WEBVTT

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Very well. Um, welcome everybody.

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Welcome to Journal House and a big
welcome to to our audiences that are

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joining us online. But above all, a big
welcome to Dario Amade, the chief exec

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of Anthropic.
>> And thank you for having me.

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>> Not at all. So Dario, um, we're at

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Davos. There's a lot going on, but I
wanted to start with a a big picture

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question, which I'll characterize like

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this. It feels to me that this time last

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year, everybody was very excited about
AI and everyone was talking about what

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AI can do, its potential,
its capabilities. It feels to me as though

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the debate has shifted somewhat this

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year to be more less what can AI do to

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what is AI doing to the world. Um, and I

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I know that you think a lot about these
things. So my question is do you think

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businesses, policy makers, governments,
whatever are doing enough to prepare for

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the impact?

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>> No. Um I'll explain the longer version

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now. um uh you know I've been watching

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this field for 15 years and I've been in

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this field for 10 years and and one of

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the things I've most noticed is that

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there's been a a surprisingly

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uh h on on a a uh you know the same

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trajectory whereas the kind
of public opinion and

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the reaction of the public has
oscillated wildly. I would say that in

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in in two different ways. One is the
capabilities of the technology. Every

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three to six months we have this reversal
of of polarity where the media

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is incredibly excited about what the
technology can do. It's going to change

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everything and then it's you know it's
all a bubble. It's all it's all going to

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fall apart. And and what I see is this

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smooth exponential line where similar to

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Moors law for compute, we basically have
a Moore's law for intelligence where the

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model is getting more and more
cognitively capable every every few

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months. And that that march has just
been constant. The up and down the we

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invented a new thing. It's all going
to crash. It's hitting a wall. It's it's

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going to go crazy. That that is a public
perception phenomenon. That's on the

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capability of the technology. I think
there's a similar thing on the polarity

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of whether the technology is good or

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bad. Um uh you know in 2023 and 2024

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there was a lot of concern about AI
right there was you know concern AI you

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know AI is going to AI are going to take

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over. There was a lot of talk about AI

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risk AI misuse. Then in 2025 the

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political wind shifted as you say to AI

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opportunity and now they're sort of

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shifting back and and I think throughout

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throughout all of this the the approach
that I have tried to take and the

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approach that anthropic has tried to

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take is is is one of constancy of saying

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that there is balance here uh and and uh

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balance of a very strange form because I

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think the technology is very extreme in

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in what it's capable apable of doing
but I think it's positive impacts and its

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negative impacts that you know they
they both exist right I wrote this essay

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machines of loving grace about a year
and a half ago it had a very radical

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view of the upside of AI that you know

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it would it would help us to you know

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cure cancer eradicate tropical diseases

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you know kind of bring bring economic
development to you know parts of the

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world that haven't seen it and I my view
hasn't changed I believe all of those

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things um but the the other side of it

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which you know I'm I'm I'm now uh uh uh

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you know kind of writing writing more

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about and and you know may may release

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something about soon is yeah there you

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know bad things will happen as well if

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we just take just for an example as as

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one of the risks we take the economic

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side of it um my my view is the

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signature of this technology is it's

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going to take us to a world where we

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have very high GDP growth and

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potentially also very high unemployment

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and inequality. Now, that's not
a combination we've we've almost ever seen

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before, right? You you think of it as high
GDP growth, that's lots of stuff to

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do, lots of jobs for everyone. It's
always been like that in the past. We've

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never had a technology that's this

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disruptive. So, the idea that we could

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have five or 10% GDP growth, but also,

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you know, 10 10% unemployment, it's it's

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not logically inconsistent at all. It's

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just never happened that that that that

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way before and I'm I'm really quite uh

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you know for those both reasons excited

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and worried. If I take an example

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something like AI coding um you know the

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latest model release claude opus 4.5 um

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I have some uh uh engineers some

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engineering leads within enthropic who

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have basically said to me I don't write
any code anymore. I I you know I just

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let Opus do the work and I edit it.
We just released a new thing called Claude

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Co-work. We can go into go you know we

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can go into that later but this was a a

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version of our tool cla code for

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non-coding. This was built in a week and
a half almost entirely with Claude Opus.

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There are still things for the software
engineers to do right. It's like even if

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the software engineers are only doing
10% of it they can you know they still

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have a job to do or they can take
a level up that's not going to last

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forever. the models are going to do more
and more and so there's an incredible

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you this is a microcosm you can
see there's an incredible amount of

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productivity here software is going to

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become cheap maybe essentially free the

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premise that you need to amortize a

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piece of software you build across
millions of users that may start to be

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false like for this meeting it might

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cost a few cents to just say I don't
know let's let's let's make some app so

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people can talk to each other or you

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know I it just it it just may be very

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flexible and recyclable but but at the

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same time there are whole jobs whole

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careers that we built built for decades
that that may not be be present and you

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know I think we can deal with it.
I think we can we can adjust to it but I

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don't I don't think there's an awareness
at all of what of what is coming here

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and the magnitude of it.
>> So how that's it's so interesting when

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you say that. So how do you think in a

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world of of high GDP growth but also

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high unemployment you know what does
that do to society and you say there are

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you know people aren't thinking about
it now can you give concrete examples of

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how society might organize
itself to adapt to such a world

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>> yeah so you know I
think I think there's

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a few things the the first thing that
that we've done that we've focused on

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and this is not a solution so much as it
is a first step is we have this thing

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called the anthropic economic index index.
We've had it for about a year.

