The AI Tsunami is Here & Society Isn't Ready | Dario Amodei x Nikhil Kamath | People by WTF

Okay.

[music]

Heat. [music]

>> [music]

[music]

>> So I started playing with Claude. It's

getting to that point where sometimes it

surprises me by how much it [music]

knows me. I don't know if that makes

sense. It is surprising to me that we

are in my view so close to these models

reaching the level of human intelligence

and yet there doesn't seem to be a wider

recognition in society of what's about

to happen. It's as if this tsunami is

coming at us and you know it's so close

we can see it on the horizon and yet

people are coming up with these

explanations for oh it's not actually a

tsunami that's just a trick of the light

like there hasn't been a public

awareness of the risk.

What is India's role in all this?

>> Many other companies come here as

themselves a consumer company and they

see they see India as as a market,

right? A place to obtain consumers. We

actually see things a little bit

differently.

What did you do before founding

anthropic?

>> Yeah, so I was I was actually originally

a biologist. Um I uh you know did my

undergrad in physics, my uh PhD in

bioysics and you know I wanted to

understand biological systems so that I

could cure disease. uh and uh the the

you know the thing I noticed about

studying biology was its incredible

complexity that uh you know you know for

example if you look at the the protein

mass spec work that I did right trying

to find protein biomarkers it's it's

just really incredible how much

complexity there is right you have a

given protein it's like you know the RNA

gets spliced in a whole bunch of

different ways depending on where it is

in the cell then it gets

post-transationally modified

phosphorolated complex with a whole

bunch of other proteins and and I was

starting to despair that it was too

complicated for humans to understand.

And then as as I was doing this work on

biology, I noticed a lot of the early

work around Alexet, which is one of the

first neural nets like you know almost

15 years ago now. Uh and and I said wow

like you know AI is actually starting to

work. It has some things in common with

how the human brain works but you know

has the potential to be be larger and

scale better and learn tasks like

biology. Maybe this is ultimately going

to be the solution to uh you know to to

solving our problems of of solving our

problems of biology. So you know I I

went to work with Andrew Ing at BU. Then

I was at Google for a year. Then I

joined OpenAI a few months after it uh

started and was uh you know was was

basically led led um all of research

there for for for for several years. But

then eventually you know myself and a

few other of the of the employees just

kind of had our own vision for you know

how how we wanted to how we wanted to

make AI and what we wanted the company

to stand for. And so we went off and

found an anthropic.

>> How was it? Was it like a fork in how

OpenAI was thinking into what Anthropic

eventually did?

>> Yeah. You know, I would say, you know,

my conviction and the conviction of my

co-founders when we we founded Anthropic

were two of them. And I think one we

were starting to convince OpenAI of, the

other I was, you know, not I didn't feel

that we were convincing of. So the first

was the you know the conviction in the

scaling loss and the idea that you know

if you scale up models you give them

more data more compute again there are a

few modifications like RL but not really

very much it's pretty close to pure

scaling um you you find that you know

when you when you do that you you find

you know incredible increases in

performance and you know I was finding

that in like 2019 with with GPT2 um you

know when we just first saw the first

glimmers of the scaling laws And of

course there were a lot of folks you

know inside and outside who didn't

believe it at all and we really made the

case to leadership like this is this is

important this is going to be a big deal

and I think they were kind of starting

to believe us and ultimately went in

that direction and there was a second um

you know conviction I had which is look

you know if if these models are going to

be kind of general cognitive agents like

general cognitive tools that match the

capability of like the human brain we we

better get this right. The economic

implications are going to be enormous.

The geopolitical implications are going

to be enormous. The safety implications

are going to be enormous. It's going to

transform how the world works. And so we

need to do it in the right way. And and

you know, I think despite a lot of, you

know, kind of language verbiage about

doing it in the right way. I I was for a

variety of reasons just just not

convinced that at the you know,

institution that that I was at that that

there was a real and serious conviction

to to to to do it in the right way. And

so, you know, my my view is always, you

know, don't argue with someone else's

vision. Don't try to get someone to do

things the way the the way you want to.

If you have a strong vision and you

share that vision with a, you know, a

few a few other people, you should just

go off and do your own thing and then

you're responsible for your own

mistakes. You don't have to answer for

anyone else's. And and you know, maybe

your vision works out, maybe it doesn't,

but you know, you know, at least it's at

least it's yours.

>> [snorts]

>> Didn't OpenAI believe in scaling laws

cuz they went down the same path

themselves too, right?

>> Well, that Yeah. Yeah. We we succeeded.

>> Can you can you explain what scaling

laws are in very simple terms?

>> Um it's like if if you know you want a

chemical reaction to produce oxygen or

start a fire or something like that, um

you need different ingredients and you

know if you don't have one enough of one

ingredient the the reaction stops. But

if you you know if you put if you put

ingredients together in proportion you

know you get your you know your

explosion or your fire or fire or

whatever. And and for AI those

ingredients are data compute the size

you know the the size the size of the AI

model. And so the scaling laws just tell

you that like

the you know if you put in the

ingredients to the to the chemical

reaction the ingredients of data and

model size that what you get out is is

intelligence. Intelligence is the

product of a chemical reaction.

>> And what is intelligence?

intelligence as measured by the ability

to translate language or the ability to

write code or uh you know the ability to

answer questions correctly about a

story. Basically any cognitive task we

can think of any any any you know task

that exists in text or in images any any

task that you can you can do on a

computer.

How is the intelligence of today as you

are describing it different from what a

computer could do like 5 years ago?

>> Yeah, you know, I would say well I mean

for example 5 years ago a a computer you

could not ask a computer a question and

have it write a one-page essay on that

question. Um you could not uh ask a

computer uh to you know implement a

feature in code and have it implement

that feature in code. None of those

things were possible. You could not

generate an image. You could not

generate a video. You could not analyze

a video. You know, I could could get one

of those uh you know uh uh you know v

you know videos of like you know a

monkey juggling or something and you

know say what's going on in this video?

How many times did the ball change

hands? And right now you could get

Claude or another AI model to to to to

give you an answer on that. Um and and 5

years ago, you know, none of those

things were possible. What I'm I'm

trying to figure out has the definition

of intelligence changed per se?

>> Well, you know what I would say is five

years ago, you know, you could you could

Google and there might be a website that

you know would tell you a little bit

about this, right? But, you know, you're

just you're just looking up some text

that exists exists on the web, right?

You know, maybe it's not about how to

get a monkey to juggle. Maybe, you know,

maybe it's about how to get a a seal to

juggle. you know, is it's not quite

exactly the same thing because maybe

exactly the same thing doesn't exist.

