Google DeepMind C.E.O. Demis Hassabis on Living in an A.I. Future | EP 137

This year, Google talked about AI very

differently. This time, they want you to

sit up. They want you to lean in. They

want you to pay them $250. And they want

you to get to work. I've been working

every hour there is for the last 20

years because I've felt the how

important and momentous this this

technology would be, whether it's 5

years or 10 years or 2 years that

they're all actually quite short

timelines when you're talking discussing

what the the enormity of the

transformation of this techn you know

this technology is going to bring. when

you when I see a Van Go, you know, hairs

going up the back of my my my spine

because of I remember what they went

through and um the struggle to produce

that right in every brush stroke of Van

Go's brush strokes. Even if the AI

mimicked that and you were told that it

was like so what?

[Music]

Now there's a very large what looks like

a circus tent over there. What do you

think's going on in there? That is Shane

amphitheater. Oh, that's the

amphitheater under that tent yesterday.

I thought that was just some carnival

that they were setting up for employees.

Okay, my mistake. I thought Ringling

Brothers had entered into a partnership

with the Google. It's a revival tent.

They're bringing Christianity back.

I'm Kevin Roose, a tech columnist at the

New York Times. I'm Casey Newton from

Platformer and this is Hardfork. This

week, our field trip to Google. We'll

tell you all about everything the

company announced at its biggest show of

the year. Then Google DeepMind CEO Demis

Hassabis returns to the show to discuss

the road to AGI, the future of

education, and what life could look like

in 2030. Kevin being very old for

starters. Somebody did ask me, text me

to ask why I freaking yell the name of

the show every episode. And did you say

it's cuz I started yelling my name? I

said it's because of the cold brew.

Well, Casey, our decor is a little

different this week. Mhm. It's I'll say

it. It looks better. Yes. We are not in

our normal studio in San Francisco. We

are down in Mountain View, California

where we are inside Google's

headquarters. I'm just thrilled to be

sitting here surrounded by so much

training data. That's what they call

books here at Google.

So, we are here because this week is

Google's annual developer conference

Google IO. There were many, many

announcements from a parade of Google

executives about all the AI stuff that

they have coming. And uh we are going to

talk in a little bit with uh Dennis

Sabis who is the CEO of Google DeepMind

uh essentially their AI division who's

been driving a lot of these AI projects

forward. Um but first let's just sort of

set the scene for people uh because I

don't think we have ever been together

at an IO before. So what is it like? So

Google IO has a bit of a festival

atmosphere. It takes place at the

Shoreline Amphitheater which is a

concert venue. Uh, but once a year it

gets transformed into a sort of nerd

concert where instead of seeing

musicians perform, you see Google

employees vibe coding on stage. Yes

there was a vibe coding demo. Um, there

were many other things. I I did actually

see as I was leaving the uh Google ac

capella group, Google was like sort of

doing their warm-ups in anticipation of

doing some concert. So, you've got some

like old school Google vibes here, but

uh also a lot of excitement around all

the AI stuff. So now I didn't see Google

Pella perform. Where was this

performance? I didn't see them perform

either. I just saw them warming up. They

were sort of doing their scales. They

sounded great. You know what? I bet it

was a classic a capella situation where

they warmed up and someone came up to

them and they said, "Please don't

perform."

All right, Kevin. Well, before we get

into it, shall we say our disclosures?

Yes. I work for the New York Times

which is suing OpenAI and Microsoft uh

over copyright violations related to

training of AI systems. And my boyfriend

works at Anthropic, a Google investment.

Oh, that's right. Yeah. So, let's talk

about some of what was announced this

week. There was so so much. We can't get

to all of it, but uh what were the

highlights from your perspective? Well

so look, I wrote a column about this

Kevin. I felt a little bit like I was in

a fever dream at this conference. You

know, I think often it is the case at a

developer conference where they'll sort

of try to break it out into one, two

three big bullet points. This one felt a

little bit like a fire hose of stuff.

And so by the end I'm looking at my

notes saying, "Okay, so email's gonna

start writing in my voice and I can turn

my PDFs into video TED talks. Sure, why

not?" Um, so I had a little bit of fever

of dream mentality. What was your

feeling? Yeah, I I told someone

yesterday that I thought the name of the

event should have been everything

everywhere all at once. Like that did

actually feel like what they were saying

is like every Google product that you

use is going to have more AI. that AI is

going to be better and is all going to

make your life better in various ways.

Uh, but it was a lot to keep track of.

Yeah. I mean, look, if we were going to

try to to to pull out one very obvious

theme from everything that we saw, it

was AI is coming to all of the things.

And it's probably worth drilling down a

little bit into what some of those

things are. Yeah. So, the thing that got

my attention and then I was sitting

right next to you. Uh the one time when

I really noticed you perking up was when

they started talking about this new AI

mode in Google search, their core search

product. So talk about AI mode and what

they announced yesterday. So Kevin, this

gets a little confusing because there

are now three different kinds of major

Google searches. I would say there is

the normal Google search which is now

augmented in many cases by what they

call AI overviews which is sort of AI

answer at the top. Yeah, that's the

little thing that will tell you like

what the meaning of phrases like you

can't lick a badger twice is. Right.

That's right. And if you don't know the

meaning of that, Google it. Um, so

that's sort of the thing one. Thing two

is the Gemini app, which is kind of like

a one for one like chat GPT competitor.

