years.

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>> Two to five years I would say.

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>> Okay. And at what
point just coming back

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to my previous question, at what point
is Enthropic the model writing 100% of

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the code? >> Yeah. Again,
it's it's a tricky question

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because maybe even if the model's
writing 100% of the code, the humans are

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still supervising the model
or prompting it or clarif

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it's it's it's it's a surprisingly

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clever thing. I don't, you know, I I'm
I I I am concerned particularly about

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people's ability to adapt as the jobs
are changing fast. The jobs are changing

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so fast, but comparative comparative
advantage is powerful. That's going to

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be one of our tools in this.
>> Well, you've mentioned healthcare

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several times, but we've seen these kind
of very conflicting studies recently in

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healthcare around accuracy, especially
because these are word models. There

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seems to be some level of accuracy

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that's not 100%. and were you know you
know and then when applied to some of

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these kind of other things like
radiology is one where we saw these very

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interesting you know studies where we

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just didn't see the accuracy of a
radiologist reflected in what the model

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was doing is that how you see it going
forward or how did you reflect back on

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that >> yeah I I would
say where we are now is I

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think of the models and I think a lot
of people who use the models in practice

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>> you know where I'm
going right where I'm

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trying to I'm trying to like understand
Where are we really in the evolution of

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the world? >> Where's the arc?
So, so I think a lot of

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you I I think and a lot of people who
use the models in practice, I think

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think of the models as maybe a bit like

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a second opinion, right? Where you don't

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necessarily trust them alone. Just like
if a doctor says something to me, I you

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know, I don't I don't necessarily take
that as fact. They're an expert, but and

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they're often right, but they're not
always right. Um uh so you know I'll

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tell you >> especially
in this one category

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radiology it seems strange
like it just can't get its

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>> I was going to say can't get its
head around it but that's not really the

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right metaphor but doesn't have >> it
does there is something about either

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it doesn't have enough data or it's not
getting it's too nuanced. What is it?

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>> Yeah. So let me let me actually tell
you a story about about one case where it

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where it where it did work. Um so
you know we're talking about you know

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founding the company with with my sister.
She recently had her second

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child while she was pregnant. Um, you
know, pregnancy can suppress your immune

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system. Uh, was was viral. For some
reason, it didn't occur to to any of

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them that it could be a bacterial
infection. She took all her data. She

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uploaded it to Claude and, you know,
Claude, you know, systematically listed

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the possibilities in the way
that the doctors had somehow missed.

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>> That is amazing, right? How many of us
have already used the models in some way

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to look at our healthcare?
Raise your hand.

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>> Yeah, I think we're
all having exactly that.

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>> That's that's a lot of people.
It's amazing, right? Where you can kind of

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say especially how many of you are the

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hypochondrics that are doing that? Raise
your hand. Yeah, I got a few. Okay, I

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know I'm >> But but but
here's the point, which is

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Claude listed possibilities. If she had

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just blindly taken the advice of Claude,

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that would not have gone well. Um Claude
was good for saying, "Hey, have you

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considered this? Have you considered
that?" So, so it has this assistive

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augmentative ability >> especially
in regards to words, right?

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It's very good on words. >> Words.
Yeah. Yeah. I mean, you know this

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specialized stuff of reading images like
I think that's more about specialized

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models like if you don't have the right
data maybe it's not doing well again.

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It's less reflective of the >>
is that the crux of the issue like in

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radiology because like when we're saying

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it's going to improve health care but we

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also saw this weird study where doctors

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who were using the models some in some
cases were relying too much of the

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models and then ending
up with bad outcomes.

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>> On the other side then we saw models
that were like trying to read radiology

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but then were not able to achieve
accuracy. There was like this kind of

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strange bifurcation between the two

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things. Yeah, you know, I think there's

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a lot still left to work out in what
the role of the human is and what the role

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of the model is. And of course, that's
changing as the models improve rapidly.

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But, you know, I think it's something
you often see in new technologies where

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there's this great technology and then
these practical issues of exactly how do

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you use it, exactly how do you interact
with it? They take surprisingly long to

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work out. And you know if you look back

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at you know the early days when people

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you know kind of you know deployed
deployed new technologies I don't know

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it's it's just it's often >> you
think this is going to be about

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humans and agents working together.