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We've updated it, I think, four or five
times now. Uh, and what that does is

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it's a real-time index that lets you

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track um, you know, what our model
Claude is being used for. It goes across

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all the conversations and kind of uses
Claude in a privacy preserving way to

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you know kind of kind of statistically
query how Claude is being used. What are

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the tasks it's being used for? To what

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extent it is is it automating versus

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augmenting tasks? um what industries
is it being used in? How is it diffusing

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across states in the United States
and countries countries in the world? We

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we've just added kind of more and more
detail here and and and my view is until

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we can measure the shape of this economic
transition. Any policy is going

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to be blind and and misinformed, right?

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Many many policies have gone wrong
because they're they're based on

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premises that are that are fundamentally

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incorrect. So So that's that's step one.

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Step two is I think we need to think

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very carefully about how do we allow
people to adapt right people can adapt

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more quickly or they can adapt more
slowly. This can mean adapting to use

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the technology within existing jobs.

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This can mean adapting you know from one

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job to another job. For example, I think

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there are probably going to be more jobs
in the physical world and less jobs in

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the knowledge work economy. Now maybe
eventually robotics you know makes

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progress but I think that's on a that's
on a slower trajectory. So so that's

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one. Are there jobs that have you know

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still kind of you know really value
a human touch? Um some of them do some of

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them don't. We may find out how
important how important that is in the

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market and where it's most important
at the level of companies. What are the

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moes when software becomes cheap and then
subsequently the rest of knowledge

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work becomes cheap? We don't know.
We've never quite asked that question. and

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we've thought about modes in a certain
way. So there's there's going to be a

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huge scramble at the level of at the level
of companies. So you know teaching

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people to adapt, teaching them what
to expect, I think is the second step. And

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and the third step is I think there's

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there's going to need to be some role

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for government in in a displacement

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that's that's this macroeconomically
large. I just I just don't see how I

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don't see how it doesn't happen. The pie
is going to grow much larger, right?

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like the money is going to be there like
you know we may you know the budget may

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balance without us doing anything
because there's so much growth um uh the

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issue is distributing it to the right
people and so and so I think I think

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this is probably a time to worry less
about disincentivizing growth and and

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worry more about making making sure that

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everyone gets gets gets a part of that
growth um which which you know which I

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know is the opposite of the prevailing
sentiment now but I think technological

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reality is about to change in a way
that forces our ideas to change.

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>> So obviously in your in your desire
to create this greater sense of urgency,

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are you speaking to people in the
administration? I mean Anthropic hasn't

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always been the sort of first on the
guest list for this administration, but

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do do you have people there
that you're talking to?

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>> I've I I I have said it to them myself.

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Um uh and you know to to be clear, there

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are plenty of things there are plenty
of things we agree on, right? You know, I

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think the AI action plan that the

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administration put out in the middle of

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this year actually had some some,
you know, some very good ideas here. You

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know, I think I think, you know,
we probably probably agreed with the vast

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vast majority of it. But I think most of
all, we just want to say these things in

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public and kind of ha and kind of have
a public debate about them, right? We

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don't we don't control policy. I think

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the most useful thing we can do is

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describe to the world what we're seeing

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and and provide data to the world and

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then and then you know it's it's left to

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the the public in in a democracy to to

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you know to take that data and and to use
it and to use it to drive policy. We

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can't drive policy on our own.

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>> Are you going to be
talking to officials

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while you're here? Have you have
you been along to USA House yet?

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>> Uh I've not I've not been to USA House.

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you know I I will I will I I you know
I will be talking to officials during my

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during my trip to Davos.

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>> Good. So um just to go back to uh

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Anthropic then. So you founded Anthropic
um specifically because you were worried

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about that you didn't think that OpenAI
was taking safety seriously enough. Now

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some people say that you know
the competitive pressures mean that you've

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gone more hawkish now. I mean, do are
those competitive pressures, you know,

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to keep up with China and keep ahead
of China, all the rest of it, have they do

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you think they have compromised
your safety principles?

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>> So, we've taken a very different route

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than some of the other players have. Uh,

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I I think one of the good choices we

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made early was to be a company that is

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focused on enterprise rather than

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consumer. Um, and I think, you know,
it's it's very hard to fight your own

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business incentives. it's easier
to choose a business model where there's

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less need to fight your own business
incentives. So, you know, I have a lot

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of worries about consumer AI that it

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kind of leads to needing to maximize

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engagement. It leads to slop. You know,

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we've seen a lot of stuff around ads

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from from some of the other players.
You know, Enthropic is not a player that

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works like that or needs to work like
that. We just sell things to businesses

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and those things directly have value,
right? We we don't need to monetize a

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billion free users. We don't need
to maximize engagement for a billion free

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users because we're in some, you know,
death race with with some other, you

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know, some other some other large player.
And and so I think that has let

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us think more carefully. But but even
with that, we have made sacrifices. You

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know, we do all these tests on our

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models that others have not done. um
uh you know some other players have done

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them but but you know I think we've been

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the most uh you know aggressive in you

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know when we when we run tests that you
know show up concerning behaviors in our

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model you know these things around
deception, blackmail, syphancy that we

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show in tests and then are present in

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all of the models but you know we make

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sure to always talk to the public about

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about these things. Um and you know we
we've we you know we've pioneered the

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science of mechanistic interpretability
for looking inside models. So you know

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have we been have we been perfect?
Of course not. I think we've done a

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generally good job. I mean you mentioned
China. I you know I think that's not

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about competition. That is that is about

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actually the public benefit mission is

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I'm I'm worried that if autocracies
lead in this technology it will be a bad

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outcome for every single
person in this room.