That's in its own, you know, standalone

app, standalone website. And then the

big thing that they announced this week

was AI mode, which has been in testing

for a little while. And I think this

sort of lands in between the first two

things, right? It is a tab now within

search. And this is rolling out to

everybody in the United States and a few

other countries. And you sort of tap

over there and now you can have the sort

of longer, you know, multi-step

questions that you might have with a

Gemini or a chat GPT, but you can do it

right from the Google search interface.

Yeah. And I've been playing with this

feature for a few weeks now. It was in

their labs um section, so you could try

it out if you were enrolled in that. Um

and it's it's really nice. Like it's a

very clean thing. There's no ads yet. uh

they they will probably appear soon. It

does this thing called the fan out which

u is is very funny to me. Like you ask

it a question and it kind of dispatches

like a bunch of different Google

searches to like crawl a bunch of

different web pages and like bring you

back the answer and it actually tells

you like how many searches it is doing

and how many different websites it's

doing. So I asked it for example like

how much does a Costco membership cost?

It search 72 websites for the answer to

that question. So AI mode is very very

eager to answer your question even if it

does verge on overkill sometimes. Yeah.

Well, so you know you and I had a chance

to meet with Robbie Stein who is uh one

of the people who is leading AI mode and

I was surprised by how enthusiastic

about you were like you said that you've

really actually found this quite useful

in a way that I think I have not so far.

So what are you noticing about this? I

mean the main thing is it's just such a

clean experience like on a regular

Google search results page. You and I

have talked about this like it has just

gotten very cluttered. There's a lot of

stuff there. There's ads, there's

carousels of images, there's sometimes a

shopping module, there's sometimes a

maps module. Like, it's just it's hard

to actually like find the blue links

sometime. Uh, and I imagine that AI mode

will become more cluttered as they try

to make more money off of it. But right

now, if you go to it, it's like a much

simpler experience. It's much easier to

find what you're looking for. Yeah. And

at the same time, they're also trying to

do some really interestingly complex

stuff. Like one of the things that they

showed off during the keynote was

somebody asked a question about baseball

statistics that required finding, you

know, three or four different kind of

you know, tricky to locate stats and

then combining them al together in an

interactive chart. That was just a demo.

We don't have access to that yet. But

that is one of the those things where

it's like, well, if that works, that

could be a meaningful improvement to

search. Yeah, it could be a meaningful

improvement to search. And we should

also say like it's a big unknown how all

of this will affect uh the main Google

search product, right? This is for now

it's a tab. Um they have not sort of

merged it into the main core Google

search uh in part because it's not

monetized uh yet. It costs a lot more to

serve those results than a traditional

Google search. But I imagine over time

these things will kind of merge which

will have lots of implications for

publishers, people who make things on

the internet, the whole sort of economic

model of the internet. But before we uh

get dragged down that rabbit hole, um

let's just talk about a few other things

that they uh said on stage at Google IO.

So I was really struck by the usage

numbers that they trotted out for their

products. Um Gemini, according to them

um the app now has 400 million monthly

users. Um that is a lot. That is not

quite as many as chatbt, but it is a lot

more than products like Claude and other

uh AI chatbots. They said that their

tokens that are being output by Gemini

has increased 50 times since last year.

Um, and is just like way like so people

are using this stuff. In other words

this is not just like some feature that

Google is shoving into these products

that people are trying to sort of

navigate around like people are really

using Gemini. I I think that that that's

right. And I think it's the Gemini

number in particular is the one that

struck me. Like 400 million is a lot of

people and I don't see that many obvious

ways that Google could be like faking

that stat. uh you know in contrast to

for example they said one and a half

billion people see AI overviews every

month it's like well yeah you just put

them in Google search results like

that's an entirely passive phenomenon

but like Gemini you got to go to the

website you got to download the app so

that tells me that people actually are

finding real utility there so that's

Gemini but they also released a bunch of

other stuff like new image and video

models do you want to talk about those

yeah so um you know like like the other

companies they're working on text to

image text to video and while open AAI's

models have gotten most of the attention

in this regard. Google's really are

quite good. I think the the marquee

feature uh for this year's IO is that

the video generating model VO3 can also

generate sound. So, it showed us a demo

for example of an owl flapping its

wings. You hear the wings flap. It comes

down to the ground. There's this sort of

nervous badger character and they

exchanged some dialogue which was

basically incomprehensible, just pure

sloth. But they were able to generate

that from scratch. And I guess that's

something. They left behind a a ball

today. It bounced higher than I can

jump.

What manner of magic is that? Yeah. Um

they also announced a new ultra

subscription to Google's AI products.

Now, if you want to be on the bleeding

edge uh of Google's AI offerings, you

can pay $250 a month for Gemini Ultra.

And Casey, I thought to myself, no one

is going to do this. Who is going to pay

$250 a month? That's a fortune for

access to Google's leading AI products.

And then I look over to my right and

there's Casey Newton in the middle of

the keynote pulling out his credit card

from his wallet and entering it into buy

a subscription to this extremely

expensive AI product. So, you might have

been the first customer of this product.

Why? Well, and I hope that they don't

forget that uh when it comes time to

feed me into the large language model.

Um, look, I want to be able to have the

the latest models. And you know, one I

think clever thing that these AI

companies are doing is they're saying

"We will give you the latest and

greatest before everyone else, but you

have to pay us a ridiculous amount of

money." And you know, if you're a

reporter and you're reporting about this

stuff every day, I do think you sort of

want to be in that camp. Now, is it true

that I now spend more on monthly AI

subscriptions than I paid for my

apartment in Phoenix in the year 2010?