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>> Yeah. No no no exactly just just the

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collaboration model needs to be worked
out a bit more than it has in these

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practical areas. code is good to point

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to like code happens so fast right

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developers are early adopters and so

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that cycle we've gone through it in like

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months right you can see people talking
in blog posts about oh you know what's

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the best way to use quad code do I
want it to suggest and I make edits or I

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write code and it makes edits and these
people riff off each other and you know

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over the course of three or four months
they work out what the best way to use

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quad code is I think that same thing
is happening in healthcare and radiology

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technology, but because there are a lot
of barriers in these industries, because

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they're high stakes, that that process

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is working itself out much slower.

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>> So, that's really kind of getting down
to it, right? That it's a collaborative

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thing. Like, here's the best way to use

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cloud code. Cloud code is writing the

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code, but here's really the best way as

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a developer now to work on cloud code.

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It's not writing 100% of the code.
It's writing code now. I'm like checking

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here's here's and now now I'm going
to have to maintain the code. I'm going to

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have to provide all the capabilities the
whole infrastructure around the code. Is

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this the right way to think about it?
I I think that's the right way to think

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about it right now. Um uh you know as as

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the models get stronger then the

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collaboration may change to more distant
supervision by the human. Um so it's

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it's very hard to say what will happen
even six months from now. But but I

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think what you've said is a very
accurate description of where we are now

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as of you know our our you know as of
Sonnet 4.5 which was the latest model

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released at >> which is probably one
of the best coders that we've seen right

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>> it it you know every every you know
we have these benchmarks that are like you

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know what fraction of you know a random

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sample of GitHub PRs can this thing
write from a description and that's over

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the last year and a half that's gone
from like 5% which was basically the

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thing can't code at all
to something like you know 77%.

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Uh so it's it's it's really >>
and it's also yielding also this whole

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other ecosystem of things on top of it
as well like even we showed agent force

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fives yesterday that I think our customers
will benefit from or other you

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know other tools lovable these things
have been built on these things right

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has that been a surprise to you to see
a whole ecosystem on top of the model

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>> I mean that's been that's been great
to see there are there are many different

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models right if you look at the GitHub
co-pilots of the world you know that's

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kind of complimentary to GitHub if you
look at the cursors of the world. That's

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kind of an IDE model. If you look at the
lovables and replets, that's more of a

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consumer model. It allows people who are
not traditionally software developers to

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write code. Um, you know, there's like a,
you know, versel is like a different

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a different way of looking at it.

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Augment code, wind surf, you know,
there's a whole ecosystem. And then

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there's the command line tools like like

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claude code. Uh, so I think it's too

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early to say, you know, what's going
to be the winner there or or even if there

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is one winner, it's just possible
there's just different things that work

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for different people or or different
>> there's a whole civilization.

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>> There's a whole eos capability, right?

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It's a whole civilization like it's
an eco it's even it's broader like it's

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really coming across the whole span.
It it's really impressive,

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>> you know? It's it's it's so fundamental
that it's able to yield all this

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incredible innovation from others.

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>> Yeah. No, this I mean >> which
one of those is the most like the

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one you did not expect?

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>> Which one is the one
that I most did not

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expect? Um I think the model of kind of

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allowing people to write code end to end

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like allowing non-coders to write code
it totally makes sense once I see it and

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it's doing really well as a business
model. But but I I I just when I was

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conceptualizing all the >> which
product do you love the best?

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>> Uh yeah this is this is one of those
questions that you know you know it's C

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your customers are like your children.

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You you you you really shouldn't give
an answer. So, I'm not going to give an

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answer.

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All right. So, are you getting a sense

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for Dario, by the way? Are you getting a

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sense for Dario? Okay. So that's I'm
trying to like you know help them to

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understand you and how you're thinking
about this and where we're going. So now

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here you are, you know, you're

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entrepreneur, you've left Open AI, this

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thing is taking off. It's you and

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Daniela, you know, um you're in the

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Slack building, you're get where
is it going to be in 5 years. What

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does it look like? We've seen zero
to five years now. You don't have to tell

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me as an investor and there's nobody
hold gonna hold you accountable for it,

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but just if you just fantasized out and

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now it's not 2025, but it's 2030 and you

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know where's Enthropic? What does it
look like? Forget about all the other

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companies and all the other competitors,
whatever. Nobody cares. Where's

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Enthropic 5 years from now? What's your

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dream big what's your big dream? So, you

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know, I I think I think maybe maybe one
of the best ways to think about this is,

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you know, I wrote this essay called
Machines of Loving Grace where where

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it's really it was written from a
societal perspective, but you know, of

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course, the company is trying to enact
that and and then contribute to it and

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so you can back out from that where we
where we hope the the company will be.