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>> What are your specific concern there?
Is it about the chips about you know

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sharing data around chips or

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>> Yeah. Well, I think I mean you know the

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the the the kind of means is selling
the chips, right? That's the that's the um

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thing that I think will have the most
impact on on who is ahead and and who's

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not. But you know the the concern and

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you know it's not about any particular
country or certainly not the people in

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any country. It's about it's about a

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form of government. Um I am concerned

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that AI may be uniquely well suited to

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autocracy and to deepening
the repression that we see in autocracies.

00:14:08.400 --> 00:14:11.597
We already see it in the kind of
surveillance state that is possible with

00:14:11.680 --> 00:14:15.917
today's technology. But if you think
of the extent to which AI can make

00:14:16.000 --> 00:14:18.397
individualized propaganda, can break

00:14:18.480 --> 00:14:21.597
into any computer system in the world,

00:14:21.680 --> 00:14:25.437
um, uh, can, uh, you know, surveil

00:14:25.520 --> 00:14:29.357
everyone, everyone in a population,
detect descent everywhere and suppress

00:14:29.440 --> 00:14:32.397
it, you know, make, you know, a a huge

00:14:32.480 --> 00:14:34.637
army of drones that could go after each

00:14:34.720 --> 00:14:39.437
each each individual person. It's it's
it's really scary. It's it's it's really

00:14:39.520 --> 00:14:44.157
scary and we have to stop it.
But again, is that something that you feel

00:14:44.240 --> 00:14:47.677
governments aren't paying enough
attention to? I mean I you know I think

00:14:47.760 --> 00:14:52.077
I think it's I think it's fair to say
that you know obviously you know

00:14:52.160 --> 00:14:53.357
different countries think of themselves

00:14:53.440 --> 00:14:55.197
as having having geopolitical

00:14:55.280 --> 00:14:59.277
adversaries but the specific focus on we

00:14:59.360 --> 00:15:01.597
we don't want autocracies to get this

00:15:01.680 --> 00:15:03.757
powerful technology and we should have

00:15:03.840 --> 00:15:06.717
targeted policies like we don't need to

00:15:06.800 --> 00:15:09.357
fight them like we just need to not sell

00:15:09.440 --> 00:15:13.597
these chips um uh that you know I I

00:15:13.680 --> 00:15:16.637
think there's not enough focus on that.

00:15:16.720 --> 00:15:20.637
I want to talk a bit more about Claude
because I think it's fair to say it's

00:15:20.720 --> 00:15:23.037
having a real moment.
I mean we >> it's having a moment.

00:15:23.120 --> 00:15:24.877
>> It is having a moment
and we we recently

00:15:24.960 --> 00:15:26.957
reported on how engineers and regular

00:15:27.040 --> 00:15:30.077
users are like are getting clawpilled.

00:15:30.160 --> 00:15:33.677
Um and I just wondered how you feel
about the state of the business today

00:15:33.760 --> 00:15:37.437
versus a year ago. Yeah,
I mean this is one of these things

00:15:37.520 --> 00:15:39.277
that's been the growth of the business

00:15:39.360 --> 00:15:42.557
has been fast but but but kind of on on

00:15:42.640 --> 00:15:47.357
the same smooth exponential curve as the
technology. So you know we you know we

00:15:47.440 --> 00:15:49.437
have this this this revenue curve that

00:15:49.520 --> 00:15:52.237
in 2023 went from zero to roughly 100

00:15:52.320 --> 00:15:54.797
100 million went in 2024 went from

00:15:54.880 --> 00:15:59.357
roughly 100 million to roughly billion
2025 went from roughly a billion to

00:15:59.440 --> 00:16:03.357
roughly 10 billion. Not exactly.
These are rounded numbers, but but that is

00:16:03.440 --> 00:16:06.957
that is roughly it. You know, through
that, if you go on Twitter, every couple

00:16:07.040 --> 00:16:10.077
months, it's like, "Oh my god,
Anthropic's changing the world. Oh my

00:16:10.160 --> 00:16:13.037
god, you know, Anthropics totally
destroy, you know, just just the

00:16:13.120 --> 00:16:15.517
excitability of the of of the moment."

00:16:15.600 --> 00:16:19.997
But but we just watch it and we watch
this curve. It's fast. It's constantly

00:16:20.080 --> 00:16:21.757
progressing. It's given us confidence.