Yes. And I don't feel great about it

but I'm trying to be a good journalist.

Kevin, please. Your family is dying. Um

another thing I that made me perk up was

uh they talked a lot about

personalization, right? This is

something we've been talking about for

years. Basically, Google has all of, you

know, billions of people's email, their

search histories, their calendars, all

their personal information, and we've

been sort of waiting for them to start

weaving that stuff in so that you can

use Gemini to do things in those

products. Um, that has been slow. Um

but they are sort of taking baby steps

and they did show off a few things

including this new personalized smart

replies feature that is going to be uh

available for uh subscribers later this

year. in Gmail so that instead of just

getting the kind of formulaic suggested

replies at the bottom of of an email

it'll actually kind of learn from how

you write and maybe it can access some

things in your calendar or your

documents and really like suggest a

better reply. You'll still have to like

hit send, but it'll like sort of

pre-populate a message for you. Yeah.

You know, I have to say I'm I'm somewhat

bearish on this one, Kevin, only because

I I I think that if this were easy, like

it would just sort of be here already

right? Like when you think about how

formulaic so much email is, it doesn't

seem to me like it should be that hard

to figure out like what kind of email

are you? Like I'm basically a two

sentence emailer, you know, that doesn't

seem like that that's hard to to mimic.

Um, so that's just like an area where

I've been a little bit surprised and

disappointed. We also know large

language models do not have large

memories. So one thing that I would love

for Gmail to do, but it cannot, is just

sort of understand all of my email and

use that to inform the tone of my voice.

But it can't do that. It can only take a

much more limited subset. Is that going

to make it sort of difficult to

accurately mimic my tone? I don't know.

So, what I'm trying to say here is I

think there's a lot of problems here and

my expectations are like pretty low on

this one. Yeah, that was the part where

I was like I I will believe that this

exists and is good when I can use this

but as with other companies like like

Apple, uh, which demoed a bunch of AI

features at its developer conference

last year and then never launched half

of them. Um, I have become like a little

bit skeptical until I can like actually

use the thing myself. Yeah, it really is

amazing how looking back, last year's

WWDC was just like a movie about what a

competent AI company might have done in

an alternate future. It had very little

bearing on our reality, but it was

admittedly an interesting set of

proposals. Okay, so that is the the

software AI portion of IO. There was

also a a demo of a new hardware product

that Google is working on, which are

these uh Android XR glasses. Basically

their version of what Meta has been

showing off. It's Orion glasses where

you have a pair of glasses. They have

like sort of chunky black frames.

They've got like sort of a hologram lens

in them and you can actually like see a

little like thing overlaid on your

vision uh telling you, you know, what

the weather is or what time it is or

that you have a new message or they have

this integration with Google Maps that

they showed off where you can like it'll

like show you, you know, the little

miniature Google map right there inside

your glasses and it'll sort of turn as

you turn and tell you where to go. Um

they did say this is a prototype, but

um, what did you make of this? Well, I

think a lot of it looked really cool.

Like probably my favorite part of the

demo was uh, when the person who was

demonstrating looked down at her feet

cuz she was getting ready to to walk to

a a coffee shop and the Google map was

actually projected at her feet and so

she know, okay, go to the left, go to

the right. If you've ever been walking

around a sort of foreign city and and

desperately wanted this feature, I think

you would see that and be pretty

excited. What did you think? Um, yeah. I

I thought to myself, Google Glass is

back.

It was away for so long in the

wilderness and now it's back. And it

might actually work this time.

Absolutely. I did get to try the the

glasses. There was a very long line for

the demo, but um I Let me guess. You

said, "I'm Kevin Roose. Let me in the

front of the line." No, they made me

wait for 2 hours. I mean, I didn't

literally wait for two hours. I went and

did some stuff and then came back. But I

got my demo. It was like 5 minutes long.

Um, and it was uh, you know, it was it

was pretty basic, but it is cool. Like

you can look now look around and you can

say, "Hey, what's this plant?" And it'll

sort of Gemini will kind of like look at

what you're seeing and tell you what the

plant is. Totally. I I did a demo um a

few months back and also like really

enjoyed it. Um so I think there's

something here. And I think more

importantly, Kevin, consumers now when

they look at Google and Meta, they

finally have a choice. Whose advertising

monopoly do I want to feed with my

personal data? And you have consumer

choice now. And I think that's

beautiful. And that's what capitalism is

all about. So, okay, those are some of

the announcements, but what did you make

of the sort of overall tenor of the

event? What stuck out to you as far as

the vibe? So, the thing that stuck out

to me the most was just contrasting it

with last year's event. Because last

year they had this phrase that they kept

repeating, let Google do the Googling

for you, which to me put me in the mind

of somebody sort of leaning back into

your like floating chair from the Wall-E

movie and just sort of letting the AI

like run rough shot over your life. This

year, Google talked about AI very

differently. This time, they want you to

sit up. They want you to lean in. They

want you to pay them $250, and they want

you to get to work. You know, AI is your

your superpower. It's your bionic arm

and you're going to use it to get sort

of further and farther than ever before.