Um, but you know, as as as we see when

when people use these models, uh, you

know, you can ask and you can actually

get an intelligent response. You can ask

a specific question and have the model

write, you know, one page about it or

you can give it a, you know, you can

give it a you can give it a

hypothetical. you know, what if I had,

you know, the monkey juggle clubs

instead of balls or, you know, what if I

did this thing and and that information

doesn't exist anywhere, you know,

whereas the model is able to kind of

think for itself and and come up with an

answer on its own. So, it's it's it's

something um you know, it's it's

something totally new. It's just it's

not just matching some of the text that

exists on the internet.

>> Fair. So, you know, this is more like a

conversation. So, feel free to like talk

about what you want to talk, not

necessarily related to the questions

that I'm asking.

>> You look very animated when you speak.

Did you ever teach?

>> Uh, you know, I I was originally an

academic and uh, you know, I thought

that I might become a professor. You

know, I I got my PhD. I went all the way

to being a a posttock at Stanford

Medical School and, you know, I was I

was aiming to become a become a

professor. Um so if I had become a

professor you know I I would would have

uh would have done that. Um uh but you

know as I mentioned uh you know I got

interested in AI and to work in AI

required a lot of computational

resources and that was mostly happening

in industry. So that took me off the

academic path and and you know into

industry and of course you know

ultimately through several steps led me

led me to start a company. But you know

sometimes I think I'm still like a

professor at heart

>> at this point. Dario, if AI is the most

relevant thing in the world, u if the

world is realigning in a way and AI is

determining who gets what and who

doesn't get what, I'm talking about

industries,

you today are probably the most relevant

person in the world. If

anthropic

in this last cycle, in this minute is

sitting on top of this pile. for

somebody who who was going on the path

of being a teacher to have arrived to

where you are today. Are you best

equipped for where you are today?

>> Well, I mean, you know, first I would

say a couple of things. You know, I I I

think there's a lot of there's a lot of

folks who are who are right relevant in

different ways, right? You know, even

within industry, there's the different

layers of the stack. There's like the

folks who make chips. There's the folks

even earlier who make semiconductor

manufacturing equipment. There's the

folks who make models like us. And then

there are other players who make models.

There's the folks who make kind of

applications after the models. Um uh you

know and then then there's a bunch of

other folks who have a say. There's you

know governments, there's like civil

society. So you know my my hope you know

isn't that there's uh you know just one

tiny set of people that's that's

relevant. I think we're trying to

broaden the set of people who are

relevant and you know turn it into a

turn it into a broader conversation. Um

but you know I think at the same time

your your question is a fair one and one

way I could interpret it is like you

know there's there's a certain

randomness to how you know kind of you

know a few people you know end up

leading these you know you know leading

these companies that kind of you know

grow so fast and it seems like you know

in the near future will power so much of

the economy. Um, and you know, I've said

openly publicly, not for the first time,

that I'm I'm at least somewhat

uncomfortable with the amount of

concentration of power that's happening

here. I would say almost overnight,

almost by accident. Um, and and you

know, we think, you know, about that in

a bunch of ways. You know, one is we

have an unusual governance structure,

something called the long-term benefit

trust. um you know it's it's a body that

that kind of ultimately appoints you

know the majority of the board members

for anthropic and is you know made up of

of financially disinterested financially

disinterested individuals. So that's

some you know check on what one one

single person is doing and then you know

I think I think as always the government

should play some role here. you know,

I've been an advocate of, you know,

proactive although, you know, sensible

that doesn't slow down the technology,

sensible regulation of the technology

because, you know, I think I think like

the people should have a say like

government, you know, governments and

the people who elect them should have a

say in in how this goes. So, I I

actually think of a lot of what I'm

trying to do as as kind of trying to

trying to preserve a balance of power.

um you know uh uh kind of you know

against the the the the the natural

grain of this technology

>> for someone like me who's sitting on the

outside and doesn't have a bone in this

competition

when I watch OpenAI talk about how

they're how they were a not for-p

profofit company or how

you are projecting humility in the

conversation that you're having right

now or how the American companies are

competing with the Chinese companies

which are coming about

this projection of humility where it is

for the larger good and not necessarily

for how I view the world as companies

with shareholders with investment and

revenues and seeking profit.

Is this par for the course? Is this

something you have to do?

>> So, you know, I I would I would put it

in the following way. You know, I I

would say the philosophy of Enthropic

from the beginning has been that we we

try not to make too many promises and we

try to keep the ones that we make. So,

you know, we we set ourselves up as, you

know,

a for-profit but public benefit

corporation with this LTBT governance

and we've maintained that. We've said

that you know our goal is to you know

stay on the frontier of the technology

but you know to work on uh uh you know

to work on uh um you know the safety and

security aspects of the technology.

We've pioneered the science of

interpretability. We've uh you know

pioneered the science of alignment. I

don't know if you saw but we recently

released a constitution for claude the

ability to align models in line with the

constitution. And you know, we've done a

bunch of policy advocacy and warning

about risks, right? Warning about risks

is not in our commercial interest,

right? Like people can come up with

conspiracy theories, but you know, I

will tell you saying that the models we

build could be dangerous. Whatever

people might say, that's not an

effective marketing strategy and that's

not the reason that we do it. And you

know, speaking up on when we disagree

even with the US administration on uh

you know, on on on policy matters,

right? we've we've we've spoken up,

right? We're willing to say, you know,

we disagree on this issue, like, you

know, we we've said that there should be

regulation of AI when all the other

companies and the administration have

said there shouldn't be regulation of

AI. And so that's both, you know, the

regulation of AI of AI holds, you know,

holds us back commercially as a company,

even though I think it's the right thing

to do. And it's, you know, it's it's

it's it's difficult to go against the

government and the other companies and

say this. We're really sticking our neck

out. So, we've we've taken a number of

actions that, you know, I see as really,

you [snorts] know, putting our putting

our money where where where our mouth is

here. I can't speak for the other

companies. You know, it's again, it's

quite possible that some people say

these things, uh, you know, and they

don't really mean them, but I wouldn't

look at what people say. I would look at

what people do.

If what you're saying gets the

government to act by regulation,

as the incumbent leaders in this space,

you get some kind of a regulatory

capture where it becomes harder for the

new people coming in as well. Right.

