WEBVTT

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Daniela, welcome to Stanford
and View from the Top.

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>> Thank you so much for having me.

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>> We're so excited to hear to be with you

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here today. As you can see, we have a

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full house and I want to start by asking

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everyone in the audience, please raise
your hand if you're a user of Claude.

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OH, WOW! >> [screaming] >> OKAY,
BUT I FEEL LIKE IF YOU had Sam

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Altman visit and you asked about ChatGPT,

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would you all have raised your hands?

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Like, let's be real. Okay. >> Still,
you have a very supportive crowd

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down here.
And you and your brother helped build

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one of the most important AI companies

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in the world. Yet, neither of you
grew up planning for any of this. Your

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background is in the arts. You studied
English literature and you your early

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career was in politics.
Can you share more about what was your

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original career plan? >> Oh my god.
First of all, so nice of you

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to use the word plan in there.
Um I don't know that I necessarily would

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have described it that way at any point
in time, but I I I really think, you

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know, this is sort of a story that I
hear from a lot of people who either

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wind up, you know, founding a company
or just doing something a little bit

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unexpected or unconventional with their
life. It was following what was most

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interesting to me at the time and the
intersection of what was I good at, what

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was I interested in, and what was going
to have a big impact in the world.

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So, for me, I think coming out of college,
that looked like, by the way, I

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graduated in 2009, which was like not
the most fun year to be a graduate. You

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were like, I have a literature degree
and no skills. Who will hire me?

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Um but at the time,
I felt this very strong

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pull towards wanting to make the world
better. I think that was always that

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sort of defining feature of both both
me and Dario from a young age, and where

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that started out for me was in in
international development, working in

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global health. And I think my desire
there was really to figure out like, how

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do we build a world that is, you know,
fair, where everybody has access to

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basic things like food and water
and medicine. And even though that's, you

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know, not what I directly work on now,
I think that early grounding sort of gave

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me this foundation for thinking about
how do you do good in the world, right?

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How do you build something that is
of consequence and has a real purpose

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behind where you're spending, you know,
50 or 60 of your hours per week, right?

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What you're working on. Um and so it was
kind of a winding journey from there. I

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worked on Capitol Hill after that.
I worked on a campaign. Uh and eventually

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I ended up coming back to Silicon
Valley. I'm originally from San

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Francisco and started uh working at
Stripe, which was this tiny company that

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at the time like nobody had heard of.
My friends on Capitol Hill were like,

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"You're leaving to go do what?

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Payments?" At like now it looks like
a great decision, but at the time it was

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about 40 people. Um and then from there
things really just snowballed into

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working at OpenAI and then
co-founding Anthropic.

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>> You've moved across
fields without being

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constrained by what you
studied or had done before.

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Where does that mindset come from? That
your background doesn't have to define

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your next move. >> So, you know,
it's really interesting. I

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think in some ways I really think
of myself as a generalist, right? That if

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you were to sort of look through
my background, you would be like, what is

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this lady actually good at, right?
She doesn't She doesn't have a She doesn't

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have a law degree. She's not a computer

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scientist. But I think this concept that

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um the ability to be curious and learn

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across a lot of disciplines, to have
a strong foundation of of wanting to have

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impact regardless of of the area
that you're working on, I think that is I

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think that's an underrated quality.
And I see it a lot in the people that we

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hire at Anthropic, and the really
talented people that I've worked with in

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the technology industry more broadly.

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People who are curious and smart
and they want to learn and they want to be

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helpful. That is like the first of all,

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the description of like every role
except for like engineer at a startup.

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Like you're like it's like what you know,
whatever. People there like I have

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this degree, I have that degree
and those are sort of the qualities that

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you're looking for.
But I think for me it was always very It

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was very interest and impact driven. So
I was like, okay, there's something that

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feels really wrong to me about the fact
that you know, I was born in America. I

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had access to all of the sort
of basic things that we

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take for granted in life. Some people
around the world just weren't They just

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That's just not where they were born.

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That's not what they were born into. How
do I make that more fair? Like how do we

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make that better as a broader
community of the world?

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And and from there I was like, I'm just
not having the level of impact that I

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want to. I need some skills. And so I
went and I worked on a campaign and I

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was like, wow, a small number of people
that work really hard who are young and

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driven can really change the world. Not
that surprising that eventually that led

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me to Silicon Valley where
you're like, you can do that but

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it turns out that at a startup you have
a lot more money and it's a lot easier

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going than the kind of 80 hours a week
of working on a campaign. But I think

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those kind of core qualities around

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you know, really following your passion
because you just want to do more when

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you care about the thing that you're
working on either intellectually or from

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an implicit implicit sense of meaning.

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>> And your career in AI started when you

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joined OpenAI in 2018 when it was still

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a relatively small research lab.

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Suddenly you were in rooms where people
were talking about neural networks and

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transformers and scaling laws.

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How did you learn to speak that language?

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>> So I think I was I was well trained
on two dimensions. I think the first is I

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had already spent almost six years at
Stripe and so I had worked with a lot of

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engineers. Obviously research
and engineering are different but I think

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there was some overlap and some grounding.

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The second is that you know, I I grew up

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with a very talented technology oriented

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physicist who's my sibling and obviously
my co-founder at Anthropic among five

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others who are also all
engineers or researchers.

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Um but I think the number one thing I
would say is both of those experiences

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just instilled in me the sense to just

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not be afraid of technology, right? It

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was ultimately it's it's a set of skills

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that are really highly prized, I think,
but it's something that anybody can

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learn. And the sort of basics behind it,

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I think the the the terminology and the

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jargon can feel overwhelming at first,
but if you just ask enough questions and

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if you have people who are kind enough
to be patient with you, which I I had in

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my life. I was very lucky for that. Um
you I just kept asking questions until I

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felt like I could understand it. Um and I
think you know, the second part of it

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was also just like knowing my lane
and their lane. So, there were a lot of

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things that uh that the researchers
I was like I I probably couldn't have

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trained GP I certainly couldn't have
trained GPT-2 or GPT-3, but like I I

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brought things to the table that they
didn't know how to do as well. And so, I

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really think understanding what your
comparative advantage is and knowing how

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you fit into the broader ecosystem, um
that takes a lot of skills that are kind

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of interpersonal. Um curiosity, I think
is is a very inherent skill, but one

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that you can learn and train. And I
think all of those sort of put together

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really put me in this position to be able
to be successful in that type of role.

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>> And in December of 2020, you,
your brother, along with a group of

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colleagues left OpenAI.

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Why did you and Dario
decide to start Anthropic?

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>> I'm going to take a big sip of water.

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That one. Take a I'll
take one more, actually.

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So, um there were seven of us that are

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originally left. Me and Dario and our
five co-founders. And then a number of

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people that kind of came over shortly

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shortly thereafter. And I I think for

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us, it really it really came down to

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just the focus on what it was we
really wanted the ultimate impact of the

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technology to be.
And I think in our own, you know, very

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different ways, all seven
of us are people that have

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a lot of integrity. We're people who

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care a lot about the impact of what it

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is that we build. And I think eventually

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it felt like it was just easier for us

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to sort of create the type of vision

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that we saw outside of the company we
were in and in a new company. And I I

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frequently say cuz I'm I know it'll
shock you, I'm actually asked this

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question not infrequently. Um and

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you know, we really, I think, were
running towards something versus running

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away from something. Like we had this

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vision in our heads of wanting to create

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an organization where the values that

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matter to us around safety,
around responsibility, um

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were the kind of forefront of what
we were doing. That's why we chose to

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incorporate it as a public benefit
corporation. That took a while to figure

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out what was the right kind of form
factor to express, look, we are going to

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be a commercial entity. We think there's
going to be a lot of economic value

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that's going to be created by artificial
intelligence, but it's really important

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to us that we do this the right way.

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And I think that was something that kind
of united the seven of us. We had We had

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all worked on a combination of both
capabilities and safety and policy work

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when we were at OpenAI. And it felt
like it was just easier to kind of create

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this structure in in a new form.

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>> And you're building Anthropic not only
with your brother, but with five other

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co-founders. Many of us here are about
to choose co-founders for the first

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time, and we all know
how often that ends badly.

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What does it take to make it work?

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>> So, you know, I think of myself as just

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extremely lucky. Like the seven of us, I

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think, are a very special group on on a

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couple of dimensions. I think
the first thing I would say is like the

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interpersonal relationships, I know this
is going to sound not that surprising,

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matter a lot more than you think. Like

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how do you guys have conflict together?