But even while presenting that vision

Kevin, they were also very much like

but it's it's going to be normal. It's

going to be chill. It's going to be kind

of like your life is now. You're still

going to be in the backyard with your

kids doing science experiments. you're

still going to be planning a girl's

weekend in Nashville, right? There was

not really a lot of science fiction

here. There was just a little bit of

like, oh, we uh we put a little bit of

AI in this. So, that was interesting to

me. Yeah. So, I had a slightly different

take, which is that I think Google is

being AGI pilled. Um, you know, for

years now, Google has sort of distanced

itself from the conversation about AGI.

You know, it had Deep Mind, which was

sort of its its AGI division, but they

were over in London and they were sort

of a separate thing. Um, and people at

Google would sort of not laugh exactly

but kind of chuckle when you asked them

about AGI. It just didn't seem real to

them or it was so remote that it wasn't

worth considering. They would say, "What

does this have to do with search

advertising?" Exactly. So now, you know

it's still the case that this is a

company that wants you to think about it

as a product company, a search company.

They're not like going all in on AGI

but once you start looking for it, you

do see that the the the sort of culture

of uh AI and how they people at Google

talk about AI has really been shifting.

It is it is starting to seep into

conversation here in a way that I think

is uh unusual and maybe indicative that

the technology is just getting better um

faster than even a lot of people at

Google were thinking it would. So, I

don't totally agree with you, Kevin

because while I'm sure that they're

having more conversations about AGI here

than they were a year ago, when you look

at what they're building, it doesn't

seem like there's been a lot of rip it

up and start again. It seems a lot like

how do we plug AI systems into Google

shaped holes? And maybe that will

eventually ladder up to something like

AGI, but I don't think we've seen it

quite yet. The other observation I would

make is that I think the Google of 2025

has a lot more swagger and confidence

when it comes to AI than the Google of

2024 or 2023. I mean two years ago um

Google was still trying to make Bard a

thing and I think they were feeling very

insecure that that OpenAI had beaten

them to a a consumer chatbot that had uh

found some mass adoption. Um, and so

they were just playing catch-up. And I

don't think anyone would have said that

Google was in the lead when it came to

generative AI just a few years ago. But

now they they feel like there is a race

and that they are in a good position to

win it. They were talking about how

Gemini stacks up well against all these

other models. It's at the top of this

leaderboard LM Marina for all these

different

categories. I don't love the way that AI

is sometimes covered as if it were like

sports. you know, who's up, who's down

who's winning, who's losing. But I do

feel like Google has the confidence now

when it comes to AI of a team that like

knows it's going to be in the playoffs

at least. And that was evident. Oh

yeah. I mean, well, when you look at the

competition, just what's happened over

the past year, you have Apple doing a

bunch of essentially fictional demos at

WWDC, and you have Meta cheating to win

at LM Arena, making 27 different

versions of a model just to come up with

one that would be good at one thing

right? So I think if you're Google

you're looking at that and you're

thinking I could be those guys. So that

is um that is what it felt like inside

Google IO. Um what was the reaction from

outside? I noticed that for example the

company's stock actually fell like not

not by a lot but like you know to a

degree that suggested that Wall Street

was kind of meh on a lot of what was

announced but what was the reaction like

outside of Google? I think the external

reaction that I saw was just struggling

a little bit to connect the dots, right?

Like that is the issue with announcing

so many things during a 2-hour period is

sometimes people don't have that one

thing that they're taking away saying, I

can't wait to try that. And when you're

just looking at a bunch of Google

products that you're already using, I

think if you're an investor, it's

probably hard to understand, well, I

don't understand why this is unlocking

so much more value at Google. Now, maybe

millions of people are going to spend

$250 a month on Gemini Ultra, but unless

that happens, I can understand why some

people feel like, hm, this feels a

little like the status quo. Yeah, I see

that. I also think there are like many

unanswered questions about how all of

this will be monetized. And, you know

it's Google has built one of the most

profitable products in the history of

capitalism in the Google search engine

and the advertising business that

supports it. Um, it is not clear to me

that whatever AI mode becomes or

whatever AI features it can jam into

search, if search as a category is just

declining across the board, if people

are not going to google.com to look

things up in the way they were a few

years ago, um, I think it's an open

question like what the next thing is and

whether Google can can seize on it as

effectively as they didn't with search.

Well, well, I think that they gave us

one vision of what that might be, and

that is shopping. A significant portion

of the keynote was devoted to one

executive talking about a new shopping

experience inside of Google where you

can take a picture of yourself, upload

it, and then sort of virtually try

things on and it will sort of use AI to

understand your proportions and, you

know, accurately map a a garment on to

you. And there was a lot of stuff in

there that would just sort of let Google

take a cut, right? Obviously, you can

advertise the individual thing to buy.

Maybe you're taking some sort of like

cut of of the payment. There's an

there's an affiliate fee that that is in

there somewhere. So, one of the things

I'm trying to do is I cover Google going

forward is understanding that yes

search is the core, but there but Gemini

could be a springboard to build a lot of

other really valuable businesses. An

important question I know that I always

ask you when I go to these things. How

was the food? Let's see. I think the

food was really nice. So, here's the

thing. Last year it was a purely savory

experience at breakfast and I am

shamefully an American who likes a

little sweet treat when I woke up. This

year they had both bagels and an apple

cinnamon coffee cake and so when I was

heading into that keynote I was in a

pretty good

mood. I had some of the they have like

little bottles of cold brew and I I'm

like a huge caffeine addict so I I took

two of them. Um and boy I was on rocket

fuel all day. I was just humming around.