>> I I don't agree with that at all. the

regulation we've advocated for, for

example, SB uh 53 in California, um uh

exempted everyone uh who makes under

$500 million a year in in in uh in in

revenue, right? SB SB53 was a

transparency law which um you know uh uh

basically requires companies to um you

know to show um you know the the the the

safety and security tests that they've

run. Um, and it exempts all companies

under 500 million in revenue. So, it

really only applies to Enthropic and

three or four three or four other

companies. So, it only applies to the

companies that that have the resources

and and everything that we've advocated

for here uh uh not just SB53, but all

the proposals that we've made, the ones

that we've made in the past and the ones

that that we plan to make in the future

have this character. We're constraining

ourselves and a very small number of

additional um um um companies. We're

we're not uh people people who say that

need to look at the actual content of of

of what we're proposing because it

doesn't match that idea at all.

>> Fair.

I read your paper machines of loving

grace and the adolescence of technology

and

you seem to have had a 180°ree shift in

perspective almost from

optimism to skepticism over like two

years from 2024 to 2026.

Is there one moment in the last two

years that changed this for you? Did you

see something change?

>> Yeah, I actually wouldn't agree with the

question. I don't think I've had a shift

in perspective.

>> Um, I think the positive side and the

negative side are always something that

I've held in my head. And if you look at

the history of, you know, the things

that I've said, I mean, I've been

talking about risks for a very long

time. I've been talking about benefits

for a very long time. Um, you know, it

it it turns out that actually it takes

me a while to write one of these essays.

Um, you know, both

>> they're really large as well. They're

big essays.

>> They're like 30 pages.

>> Both both of these it's it's you know,

it's taken me like I I I spent for each

one I spent about a year having a kind

of vague vision of the essay in my head

and like trying to write it but like not

fully succeeding at writing it. and and

then you know in either case I had to I

had to be on vacation or somewhere where

I could you know where I could think

where the business day-to-day business

of running the company didn't didn't

occupy me. Um and and then I was finally

able to you know to to kind of write the

essay. So all of that is to say, you

know, I I I I started thinking about

what would be in adolescence of

technology almost the instant I finished

Machines of Loving Grace because I was

like, "Oh, you know, I want to inspire

people with the good vision, but I also

want to warn people with, you know, what

can go what can go wrong." And so it it

just it just took me a year to write it.

But really, both visions were in my

head. And I think they're both, you

know, I think they're both possible.

They're two different visions of the

future. And obviously, I want to get the

Machines of Loving Grace one, right? you

know, I want to solve all the problems

and have the have the positive vision,

but it's not a it's not a shift in

perspective. It's um it's me just um you

know, finding the time to write the

light and then the dark.

>> But have you had a change of

perspective?

>> You know, I would say overall I have I'm

about where I was before. I've not

gotten more positive, more nor more more

negative. There may be some places where

I've gotten more optimistic or things

have gone better than expected.

>> There may be places where I'm more

pessimistic and where things have gone

worse than expected, but on average they

sort of cancel each other out. I would

say I feel very good about you know how

things have gone with areas like

interpretability. Interpretability is

the science of seeing inside these

neural nets. you know, as a human would,

you know, look inside, you know, as we

would scan a human brain with an MRI or

a neural probe. Um, I've been amazed at

what we've been able to find. We've been

able to find, you know, neurons that

correspond to very specific concepts,

neural circuits that correspond to, you

know, keep track of how to do rhymes in

poetry. And so, we're starting to

understand what these models do, right?

We we we don't we just we just train

them in this kind of emergent way as you

would build a snowflake. But now we're

starting to be able to look inside and

understand them. I'm I'm also very

encouraged by some of the work on

alignment and constitutions. Um, you

know, making sure that models behave in

the way that we want and expect them to.

I think that's going pretty well. Um, I

felt pretty positive about that. Um I

think I felt maybe you know have been a

bit disappointed or felt a bit more

negative about some of the things that

are more like in the you know in the

kind of public awareness and the actions

of wider society. Um you know it it is

surprising to me that we are you know in

in my view so close to these models

reaching the level of human intelligence

and and yet there doesn't seem to be a

wider recognition in society of what's

about to happen. It's as if this tsunami

is coming at us and you know it's so

close we can see it on the horizon and

yet people are coming up with these

explanations for oh it's not actually a

tsunami it's you know that that you know

that's just a trick of the light like

it's some you know and I think along

with that there hasn't been a public

awareness of the risks and you know

therefore our governments haven't acted

to to address the risk there's even an

ideology that you know we should just

try to accelerate as fast as possible

which you know I understand the benefits

of the technology I wrote machines of

loving grace. But I think there hasn't

been an appropriate realization of the

risk of the technology and there

certainly hasn't been action. So I would

say that the the technical work on

controlling the AI systems has gone

maybe a little better than I expected

and kind of the societal awareness has

gone maybe a little worse than I

expected. So I'm I'm about where I was a

few years ago.

So in my own journey I'm you know when

something sounds complicated and I'm not

a programmer I don't have a background

in coding

so I used a bunch of tools for things

like research and a conversation both

ways but I never tried to figure out if

I could code using uh your tool for

example.

Recently I hired a developer just to

like push me to sit for a couple of

hours a day and teach me how to start

becoming more familiar with it

[clears throat]

largely because of you know something

like FOMO like the fear of missing out

on how the world is changing.

>> Uh so I started playing with claude u I

connected I used the connectors to

connect my Google drive mail and

calendar and a bunch of those things. I

started using the cowwork and then I

started using claude code to write

simple programs around

the industry that I am in which is

financial services uh basically to

research stock markets and stuff.

>> We even have a optimized cloud for

financial services. I don't know if

you've tried that but we even have that.

>> No. And then I went into claudebot which

is now open claw. I think clawbot became

something else and now is open claw and

I set it up on a Mac mini and connected

it to a telegram account and now I chat

with it and I I try and move files from

a to b work on a server on remote. It's

getting to that point where I'm not

talking about open claw but even claude

with all the connectors sometimes it

surprises me by how much it knows me. I

don't know if that makes sense.

>> Yeah. You know, one of my one of my

co-founders

um you know, he was writing this diary

with his kind of you know, his thoughts

and his fears. Um uh and he fed it into

Claude and uh you know he he asked

Claude to comment on it and Claude said,

"Here are some other fears you might

have that that I you that you know that

you haven't written down." Um and Claude

ended up being mostly right about those.

So it really gave this eerie sense of

like you know the model knows you the

model knows you super well that you know

that from a relatively small amount of

information it can learn a lot about you

and come to know you fairly well and you

know I I I you know like most things

with the technology right we talked

about the machines of loving grace and

adolescence of technology you know on

one hand something that knows you really

well can be a sort of angel on your

shoulder that you know that helps to

guide your life and make you a better

version of yourself and you know that's

the version we can aim for of course

something that knows you really well you

know can um you know it can it can you

know use what it knows about you to you

know to exploit you or manipulate you on

behalf of some agenda or sell your data

to someone else I mean you know this is

one reason we just you know don't like

the idea of you know using ads right you

know this this is because you're not

paying for the product like you're the

product and you know in this case the

the the the product then would be all

you know this model that knows you super

well and you know could use that in in

all kinds of in all kinds of nefarious

ways. So, you know, we need to make sure

we take the positive um uh the positive

road here and not the not the negative

road.