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So, for example, Dario and I have been
fighting and getting over it for almost

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40 years. >> [laughter] >> Because
he's my brother. And like, I

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used to steal his toys, right? So, like,
we know how to work through conflict

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together and like, there's no question
like, we will love each other at the end

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of that, no matter what. Um with our

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co-founders, it's like, I've known Jared

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for like 15 years. I've known Chris for

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15 years. Tom and Sam were roommates.
Um Jared and Sam worked together at

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Stanford when they were in their PhD
program. So, there was this kind of long

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history. Um both Dario and I actually
managed all of the other co-founders and

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like, I think they had either reported
to one of us or both of us. I think the

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majority reported to both of us at OpenAI.
So, we already had this kind of

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existing structure and framework of like,
we knew how to give each other

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feedback. We knew how to work together.

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We'd like, understood like, who we
were as people. And I I think the other

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really important thing is just making
sure you have a very strong sense of

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what it is you're trying to do and that
that picture is the same. Like, if you

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locked yourself and and your co-founder
in another room and you wrote down or

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drew a picture of like, what it is you're
trying to build, you're not going

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to walk out and like, one has drawn
a unicorn and the other has drawn a

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platypus, right? Like, that's the type
of situation where you think you're

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doing the same thing, but I think it
just doesn't end well. And I think for

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us, this kind of vision of what it is we
wanted to build, in some ways we were

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lucky because we had we had been in an
environment where we were like, "Ooh,

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it's not quite this. We want to do this
other thing." But, we were like, in the

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same zone already. So, we're sort of
pre-selected for that level of interest.

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But, I think really being able to like,
pressure test to the degree that you

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can, like, instead of like, starting
a company together, like, go on vacation

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together. Just like, see how that goes.

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Share a room with them. Be like,
"How did that go?" Um and if you're like,

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"Man, all I want to do is spend more
time with you." Great. If you're like,

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"Really I'm going to need a vacation
to recover from my vacation." You just

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might it might be the wrong choice.

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>> I want to circle back to what you
just said a few moments ago. Anthropic is

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deeply associated with AI safety,
but I want to make sure that everyone

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understands what that actually means.

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So, when you say AI safety,
what do you mean?

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>> Yeah, this is a great question. And I
think this is a term that's gotten like

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almost a little bit overloaded
in the past few years because it's sort of

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it's kind of a catchall
that I think has been

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reached for in a lot of contexts.
But I think to us the the kind of highest

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level of framing is just taking a form
of like radical responsibility for the

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technology that we're developing. And
the analog that we often use or that we

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often point to is social media
companies, which like it's

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so in vogue to like crap all over them
in public now, so I'm going to do that.

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But basically, you know, like if you

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imagine going back in time, I actually

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think developers who created these these

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technology companies, they weren't like
I'm setting out to cause a pandemic of

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eating disorders for teen girls. Right?

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That was not their intention. But they
were like, what are the metrics that I'm

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trying to optimize for? Right? I'm
trying to build a company. I would like

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to see rapid rapid scale growth. Like
let's just build towards that. And there

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wasn't this sort of at the time it just
wasn't necessary cuz we'd never seen

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something on the level of scale that we
have seen today, how quickly these

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companies grew, how many people adopt

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the technologies so quickly. But if you
could imagine, you could go back in time

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and say like, wow, you're starting

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Facebook or Instagram or Snapchat
or Twitter, and you're like, what if I

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really try to think through all of the
ways that this could go wrong? Like what

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if what if I could think about what
all the unintended externalities are and

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like really just kind of tried
in advance to like prevent some of those

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from happening. And it's a little bit
unfair because in AI, we've had this

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whole generation of technologies before
us where they've like gotten to up

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and we've gotten to be like, haha, we're
not going to do that thing again. But

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that is a huge privilege, right? We're
able to say, "Okay, you guys made this

00:13:30.600 --> 00:13:34.317
mistake. We are not going to make that
mistake this time, right? We're going to

00:13:34.400 --> 00:13:38.877
be careful and we're going to say, how
do we make sure to think about the other

00:13:38.960 --> 00:13:43.557
things that might not go wrong because
we understand the technology better?" Um

00:13:43.640 --> 00:13:48.437
how can we how can we
imagine a world where like

00:13:48.520 --> 00:13:51.997
everything goes right, but also a world
where everything goes wrong? And I think

00:13:52.080 --> 00:13:56.517
for us safety means like all of the big
stuff, so preventing chemical and

00:13:56.600 --> 00:14:00.157
biological weapons from being developed
using our technology, which by the way,

00:14:00.240 --> 00:14:05.037
they they could have the potential to do.
Um but also like cyber, right? Cyber

00:14:05.120 --> 00:14:08.517
warfare, there's we've been in the news
a lot lately about our decision to not

00:14:08.600 --> 00:14:11.557
um you know, release our Mythos class
model because of the potential for cyber

00:14:11.640 --> 00:14:14.277
warfare. There's also
a lot of work that happens

00:14:14.360 --> 00:14:16.157
around things like user wellness, child

00:14:16.240 --> 00:14:21.517
safety, um a lot of misinformation,
election integrity work. This is not

00:14:21.600 --> 00:14:25.117
something new that we invented. We've
been able to stand on the shoulders of

00:14:25.200 --> 00:14:29.437
previous safety and security teams who
worked on this at some of the other most

00:14:29.520 --> 00:14:32.997
consequential technology companies
in history and say, "How do we learn from

00:14:33.080 --> 00:14:35.837
you, right? How do we do this better?"

00:14:35.920 --> 00:14:38.317
>> Anthropic is an AI safety company that

00:14:38.400 --> 00:14:43.157
also has to generate revenue.
How do you manage the tension between

00:14:43.240 --> 00:14:46.917
the two? >> We're asked
this question a lot, too.

00:14:47.000 --> 00:14:49.637
And I think

00:14:49.720 --> 00:14:54.237
in general, the two don't come into
conflict as much as you would expect. So

00:14:54.320 --> 00:14:58.877
what I would say is most
businesses in particular, which is

00:14:58.960 --> 00:15:00.197
by the way, the majority of our revenue

00:15:00.280 --> 00:15:02.757
comes from businesses, are not looking

00:15:02.840 --> 00:15:06.477
to have models that are unsafe, right?

00:15:06.560 --> 00:15:10.717
They're not like, "Wow, we would love
for Claude to hallucinate more." Or it

00:15:10.800 --> 00:15:14.917
would be great if um Claude just
produced like harmful outputs when

00:15:15.000 --> 00:15:20.117
you're asking it a question. And so I
think until like pretty recently, this

00:15:20.200 --> 00:15:23.477
these things were just like 100%
in alignment, You were like, it's actually

00:15:23.560 --> 00:15:28.557
really good for business to be safe
because businesses are correctly the

00:15:28.640 --> 00:15:31.397
risk of us. Right? They're like,
we don't want AI technologies that are

00:15:31.480 --> 00:15:34.237
going to be super
unpredictable or unreliable.

00:15:34.320 --> 00:15:38.237
That said, I think we're now entering an
era where the capabilities of the models

00:15:38.320 --> 00:15:40.237
are developing so rapidly that the

00:15:40.320 --> 00:15:42.437
tension is about time.

00:15:42.520 --> 00:15:45.877
So, it's not necessarily the case that

00:15:45.960 --> 00:15:49.957
the models can't do amazing things. It's
just we don't fully understand at this

00:15:50.040 --> 00:15:51.797
stage and I think it will be more the

00:15:51.880 --> 00:15:55.197
case going forward. Like, how

00:15:55.280 --> 00:15:58.397
how serious are the risks? What are all

00:15:58.480 --> 00:16:02.797
of the risks and how do we help mitigate
them? That sometimes means that we take

00:16:02.880 --> 00:16:06.597
slightly unusual actions like we did
with Project Last Wing and say this new

00:16:06.680 --> 00:16:10.437
class of model, um, it would be great if
we could just release this to all of our

00:16:10.520 --> 00:16:14.397
customers. They all would love to use it,
but we're just not confident enough

00:16:14.480 --> 00:16:17.037
yet. Like, we need a little more time to

00:16:17.120 --> 00:16:20.397
do some of the work to make

00:16:20.480 --> 00:16:25.197
the models safer to use, um,
but that's uncomfortable. Right? It's

00:16:25.280 --> 00:16:27.797
uncomfortable to say that to your
customers. Right? They're like, look, we

00:16:27.880 --> 00:16:31.117
all believe in cyber defense, but like
I really want access to that model. And I

00:16:31.200 --> 00:16:33.917
think this is the place where we just
come back to the mission. Right? We say,

00:16:34.000 --> 00:16:37.077
okay, we understand that desire. We want
to get this technology to you as quickly

00:16:37.160 --> 00:16:42.477
as possible, but it is irresponsible
of us to release it until we are confident

00:16:42.560 --> 00:16:46.117
that all of the patching that
needs to be done has been done.