00:16:21.840 --> 00:16:25.517
We never know for sure if it's going to
continue. It might not. But but that has

00:16:25.600 --> 00:16:29.277
been empirically what we have observed
the whole time. And then there are these

00:16:29.360 --> 00:16:32.717
moments where even though the curve
is smooth, there's a breakout moment. And

00:16:32.800 --> 00:16:34.957
so right now I think there's a breakout

00:16:35.040 --> 00:16:37.837
moment around claude code among

00:16:37.920 --> 00:16:42.557
developers. Um you know this this thing
about being able to make whole apps and

00:16:42.640 --> 00:16:44.557
doing things end to end. Again that

00:16:44.640 --> 00:16:47.037
advanced gradually but with our most

00:16:47.120 --> 00:16:52.077
recent model opus 4.5 it just kind
of reached an inflection point right where

00:16:52.160 --> 00:16:55.757
the you know the the improvement was
gradual but you know it's it's just like

00:16:55.840 --> 00:16:59.357
you know boiling the fraud. you know,
you see you see the gradual improvement

00:16:59.440 --> 00:17:03.437
and then there's a specific point
at which suddenly that's the point people

00:17:03.520 --> 00:17:07.357
notice. I think the second thing
that has that has maybe accelerated that

00:17:07.440 --> 00:17:10.077
further is we looked at claude code and

00:17:10.160 --> 00:17:12.877
one of the things we we noticed is there

00:17:12.960 --> 00:17:14.797
were a lot of people inside enthropic

00:17:14.880 --> 00:17:17.197
and outside enthropic who were not

00:17:17.280 --> 00:17:20.557
technical but who realized that claude

00:17:20.640 --> 00:17:24.397
code could do these incredible agentic
tasks for you. It couldn't just write

00:17:24.480 --> 00:17:26.557
code. It could also organize your to-do

00:17:26.640 --> 00:17:31.917
list or plan your projects or organize
your folders or, you know, process a

00:17:32.000 --> 00:17:35.757
bunch of information and kind
of summarize. So, the the idea that not

00:17:35.840 --> 00:17:38.477
just a chatbot, but a gentic tasks were

00:17:38.560 --> 00:17:43.837
were needed. Um, non-technical people
were realizing it and they wanted it so

00:17:43.920 --> 00:17:46.957
much that they were wrestling with the
command line, right? Nontechnical, you

00:17:47.040 --> 00:17:50.237
know, they have no reason if you're
not a programmer. It's such a terrible

00:17:50.320 --> 00:17:53.037
interface to use if you're not
a programmer. But people were going

00:17:53.120 --> 00:17:55.517
through and using it anyway. And so
I looked at that and I said that looks

00:17:55.600 --> 00:17:59.037
like unmet demand. And so we we used we

00:17:59.120 --> 00:18:01.037
used claude code again in like two

00:18:01.120 --> 00:18:03.997
weeks, you know, to make basically kind

00:18:04.080 --> 00:18:07.837
of a version with a better UI that's

00:18:07.920 --> 00:18:10.957
customized for tasks that tasks other

00:18:11.040 --> 00:18:13.997
than other than code. And you know you

00:18:14.080 --> 00:18:15.917
know we released it and you know within

00:18:16.000 --> 00:18:18.397
like a day it you know it had like you

00:18:18.480 --> 00:18:21.837
know you know you know most of the the

00:18:21.920 --> 00:18:25.837
metrics on it were like four times
as much as anything anything we'd ever

00:18:25.920 --> 00:18:29.837
released. Uh so you know those are the two
moments. I don't know that these are

00:18:29.920 --> 00:18:33.597
new capabilities but you know there
was just one of these kind of consensus

00:18:33.680 --> 00:18:37.757
moments where people got really excited
and it's it's it's driving adoption

00:18:37.840 --> 00:18:41.677
really fast. I think people are catching
up to what the technology is is capable

00:18:41.760 --> 00:18:44.637
of because it's reached a certain point
and because we built interfaces that

00:18:44.720 --> 00:18:47.277
have made it accessible.
>> Can you tell us a bit about how you

00:18:47.360 --> 00:18:51.357
personally in your life,
your family life, use a Gentic AI?

00:18:51.440 --> 00:18:55.997
>> Yeah. Um, so you know when I'm writing
like you know an essay or something or

00:18:56.080 --> 00:18:59.677
like you know things I say in front of
the company um I feel like a fair amount

00:18:59.760 --> 00:19:03.837
of my job is writing and so I kind of help
I have Claude you know come up with

00:19:03.920 --> 00:19:08.717
sources, help me help me with my writing
that that kind of thing. And then

00:19:08.800 --> 00:19:11.517
obviously you're having this great
moment and I think there you're it's

00:19:11.600 --> 00:19:14.637
widely expected that you're going to IPO
this year. Can you tell us a bit about

00:19:14.720 --> 00:19:17.597
your plans for that? >> Yeah,
I mean you know we we don't know

00:19:17.680 --> 00:19:21.277
for sure we don't know for sure what
we're going to do and you know I would

00:19:21.360 --> 00:19:23.437
say we're we're more focused on just

00:19:23.520 --> 00:19:26.477
keeping the revenue curve going better

00:19:26.560 --> 00:19:30.477
selling the models to people you know
warning about the societal impacts and

00:19:30.560 --> 00:19:33.357
bringing bringing the good societal
impacts. So, you know, that's the that's

00:19:33.440 --> 00:19:36.797
that's kind that's kind of the highest
that's kind of the highest priority

00:19:36.880 --> 00:19:40.557
right now. But, you know, I'm not saying
anything novel if I say that this is an

00:19:40.640 --> 00:19:42.797
industry with very high capital demands.