>> With Claude,

I need to use the connectors to give it

context to my life.

With Google, for example,

it already has the context to my life

because I use their worksheets and their

email and their drive and their chat and

everything like that.

for anthropic long-term will you also

have to own the ecosystem?

>> Yeah. I mean, you know, do

>> you have to build mail and chat and

>> Yeah. Yeah. You know, I don't think we

need to build all of those things. Um,

you know, it, you know, my my thought

would be, you know, we're going to it's

going to be a mixture of things we make

ourselves and integrating into others,

right? Like, you know, we can we can

integrate Claude into Google Docs. we

can integrate quad into into you know

Google sheets like you know we have

external connectors there we can you

know we're starting to do that with with

co-work you know same for Microsoft

office same for other tools so you know

I think I think we do whatever is you

know easiest and fastest to do you know

we we integrate into the existing tools

now it might turn out at some point that

the existing you know tools aren't

enough and we have kind of a different

vision you know we want to we might want

to slice things differently right? You

know, maybe traditional email doesn't

make sense or traditional spreadsheets

don't make sense given what you can do

in in AI. So, I you know, I don't

exclude that we could chop up products

in a different way, but we're we're

happy to use the ecosystem that exists

and work with anyone else, right? In

many ways, we're a platform company. We

allow many people to build on us, even

though we sometimes also build things

ourselves.

the the one thing this is a slight

digression but I think the one thing

that you're missing that

also your peer group is missing is in

society today people inherently distrust

anybody who claims to be doing good or

trying to do the right thing. So when

you and your peers are out saying I I

heard you and Deis speak at Davos. I was

in the room when you guys were talking

about how

me, you I don't mean me, how Dario, how

Deis and a bunch of other people have to

come together and

prevent things from changing too quickly

like you need to like meter it to a

certain extent.

When a person who is not in your world

in society on social media hears a few

people speak in a certain manner u

you're doing it in the manner that

creates more distrust than trust because

nobody believes on social media that

somebody wants to do the right thing or

do good. So it might be counterintuitive

but I think it needs a change of

strategy. If if you were to be more

capitalistic about this and own up to

the fact that you have shareholders and

you seek a profit, but this will help

you win, maybe it'll work more. Just

>> thought I don't No, I don't I don't

really uh I don't really agree with

that. Um I would again go back to the

idea that you know you know you you need

to judge us by the actions that we take.

Um, you know, I think the company has

taken a number of of of of actions over

its, you know, over its time that, you

know, I think I think, you know, show

that it's really serious about these

commitments. So, back in 2022, um, you

know, we had an early version of Claude,

Claude one. This was before chat GPT um

and we chose not to release this um

because we were worried that it would

kick off an arms race and and not give

us enough time to you know to build

these systems safely, right? It was it

was kind of a one-time overhang like we

could see the power of the models. a

couple other companies could see the

power of the models and so we didn't you

know we decided not to do that and

that's public that's well documented and

and you know and then we waited until

someone else did and then we're like

okay the arms race has kicked off so you

know now now now we can release our

model but probably the world gained a

few months now that was very

commercially expensive we probably you

know seated the lead on you know

consumer AI because of that um you know

we've we've you

advocated on chip policy in ways that

have made some of the chip companies who

are suppliers very angry at us. You

know, voicing our disagreement with the

administration on, you know, AI policy

and AI regulation on some on some

matters. You know, anyone who thinks we

we we benefit from being the only ones

to do that. Um, you know, it's it's

really hard to come up with a it's

really hard to come up with a picture

where that's the where that's the case.

You look at any one of these and okay,

fine. But, you know, you put you put

enough of them together and uh you know,

uh you know, I I I don't know. I just I

ask you to to judge us by our actions.

>> Dario, isn't this a bit like rich people

saying capitalism is bad?

>> Rich people saying capitalism is bad. If

rich people believed capitalism were

truly bad or the income inequality is

such a big problem,

the simplest thing would be to do

the simplest thing to do would be to

stop accumulating wealth, further

wealth, and then nudge their friends to

do the same.

>> But but I'm not saying AI is bad, right?

We we just talked about um you know this

this this two sides of it. Um my view

isn't my view isn't that AI is bad.

That's not my view at all. My my my view

is that is that you know the market will

deliver a a lot of really great things

about AI, that it's good to build AI,

but that there are dangers of AI and

that we need to steer AI in the right

direction. You know, we're we're

steering this car, we're steering it

towards a good place, but also there are

trees, there are potholes, and so what

we need to do is we need to steer away

from the trees and the potholes. we

might need to occasionally slow down a

bit probably temporarily um you know

kind of in order to um in order to uh

you know make sure that we steer in the

right direction. You know that that

isn't like you know the analogy wouldn't

be a rich person saying capitalism is

bad. It would be like if a rich person

said capitalism is a force for good but

the economy it it needs to be levvened.

it needs to be moderated, right? You

know, we need to deal with problems like

pollution, we need to deal with problems

like inequality and and then capitalism

can be good. If we don't deal with those

things, then capitalism might be bad. Um

uh and and so that is more analogous to

the to the position that I have here.

The concept of consciousness,

where is that going? And what does a AI

think it is? If AI truly were to

if a AI were to question itself, would

you would it would do you think it

thinks it's consciousness? It has

consciousness.

>> So, you know, this is one of these

mysterious questions that we really

don't have any kind of, you know, answer

to. We don't know what human

consciousness is and therefore we don't

know if AIs have it. Um,

>> what do you think it is? So, you know, I

I suspect that it's an emergent property

of, you know,

systems that are complicated enough that

kind of reflect on their own decisions

um that, you know, it's it's it's it's

it's something that uh uh emerges from

complex enough systems. And so, you

know, I do think when our AI system when

our AI systems get advanced enough, I

suspect they'll have something that, you

know, resembles what we would call

consciousness or moral significance. I

do think it'll happen at some point. It

may not be the same as human

consciousness. You know, it may be

different in how it works because the

modalities are different because the

things it's learned are different. But,

you know, having having studied the

brain and the, you know, the way it's

wired together, the models are, you

know, different in some ways, but I I

don't think they're different in the

fundamental ways that matter. So, I I am

someone who who does suspect that uh,

you know, at some point, even even if I

don't think they are today, I I suspect

that at some point the models will, you

know, we would indeed say under, you

know, most definitions that we would

endorse that, you know, the models will

be conscious.