00:16:46.200 --> 00:16:47.837
>> We count online that when it comes to

00:16:47.920 --> 00:16:51.557
AI, there's a lot of fear. Fear that AI

00:16:51.640 --> 00:16:53.957
means fewer jobs because of less need

00:16:54.040 --> 00:16:58.157
for human judgment. Is that fear valid?

00:16:58.240 --> 00:17:01.717
>> I think this is actually
a very complicated question. Um,

00:17:01.800 --> 00:17:03.797
my sense is that this probably will not

00:17:03.880 --> 00:17:06.917
surprise people here. Um, AI is going to

00:17:07.000 --> 00:17:10.437
change what types of jobs are available

00:17:10.520 --> 00:17:12.837
and what types of jobs

00:17:12.920 --> 00:17:15.197
people do. So,

00:17:15.280 --> 00:17:20.157
today, I think there are jobs that exist
that did not exist 5 years ago because

00:17:20.240 --> 00:17:25.197
of AI. I also think there will be some
jobs that will not exist in the future

00:17:25.280 --> 00:17:29.957
because of AI. But what we
have seen so far today is that

00:17:30.040 --> 00:17:33.237
according to our economic index, where
we study like how are people actually

00:17:33.320 --> 00:17:36.077
using artificial intelligence technology

00:17:36.160 --> 00:17:38.797
right now, it mostly looks like

00:17:38.880 --> 00:17:41.517
complementary skills, right? So, you

00:17:41.600 --> 00:17:45.277
have artificial intelligence as

00:17:45.360 --> 00:17:47.957
an enabler of work. You don't have it as

00:17:48.040 --> 00:17:50.757
a replacer of work. Um except in a very

00:17:50.840 --> 00:17:55.037
vanishingly small number of cases,
which is mostly customer service.

00:17:55.120 --> 00:17:58.997
Sorry if you have to email Comcast.
It will never be a human again, probably.

00:17:59.080 --> 00:18:00.717
Um but I don't know that that was

00:18:00.800 --> 00:18:02.797
actually different 5 years ago. So, um I

00:18:02.880 --> 00:18:06.077
think I think in reality, what I expect

00:18:06.160 --> 00:18:10.357
is like there will be a number of

00:18:10.440 --> 00:18:13.557
types of work that will feel a lot like

00:18:13.640 --> 00:18:17.597
they rhyme with a job that exists today,
but they're not necessarily the same as

00:18:17.680 --> 00:18:20.717
a job that exists today. And I think we
just don't know the shape of what all of

00:18:20.800 --> 00:18:24.237
that is, right? Today, I think the thing
that's most talked about is coding,

00:18:24.320 --> 00:18:27.717
right? Software developers. Um I always,
you know, in business meetings, right?

00:18:27.800 --> 00:18:31.917
People will say to me like we're
talking shop about Claude, and

00:18:32.000 --> 00:18:35.437
then like two thirds of the way through
the conversation, like a CEO will kind

00:18:35.520 --> 00:18:38.677
of conspiratorially lean
across the table and say,

00:18:38.760 --> 00:18:44.077
you know, my daughter is a
sophomore at Stanford. Like, what

00:18:44.160 --> 00:18:47.637
should she study? Right?
Like, she was going to be a CS

00:18:47.720 --> 00:18:50.637
major. Should she not major in computer
science? And I think, you know, the

00:18:50.720 --> 00:18:54.757
truth is we don't know. But my guess
is software developers will still exist,

00:18:54.840 --> 00:18:56.877
but like they won't write as much code.

00:18:56.960 --> 00:19:00.277
Right? Like a lot of what software
developers do is much bigger than just

00:19:00.360 --> 00:19:04.317
hands-on keyboard. They're talking
to product managers. They're working

00:19:04.400 --> 00:19:07.877
closely with customers. And I think
the percentage of that work is going to

00:19:07.960 --> 00:19:10.877
expand. Um and I think the sort of

00:19:10.960 --> 00:19:13.197
things that can be more easily done by

00:19:13.280 --> 00:19:17.997
AI, that will contract, but my sense is
that is going to create a very different

00:19:18.080 --> 00:19:21.917
scope of what's possible.
>> What needs to happen in education,

00:19:22.000 --> 00:19:26.957
leadership, society, that people
feel prepared and excited and not just

00:19:27.040 --> 00:19:28.517
anxious?

00:19:28.600 --> 00:19:30.997
>> So I think there's a few things here. I

00:19:31.080 --> 00:19:34.037
think the first one is

00:19:34.120 --> 00:19:38.357
we need to start and lead from a place
of like humility and not knowing the

00:19:38.440 --> 00:19:42.517
answers, but doing the research. And I
think at Anthropic, something that we've

00:19:42.600 --> 00:19:45.717
always aimed to do is be as kind
of radically transparent about all of this

00:19:45.800 --> 00:19:47.957
as as we possibly can be, right? We've

00:19:48.040 --> 00:19:53.037
always said, "Look, we we don't have all
the answers. We do need to study this so

00:19:53.120 --> 00:19:56.637
that we're able to tell people what
we see coming." And I think, you know,

00:19:56.720 --> 00:19:59.597
sometimes fairly people can say like,
"Wow, you guys are just this is like a

00:19:59.680 --> 00:20:02.837
lot of negativity, right? You're like,
here's here's what we think might happen

00:20:02.920 --> 00:20:06.437
in the future." But I think it's more
important that we start the conversation

00:20:06.520 --> 00:20:10.237
sooner because we don't want people
to be caught off guard, right? We're we're

00:20:10.320 --> 00:20:14.197
publishing the academic index to say,
"Here's how people are using artificial

00:20:14.280 --> 00:20:18.877
intelligence today because we want
people to have an understanding of like

00:20:18.960 --> 00:20:22.197
where do we think this is going?"
So I think that's step one, is we have to

00:20:22.280 --> 00:20:25.837
actually all agree on what reality
is to the degree that's possible.

00:20:25.920 --> 00:20:31.077
I think step two is, you know, we have
to be creative and experimental at many

00:20:31.160 --> 00:20:34.757
different layers. So how do we have
artificial intelligence really be

00:20:34.840 --> 00:20:39.277
something that is a grounding and a
unifying force for people outside of

00:20:39.360 --> 00:20:42.317
just like, "Oh, I'm using it at my
kind of job," right? Which is really

00:20:42.400 --> 00:20:45.237
important, but I think in some ways
we need to sort of be rethinking the

00:20:45.320 --> 00:20:48.077
paradigm of this connection between

00:20:48.160 --> 00:20:53.917
work and meaning and like social life,
like all of these things I think are

00:20:54.000 --> 00:20:57.277
going to look very different in the future
and we need to practice. And then

00:20:57.360 --> 00:21:00.557
I think the third one, which is really
outside of the realm of what a

00:21:00.640 --> 00:21:05.317
technology company can do alone,
is this is going to become a social and

00:21:05.400 --> 00:21:09.797
political issue. People are going
to care if it feels like their jobs are

00:21:09.880 --> 00:21:12.557
being displaced by AI. People are
already care about this, right? It comes

00:21:12.640 --> 00:21:15.637
up in polling. People are like,
I have anxiety about what artificial

00:21:15.720 --> 00:21:18.997
intelligence is going to mean for my
future, for my kids' future, right? That

00:21:19.080 --> 00:21:23.277
CEO that's leaning across the table from
me, it's not just them. And so, I think

00:21:23.360 --> 00:21:26.677
there is a broader discussion that needs
to happen at many different levels of

00:21:26.760 --> 00:21:30.477
government, with civil society, with
universities, by the way, to say like,

00:21:30.560 --> 00:21:33.557
what does this mean? Where What is the
type of world that we want to be able to

00:21:33.640 --> 00:21:36.877
build where artificial intelligence is
capable of doing many of the things that

00:21:36.960 --> 00:21:41.157
humans do today. >> At the core,
it is all about adoption.

00:21:41.240 --> 00:21:45.997
Here at Stanford, we live in the AI, but
Stanford and Silicon Valley are not the

00:21:46.080 --> 00:21:49.877
whole world.
What currently hinders AI adoption

00:21:49.960 --> 00:21:53.597
outside of this bubble? >> You know,
I think this is a I think this

00:21:53.680 --> 00:21:57.117
is a really, really great call out.
Um it's so interesting because it feels

00:21:57.200 --> 00:22:00.237
like, you know, certainly to us
at Anthropic, and I'm sure at Stanford,

00:22:00.320 --> 00:22:03.917
too, it's like the only thing people
want to talk about is AI. Certainly the

00:22:04.000 --> 00:22:08.277
only thing people want to talk to me about
is AI, but that's probably a a me problem.