00:19:42.880 --> 00:19:47.037
Um uh and you know that the there's only
so much at some point that the private

00:19:47.120 --> 00:19:49.757
markets the private markets can provide.

00:19:49.840 --> 00:19:54.077
>> So, another model that's absolutely
having a moment is Gemini and it sort of

00:19:54.160 --> 00:19:58.237
surged to the top of the app store
recently and OpenAI declared code red

00:19:58.320 --> 00:19:59.917
and so everyone was got very excited

00:20:00.000 --> 00:20:02.877
about that. Do you worry about your

00:20:02.960 --> 00:20:05.357
ability to compete against uh Gemini

00:20:05.440 --> 00:20:08.077
given the sheer size of Google?

00:20:08.160 --> 00:20:10.237
>> So I think I think
this is another place

00:20:10.320 --> 00:20:12.637
where just just being different helps.

00:20:12.720 --> 00:20:15.517
So you know the enterprise strategy

00:20:15.600 --> 00:20:20.477
Google and open AI are fighting it out
in consumer right it they it is both is

00:20:20.560 --> 00:20:22.317
existential to both of them. Existential

00:20:22.400 --> 00:20:27.517
to open AI because that's their whole
business existential to Google because

00:20:27.600 --> 00:20:30.317
they have the search business and that's
that's what's being disrupted by this.

00:20:30.400 --> 00:20:34.557
So they they need to you know replace
replace themselves and and fight the

00:20:34.640 --> 00:20:36.877
disruption. So that's always their first

00:20:36.960 --> 00:20:41.117
priority and you know they they seem

00:20:41.200 --> 00:20:43.437
more f they seem much more focused on

00:20:43.520 --> 00:20:45.997
that than kind of operating in the

00:20:46.080 --> 00:20:50.557
enterprise. It's been great to see what
what Gemini is is capable of you know

00:20:50.640 --> 00:20:53.517
capable of in consumer. You know I think
I think they're you know I think they're

00:20:53.600 --> 00:20:57.277
going about it a different way. I was you
know I was just on a panel with with

00:20:57.360 --> 00:21:00.797
Demis who leads research at Google. you
know, I think I think he's a great guy.

00:21:00.880 --> 00:21:03.837
I've known him for 15 years,
so I'm rooting for him.

00:21:03.920 --> 00:21:07.197
>> Um, what you talk about differences.
One difference is I believe is that

00:21:07.280 --> 00:21:11.437
Anthropic doesn't have the ability
to generate videos and photos. Do you see

00:21:11.520 --> 00:21:13.597
that as a a potential weakness?

00:21:13.680 --> 00:21:17.357
>> Um, I you know, I think for enterprise
business, you know, there's there's not

00:21:17.440 --> 00:21:19.837
really a demand for like, you know, you

00:21:19.920 --> 00:21:22.797
know, photos of, you know, the cats

00:21:22.880 --> 00:21:26.397
riding donkeys or, you know, whatever

00:21:26.480 --> 00:21:28.477
whatever consumer video people want.

00:21:28.560 --> 00:21:32.237
There's maybe an edge case around like
slides and presentations, but if we ever

00:21:32.320 --> 00:21:35.917
need it, we can just buy a, you know, we
can just contract a model from from from

00:21:36.000 --> 00:21:38.557
from contract a model from someone else.

00:21:38.640 --> 00:21:41.837
So, you know, I don't I don't know
what will happen. I don't know what will

00:21:41.920 --> 00:21:45.917
happen in the future, but I at least
don't anticipate needing this. Um, and I

00:21:46.000 --> 00:21:49.277
think there are problems associated with
this. Like, you know, I think I think,

00:21:49.360 --> 00:21:52.557
you know, we look at the amount of short
form video out there, like a lot of it's

00:21:52.640 --> 00:21:54.477
fake, a lot of it's pretty pretty

00:21:54.560 --> 00:21:57.357
addictive, a lot of it's slop. Um, not

00:21:57.440 --> 00:22:01.757
to say that all of it is bad or that
necessarily doing it means you're bad,

00:22:01.840 --> 00:22:04.077
but but it's not it's not a part of the

00:22:04.160 --> 00:22:08.957
market that I'm I'm like, you know,
tripping over myself to to get involved

00:22:09.040 --> 00:22:11.677
in. >> [snorts] >> You mentioned
that you were on a panel

00:22:11.760 --> 00:22:15.997
with Disa Savis and when we were
chatting earlier yesterday, you talked

00:22:16.080 --> 00:22:19.757
you said something that I thought
was very interesting that scientists are

00:22:19.840 --> 00:22:23.277
approaching the AI AI era or scientists
who are leading these big AI companies

00:22:23.360 --> 00:22:28.157
are approaching the era differently from
sort of uh tech entrepreneurs. Can you

00:22:28.240 --> 00:22:30.077
say a bit more about
what you mean by that?