I was like bouncing off the wall. I was

like doing parkour. I was like I was

feeling great. I thought I saw you

warming up with the capella team. Now it

all makes sense.

When we come back, we'll talk with

Deisabis, CEO of Google Deep Mind, about

his vision of the AI future.

[Music]

Deis, welcome back to Hardfork. Thanks

for having me again. A lot has happened

since the last time you were on the

show. Um, most notably, you won a Nobel

Prize. Congrats on that. Um, ours must

be still in the mail. Can you put in a

good word for next year with the

committee? I will do. I will do. I

imagine it's very exciting to win a

Nobel Prize. I know that have been a

goal for a long time of yours. Um, I

imagine it also leads to like a lot of

people giving you crap like during

everyday activities like if you're, you

know, struggling to work the printer and

people are just like, "H, oh, Mr. Nobel

Laur does that happen?" Um, a little

bit. I mean, look, I tried to say

"Look, I can't, you know, that maybe

it's a good excuse to like not have to

fix uh those kinds of things, right?"

So, it's more shield.

Um, so you just had Google IO and it was

really the Gemini show. I mean, I think

Gemini's name was mentioned something

like 95 times in the keynote. Of all the

stuff that was announced, what do you

think will be the biggest deal for the

average user?

Wow. Well, I mean, we did announce a lot

of things. I think for for the average

user, I think it's the new powerful

models and I hope uh uh this Astrotype

technology coming into Gemini live. I

think it's really magical actually when

people use it for the first time and

they realize that actually AI is capable

already today of doing much more than

what they thought. Uh and then I guess

V3 was the big uh uh the biggest

announcement of the show probably and

seems to be going viral now and that's

pretty exciting as well I think. Yeah.

One thing that struck me about IO this

year compared to previous years um is

that it seems like Google is sort of

getting AGI pill as they say. Um I

remember interviewing people

researchers at Google even a couple

years ago and um there was a little

taboo about talking about AGI. They

would sort of be like, "Oh, that's like

Demis and his deep mind people in

London. That's sort of like their crazy

thing uh that they're excited about, but

here we're doing like, you know, real

research." Um but now you've got like

senior Google executives uh talking

openly about it. What explains that

shift? I think the sort of AI part of

the of the equation becoming more and

more central like I sometimes describe

uh Google deep mind now as the engine

room of Google and I think you saw that

probably in the keynote yesterday really

if you take a step back um and then it's

very clear uh I think you could sort of

say AGI is maybe the right word that

we're quite close to this uh human level

general intelligence u maybe closer than

people thought even a couple of years

ago and it's going to have broad

crosscutting impact and I think it's

another thing that you saw at the

keynote, it's sort of literally popping

up everywhere because it's this

horizontal layer that's going to

underpin everything and I think everyone

is starting to understand that and um

maybe a bit of the deep mind ethos is

bleeding into the into the general

Google which is which is great. You

mentioned um that project Astra is

powering some things that maybe people

don't even realize that AI can yet do. I

think this speaks to a real challenge in

the AI business right now, which is that

the models have these pretty amazing

capabilities, but either the products

aren't selling them or the users just

sort of haven't figured them out yet.