00:22:08.360 --> 00:22:11.077
>> [laughter] >> But I I
think I think that feeling

00:22:11.160 --> 00:22:13.597
you're totally right, that even in other

00:22:13.680 --> 00:22:17.037
parts of America, it's it's not it's not

00:22:17.120 --> 00:22:21.277
something that people are comfortable
with yet on the whole, and it's not even

00:22:21.360 --> 00:22:25.277
something that people like necessarily
know how to use with very high fluency.

00:22:25.360 --> 00:22:29.037
Right? So, you read these like really
astounding, super impressive numbers

00:22:29.120 --> 00:22:31.157
about how many people are using AI

00:22:31.240 --> 00:22:34.317
tools. Um but there's there's

00:22:34.400 --> 00:22:36.397
there's a demographic component to that.

00:22:36.480 --> 00:22:38.477
Right? So, it's generally people who are

00:22:38.560 --> 00:22:41.117
college educated, not uniquely. Uh it's

00:22:41.200 --> 00:22:45.757
more men than women. Um there's racial
demographics involved. There's like, um

00:22:45.840 --> 00:22:49.517
wealth demographics involved. And if you
look around the world, it's really not

00:22:49.600 --> 00:22:55.317
equally distributed. And so, I think
what's interesting is that you pair that

00:22:55.400 --> 00:22:58.437
with some other data that we've
collected, which is that people in

00:22:58.520 --> 00:23:02.357
developing countries
are much more optimistic

00:23:02.440 --> 00:23:04.637
about AI than people in

00:23:04.720 --> 00:23:06.357
higher income countries. So, the global

00:23:06.440 --> 00:23:11.757
south is almost universally like,
"Wow, this is a huge opportunity for us,

00:23:11.840 --> 00:23:15.197
right? This is This is the moment where
perhaps we could have an equalizing

00:23:15.280 --> 00:23:18.157
force that will make things more fair."

00:23:18.240 --> 00:23:22.877
But, I think in the US and in Europe and
in parts of Asia, people have a lot more

00:23:22.960 --> 00:23:26.197
anxiety. They're like, "I like things
the way they are. I don't want AI to

00:23:26.280 --> 00:23:29.117
come in and disrupt that.
That doesn't sound as good to me."

00:23:29.200 --> 00:23:31.837
What do we do with this information?
I have no idea. But, I think it's really

00:23:31.920 --> 00:23:33.517
interesting to say like there are

00:23:33.600 --> 00:23:36.237
different questions of access and

00:23:36.320 --> 00:23:39.357
adoption around the technology.

00:23:39.440 --> 00:23:44.077
And I think we we are we are actually
still very early in the game. And that's

00:23:44.160 --> 00:23:47.717
the thing that I think can be missed
in Silicon Valley, in our bubble, that

00:23:47.800 --> 00:23:51.397
we're like, "It's already Everybody is
a software engineer. It's like, I'm using

00:23:51.480 --> 00:23:55.557
cloud code. I'm using Codex." That is
not the vast majority of developers in

00:23:55.640 --> 00:23:57.677
the world at large. And so, I think the

00:23:57.760 --> 00:24:00.637
the the race is is still like we're at

00:24:00.720 --> 00:24:04.797
the like the gun just went off to start
the race. And I think there's a lot of

00:24:04.880 --> 00:24:09.837
opportunity to still positively shape
how this technology is going to be used

00:24:09.920 --> 00:24:13.317
and developed, what access looks like,
and just what the values that are baked

00:24:13.400 --> 00:24:15.917
into it are going to ultimately be.

00:24:16.000 --> 00:24:18.637
>> So, let's forward
to the future where AI

00:24:18.720 --> 00:24:21.477
is far more widely adopted.

00:24:21.560 --> 00:24:25.117
What are the things we risk losing

00:24:25.200 --> 00:24:29.117
if we start delegating too much to AI?

00:24:29.200 --> 00:24:32.597
>> Yeah. Um we did
this very large qualitative

00:24:32.680 --> 00:24:36.597
survey at Anthropic. I think it's the
largest qualitative study ever done that

00:24:36.680 --> 00:24:41.237
we know about. We talked to 81,000
people about their use of artificial

00:24:41.320 --> 00:24:44.957
intelligence. And so, some of them were
Claude users, some of them were users of

00:24:45.040 --> 00:24:48.037
other of other AI tools. Um

00:24:48.120 --> 00:24:50.397
And what was interesting is, you know,

00:24:50.480 --> 00:24:53.477
people people have a lot of different

00:24:53.560 --> 00:24:56.837
feelings about AI, right? And again, you

00:24:56.920 --> 00:25:00.557
know, cuts differently depending
on where you are and and what you do, but

00:25:00.640 --> 00:25:05.357
some people are like, I've never been
able, you know, it it enabled me to do

00:25:05.440 --> 00:25:09.157
things that I never thought I could do,
right? I think for myself as an example,

00:25:09.240 --> 00:25:12.117
like, I didn't think I could build
a website, right? And now using Claude,

00:25:12.200 --> 00:25:14.917
I'm like, oh man, that's so easy. Like,
I just click a couple buttons and Claude

00:25:15.000 --> 00:25:18.117
like built a website for me. What?
That would have That would have taken me

00:25:18.200 --> 00:25:21.197
probably a year if I tried to do it
by myself, and it would not have been a

00:25:21.280 --> 00:25:23.437
very good website. Um

00:25:23.520 --> 00:25:26.917
there's some people though who express a

00:25:27.000 --> 00:25:32.117
feeling of there's not like a a specific
term for it, but I think there might be,

00:25:32.200 --> 00:25:36.837
or there might be in another language
one day. Um like, I don't I don't engage

00:25:36.920 --> 00:25:38.477
my brain because I don't have to.

00:25:38.560 --> 00:25:40.757
>> Yeah. >> So, it's not
the same feeling as like

00:25:40.840 --> 00:25:45.037
scrolling on your phone, but it's like,
I could have reached for this idea, I

00:25:45.120 --> 00:25:48.757
could have thought through it, but it
was so much easier to not do it and to

00:25:48.840 --> 00:25:53.357
just trust what the AI tool was giving me.
And I think this is the source, I I

00:25:53.440 --> 00:25:55.037
actually believe, of a lot of the

00:25:55.120 --> 00:25:58.957
anxiety around AI. As humans like to, I

00:25:59.040 --> 00:26:02.037
think, have an inherent desire to learn,

00:26:02.120 --> 00:26:05.517
to be curious, to want to expand the

00:26:05.600 --> 00:26:08.117
aperture of things that they know about.

00:26:08.200 --> 00:26:13.197
And AI in some ways enables that,
but if you if used incorrectly can sort of

00:26:13.280 --> 00:26:16.157
disable that, right? It's like, I've
done this sometimes, right? I was like,

00:26:16.240 --> 00:26:18.837
oh, I could like look this up and figure
it out myself, but I'll just ask an AI

00:26:18.920 --> 00:26:21.557
tool and then I'll blindly trust that what
it says is correct. It's not always

00:26:21.640 --> 00:26:22.877
correct, by the way. Sometimes Claude is

00:26:22.960 --> 00:26:26.197
wrong. Heretical to say, but a fact. Um

00:26:26.280 --> 00:26:29.277
and I I think I think the anxiety there

00:26:29.360 --> 00:26:31.557
is around

00:26:31.640 --> 00:26:34.637
how do we actually set some guardrails

00:26:34.720 --> 00:26:38.997
in place so that it's just it's not
impossible to do that, but you actually

00:26:39.080 --> 00:26:43.117
have to really be trying to do it,
right? Like, I think some of the work

00:26:43.200 --> 00:26:45.917
that we do with universities is maybe an

00:26:46.000 --> 00:26:50.477
interesting microcosm for this. We have
this concept of learning mode. Maybe

00:26:50.560 --> 00:26:51.797
some of you even use it. I know if we're

00:26:51.880 --> 00:26:54.677
at the GSP yet, but um

00:26:54.760 --> 00:26:58.397
faculty and professors and students,

00:26:58.480 --> 00:27:03.637
like one version of this is
like you put your homework

00:27:03.720 --> 00:27:07.317
in chat GPT, I'm going to use that one
instead, and you're like, "Haha, it just