00:22:30.160 --> 00:22:32.317
>> Yeah. Well, so you know when I when you

00:22:32.400 --> 00:22:34.797
think about this technology, it's really

00:22:34.880 --> 00:22:37.757
it's really the intersection of research

00:22:37.840 --> 00:22:42.237
that has going been going on for many
decades, much of which was academic in

00:22:42.320 --> 00:22:44.237
nature until you know a decade decade

00:22:44.320 --> 00:22:48.717
and a half ago. um and the kind of scale

00:22:48.800 --> 00:22:52.237
needed to um you know needed to develop

00:22:52.320 --> 00:22:56.237
and deploy these technologies over
the last decade and a half which has only

00:22:56.320 --> 00:23:00.477
come from the large scale kind
of internet and social media companies

00:23:00.560 --> 00:23:04.797
right they have the infrastructure they
have the cash so we've seen a world in

00:23:04.880 --> 00:23:09.117
which some of the companies are
essentially led by people who who have a

00:23:09.200 --> 00:23:12.317
scientific background that's
my background that's Dennis's background

00:23:12.400 --> 00:23:16.717
some of them are led by the generation
of entrepreneurs that social media. Um,

00:23:16.800 --> 00:23:18.877
and I I I think that's very different

00:23:18.960 --> 00:23:21.677
that um, you know, scientists, there's a

00:23:21.760 --> 00:23:23.837
long tradition of scientists thinking

00:23:23.920 --> 00:23:27.277
about the effects of the technology they

00:23:27.360 --> 00:23:29.197
build, of thinking of themselves as

00:23:29.280 --> 00:23:34.637
having responsibility for the technology
they build, not ducking responsibility,

00:23:34.720 --> 00:23:38.157
right? They're they're they're mo
they're motivated in the first place by

00:23:38.240 --> 00:23:40.557
creating something for the world. And so

00:23:40.640 --> 00:23:42.957
then they they worry in the cases where

00:23:43.040 --> 00:23:45.757
that something can go wrong. Um I think

00:23:45.840 --> 00:23:48.397
the motivation of entrepreneurs

00:23:48.480 --> 00:23:53.357
particularly the generation of uh
uh social media entrepreneurs is very

00:23:53.440 --> 00:23:57.757
different. The selection effects that
operated on them the way in which they

00:23:57.840 --> 00:24:01.437
interacted with you might say
manipulated consumers is very different.

00:24:01.520 --> 00:24:04.557
Um and and so I think that that leads to

00:24:04.640 --> 00:24:07.197
that leads to different attitudes.

00:24:07.280 --> 00:24:11.517
Um now we've been taking some questions
from readers who submitted their

00:24:11.600 --> 00:24:14.557
questions online but before we do that I
just wanted to ask you one more thing

00:24:14.640 --> 00:24:18.717
which is that you know again big picture
tensions are running very high at the

00:24:18.800 --> 00:24:23.357
moment between the US and the EU. Do you
wonder about how that might impact how

00:24:23.440 --> 00:24:26.237
you operate your business
should things escalate.

00:24:26.320 --> 00:24:29.997
>> Look I mean you know we we have we
have always you know we only speak for

00:24:30.080 --> 00:24:33.597
ourselves. We've always thought
of ourselves as as as, you know, kind of

00:24:33.680 --> 00:24:36.157
kind of our own thing and and and and

00:24:36.240 --> 00:24:38.637
and independent, right? We don't go out

00:24:38.720 --> 00:24:42.557
of our way to be for or against anyone.

00:24:42.640 --> 00:24:45.437
But when we, you know, when we disagree

00:24:45.520 --> 00:24:47.757
on policy, we say so. When we agree on

00:24:47.840 --> 00:24:50.557
policy, we say so. And we we really keep

00:24:50.640 --> 00:24:53.037
it focused on AI. And and so, you know,

00:24:53.120 --> 00:24:57.677
I, you know, I haven't seen any
reluctance in folks in other parts of

00:24:57.760 --> 00:25:01.757
the world to work with us, right?
We're our own thing. We're providing AI

00:25:01.840 --> 00:25:03.997
models. We try to do that responsibly.

00:25:04.080 --> 00:25:07.357
>> I mean, there's been a lot of talk
this week about AI sovereignty. I'm not

00:25:07.440 --> 00:25:09.677
entirely sure what
everybody seems to have.

00:25:09.760 --> 00:25:11.197
>> I don't know what it means either.

00:25:11.280 --> 00:25:12.987
>> You don't have your own definition.

00:25:13.070 --> 00:25:14.557
[laughter]

00:25:14.640 --> 00:25:17.597
>> Good. Okay. Well, look, so um we have

00:25:17.680 --> 00:25:20.477
solicited questions from readers online.

00:25:20.560 --> 00:25:22.557
Um so I'm going to start now with one

00:25:22.640 --> 00:25:25.677
from Trevor Lumis. Um and his question

00:25:25.760 --> 00:25:27.997
is what is the single most important

00:25:28.080 --> 00:25:30.317
technical breakthrough still missing to

00:25:30.400 --> 00:25:32.957
make frontier AI reliably safe and

00:25:33.040 --> 00:25:36.557
controllable in real world deployment?