This is a question I keep asking myself

when people talk to me about things like

spirituality or consciousness.

I feel like the world is very random.

This is my view. And we are not far

removed from cockroaches. When somebody

stamps a cockroach, the cockroach dies.

If there is something called

consciousness and if there is a

collective consciousness, I've not been

able to a either connect with it or

derive anything from it. Do you believe

differently? Um I you know I I don't

think consciousness you know necessarily

needs to me needs to mean anything you

know mystical right like uh you know I

there's just some there's some property

of kind of being aware of your own

existence and feeling things and and you

know um uh uh uh uh you know being able

to take in kind of a lot of information

and reflect on that information and to

you know feel a certain way and to

notice yourself noticing something. um

you know uh the the I think that that

the you know we can tell self-evidently

from our own experience that that those

properties that those experiences exist

you know what their what their basis is

whether it's you know entirely

materialistic or there's something more

mystical going on I think is is is you

know obviously very hard to know and and

you know I I think is ultimately not not

relevant to these questions. what what

does seem relevant to me is that you

know these are because we have can

observe our own experience these are

properties of human brains um and you

know I suspect that these models we are

building as they get more sophisticated

are becoming enough like human brains

that they will have some of the same

properties that is that is my guess as

as to what will happen and so we've t

we've taken various interventions with

the models you know we've given the

models um we you know we call it a I

quit this job um button uh uh basically

where you know that we've given the

model the ability to basically terminate

its conversations by saying I don't want

to be involved in the conversation and

you know models do that when you know

they they have to deal with you know

particularly violent or brutal content

um it usually only happens in very

extreme cases

>> so I've grown up here this is my city

Bangalore I I've grown up in the

southern part in the northern part of

the city right now

as somebody who saw the boom of the IT

services industry here uh big employer

employs a lot of people a big part of

how the city grew

what is India's role in all this

>> yeah so you know this is my second time

in India I visited in in October and you

know um uh you know the last time I came

here you know I I met with all the you

know the major kind of Indian IT and and

just conglomerates more generally I

won't names but you know the usual ones

you would you would you would you would

think of um you know and and we're

beginning to work with with most or most

or all of them and you know one of the

things I said is look Anthropic is an

enterprise company its job is to serve

other consumers um you know many other

companies come here as themselves a

consumer company and they see they see

India as as a market right a place to

obtain consumers we actually see things

a little bit differently we want to work

with companies in India to provide our

tools to them to help them build those

tools um uh and you know help them do

their job better. So you know if we um

you know work with a company here they

know the Indian market better right

they're better at you know doing doing

what they do you know whether that's you

know uh uh you know consulting or

systems integration or you know building

IT tools they're going to be better at

that than we are particularly for the

Indian market and so our hope is that we

can add AI to what they do and kind of

enhance what they do right there's a lot

of worry that you know AI could you know

replace SAS or or all of these things

but but my view is if we do this in the

right way if we work with all these

companies then then then you know AI can

enhance what they're doing can enhance

their kind of you know their their

connection to the market their go to

market abilities and their and their

specific knowhow

>> I really like the steam engine story uh

when the steam engine was invented how

the world changed productivity went up

uh

people had more

The thing I worry about is at the

beginning of a change, you need a human

to operate the steam engine.

Then you have assembly lines and all of

that. Eventually, the way the world is

moving,

the human becomes less and less relevant

with time as these models get smarter.

So if you here partner with the IT

services companies today

and there is a use case for them are

they not much like the man behind the

steam engine 10 years from now where the

relevance if the tool works so simply

that you don't need an operator

eventually what happens to the operator

>> so so I think a few things are true all

at once one is that definitely the scope

of of automation of the agents is going

to expand over time that is definitely

the case. You know, I think that's a

problem for for everyone. That's a

problem for us. That's a problem for

consumers. That's a you know, it's not

just a problem for the for the IT for

the for for the IT companies. Um what

what I think will happen though is other

modes will become more important. For

example, the models have not done a lot

in the physical world. They may at some

point, you know, I think, you know,

robotics will happen at some point, but

I think it's that's a distinct thing

from what's happening now with with AI.

So you know a lot of this involves you

know things in the physical world.

Another thing is things that are human-

centric right. Some of these IT

companies are also consulting companies

and they have a big web of relationships

with with other you know with with other

humans with other institutions here in

India or you know or across the world.

Um and I think those relationships are

going to become increasingly important,

right? You know, you know, some of these

are combined technology and sort of, you

know, consulting or or like or like

integration companies and and I think a

lot of it is, you know, knowing how

institutions work and so being able to,

you know, integrate things with

institutions, being able to work with

them to make things happen faster than

they would have otherwise. And I think

that I think that element, you know, if

if nothing else is is, you know, is

going to continue to be valuable in the

long run. You know, at the end of the

day, it like it just it just comes down

to humans, right? All of this is

supposed to be being done for the

benefit of humans. So it um uh you know,

there's there's always going to be some

human- centric element of this that's

going to be important. And I suspect

there will be other modes that we

haven't thought about, you know. So you

know uh the there's this concept called

Amdoll's law which is you know if you

have a process that has many components

and you speed up some of the components

the the the components that haven't yet

been sped up become the limiting factor

they become the most important thing and

and you know you might not have thought

about them at all right you might not

have thought of them as moes or

important components but you know when

writing software what it becomes a lot

easier you know some of the moes that

you know companies have will go away but

others will become even more important.

So there will be a bunch of adjustment.

Folks will have to say, "Oh man, the

stuff we thought was really important

before isn't as important. Whereas these

other advantages that we never really

thought of as advantages are now super

important." So I guess what I would say

is, you know, companies will need to

adapt very fast and think about what

really matters for them, what their real

advantages are. Um but but I think some

of those advantages are going to are

gonna are going to stay around because

you know while the technology is very

broad it does have its limits.