00:27:07.400 --> 00:27:10.317
gave me the answer." There's a word
for that, it's called cheating, right? And

00:27:10.400 --> 00:27:14.837
you're like, "That was great." There's
another version where you use Claude in

00:27:14.920 --> 00:27:20.197
learning mode and you're like, "I don't
I'm I I'm stuck, right? I'm trying to

00:27:20.280 --> 00:27:22.837
write this essay and I like there's
something about the format that doesn't

00:27:22.920 --> 00:27:26.357
feel right to me."
And Claude is this sort of

00:27:26.440 --> 00:27:28.197
patient tutor. It's almost like you have

00:27:28.280 --> 00:27:30.597
an individualized professor who like

00:27:30.680 --> 00:27:35.237
knows you and understands like what you
most want to learn and why this class is

00:27:35.320 --> 00:27:38.477
important to you. It's like, "Let me
help get you unstuck, right? Do you want

00:27:38.560 --> 00:27:41.037
to go back and read this section together?
Could we talk through this?" I

00:27:41.120 --> 00:27:44.677
think that's the version where like
these tools can make you smarter, they

00:27:44.760 --> 00:27:48.917
can make you um expand the set
of things that you think

00:27:49.000 --> 00:27:52.317
you can learn. And then I
think there's the version

00:27:52.400 --> 00:27:54.837
that's just turn your brain off.

00:27:54.920 --> 00:27:57.997
And I'm hoping that as
an industry we're going to

00:27:58.080 --> 00:28:00.997
choose to do the second versus the first.

00:28:01.080 --> 00:28:03.477
>> So, if you had to prioritize,

00:28:03.560 --> 00:28:06.677
what human skills most likely going to

00:28:06.760 --> 00:28:10.717
be more important in an AI-driven world?

00:28:10.800 --> 00:28:11.757
>> So,

00:28:11.840 --> 00:28:16.117
I I have my own views on this, which is

00:28:16.200 --> 00:28:17.757
I think

00:28:17.840 --> 00:28:21.757
like we talked about, a lot of specific

00:28:21.840 --> 00:28:25.117
like task-oriented things, right?
Like I'm a financial analyst or I'm a

00:28:25.200 --> 00:28:30.157
developer or I'm a copy editor,
um those jobs are going to change a lot.

00:28:30.240 --> 00:28:31.597
And a lot of that work I think will be

00:28:31.680 --> 00:28:34.397
able to be done by by AI tools.

00:28:34.480 --> 00:28:38.477
But I think ultimately there's this very

00:28:38.560 --> 00:28:42.757
real phenomenon, which is that humans
like to be with other humans.

00:28:42.840 --> 00:28:46.877
We like to spend time together
with each other. We like to

00:28:46.960 --> 00:28:50.797
um learn from each other. We like
to be creative. We like to spend time

00:28:50.880 --> 00:28:55.517
understanding the other person and we're
social creatures, right? And I think I'm

00:28:55.600 --> 00:28:58.957
imagining that in a world where AI
is able to do a lot more of the productive

00:28:59.040 --> 00:29:03.037
day-to-day work that we do,
those skills are going to become a lot

00:29:03.120 --> 00:29:05.517
more important and much more prized.

00:29:05.600 --> 00:29:09.517
Because ultimately, like if you're in a
work environment and you're like, "Well,

00:29:09.600 --> 00:29:12.997
I could just ask Claude like write
a bunch of code for me." You are You are

00:29:13.080 --> 00:29:16.477
going to choose to talk to the developer
that's going to explain to you like why

00:29:16.560 --> 00:29:21.237
something broke or why we chose to build,
you know, a tool the way that we

00:29:21.320 --> 00:29:25.517
did. And I think sort of expanding
this outside of the realm of just the

00:29:25.600 --> 00:29:27.557
technology industry, the example I often

00:29:27.640 --> 00:29:31.157
use is in medicine. Right today,

00:29:31.240 --> 00:29:35.317
we hire doctors who are really good
diagnosticians. We're like, "Hey, can

00:29:35.400 --> 00:29:37.957
you tell me what's wrong with me?
I don't feel well." And you're like

00:29:38.040 --> 00:29:40.597
basically paying this doctor to say like,
"Here's a set of things that could

00:29:40.680 --> 00:29:43.997
be wrong with you. This is the one that's
the most likely. Let me run some tests."

00:29:44.080 --> 00:29:46.917
Guess what? AI is going to get
really good at doing that.

00:29:47.000 --> 00:29:51.277
But the thing that an AI tool can't do
is actually like look at you and examine

00:29:51.360 --> 00:29:55.237
you and also like help
understand how you're

00:29:55.320 --> 00:29:57.197
feeling and help you feel better.

00:29:57.280 --> 00:30:00.517
Right? There's a There's a reasonable
body of medical literature that

00:30:00.600 --> 00:30:03.197
indicates that people who have a good
relationship with their doctor, they

00:30:03.280 --> 00:30:07.517
just like their doctor, um have better
clinical outcomes than people who do not

00:30:07.600 --> 00:30:10.477
like their doctor. Like that's really
hard to explain. But like what's

00:30:10.560 --> 00:30:13.877
probably going on?
Probably the doctor like

00:30:13.960 --> 00:30:16.677
tries a little bit harder to understand
what's what's wrong with you. Maybe they

00:30:16.760 --> 00:30:18.997
run a set of tests that were unexpected.

00:30:19.080 --> 00:30:23.157
Um and I think those skills, right? That
bedside manner is going to be like five

00:30:23.240 --> 00:30:26.877
times more important in a world where
you're not trying to cram that into one

00:30:26.960 --> 00:30:31.477
of seven things that you're looking for
to make a doctor qualified to treat you.

00:30:31.560 --> 00:30:34.077
>> And when you are
thinking about the future,

00:30:34.160 --> 00:30:38.757
what AI use cases you're
most personally excited about?

00:30:38.840 --> 00:30:43.997
>> Oh man. Um I think I think
for me personally, um I'm I mean

00:30:44.080 --> 00:30:47.797
I'm a career manager. So, I spend most
of my time with people. And we have this

00:30:47.880 --> 00:30:50.277
sort of I don't know,
again, it's maybe one day

00:30:50.360 --> 00:30:53.317
there's going to be a word for it, but
there's this phenomenon where everybody

00:30:53.400 --> 00:30:56.997
thinks that AI is not going to come
for their job because they're so special.

00:30:57.080 --> 00:30:59.357
And I am I am extremely guilty of this.

00:30:59.440 --> 00:31:01.837
I was like, "People love people.

00:31:01.920 --> 00:31:04.517
People are going to want to report to me.
They're not going to want to report

00:31:04.600 --> 00:31:07.757
to Claude, obviously." Um but I actually

00:31:07.840 --> 00:31:10.037
think Claude is incredibly powerful as a

00:31:10.120 --> 00:31:15.157
management coach and as a person
to help you be a better leader. So, I use

00:31:15.240 --> 00:31:18.797
Claude. We write performance reviews
at Anthropic. And I've uploaded, you know,

00:31:18.880 --> 00:31:21.757
a lot of the people are that I work with
have reported to me for like, you know,

00:31:21.840 --> 00:31:25.117
three or four years. So, in general,
it's like you're the same person,

00:31:25.200 --> 00:31:28.157
they're the same person. You give them
feedback, but like, how much has really

00:31:28.240 --> 00:31:30.077
changed just like in the past 6 months?

00:31:30.160 --> 00:31:33.877
But I think Claude has been really
powerful in helping me to spot patterns

00:31:33.960 --> 00:31:38.197
about somebody. Um so, if you you know,
more data is better, but if you look

00:31:38.280 --> 00:31:41.997
back over the course of 3 to 4 years
of time of working with somebody, and

00:31:42.080 --> 00:31:45.197
you're like, "Wow, you know, you guys
have been circling around like this

00:31:45.280 --> 00:31:48.917
topical issue for the past 3 to 4
years." Like, maybe they need some

00:31:49.000 --> 00:31:52.357
additional coaching. Um or maybe they
need somebody sort of outside of you.

00:31:52.440 --> 00:31:55.317
It's just the type of thing that I think
tends to get missed because you're just

00:31:55.400 --> 00:31:56.957
in it day-to-day.

00:31:57.040 --> 00:31:59.357
And in the in the opposite direction,

00:31:59.440 --> 00:32:02.397
Claude is great at giving you feedback.