So, how are you thinking about that

challenge and how much do you bring

yourself to the product question as

opposed to the research question? Yeah

it's great great question. I mean I

think um one of the challenges I think

of this space is obviously the

underlying tech is moving unbelievably

fast and I think that's quite different

even from the other big revolutionary

techs internet and mobile at some point

you get some sort of stabilization of

the tech stack so that then the you know

the focus can be on product right or or

exploiting that tech stack and what

we've got here which I think is very

unusual but also quite exciting from a

researcher perspective is that the the

tech stack itself is evolving incredibly

fast as you guys know. So I think that

makes it uniquely challenging actually

on the product side. um not just for us

at Google and Deep Mind, but for

startups, for for anyone really, any any

any uh company, small and large, is

where do you what do you bet on right

now when that could be 100% better uh in

a year as we've seen and and so you've

got you've got this interesting thing

where you need kind of fairly um deeply

technical sort of product people

product designers and managers I think

to in order to sort of intercept where

the technology may be in a year. So

there's things it can't do today and you

want to design a product that's going to

come out in a year. So you've got a kind

of you got a pretty deep understanding

of the tech and where it might go to to

sort of work out what features you can

rely on. And so it's it's it's an

interesting one. I think that's what

you're seeing so many different things

being tried out and then if something

works we got to really double down

quickly on that. Yeah. During your

keynote, you talked both about Gemini as

powering both uh sort of productivity

assistant style stuff and also

fundamental uh science and and research

challenges and I wonder in your mind is

that the same problem that sort of like

one great model can solve or are those

sort of very different problems that

just require different approaches? I

think you know when you look at it it

looks like an incredible breadth of

things which is true and how are these

things related uh other than the fact

I'm interested in all of them but is

that uh that was always the idea with

building general intelligence you know

truly generally and and and and this in

this way that we're doing it should be

applicable to almost anything right that

being productivity which is very

exciting help billions of people in

their everyday lives to cracking some of

the biggest problems in science um 90% I

would say of it is is the underlying

core general models uh you know in our

case Gemini especially 2.5 and the in

most of these areas you still need

additional applied research or some a

little bit of um special casing from the

domain maybe it's special data or

whatever um to tackle that problem and

you know maybe we work with domain

experts in in the scientific uh areas uh

but underlying it you all the when you

crack one of those areas you can also

put those learnings back into the

general model and then the general model

gets better and better. So, it's a kind

of very interesting flywheel and um it's

great fun for someone like me who's very

interested in many things. You get to

use this technology and sort of um uh uh

uh go into almost any field that you

find interesting. A thing that a lot of

AI companies are wrestling with right

now is how many resources to devote to

sort of the core AI push on the

foundation models, making the the models

better at the basic level versus how

much time and energy and money do you

spend trying to spin out parts of that

and commercialize it and turn it into

products. And I I imagine this is both

like a resources challenge but also like

a a personnel challenge because say you

join Deep Mind as an engineer and you

want to like build AGI and then someone

from Google comes to you and says like

we actually want your help like building

the shopping thing that's going to like

let people try on clothes. Is that a

challenging conversation to have with

people who joined for one reason and

maybe asked to work on something else?

Yeah. Well, we we don't you know it's

sort of self- selecting internally. We

don't have to that's one advantage of

being quite large. there were enough

engineers on the product teams and the

product areas, you know, that can deal

with the the product development product

and the researchers if they want to stay

in core research that they're absolutely

that's fine and and we need that. Um but

actually you'll find a lot of

researchers are quite um motivated by

real world impact be that in medicine

obviously and and things like isomorphic

but also um uh to to have billions of

people use their research. it's actually

really motivating and so there's plenty

of people that like to do both. So, um

yeah, we don't there's no need for us to

sort of have to pivot people to certain

things. Um you did a panel yesterday

with uh Sergey Brin, Google's

co-founder. Yeah. Um who has been

working on this stuff back in the office

and uh interestingly he has shorter AGI

timelines than you. um he thought AGI

would arrive before 2030 and you said

just after. He actually accused you of

sandbagging, basically like artificially

pushing out your estimates so that you

could like underpromise and overd

deliver. Um but I'm curious about that

because you will often hear people at

different AI companies arguing about

when the timelines are, but presumably

you and Sergey have access to all the

same information and the same road maps

and you you understand what's possible

um and what's not. So what is he seeing

that you're not or vice versa that leads

you to different conclusions about when

AGI is going to arrive? Uh look, well

first of all, there wasn't that much

difference in our timelines if he's just

before 2030 and I'm just after. Also

our my timeline's been pretty consistent

since the start of Deep Mind in 2010. So

we thought it was roughly a 20-year

mission and amazingly we're on track. So

it's it's somewhere around then, I would

think. And I and I I feel like between I

actually have obviously a probability

distribution of you know where the ma

most mass of that is between five and 10

years from now. And I think partly it's

to do with predicting anything precisely

5 to 10 years out is very difficult. So

there's uncertainty bars around that.

And then also um there's uncertainty

about how many more breakthroughs are

required right and also about the

definition of AGI. I have quite a high

bar which I've always had which is it's

it it should be able to do all of the

things that the human brain can do right

even theoretically and so that's that's

a higher bar than say what the typical

individual human could do which is

obviously very economically important

and that would be a big milestone but

not in my view enough to call it AGI. Um

and and we talked on stage a little bit

about what is missing from today's

systems. Sort of true out of the box

invention and thinking um sort of

inventing a conjecture rather than just

solving a math conjecture. Solving one's

pretty good but actually inventing like

the reman hypothesis or something as

significant as that that mathematicians

agree is really important is very is

much harder. Um and also consistency. So

the consistency is a requirement of

generality really and you should it

should be very very difficult for even

top experts to find uh flaws especially

trivial flaws in the systems which we

can easily find today and you know the

average person can do that. So there's a

sort of capabilities gap and there's a

consistency gap before we get to what I

would consider AGI. And when you think

about closing that gap, do you think it

arrives via incremental two 5%

improvements in each successive model

just kind of stacked up over a long

period of time? Or do you think it's

more likely that we'll hit some sort of

technological breakthrough and then all

of a sudden there's liftoff and we hit

some sort of intelligence explosion? I I

think it's I think it could be both. and

and and I and I think for sure both is

going to be useful which is why we push

unbelievably hard on the scaling and the

you know what you would call incremental

although actually there's a lot of

innovation even in that to keep moving

that forward pre-training post-training

inference time compute all of that stack

so there's actually lots of exciting

research and we showed some of that that

diffusion model um the deep think model

um so we're innovating at all parts of

that the traditional stack should we

call it and then on top of that we're

doing uh more green field things, more

blue sky things like Alpha Evolve maybe

you could you could include in that

which um is there a difference between a

green field thing and a blue sky thing?

I'm not sure. Maybe they're maybe

they're pretty

similar. So uh some new area, let's call

it. And uh and uh and then that could

come back into the main branch, right?

And we've I've I mean as you both know

I've been fundamental believer in sort

of foundational research. We've always

had the broadest, deepest research

bench, I think, of any lab out there.

Um, and that's what allowed us to do

past big breakthroughs, obviously

transformers, but Alpha Go, Alpha Zero

all of these things, distillation. Um

and if to the extent any of those things

are needed again, another big

breakthrough of that level, um, I would

back us to do that. And uh we're you

know pursuing lots of very exciting

avenues that could bring that sort of

step change uh as well as the

incremental and then they of course also

interact um because the better you have

your your your base models the more you

things you can try on top of it um again

like alpha evolve you know add in

evolutionary programming in that case on

top of the the the LLMs.