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So, you know, I I really thought of it
as like the upside of AI, right? There's

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lots of concerns with AI. I I you know

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I'm one of the loudest in voicing them

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but but I am you know I am I am I am

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incredible believer in the upside of AI
you know I think I think more so than

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people who code or describe themselves

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as more optimistic I have just a really

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expansive view I am perhaps most excited

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about what it can do for curing diseases
um you know going back to my background

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in biology like you know we look at

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these tough diseases like cancer like

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Alzheimer's um you know like like you

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know various kinds of metabolic disease
like I I I just think AI is the right

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thing to cure them and so I think
of all the work we can do working with

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pharmaceutical companies working
with scientific research institutions and

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working with the health care system
to kind of enact it out and you know you

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again our partnership is about regulated
industries and one of the regulated

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industries I think especially is is

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healthcare but you know that is one
of the most positive applications but I

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think that we will see applications
across the industry I'm very excited

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about financial services. I'm very

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excited about insurance. I'm very

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excited about taking basically every

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enterprise um and learning to serve

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every enterprise with smarter and smarter
models. And the skill ceiling is

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very high. One way to say it is what
is an enterprise? It's kind of a super

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intelligence, right? It it is an entity

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that acts in the world in in in in in in

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a way with a level of strategy and and
knowledge and power in the world that

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goes far beyond what any individual
human can do. So, you know, even as

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these models get smarter than than than
any than any individual human, there's a

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ceiling substantially higher than
that in terms of doing the things that a

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company tries to do in the world. Um,
you know, most of which are good for the

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world, most of which create economic
value. Um and and and so then my my

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vision of Anthropic is a company that
serves the enterprise of the world that

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transforms every every enterprise puts

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AI at the center at the center of every

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enterprise. Um uh you know and and and

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and and you know I think I won't give

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specific numbers but like if you if you

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think about the actual market potential

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of that it's very very high right our
revenue has been going up 10x per year.

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It's currently, I think it was reported
this morning, which I'm happy to confirm

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is correct, is approaching approaching 7

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billion run rate. Um, that's gone up 10x
a year for the for the last three years.

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It can't continue like that for much
longer, of course, but uh you know,

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>> as an investor, I'm looking forward
to the $70 billion year next year.

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>> You should not feel bad investing at 4

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bill. You should not feel bad.

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>> Um uh uh uh so, you know, but I I I

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think the potential is is is very large.

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But but I think even even more important
than that or perhaps even necessary to

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that success, we want to be known
for, you know, the company that is

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trustworthy, the company that behaves

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responsibly, the company that has a that

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has a reputation for, you know, doing

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what it says, saying what it does,
and building products that are trustworthy

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that, you know, if you if you if
you build on us, you won't get any

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surprises. You won't get anything crazy.

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You won't get the model, you know,
saying it's Hitler. you won't you won't

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get, you know, all kinds of um you won't

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get all kinds of crazy stuff. Um and
and I think that's very important to us

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being good stewards of this.
>> Are there certain companies that you're

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00:18:39,600 --> 00:18:42,077
trying to imply that that
might be >> None whatsoever.

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>> There's an entrepreneur
down in Palo Alto.

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>> None at all. I don't know
what you're talking about.

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>> Okay, I see.
That's a certain letter in the alphabet.

331
00:18:50,800 --> 00:18:53,437
>> I don't I don't I I have no
idea what you're talking about. Um

332
00:18:53,520 --> 00:18:57,037
>> Okay, it's before the letter Y. I sorry

333
00:18:57,120 --> 00:18:59,197
I still don't know what you mean.