00:32:02.480 --> 00:32:06.117
So, like, I have I upload all of my
reports, upward feedback for me. And

00:32:06.200 --> 00:32:09.597
sometimes Claude will kind of very
kindly be like, "It sounds like you

00:32:09.680 --> 00:32:12.517
haven't improved on this in the past year.
Like, maybe you should get some

00:32:12.600 --> 00:32:17.237
extra coaching, Danielle." Um but but I
think those I think Claude's ability to

00:32:17.320 --> 00:32:20.797
kind of coach and help people be
the best versions of themselves, I think

00:32:20.880 --> 00:32:24.077
there's a version of that that makes
sense in the workplace, uh in people's

00:32:24.160 --> 00:32:27.797
personal lives, that I think could
be done kind of, you know, quite quite

00:32:27.880 --> 00:32:31.757
carefully, but that I think could be
really powerful. Um and then the second

00:32:31.840 --> 00:32:35.677
is I have two little kids. So, I have
a almost 5-year-old and almost 1-year-old.

00:32:35.760 --> 00:32:39.477
And I have to tell you, number one,
best thing Claude has ever done is help me

00:32:39.560 --> 00:32:42.357
through potty training. Um that was

00:32:42.440 --> 00:32:46.517
not a fun experience. And Claude
made it just like a little bit it was

00:32:46.600 --> 00:32:50.077
empathetic, like very actionable. There
were some diagrams, I don't need to tell

00:32:50.160 --> 00:32:53.157
you guys, but um it was really really

00:32:53.240 --> 00:32:57.117
useful. And I think Claude's ability
to help in particular um overwhelmed

00:32:57.200 --> 00:33:01.557
parents is going to be really powerful
because there's so much bad information.

00:33:01.640 --> 00:33:03.797
It's like every time you Google
is something wrong with your kid, the

00:33:03.880 --> 00:33:08.077
answer is yes. And I think Claude
is a lot more measured and like can be

00:33:08.160 --> 00:33:11.477
interactive in a way that I
think is really, really helpful.

00:33:11.560 --> 00:33:13.077
>> Daniela, before we turn to student

00:33:13.160 --> 00:33:15.997
questions, when you think about the next

00:33:16.080 --> 00:33:18.837
generation of AI leaders and builders

00:33:18.920 --> 00:33:21.717
who are with us here today in this room,

00:33:21.800 --> 00:33:26.880
what's the one thing you hope
they take from your journey?

00:33:27.520 --> 00:33:30.197
>> I would say, can I do two? >> Go ahead.

00:33:30.280 --> 00:33:33.877
>> Okay. Um I would say
the first is it sounds so

00:33:33.960 --> 00:33:36.637
trite and so I almost like I almost

00:33:36.720 --> 00:33:41.277
won't say it, but I truly think like
following something that you really care

00:33:41.360 --> 00:33:45.317
about, something that you are passionate
about is the most important thing you

00:33:45.400 --> 00:33:48.157
can do. Um there are so many great ideas

00:33:48.240 --> 00:33:52.557
and if you don't feel the sort of like
burning feeling of like this is a thing

00:33:52.640 --> 00:33:54.877
that needs to exist in the world and I I

00:33:54.960 --> 00:33:57.157
I will just run through walls to be able

00:33:57.240 --> 00:34:00.517
to do it, um it's just it matters for

00:34:00.600 --> 00:34:04.877
the times when like it's not fun and when
it sucks. Like you just have to be

00:34:04.960 --> 00:34:08.397
able to say like I remember why
this matters to me, I remember why it's

00:34:08.480 --> 00:34:12.157
important, whether it's to me personally
or it's because of a type of change that

00:34:12.240 --> 00:34:16.317
I want to see happen in the world. There
will be time, there certainly have been

00:34:16.400 --> 00:34:20.197
at Anthropic, times where we're like
this is not the most fun part, right?

00:34:20.280 --> 00:34:24.237
There are parts that are tough. And so
being able to relate it back to why you

00:34:24.320 --> 00:34:26.917
decided to do this in the first place
and what matters to you I think is so

00:34:27.000 --> 00:34:31.637
important. And then the second I would
say is I I for you know, this generation

00:34:31.720 --> 00:34:33.357
in particular and I think really in the

00:34:33.440 --> 00:34:35.957
past, you know, 5 to 10 years, this

00:34:36.040 --> 00:34:38.917
concept that like

00:34:39.000 --> 00:34:41.557
being in business doesn't have to be in

00:34:41.640 --> 00:34:44.157
tension with with doing good. I think

00:34:44.240 --> 00:34:46.997
that is very that is a very new idea and

00:34:47.080 --> 00:34:49.517
I think it is really special and I have

00:34:49.600 --> 00:34:51.877
been so impressed at the sort of

00:34:51.960 --> 00:34:54.677
generation of

00:34:54.760 --> 00:34:57.837
founders and you know, just like

00:34:57.920 --> 00:35:00.557
creators who are thinking in that in

00:35:00.640 --> 00:35:02.797
that way, right? Like there's this kind

00:35:02.880 --> 00:35:07.837
of marriage of innovation
and social impact. I think

00:35:07.920 --> 00:35:11.877
Stanford has always been exceptional
at this, but I think that is a very new

00:35:11.960 --> 00:35:16.317
concept and I think there's like more
appetite for it today, right? I think

00:35:16.400 --> 00:35:20.597
there can sort of be this feeling
of like only the kind of like mean sucky

00:35:20.680 --> 00:35:25.437
people can like build a business.
I just don't think that's true and I think

00:35:25.520 --> 00:35:27.437
increasingly I feel that the desire to

00:35:27.520 --> 00:35:32.117
do good is a strong

00:35:32.200 --> 00:35:35.917
it's a strong correlate
with actually doing well.

00:35:36.000 --> 00:35:39.000
>> Turn to the students.

00:35:39.480 --> 00:35:43.317
>> Hi Daniela, my name is Brandon. I'm
a second-year MBA student here. Thank you

00:35:43.400 --> 00:35:47.117
for joining us.
There's a debate about whether we're in

00:35:47.200 --> 00:35:51.037
an AI bubble and people usually mean
three different things when they say

00:35:51.120 --> 00:35:54.757
that. Company valuations,
how much companies

00:35:54.840 --> 00:35:58.717
are spending on infrastructure,
or whether the pace of AI progress is

00:35:58.800 --> 00:36:02.237
actually sustainable.
Which of those three are you most

00:36:02.320 --> 00:36:05.837
worried about and which do you
think people are most wrong about?

00:36:05.920 --> 00:36:08.117
>> Mhm, that's a great question.

00:36:08.200 --> 00:36:13.197
Um, yeah, we need like different like we
need like air bubbles and glass bubble.

00:36:13.280 --> 00:36:17.237
They're very different.
I see I see what you're saying. Um,

00:36:17.320 --> 00:36:22.237
I think I think the one
that probably I don't

00:36:22.320 --> 00:36:26.237
know if I would say I'm the most worried
about, but I think is is is a is valid

00:36:26.320 --> 00:36:28.037
as a concern about this industry in

00:36:28.120 --> 00:36:32.637
general is it is a high capital

00:36:32.720 --> 00:36:37.837
expenditure business. And that inherently
brings some risks along with it.

00:36:37.920 --> 00:36:41.397
This is probably stuff you all already
know, but it is really expensive to

00:36:41.480 --> 00:36:45.797
train these models. It takes a lot
of compute and that compute is in scarce

00:36:45.880 --> 00:36:49.317
supply and when you put those things
together like there's a lot of demand,

00:36:49.400 --> 00:36:51.877
there's not a lot of supply.
I'm not [laughter] an economics

00:36:51.960 --> 00:36:56.467
professor, but I think that means
the price goes up. Um and so

00:36:56.550 --> 00:36:59.837
>> [laughter] >> So the compute

00:36:59.920 --> 00:37:03.797
that is sort of the lifeblood
of these companies.

00:37:03.880 --> 00:37:07.197
Um you have to buy really far in advance.

00:37:07.280 --> 00:37:09.997
And so you're essentially making a bet
on the future. You're like we think

00:37:10.080 --> 00:37:13.797
we're going to need this much
compute at this period of time.

00:37:13.880 --> 00:37:17.677
That's a really that's a really big
expenditure to make and like it is it is

00:37:17.760 --> 00:37:20.997
a little bit harrowing to work at any
of these companies. I think if I think if

00:37:21.080 --> 00:37:23.397
someone doesn't tell you that maybe
with the exception of Google because they

00:37:23.480 --> 00:37:27.357
have their public company have so
much money. But I think certainly for

00:37:27.440 --> 00:37:31.317
Anthropic, for Open AI like you're kind
of making a calculated bet that you're

00:37:31.400 --> 00:37:35.277
going to be able to pay
that money back over time.