Um we recently talked to Karen how who's

a journalist um just wrote a book about

AI um and she was making an argument

essentially against scale um that you

don't need these big general models that

are incredibly energyintensive and

compute intensive and require billions

of dollars and new data centers and and

all kinds of uh of resources to make

happen that instead of doing that kind

of thing you could build smaller models.

You could build narrower models. You

could have a model like Alphafold that

is just designed to uh predict the 3D

structures of proteins. You don't need a

huge behemoth of a model to accomplish

that. What's your response to that?

Well, I think you need those big models.

We we we're, you know, we love big and

small models. So, you need the big

models often to train the smaller

models. So, uh we're very proud of our

kind of flash models, which are the

most, you know, we call them our

workhorse models. Really efficient, some

of the most popular models. We use a ton

of those types of size models

internally. But you can't build those

kind of models without distilling um

from the larger teacher models and um

and even things like alpha which

obviously I I I'm huge advocate of more

of those types of models that can tackle

right now. We don't have to wait to AGI.

we can tackle now really important

problems in science and medicine uh

today and uh that will require taking

the general techniques but then

potentially specializing it you know in

that case around protein structure

prediction and I think there's huge

potential for doing more of those things

um and we are largely in our science

work AI for science work um and I think

you know we're producing something

pretty cool on that pretty much every

month these days and um I think there

should be a lot more exploration on that

probably a lot of startups could be

built uh combining some kind of general

model that exists today with some domain

specivity and um but if you're

interested in AGI you've got to push the

the again both sides of that it's it's

not an either or in my mind I'm I'm an

and right like let's scale let's let's

look at specialized techniques combining

that and hybrid systems sometimes

they're called and let's look at um new

uh uh blue sky research that could

deliver the next transformers Um, you

know, we're betting on all of those

things. You mentioned Alpha Evolve

something that Kevin and I were both

really fascinated by. Tell us what Alpha

Evolve is. Well, a high level, it's

basically taking our um latest Gemini

models, actually two different ones, uh

uh to generate sort of ideas, hypotheses

about programs and other uh mathematical

functions. And then it goes they go into

sort of evolutionary programming process

to decide which ones of those are most

promising and then that gets sort of

ported into the next step. And tell us a

little bit about what evolutionary

programming. It sounds very exciting.

Yeah. So it's it's basically a way for

uh systems to kind of uh uh explore new

space right. So like what things should

we you know in genetics like mutate to

uh to give you a kind of new organism.

So you can think about the same way in

programming or in mathematics. You know

you change the program in some way and

then uh you compare it to some answer

you're trying to get and then the ones

that fit best according to sort of

evaluation function you put back into

the next set of generating new ideas. uh

we have our most efficient model sort of

flash model generating uh possibilities

and then we have the pro model uh

critiquing that right and deciding which

one of those is most promising for the

to be selected for the next uh next

round of evolution. So it's sort of like

an autonomous AI research organization

almost where you have some AI coming up

with hypotheses, other AI testing them

and supervising them and the goal as I

understand it um is to have an AI that

can kind of improve itself or over time

or suggest improvements to existing

problems. Yes. So it's the beginning of

I think that's why people so excited

about and we're excited about is the

beginning of a kind of automated

process. It's still not fully automated

and also it's still relatively narrow.

We've applied it to many things like

chip design, uh scheduling, uh AI tasks

on our on our data centers more

efficiently, um even improving matrix

multiplication, one of the most

fundamental units of training uh uh

training algorithms. Uh so it's it's

actually amazingly useful already, but

um it's still constrained to domains

that are kind of provably correct

right, which obviously maths and coding

are, but we we need to sort of fully

generalize that. But it's interesting

because I think for a lot of people the

knock they have on LLMs in general is

well all you can really give me is the

statistical median of your training

data. But what you're saying is we now

have a way of going beyond that to

potentially generate novel ideas that

are actually useful in advancing the

state-of-the-art. That's right. And and

but we we already had these type this is

another approach alpha evolve using

evolutionary methods but but we already

had evidence of that even way back in

Alph Go days. So, you know, it's Alph Go

came up with new Go strategies. Most

famously, move 37 in game two of our big

Lisa doll world championship match. And

okay, it was limited to a game, but it

was a genuinely new strategy that had

never been seen before, even though

we've played Go for hundreds of years.

So, that's when I kicked off our sort of

alpha fold projects and science projects

because I was waiting for to see

evidence of that kind of spark of um

creativity, you could call it, right? or

originality at least in within the

domain of what we know. But there's

still a lot further that has to you know

so we we we know that these kinds of

models paired with things like Monte

Carlo research or reinforcement learning

planning techniques uh uh can get you to

new regions of space to explore and

evolutionary methods is another way of

going beyond what the current model

knows to explore force it into a new

regime where it's not seen it before.

I've been looking for a good Monte Carlo

tree for so long now. If you could help

me find one, it would honestly be a huge

help. One of these things could help.

Um, so I read the Alpha Evolve paper, or

to be more precise, I fed it into

Notebook LM and had it make a podcast

that I could then listen to that would

explain it to me at a slightly more

elementary level. Um, and one

fascinating thing that stuck out to me

um, is a detail about how you were able

to make Alpha Evolve more creative. And

one of the ways that you did it was by

essentially forcing the model to

hallucinate. M I mean so many people

right now are obsessed with eliminating

hallucinations but it seemed to me like

one way to read that paper is that

there's there's actually a scenario in

which you want models to hallucinate or

be creative whatever you want to call

it. Yes. Well I think that's right. I

think you you know hallucination in when

you want factual things obviously is you

don't want um but in creative situations

where you know you can think of it as a

little bit like lateral thinking in an

MBA course or something right uh is just

just create some crazy ideas most of

them don't make sense um but the odd one

or two may get you to a a region of the

search space that is actually quite

valuable it turns out once you evaluate

it afterwards uh and so um you can

substitute the word hallucination maybe

for imagination at that point, right?