334
00:18:59,280 --> 00:19:03,197
>> Okay, one more thing. I'm going to take
a couple I'm going to use moderator

335
00:19:03,280 --> 00:19:04,477
privilege here and take a couple extra

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minutes with you. So number one, we see

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right now a tremendous fever on building

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data centers and you know we've seen a

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tremendous fever around the chips, the

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00:19:18,080 --> 00:19:22,077
financing, the size and scale of the
data centers, the energy of the data

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00:19:22,160 --> 00:19:23,837
centers. you haven't really addressed it

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00:19:23,920 --> 00:19:26,157
in your comments per se besides the

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00:19:26,240 --> 00:19:31,757
scaling laws but let's talk about it
just in part and parcel to anthropic and

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when you think about obviously one
of your core partners has been Amazon it's

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00:19:35,200 --> 00:19:40,477
been very important for your growth very
important company um even though you do

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have you know obviously relationship
with others as well but that one has

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been especially important to you
know just give us a vision of what's

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happening with these data centers right
now what are we going to see in the next

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few years with these these data centers
and how do you think about it as an

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00:19:55,040 --> 00:19:59,117
entrepreneur who's going to need data
centers to grow? Who will you go to? How

351
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will you do it? Will you build your own?

352
00:20:00,960 --> 00:20:04,157
Will you just continue to partner?
What what is your plan?

353
00:20:04,240 --> 00:20:07,677
>> Yeah. So, you know, my my my view is is

354
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that there is going to be an enormous
need for compute for for all the reasons

355
00:20:12,000 --> 00:20:15,597
that uh you know that that we've laid out,
right? The the scaling laws suggest

356
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that we're going to need more and more
compute to train smarter and smarter

357
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models. And then as the revenue goes up,
we're gonna we're, you know, we're going

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to need to serve more. We're going
to need to serve more of our models. So

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when that revenue is not seven billion,
but a hundred billion or more. Um uh and

360
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you know, when when model training
requires millions of chips, you know,

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we're we're going to need many many
gigawatts of power to to train the

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models. So we're trying to secure that

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power basically wherever we can. Um you
know, and I'm confident in the company's

364
00:20:45,040 --> 00:20:46,637
uh I'm confident in the company's

365
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ability to do so. I I I do get a bit

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worried when the press focuses too much

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on the building of the data centers and

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deals to build data centers. Some
of these deals seem a little bit fishy.

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They seem like there may be double
counting. And I I also worry again

370
00:21:03,680 --> 00:21:05,117
>> deals are you ask talking about?

371
00:21:05,200 --> 00:21:06,237
>> I don't know what you're talking about.

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I have no idea. Um uh uh uh >> who
is double counting? These are all

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public companies. I I >> we're going
to want to know who's double counting.

374
00:21:15,200 --> 00:21:17,677
>> I don't I don't I don't know.
I don't know anything more than you.

375
00:21:17,760 --> 00:21:19,197
>> What about the ones who
are doing triple counting?

376
00:21:19,280 --> 00:21:21,517
>> I I don't There might
be some of that, too.

377
00:21:21,600 --> 00:21:26,077
>> Oh, wow. All right.
>> I don't know. Um uh uh uh but but but

378
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look look look look at the end of the

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day all this data center stuff is

380
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spending money, right? Um I think the

381
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thing that's most important is making

382
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money. Um and and so this is why I focus

383
00:21:38,480 --> 00:21:41,357
on, you know, the revenue and the actual

384
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work that we're doing with businesses.

385
00:21:43,600 --> 00:21:46,557
ROIC is growing faster than any other

386
00:21:46,640 --> 00:21:50,557
company in the space. The revenue
is growing faster and you know I think I

387
00:21:50,640 --> 00:21:54,557
think basically if if you know if the if
the demand is there if customers want

388
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the money and they're willing to pay you
then you are willing to pay your compute

389
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providers and you'll get the compute
you need at the end of the day.

390
00:22:01,840 --> 00:22:04,157
>> Daria, we could not
be more grateful for

391
00:22:04,240 --> 00:22:09,517
you to be here to be in San Francisco
to be a native son and to Daniela as well

392
00:22:09,600 --> 00:22:10,957
and for everything that you're doing

393
00:22:11,040 --> 00:22:17,240
with anthropic congratulations.
Please give Dario a round of applause.