00:37:35.360 --> 00:37:38.917
We obviously are very bullish on this.
I think the revenue from both of those

00:37:39.000 --> 00:37:41.797
companies is like unbelievable.
It's something that I

00:37:41.880 --> 00:37:45.637
think you know we hear all the time like
venture capital is nothing like this has

00:37:45.720 --> 00:37:48.837
ever happened before, right?
It's impossible to imagine a business you

00:37:48.920 --> 00:37:52.237
know getting to the kind of revenue
numbers that are being talked about on

00:37:52.320 --> 00:37:56.637
such a short time scale. And if that
ever were to change, there would be a

00:37:56.720 --> 00:37:59.437
problem, right? Both of these companies
have bought a lot of compute for the

00:37:59.520 --> 00:38:03.557
future. It's very expensive. And so
I think that is probably the risk that

00:38:03.640 --> 00:38:06.717
feels it's not crazy
to be worried about that.

00:38:06.800 --> 00:38:10.317
We obviously think we're in a very
good position. I think the industry as a

00:38:10.400 --> 00:38:13.157
whole is, but that could
change anytime. And I

00:38:13.240 --> 00:38:16.477
think it's important to remember that
like this is ultimately a bet, right?

00:38:16.560 --> 00:38:19.317
It's ultimately the industry thinking
hey this is going to have a lot of

00:38:19.400 --> 00:38:23.960
returns to it, but like we
could absolutely be wrong.

00:38:25.000 --> 00:38:29.997
>> Hi, I'm Daniel Yash, MBA student. Uh my
question is, what does Anthropic believe

00:38:30.080 --> 00:38:33.997
is the right balance between government
regulation and AI innovation? And what

00:38:34.080 --> 00:38:37.437
do you wish governments around
the world were doing differently?

00:38:37.520 --> 00:38:42.637
>> Great question.
Um So I I think this is an area where

00:38:42.720 --> 00:38:46.077
um I think the conversation about it has

00:38:46.160 --> 00:38:48.317
been you know, sadly as sort of is the

00:38:48.400 --> 00:38:53.117
case I think today just just in the sort
of political climate, it's like hard to

00:38:53.200 --> 00:38:57.077
have a nuanced discussion.
And I I think that's a shame

00:38:57.160 --> 00:38:59.197
because I think it is a really nuanced

00:38:59.280 --> 00:39:01.677
question.

00:39:01.760 --> 00:39:06.397
I think sensible regulation will
need to be part of the story for

00:39:06.480 --> 00:39:09.757
artificial intelligence, right? It's
a very different technology than any other

00:39:09.840 --> 00:39:13.197
technology that's ever been built before.
And even the last generation of

00:39:13.280 --> 00:39:16.197
technologies probably could have
benefited from slightly more regulation

00:39:16.280 --> 00:39:19.117
in my personal opinion. Um that being

00:39:19.200 --> 00:39:23.837
said, we're not blind to the fact
that like you need to have some room to

00:39:23.920 --> 00:39:27.677
maneuver as a company to be able to try
things if you want to come up with the

00:39:27.760 --> 00:39:31.037
next generation of incredible products
that people want to use and that are

00:39:31.120 --> 00:39:33.197
adopted. And I think this is one of

00:39:33.280 --> 00:39:38.237
these things that my actual most
kind of critical hope for the

00:39:38.320 --> 00:39:41.437
conversation is that it doesn't become
politicized, which I fear it already

00:39:41.520 --> 00:39:45.717
has, right? It's sort of like, oh,
like regulation bad, innovation good, or

00:39:45.800 --> 00:39:50.077
innovation bad, regulation good. I think
it's just really complicated. Um I think

00:39:50.160 --> 00:39:53.557
there are areas of regulation that just
don't make a lot of sense. And then I

00:39:53.640 --> 00:39:57.077
think there are areas of regulation
that are absolutely critical to make the

00:39:57.160 --> 00:40:00.117
technology be developed in a way that
is good for people and that will prevent

00:40:00.200 --> 00:40:03.757
bad things from happening to the people
that rely on it every day and to the

00:40:03.840 --> 00:40:07.237
broader world.
My hope is that in an ideal world what

00:40:07.320 --> 00:40:09.397
that would look like is

00:40:09.480 --> 00:40:12.517
technology companies and regulators like

00:40:12.600 --> 00:40:15.157
working hand-in-hand because we have the

00:40:15.240 --> 00:40:20.397
information about how the technology can
be abused because we see it every day,

00:40:20.480 --> 00:40:23.637
right? We have safeguards teams and
security teams that look at like how are

00:40:23.720 --> 00:40:27.277
people poking on this and what are
the what are the actual risks, but

00:40:27.360 --> 00:40:31.397
regulators know how to provide
a framework and a system that can actually

00:40:31.480 --> 00:40:36.037
be followed and and enforced. And so
maybe it's overly optimistic, but I I

00:40:36.120 --> 00:40:39.197
still hold out hope that that future's
possible where I think the two sides can

00:40:39.280 --> 00:40:43.477
find common ground and say like how do
we ensure that we're able to develop the

00:40:43.560 --> 00:40:46.717
next, you know, amazing technology
that doesn't even exist yet or the next

00:40:46.800 --> 00:40:49.837
company that's going to be like
whatever, the next Google or the next

00:40:49.920 --> 00:40:52.117
meta, um but that we just put some

00:40:52.200 --> 00:40:57.760
common sense regulations in place
that that help protect people.

00:40:58.200 --> 00:41:01.317
>> Hi Daniela, thanks for being here.
My name's Jackie Kimmel, I'm an MSX

00:41:01.400 --> 00:41:04.077
student. My question is AI's gaining

00:41:04.160 --> 00:41:09.037
access to increasingly sensitive
personal data such as our health data.

00:41:09.120 --> 00:41:12.677
Uh what do you think individuals should
actually be doing to protect their

00:41:12.760 --> 00:41:18.197
privacy? >> I think this is also
an excellent question. Um

00:41:18.280 --> 00:41:22.637
you know, the first thing that I'll say
is you would be surprised at how common

00:41:22.720 --> 00:41:25.877
of a use case it is to ask Claude
medical questions. I think it's like one

00:41:25.960 --> 00:41:29.477
of our most common just casual use cases.
People I use it all the time,

00:41:29.560 --> 00:41:33.077
right? I'm like this is wrong with my kid,
this is wrong with me, help me.

00:41:33.160 --> 00:41:36.357
I think there's I think there's two I
think there's two sides to this. I think

00:41:36.440 --> 00:41:38.757
the first is it's actually

00:41:38.840 --> 00:41:42.597
it's actually the companies' jobs to use

00:41:42.680 --> 00:41:45.517
and protect your data with care.

00:41:45.600 --> 00:41:49.997
I think that's extremely important.
Um I bluntly think like people should hold

00:41:50.080 --> 00:41:54.237
companies accountable for using their
data carefully, um because it is very

00:41:54.320 --> 00:41:56.837
personal. And I think for example, our

00:41:56.920 --> 00:41:59.997
decision to not put ads in Claude, um is

00:42:00.080 --> 00:42:03.917
partly predicated on this belief that AI
technology is just different, right?

00:42:04.000 --> 00:42:06.797
People have conversations with AI tools

00:42:06.880 --> 00:42:11.437
that are are much more personal
than even what you would put on a an

00:42:11.520 --> 00:42:14.157
Instagram account or um

00:42:14.240 --> 00:42:18.557
on social media of any form.
And so I think the first is like with that

00:42:18.640 --> 00:42:21.197
knowledge comes more responsibility
for the technology companies to actually

00:42:21.280 --> 00:42:24.837
protect your data. Um the second is I

00:42:24.920 --> 00:42:28.077
think from a personal perspective like

00:42:28.160 --> 00:42:31.997
I don't know that I have the perfect
answer. Um I can certainly tell you like

00:42:32.080 --> 00:42:35.437
a lot of people use it
for medical questions.

00:42:35.520 --> 00:42:37.957
I would really think about safety from

00:42:38.040 --> 00:42:43.157
the perspective of like don't take
the models on faith about medical things.

00:42:43.240 --> 00:42:46.037
Um in my own experience Claude has been

00:42:46.120 --> 00:42:48.637
right more often than my doctors about

00:42:48.720 --> 00:42:51.637
complex medical cases and I would never

00:42:51.720 --> 00:42:56.597
do something without checking with a
licensed medical professional. Right? We

00:42:56.680 --> 00:43:00.157
we um we are very open about the fact that

00:43:00.240 --> 00:43:04.277
like the models make things up sometimes.
They get confused. They don't

00:43:04.360 --> 00:43:05.957
know you they can't examine you, right?