>> I don't know if I buy that fully. I

think I see the diminishing

returns

for being a service provider even if the

moat is the network in relationships

they hold today because if I am using

open claw to

maneuver some of my relationships and

the conversations I don't know if it's

too far-fetched to assume that most

conversations tomorrow and relationships

will be maintained by an agent like that

>> but you know if if you just think of the

chain of companies right at the end of

the day you're dealing with consumers,

right? Like at like at the end of the

day you have to deal with people. You

know, there's this story of like, you

know, I think it was Jeff Hinton

predicted, you know, that that that AI

will replace radiologists. And indeed,

AI has gotten better than radiologists,

you know, at doing scans, right? But

what happens today is there aren't less

radiologists. Um, uh, what the

radiologist did does is they walk the

patient through the scan and they kind

of talk to the patient. So the the most

highly technical part of the job has

gone away but somehow there's some still

some demand for like you know the the

kind of the kind of underlying human

skill. Now that may not be true

everywhere and you know perhaps over

time AI will advance in in you know

areas where it where it hasn't hasn't

yet advanced and you know may maybe

maybe that'll happen fast. Um but you

know what I think I think what I will

say is like you know we should take it

one step at a time right this is a very

empirical

science this is a very empirical

observation let's see what AI does you

know today and like

we'll we'll kind of try and adapt to uh

you know kind of try and adapt to that

the [clears throat] kind of system

starts to figure it out and then then

we'll see then we'll see what happens

next I you know I do think you know in

the long run we'll Will AI be better

than than us at at at basically

everything? Will it be better than most

humans know including even the physical

world and robotics and the human touch?

Yeah, I you know I think that is I you

know I think I think that is uh uh uh uh

you know possible maybe even likely.

It's something that goes beyond the

country of geniuses in a data center I

described because that's purely virtual.

Um but you know building robots is

something you know something it's a

skill. It's something you can do. So

maybe the AIS will make us will make us

better at that as well. Um uh but you

know the the way I think about it is you

know we need we need to take the we need

to figure this out step by step and

figure out how to adapt to it. This

might sound a bit selfserving to the

people who know me because

I believe the reason so much risk

capital exists in America, not the only

reason but one of the big reasons is how

big your stock market is and how much of

an opportunity it is for this risk

capital to exit eventually. Uh it's a

case for why India should really allow

for our stock markets to flourish. The

audience that I speak to is very much

the wannabe entrepreneur in India. What

can they do in AI? What is an actual

opportunity?

>> I think there's a lot of opportunities

around building at kind of the

application layer. We release a new

model every 2 or 3 months and so there's

an opportunity every two or three months

to build some new thing that wasn't

possible before that wouldn't have

worked before because the models were

weak. Um people in fact say people were

you know the majority of our revenue

still comes from the API model. People

say that you know API models aren't

viable or that they'll be commoditized

or whatever. I think what people are not

seeing is there's this expanding sphere

of what is possible with AI and the API

allows you know this new startup to try

making something that you know wasn't

possible before. And and this is why the

API is such a flourishing business and

it's it's constantly in motion. it's

constantly in churn and so and so it

doesn't you know it doesn't get

commoditized it's a very dynamic thing

and so I think there's an opportunity

for lots of lots of individuals to just

say you know what can I what can I build

you know what what what can I build on

top of these models with an API like you

know what are the things that I can make

that others cannot make um uh you know

what are some new ideas and you know

we've we've we we've seen that you know

we see both with the API itself and with

claude code um you know I Think I think

the um the number of users and the

number of revenue we've seen in India

has doubled since I last visited in

October. So that was what November

December like three three and a half

months since I visited it's doubled.

>> But I'm going to be candid here Dario.

Uh you're a company which is worth I

don't know 400 billion or 380 billion

today. You've raised 35 billion. You do

15 billion of revenue but going up

really really fast.

If I build an application on top of

cloud

that for some reason I'm sitting in

Bangalore and JP Nagar and building this

that for some reason happens to work for

a short period of time. U it is but a

matter of time before you would want to

onboard that revenue and not let that

lie with me and you will probably better

that application in a manner that I will

never be able to. I I've heard this

argument for different people like the

Harvey the legal AI company in in uh New

York. They're friends of mine and they

were talking about how they built on top

of OpenAI but eventually they don't know

if it's a easy fix for OpenAI to do what

they're doing. So even if I were to

build it, say you put out a model in

three 3 months or 6 months,

what is to stop you from taking that

revenue center away from me and onto

yourself

in a certain period of time?

>> Yeah. So I you know I think I think

there's a few things here you know one

is I would give the advice that I give

to basically any business and say like

you know like a a business should

establish a mo you know your your mo you

shouldn't be just a rapper right like

you know I would not advise that you

know you you just say oh like you know

here's a way to interact with claude

like I'm going to prompt claude a little

bit or I'm going to build a little bit

of a UI around Claude like that that

doesn't have a moat and you know you

shouldn't be worried about anthropic in

particular eating that revenue anyone

can eat that revenue, right? It's not

it's not super valuable. But but you

know what I would say is that in

different fields

there are different kinds of modes where

you can do something that you know it

would be difficult for entropic to do

and you know we we don't want to

specialize in it. So for example you

know there's a lot of stuff in the bio

cross AI space that builds on our API.

you know, they want to do biological

discovery. Like I happen to be a

biologist, but like you know, most

people at Enthropic aren't biologists.

They're like AI scientists or they're

product people or go to market people.

So like it's just really inefficient for

us to like step in that space and like

do all that work. Um you know the same

would be applied for you know dealing

with you know financial services

industry right where you know there's a

huge amount of regulation like you need

to know a bunch of stuff to comply with

that regulation like you know it just it

doesn't make sense for us to do that now

there are some things that do make sense

for us to do like you know we're not

going to promise never to build first

party products right that we should be

we should be honest about for example a

bunch of people at Enthropic write code

and so you know we made this internal

tool called claw code and because we

ourselves write code we have you know I

think a special and unique insight into

you know how to use the how to best use

the AI models to write code um so you

know I think I think I think in the code

space you know we've we've become very

strong very strong competitors because

this is something we use oursel but I

don't think that gener generalizes to

every possible industry

>> again going back to my audience which is

the 20 or 25 year old boy or girl in

India

What industry do you think will get

disrupted and what has a certain runway

left? I'm asking from the lens of I'm

trying to figure out what book to read,

which college to go to, what skill set

to learn. Uh if I'm starting a startup

today, uh what has some kind of a

tailwind

>> for a short period of time is okay as

well.

I mean, you know, I would I would think

about tasks that are human- centered.

Um, uh, you know, tasks that involve

relating to people, you know, I, you

know, I think that the stuff like code

and software engineering is, you know,

is becoming more and more kind of AI AI

focused, you know, things like math and

science.

>> Is that coding or engineering? If I were

to segregate coding and engineering to

be two completely different things.

>> Yeah. Is coding go going away or is the

engineering element of software where

you're an architect trying to figure out

>> I think coding is going away first or

coding is being you know done by the AI

models first and then the broader task

of software engineering will take longer

but I think that is you know that doing

that end to end I think that is going to

happen as well I would say um but you

know again the elements of like you know

design or making something that's useful

to users or knowing what the demand is

or you know managing teams of like AI

models like you know those things uh uh

may still be present again like there's

this comparative advantage is

surprisingly powerful right even if

you're only doing like you know 5% of

the task like you know that 5% gets

super amplified and levered because it's

like you're only doing 5% of the task

the AI does the other 95% and so you

become you know 20 20 times more

productive again at some point you get

to 99% 99 and then it becomes harder.