00:25:36.640 --> 00:25:39.517
>> So I think we need
to make more progress

00:25:39.600 --> 00:25:43.197
on mechanistic interpretability which
is the science of looking inside the

00:25:43.280 --> 00:25:45.677
models. One of the problems when we

00:25:45.760 --> 00:25:51.117
train these models is that we don't know
you can't be sure they're going to do

00:25:51.200 --> 00:25:54.557
what you think they're going to do. You
can talk to the model in one context. It

00:25:54.640 --> 00:25:59.117
can say all kinds of things. Just as
with a human, that may not be a faithful

00:25:59.200 --> 00:26:03.437
representation of what they're actually
thinking. If they tell you, "I'm doing X

00:26:03.520 --> 00:26:06.957
because why?" They might be doing X
for a completely different reason. They

00:26:07.040 --> 00:26:10.557
might be lying about doing X. Like,
we're very used to these problems with

00:26:10.640 --> 00:26:15.117
humans, but but they exist
with AI as well. And so any kind of

00:26:15.200 --> 00:26:17.517
phenomenological testing or training we

00:26:17.600 --> 00:26:21.037
can't be certain of. But similar to how

00:26:21.120 --> 00:26:25.597
you know you can learn things about
human brains by doing an MRI or an X-ray

00:26:25.680 --> 00:26:29.437
that you can't learn just by talking
to a human, the science of looking inside

00:26:29.520 --> 00:26:31.997
the AI models. I am convinced that this

00:26:32.080 --> 00:26:35.357
ultimately holds the key to making the

00:26:35.440 --> 00:26:37.837
model safe and controllable because it's

00:26:37.920 --> 00:26:39.837
the only ground truth we have.

00:26:39.920 --> 00:26:42.957
>> Right. Okay. Um,
I have another question

00:26:43.040 --> 00:26:45.597
here from Jim O' Connell. How will AI

00:26:45.680 --> 00:26:50.477
affect current K12 educational
achievement gaps? Very practical

00:26:50.560 --> 00:26:52.477
question there from no doubt a parent.

00:26:52.560 --> 00:26:56.397
>> Yeah. So, you know, I I my you know,
there there's kind of the short-term

00:26:56.480 --> 00:26:59.997
stuff about, you know, people using
AI for cheating, which I think is, you

00:27:00.080 --> 00:27:04.717
know, I think is problematic. But,
you know, in in in relative terms, okay,

00:27:04.800 --> 00:27:09.997
fine, you can have a kind of a different
way of um uh you know, of teaching using

00:27:10.080 --> 00:27:12.877
AI. And you know, we've we've thought
about that. We've released versions of

00:27:12.960 --> 00:27:15.197
of Claude for for education that are

00:27:15.280 --> 00:27:20.637
kind of designed around that. But I
think the harder problem behind that is

00:27:20.720 --> 00:27:24.077
okay, what skills are we actually
teaching in in the world of AI? What

00:27:24.160 --> 00:27:26.077
does education look like look like in

00:27:26.160 --> 00:27:28.397
the world of AI? And it's it's not so

00:27:28.480 --> 00:27:33.277
easy because the disruption is broad
and we don't you know, if someone asked me

00:27:33.360 --> 00:27:37.517
what exactly what career should I go
into, the the uncomfortable truth is I

00:27:37.600 --> 00:27:41.517
I'm I'm not sure. I can't tell
the direction that it's going to I can't

00:27:41.600 --> 00:27:43.117
tell the direction that it's going to go

00:27:43.200 --> 00:27:46.237
yet. I I will say that I think we should

00:27:46.320 --> 00:27:48.957
go back to some concepts that we had

00:27:49.040 --> 00:27:50.877
earlier about education like we've we've

00:27:50.960 --> 00:27:53.597
had a very kind of like um like

00:27:53.680 --> 00:27:56.477
economically inflected almost mercenary

00:27:56.560 --> 00:28:00.877
notion notion of education. Um and and
and I think one of the things that we

00:28:00.960 --> 00:28:04.877
should do is we should maybe move away
move away from that notion back to the

00:28:04.960 --> 00:28:07.357
idea that like you know education is is

00:28:07.440 --> 00:28:09.837
is designed to shape you as a person is

00:28:09.920 --> 00:28:12.317
designed to build character is is you

00:28:12.400 --> 00:28:15.357
know is is is is kind of designed to

00:28:15.440 --> 00:28:19.357
enrich you and and like make you
a better person. I think that's actually a

00:28:19.440 --> 00:28:21.837
safer foundation for for
education in the future.

00:28:21.920 --> 00:28:25.437
>> That sounds I'm rather envious of
the kids kids who are yet to be educated.

00:28:25.520 --> 00:28:28.637
It's the kind of education I think we'd
all have liked to have. Um, so to be

00:28:28.720 --> 00:28:31.517
fair to everybody in the room then
I just I think we've got time for one

00:28:31.600 --> 00:28:36.237
question if anybody would
like to ask a question. Yeah.

00:28:36.320 --> 00:28:38.077
This lady here.

00:28:38.160 --> 00:28:41.277
>> Yeah. Yeah. No, no, he's the mic. Um, I

00:28:41.360 --> 00:28:45.677
wanted to ask from a point of view
of the AI labs, uh, what kind of

00:28:45.760 --> 00:28:49.677
responsibility do you hold when there
are economies, countries, and people

00:28:49.760 --> 00:28:51.997
that are being left behind? um would

00:28:52.080 --> 00:28:54.877
that expand into structurally involving

00:28:54.960 --> 00:28:58.797
them um slowing down or actually making

00:28:58.880 --> 00:29:00.637
sure that they're not being left out?