00:43:06.040 --> 00:43:07.917
Just like I was saying on stage. So I

00:43:08.000 --> 00:43:09.477
think like having some healthy

00:43:09.560 --> 00:43:13.677
skepticism is extremely correct. Um but

00:43:13.760 --> 00:43:18.397
think of it as like if you had a friend
who was a really good doctor who maybe

00:43:18.480 --> 00:43:21.517
wasn't a specialist. So you're like
I'm seeing a specialist and I would like

00:43:21.600 --> 00:43:25.477
help kind of being guided in this
conversation with my doctor. I think

00:43:25.560 --> 00:43:30.037
Claude is a great tool for that. Um
it's great for helping you think of like

00:43:30.120 --> 00:43:33.197
things that might be going on that you
might not know. But I think my number

00:43:33.280 --> 00:43:36.357
one recommendation is please do not just
do medical things that any I I mean I

00:43:36.440 --> 00:43:37.637
know you're all too smart to do this.

00:43:37.720 --> 00:43:41.477
But like any AI tool just
says like go do X like

00:43:41.560 --> 00:43:45.277
look at it with some skepticism
and actually talk to a professional.

00:43:45.360 --> 00:43:47.797
>> Thank you to the students. And Daniela,

00:43:47.880 --> 00:43:52.677
before we let you go we have to do our
view from the top rapid fire tradition.

00:43:52.760 --> 00:43:54.877
>> Love it. >> Are you ready?
>> I'm so ready.

00:43:54.960 --> 00:44:01.557
>> Okay, let's go. What would you
major in if you were back in college?

00:44:01.640 --> 00:44:05.237
>> [laughter] >> Can I if I
say business does that just

00:44:05.320 --> 00:44:08.477
get me out of this [laughter] entirely?

00:44:08.560 --> 00:44:13.197
Um What would I major in? I I would
probably major in literature again. I

00:44:13.280 --> 00:44:16.957
know that sounds crazy.
I loved I like to read.

00:44:17.040 --> 00:44:20.077
>> What's your favorite thing
about working with your brother?

00:44:20.160 --> 00:44:23.827
>> Oh, um Thanksgiving
dinner. No, I'm just

00:44:23.910 --> 00:44:24.517
>> [laughter]

00:44:24.600 --> 00:44:28.037
>> Um I would say
that we like know so much

00:44:28.120 --> 00:44:32.117
about each other and we're able to have
like sometimes we're able to say things

00:44:32.200 --> 00:44:35.237
to each other that nobody else in the
company can. Right? Like sometimes we

00:44:35.320 --> 00:44:38.397
can get away with something that no
one else feels like they could.

00:44:38.480 --> 00:44:41.197
>> What about Lee's favorite thing
about working with your brother?

00:44:41.280 --> 00:44:44.557
>> Um Thanksgiving dinner. Uh no, um

00:44:44.640 --> 00:44:47.957
I think just like the um needing to also

00:44:48.040 --> 00:44:52.077
have separation between like
our personal relationship and our work

00:44:52.160 --> 00:44:56.077
relationship. So, we build in time every
week when we hang out outside of the

00:44:56.160 --> 00:44:59.557
office, but I think it that's hard.
Just that like we were siblings for a long

00:44:59.640 --> 00:45:02.997
time before we were co-founders. We will
be siblings for a long time after we're

00:45:03.080 --> 00:45:06.237
co-founders and making sure to just like
con- continue to water that relationship

00:45:06.320 --> 00:45:10.077
outside of work. >> Favorite
book you found at the library

00:45:10.160 --> 00:45:14.037
in your office. >> Ooh, oh man.
Um I don't know if I've

00:45:14.120 --> 00:45:17.357
discovered a new book, which maybe means
I should get I was like, I like reading.

00:45:17.440 --> 00:45:19.757
Just kidding. Um I should probably

00:45:19.840 --> 00:45:24.397
probably go look more, but um I did
I was reminded of a favorite book

00:45:24.480 --> 00:45:27.917
there. Maybe that counts. Um there's
a book called The Guns of August. I don't

00:45:28.000 --> 00:45:31.037
know if there's any World War I
like I'm not saying I'm seeing some

00:45:31.120 --> 00:45:34.757
blank faces, so maybe not. It's a great
book. If you're curious about World War I,

00:45:34.840 --> 00:45:39.357
um I I picked it up from the library and
actually reread it um because I read it

00:45:39.440 --> 00:45:42.517
I think maybe right after college
or something. But I think it's a really

00:45:42.600 --> 00:45:44.237
important study of just like the

00:45:44.320 --> 00:45:47.157
individual people and personalities that

00:45:47.240 --> 00:45:51.677
resulted in and sort of led to um you
know, the beginnings of what became

00:45:51.760 --> 00:45:55.517
World War I and just how
much like so many individual

00:45:55.600 --> 00:45:58.797
events and and sort of people and
personalities hinged on this like really

00:45:58.880 --> 00:46:03.357
ultimately sort of tragic and terrible
thing that happened. So, anyway,

00:46:03.440 --> 00:46:05.037
Blinkist shoutout of read that book.

00:46:05.120 --> 00:46:08.757
It's great. >> Amazing.
>> And if Anthropic had ended up with a

00:46:08.840 --> 00:46:11.117
different name, what would it be?

00:46:11.200 --> 00:46:16.497
>> Oh, man. Um, wow, we went through
some like truly tragic ideas before

00:46:16.580 --> 00:46:19.277
[laughter] we came up with Anthropic.
I think for some reason we were like very

00:46:19.360 --> 00:46:23.077
into birds at the time.
We were like Sparrow Systems. I think

00:46:23.160 --> 00:46:26.797
no idea where that one came from,
but um, I think actually now that I'm

00:46:26.880 --> 00:46:28.757
remembering, I think some of the like

00:46:28.840 --> 00:46:30.917
model names from the early days were all

00:46:31.000 --> 00:46:34.357
birds. So, there was like Bert, which

00:46:34.440 --> 00:46:36.797
then became like we had a Snuffleupagus.

00:46:36.880 --> 00:46:40.717
That's not a bird, but like then anyway,
we had this kind of like bird thing. I

00:46:40.800 --> 00:46:43.877
don't know. There were some
terrible bird names. Thankfully,

00:46:43.960 --> 00:46:47.797
better decisions prevailed and we named
it Anthropic, which is now impossible to

00:46:47.880 --> 00:46:50.037
imagine as not her name. >> Imagine.

00:46:50.120 --> 00:46:53.117
And finally, not a rapid-fire question.

00:46:53.200 --> 00:46:55.117
Best advice you ever received.

00:46:55.200 --> 00:46:59.557
>> Ooh. Drop the

00:46:59.640 --> 00:47:01.957
ant. No, um, I'm trying to think of

00:47:02.040 --> 00:47:03.517
um,

00:47:03.600 --> 00:47:07.237
best advice I've ever received.

00:47:07.320 --> 00:47:11.917
I think probably, you know, I will
say when we were thinking about leaving

00:47:12.000 --> 00:47:15.197
and it was it was

00:47:15.280 --> 00:47:19.677
it's now in retrospect. People are like,
"Of course you guys you all left and you

00:47:19.760 --> 00:47:24.397
founded Anthropic." It didn't feel that
way at the time. We were like, "This is

00:47:24.480 --> 00:47:28.117
a really, you know, this is kind of a
crazy thing to do. Maybe we should maybe

00:47:28.200 --> 00:47:32.197
we should stay. Maybe we could make
this work." Um, but we talked I talked to

00:47:32.280 --> 00:47:34.197
one, you know, sort of friend and mentor

00:47:34.280 --> 00:47:38.197
outside of work and she was like,

00:47:38.280 --> 00:47:41.517
"Honestly, I don't think you really
need to be on the phone with me. Like you

00:47:41.600 --> 00:47:43.277
already know what the right answer is."

00:47:43.360 --> 00:47:47.797
And I think that is I think in general
like when you're in a moment of

00:47:47.880 --> 00:47:49.077
"Is this the right thing for my life?"

00:47:49.160 --> 00:47:52.117
often you actually know what the right
answer is. Um, and I think that was

00:47:52.200 --> 00:47:55.517
really good advice.
>> Daniela, it's been a pleasure.

00:47:55.600 --> 00:47:59.680
>> Thank you. >> Thank you so much.
Thanks for having me.

00:48:04.650 --> 00:48:06.670
>> [music]

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