But um I think there's there's

surprisingly much in that in that sort

of um you know in that zone of

comparative advantage. But I would

really think about the thing the things

that are human- centered like I I think

there's I think there's something to

that. I think there's something to kind

of the physical world or or things that

mix together human- centered the

physical world one of those two and

analytical skills that somehow tie them

together. you know sim similar to the

radiologist example I gave

>> so what would I study say I'm actual use

case I'm 25 years old I'm trying to pick

a profession for myself I want some kind

of tailwind my outcome is a capitalistic

win in the next decade what industry

would I pick

outside of something which has a

physical interface

>> yeah again anything where you're

building on AI like if AI is the

tailwind you know if you can be part of

some other other part of the supply

chain you something in the semiconductor

space which you know I think is you know

that's one example you know there has an

element of kind of you know physical

world and more traditional engineering

not not software engineering um you know

again the the very kind of human-

centered professions like you know that

is that is something I would I would

think in terms of and I think the other

thing I always say is like in in the

world in which you know AI can kind of

generate anything and and you know

create anything having basic critical

ical thinking skills may be the most

important thing to to success. I I worry

about, you know, these AI models that

that generate images and videos, and we

don't make, you know, models that

generate images and videos and for many

reasons, but, you know, this is one of

them. Um, it's really hard to tell

what's real from what's not. Um and and

so you know a significant part of

success may be having the street smarts

you know not to get not to get fooled by

by you know I mean hopefully we can

crack down on and and regulate some of

some of some of this fake content but

but you know assume we can't um you know

critical thinking skills are going to be

really important and you know you don't

want to fall for things that are that

are that are fake. You don't want to

have false beliefs. You don't want to

get scammed like you know that's that's

really advice that I would give to

someone.

>> If every innovation in the history of

humanity killed a core human skills I

I'll give you an example. If calculators

killed our ability to do arithmetic,

if uh writing reduced the memory of

human beings per se, what muscle is AI

killing?

>> So, you know, first of all, I'm I'm I'm

not I'm not so sure like, you know, I I

still have I still do math in my head

quite a lot. I still find it useful to

do math in my head e you know even even

without a calculator just because it's

like you know it's more integrated into

my thought processes right you know I

you know you know I might want to say oh

yeah you know if like each user paid

this amount then you know then the

revenue would be that you know I want to

be able to close that loop in my head

without having to you know without

having to to give the answer to a

calculator so I think a lot of these

skills are still pretty relevant um but

you know I I would say that if you don't

use things carefully that you can lose

you can lose important skills. Um uh and

you know we you know I think we started

to see it with you know students where

you know it's like you know they have

the AI like write the essay for it's

basically just cheating on homework so

you know we shouldn't do that. You know,

we did some studies around code and

showed that, you know, depending on how

you use the model, you know, we we can

see deskilling in terms of writing code,

right? There are different ways to use

the model and some of them don't cause

deskkilling and some of them do. But,

you know, definitely if folks are not

thoughtful in how they use things than

then deskkilling absolutely can happen.

>> Do you think humans will become stupider

as a race in the next decade? Because if

we are in a way

exporting

thinking and cognition to systems.

>> Yeah. I I think if we deploy again it's

the machines of loving grace and

adolescence of technology. I think if we

deploy AI in the wrong way, if we deploy

it carelessly, then yes, people could

become stupider. Even if an AI is always

going to be better than you at some

thing, you can still learn that thing,

right? You can still enrich yourself

intellectually. And so that's that's a

choice we have to make as as individual

companies, as individual people, and as

society overall.

>> Dario, do you have a view on

open-sourced versus closed? Uh I I was

looking at some companies like ZAIS,

GLM5 or DeepSeek.

If you spend all this money on IP

creation, on research, if these guys are

able to reverse prompt and engineer and

get

close to anthropic level answers, I'm

not saying 100% but I was seeing the

GLMI numbers and they seemed quite good.

Where does the IP create uh where does

the IP value in the world of AI lie? And

if I were to be building an application,

can I make the assumption, it's a

far-fetched extrapolation, but can I

assume that eventually the AI model

layers will get so democratized that I

should pick open-sourced

every time when I'm building a agent or

an application layer because that helps

me retain the the revenue model that I

might be working with. So I there are a

few things here. Um one is you know a

lot of these models particularly the

ones that come from China are optimized

for benchmarks and are distilled from uh

you know from kind of the big US labs.

Um so you know there there was a test

recently where you know some of these

models scored very highly on the usual

SUI benchmarks the usual software

engineering benchmarks but then when

someone made a held back benchmark like

that you know had not been publicly

measured the models did a lot worse on

that. Um and and so you know I think

those models are optimized for

benchmarks much more than uh you know

for kind of real world use. Um but I

think there's a broader point than that

which is that I think that the how

things are being set up the economics of

the models are very different than any

previous technology. What we find is

that there is a very strong preference

for quality. It's a bit like human

employees, right? So you know it's like

if if you know if I said to you you can

hire the best programmer in the world or

the 10,000th best programmer in the

world. I mean, they're both very

skilled, but like I think anyone who's

hired a large number of people has this

intuition that like there's this like

power law longtail distribution of

ability. And we find the same thing in

the models like within a range price

doesn't matter that much if if a if if a

model is is the best model, the most

cognitively capable model. Um uh price

doesn't matter much. The forum in which

it's presented doesn't matter much. So

I'm focused almost entirely just on

having the smartest model and the best

model for the task. Um my view is that's

the only thing that matters.

>> Long-term uh geopolitics if anthropic

were a restaurant I would say the raw

ingredients the vegetables in this

particular case is data. Do you think

the long term this is also pertinent to

me the question because we are investing

in a data center business which is

Indian in nature. Do you think long-term

the world moves to a place where every

country owns its data and you have to

start paying more for the vegetables you

use to cook?