00:29:00.720 --> 00:29:05.117
>> Yeah, I worry about that on a whole
bunch of scales and it's you know it's

00:29:05.200 --> 00:29:07.117
not just country versus country.

00:29:07.200 --> 00:29:11.117
Certainly I worry about the developing
world versus the developed world. Um

00:29:11.200 --> 00:29:14.077
where you know sometimes the the
developed world developing world will

00:29:14.160 --> 00:29:15.677
get passed by by by technological

00:29:15.760 --> 00:29:18.077
revolutions. But I but you know I also

00:29:18.160 --> 00:29:21.117
worry about divisions within a country.

00:29:21.200 --> 00:29:23.037
It it has occurred to me as I've looked

00:29:23.120 --> 00:29:26.077
across our customers as the startups are

00:29:26.160 --> 00:29:30.637
so fast to adopt AI and the traditional
enterprises because they're bigger

00:29:30.720 --> 00:29:35.117
because they do a specific thing they
move much slower. Um and we can see it

00:29:35.200 --> 00:29:39.677
in our economic data. We can see the
diffusion of the technology from states

00:29:39.760 --> 00:29:43.517
within the US that adopt it quickly
and states that move slowly. It is

00:29:43.600 --> 00:29:47.197
diffusing. It's getting out there. But
there's there's no question that there's

00:29:47.280 --> 00:29:50.477
a differential here. If I were
to describe the nightmare and then then

00:29:50.560 --> 00:29:54.957
I'll try to describe some you know what
I think of as a solutions. The nightmare

00:29:55.040 --> 00:29:57.037
would be that there's there there's like

00:29:57.120 --> 00:30:00.317
this emerging zeroth world country of

00:30:00.400 --> 00:30:03.277
like 10 million people that's like seven

00:30:03.360 --> 00:30:07.437
million people in the b you know in like
Silicon Valley and you know 3 million

00:30:07.520 --> 00:30:11.277
people kind of scattered scattered
scattered throughout that that you know

00:30:11.360 --> 00:30:15.677
is kind of forming its own economy
and is becoming decoupled or disconnected

00:30:15.760 --> 00:30:20.477
right maybe the 10% GDP growth looks
like 50% GDP growth in that part of like

00:30:20.560 --> 00:30:25.357
this technology is so crazy it can pull
things apart that way. Um I think that

00:30:25.440 --> 00:30:28.637
would be a really bad world.
I would almost say that it was a dystopian

00:30:28.720 --> 00:30:33.517
world, right? And and we should think
about how to stop that. Um there are,

00:30:33.600 --> 00:30:35.437
you know, a number of things anthropic

00:30:35.520 --> 00:30:38.557
is thinking about or doing. Um one is as

00:30:38.640 --> 00:30:42.797
regards the developing world, we're we
are starting to do a lot of work around

00:30:42.880 --> 00:30:46.317
public health. Um we've announced
stuff with, you know, Ronda Ministry of

00:30:46.400 --> 00:30:50.797
Education. We're doing a a lot of work
with the Gates Foundation. Um you know,

00:30:50.880 --> 00:30:53.917
it it would I wrote about this in machines
of loving grace. it it would be

00:30:54.000 --> 00:30:57.357
really great to get these fast economic
growth rates which in theory should be

00:30:57.440 --> 00:31:01.517
even faster because it's catch-up
growth um in the developing world that I

00:31:01.600 --> 00:31:03.037
predict we're going to get in in the

00:31:03.120 --> 00:31:05.437
developed world um you know within

00:31:05.520 --> 00:31:08.557
within countries we need to you know we

00:31:08.640 --> 00:31:13.197
need to think about you know how how
not to have a part of the world that just

00:31:13.280 --> 00:31:16.637
that just kind of decouples right
how do we get the economic growth to

00:31:16.720 --> 00:31:20.957
Mississippi that that you know that is
coming to this contained contained area

00:31:21.040 --> 00:31:24.797
of of of of Silicon Valley And so, you
know, there we've done work around kind

00:31:24.880 --> 00:31:29.517
of e economic mobility and e economic
opportunity. But I think both of these

00:31:29.600 --> 00:31:32.557
again are going to need to have some
some involvement of the government.

00:31:32.640 --> 00:31:34.877
We're going to find that that you know

00:31:34.960 --> 00:31:37.917
ideology will will not survive the

00:31:38.000 --> 00:31:39.597
nature of this technology. It won't

00:31:39.680 --> 00:31:45.037
survive reality. The things I'm talking
about, you know, while you could today

00:31:45.120 --> 00:31:48.157
say, "Oh, yeah, they're they're like
politically coded in some way," they're

00:31:48.240 --> 00:31:51.437
going to become bipartisan and universal
because everyone will recognize the

00:31:51.520 --> 00:31:55.277
necessity of it. Just mark my words,
we come back, if not next year, the year

00:31:55.360 --> 00:31:57.917
after, everyone's going to think this.

00:31:58.000 --> 00:32:01.437
>> Well, you've managed to end on a more
or less positive note. So, I'm going to

00:32:01.520 --> 00:32:04.957
draw a line there and say thank you
very much, Dario. That was really, really

00:32:05.040 --> 00:32:09.050
fascinating. >> Thank you
for having me. [applause]