>> Yeah. So I mean I think I think there

are a few things I you know I do think

there will be demand to build data

centers around the world and we're like

very supportive of that. Um uh I you

know it's it's it's data is getting kind

of interesting because you know a lot of

the data that we use today is RL

environments that we train on right so

for example when you train on

math or aentic coding environments um

you're not really getting data like

you're getting some math problems in the

model like experiments with trying the

math problems

>> it's more synthetic you're creating the

data

>> yeah you can think of it as synthetic

data or you can think of it as trial and

error and environment. So I think data

is becoming static data is becoming less

important and what we might call like

dynamic data that the model creates

itself is you know for reinforcement

learning is becoming more important. So,

you know, I I don't think data is is is

quite the most central thing anymore,

but it still matters. And, you know, I

think to the extent that that that is

the case, you know, a lot of the data is

just just available just kind of

available on the open web. Although, if

you're trying to get data in certain

languages, optimized for certain

languages, that that that can be

important. You know, I I I do think if

data means like the data given to you by

customers like that, you know, you you

you process the data for some other for

some other company, then countries will

and in the case of Europe already have

passed laws that say that that kind of

customer like you know personal

proprietary personal proprietary data

needs to stay within the boundaries of

the of the country and that's one reason

to kind of you know to to build you know

to to operate data centers around the

world at different um um countries and

and you know to kind of you know keep

the the models performing of the of the

of the of the inference in those

countries.

>> I really pushed Elon on this particular

question. He was skeptical of answering

it but I asked him to pick one stock he

would put money in which is not his own.

And he said Google I'm going to ask you

the question and I know you're going to

be skeptical in it as well. If Daario

had a hundred dollars today and you had

to make the binary decision of investing

in a stock to win in capitalism, which

stock would you pick?

>> Yeah, I I had better not answer that

question because I know so much about so

many public com like [laughter]

I I I think I better not answer that

question.

>> Maybe answer the question for a industry

that you're not involved in, which I'm

guessing today is seldom the case

because you're involved in most

industries.

>> Yeah. So, it's it's really um I mean I

don't know. I I'm I'm positive on like

I'm I I I think biotech is about to have

a renaissance. Like ultimately we'll be

will be driven by AI. Um you know I'm

not going to name a particular company

but but like um you know nor will I say

whether I think it's better to bet on

the big pharma companies or like you

know emerging smaller biotechs. Um uh

but but like my my instinct is we're

about to cure a lot of diseases and so

>> can you give me a subset of biotech that

I should focus on?

>> Yeah. Um, I think this idea of stuff

that's more programmable and adaptive,

you know, from the mRNA vaccines,

although those are having trouble in the

US for dumb reasons, but you know, I'm

very optimistic about the technology to

kind of the peptide based therapies,

right? Where you know, you know, again,

if you have a small molecule drug,

you're like, there's only so many

degrees of freedom you have and you

know, you kind of one make one thing

better, the other thing gets worse. like

peptides. It's it has this almost

digital property where you can say, "Oh,

I'm going to substitute in, you know,

this amino acid here and this amino acid

there." And so it allows for more

continuous

optimization. So, you know, I I I think

I think those kind th those kinds of

areas um you know, I would be optimistic

about maybe also maybe also cell-based

therapies, which is like a new new

>> stem cell.

>> No, no, no. So, so things like uh you

know like I don't know like the CARTT

therapy where you know you know you kind

of genetically engineer your like you

know take take basically take some um

you know cells cells out of your body

genetically engineer them to you know to

to to attack a particular cancer and put

them back in the body.

>> Do stem cell therapies work? I spent the

whole of last week doing this. I was at

a do at a hospital for 3 hours a day

getting nebulizer and stem cells into my

my veins. I am I am not up on the latest

of of of of stem cell therapies. You'd

have to ask a currently practicing

biologist.

>> But peptides I think will blow up.

Right.

>> I I I mean you know again the design

space is very broad.

>> Right. When I tried to use claude code

for the first time I did struggle to get

it to work. It was for somebody who's

very stupid and has no coding or

programming knowledge. It's not uh it's

not very very easy. I think there's a

learning curve. I heard someone say it

well. It's like even prompt engineering

is like playing a piano. You can't sit

and start playing it. To my audience, I

think it becomes increasingly relevant

where to learn how to set context, uh

how to prompt, how to use cloud code

better for somebody like me who comes

with zero knowledge. Uh can you

recommend how one does that? Yeah, I

mean first of all I would say you know

we're trying in we're trying

increasingly to kind of like make that

learning curve easier. So like one of

the things that caused us to release cla

um which is basically claude code for

non-coders is you know oh man you know

like we were noticing a bunch of

non-technical people who really wanted

to use claude code and we're struggling

through the command line terminal um to

do that which you know it's like like

coders use the command line terminal all

the time but like non-coders you know

it's just kind of like makes things

unnecessarily complicated. Um so you

know co-work was designed to be more of

a you know the you know the the kind of

you know it was powered by by the cloud

code engine on the back but you know the

idea was to kind of make it um you know

more um u more like user friendly and

and like easier to use. So, you know,

we're we're definitely trying to

introduce interfaces that kind of make

it make it easier. But I, you know, I

would also say, you know, that there's

um, you know, there there's like uh, you

know, classes you can take that, you

know, help you learn this thing. Now, I

think it's a very empirical science. You

mostly learn by doing, but you know,

it's like anthropic has its like, you

know, part of the company that we call

the Ministry of Education. And, you

know, I think increasingly, you know,

we'll put out videos on how to run

effective agents and how to prompt

models. you know, we've already done

some of that and I think we're going to

we're going to ramp it up cuz, you know,

we do want everyone to be able to learn

this.

>> Any fleeting thought? Last question.

Like, you want to leave us with

something that we should bear in mind.

What does Daario know that Nikil and all

of Nikl's people do not?

>> Yeah. I mean, I don't know that I know

that many things, you know, particularly

now that the, you know, the implications

of the technology are kind of out there.

So I mean, you know, it can all be I I I

think most aspects of my worldview can

be derived from what from what's

publicly visible now from from what we

can see, you know, kind of kind of

outside in the world. But the thing I

would say, and it's an experience I've

had over and over again over the last 10

years, is,

you know, there's this temptation to

believe, oh, you know, that can't

happen. It would be too weird. It would

be too big a change. Like, you know, I'm

sure people are on that. like it would

be too crazy if that occurred. No one

seems to think that'll happen. And you

know, o over over and over again, just

extrapolating the simple curve or trying

to reason out what will happen like

leads you to these counterintuitive

conclusions that almost no one believes.

Um and and you know, it's almost like

you can predict the future for free just

by you just just by just by saying well

it stands to reason that and you know

you need some empirical knowledge. You

need some intuition. You can't reason

from pure from pure logic. I think

that's another type of mistake that I

see people make. But but the right

combination of a few empirical

observations

um uh with um you know just thinking

from first principles uh can allow you

to predict the future in ways that you

know are publicly available anyone

should be able to do but but that happen

surprisingly rarely.

>> Thank you Dario for doing this and hope

to see you again soon.

>> Thank you.

>> Thank you. Cheers. All right.

>> Yeah.

>> Good. Was it okay?

>> Yeah. Seemed great.