They're obviously two sides of the of

the same coin. Yeah. I did talk to one

AI safety person who was uh a little bit

worried about Alpha Evolve, not not

because of the actual technology and the

experiments, which this person said, you

know, they're fascinating, but because

of the way it was rolled out. So, uh

Deep Google Deep Mind created Alpha

Evolve and then used it to optimize some

systems inside Google and kept it sort

of hidden for a number of months. um and

only then sort of released it to the

public. And this person was saying

well, if we really are getting to the

point where these AI systems are

starting to become recursively

self-improving and they can sort of

build a better AI, doesn't that imply

that when Google, if Google DeepMind

does build AGI or even super

intelligence that it's going to keep it

to itself for a while rather than doing

the responsible thing and informing the

public? Well, I think it's a bit of both

actually. You need to for first of all

Alphveolve is a very naent

self-improvement thing, right? And it's

still got human in the loop and it's um

and it's only shaving off, you know

albeit important percentage points off

of already existing tasks. You know

that's valuable, but it's not some it's

not creating any kind of step changes.

Uh and there's a there's a trade-off

between, you know, carefully evaluating

things internally before you release it

to the public out into the world. Um and

then also getting the extra critique

back which is also very useful from the

academic community and so on. And also

we we have a lot of trusted tester type

of programs that we talk about where

people get early access to these things

um and um and then give us feedback and

and stress test them uh including

sometimes the the safety institutes as

well. But my understanding was you

weren't just like red teaming this

internally within Google. we were

actually like using it to make the data

centers more efficient, using it to make

the kernels that train the AI models

more efficient. So I guess what this

person is saying is like it's just we we

want to start getting good habits around

these things now before they become

something like AGI and uh they were just

a little worried that maybe this is

going to be something that stays hidden

for longer than it needs to. So I don't

like you I would love to hear your

response to that. Yeah. Well, look, I I

mean I think that that system is not uh

uh anything really that I would say, you

know, has any risk on the AGI type of

front. I think as we get and I think

today's systems still are not although

very impressive are not that powerful um

from a you know any kind of AGI risk

standpoint that maybe this person was

talking about. Um and I think you need

to have both. You need to have

incredibly rigorous internal tests of

these things and then you need to also

get collaborative inputs from external.

So I think it's a bit of both. I

actually don't know the details of uh of

of the alpha uh uh process for the last

few you know the first few months. It

was just function search before and then

it become more general. So it's it's

sort of evolved it's evolved itself over

the last year in terms of becoming this

general purpose tool. um and it still

has a lot of um way to go before we can

actually use it in our main branch which

is at that point I think then becomes

more serious like with Gemini it's sort

of separate from from that currently.

Let's talk about AI safety a little bit

more broadly. It's been my observation

that it seemed like if the further back

in time you go and the less powerful AI

systems you have the more everyone seem

to talk about the safety risk and it

seems like now as the models improve we

we hear about it less and less including

you know at the keynote yesterday. So

I'm curious what you make of this moment

in AI safety. Uh, if you feel like

you're paying enough attention to the

risk that could be created by the

systems that you have and if you are as

committed to it as you were say 3 or

four years ago, a lot of these outcomes

seem less likely. Yeah. Well, we're

we're just as committed as we've ever

been. I mean, we we've we've from the

beginning of Deep Mind, we plan for

success. So, success meant something

looking like this. This is what we kind

of imagined. And I mean, it's sort of

unbelievable still that it's actually

happened, but it's it is sort of in the

in the Overton window of what we thought

was going to happen if if these

technologies really did develop the way

we thought they were going to. Um, and

the risk and attending to mitigating

those risks was was part of that. And so

we do a huge amount of work on our

systems. Uh, I think we have very robust

red teaming uh uh uh uh processes pro

both pre and post launches. Um, and

we've learned a lot. Uh and I think

that's what's the difference now between

having these systems have albeit early

systems contact with the real world. I

think that's actually been I'm sort of

persuaded now that that has been a

useful thing overall. And I wasn't sure

um I you know I think 5 years ago 10

years ago I may have thought maybe it's

better staying in a research lab and and

you know kind of collaborating with

academia and that but actually there's a

lot of things you don't get to see or

understand unless millions of people try

it. So, it's it's this weird trade-off

again between um you you can only do it

when there's there's millions of smart

people uh uh try your uh technology and

then you find all these edge cases. So

you know, however big your your testing

team is, it's only going to be, you

know, 100 people or thousand people or

something. So, it's not comparable to

tens of millions of people using your

your your systems. But on the other

hand, you want to know as much as

possible uh ahead of time so you can

mitigate the risks before they happen.

So, and this is so this is interesting

and it's good learning. I think what's

happened in the industry in the last two

three years has been great because we've

been learning when the systems are not

that powerful or risky as you were

saying earlier, right? I think things

are going to get very serious in 2 three

years time when these agent systems

start becoming really capable. We're

only seeing the beginnings of the agent

era, let's call it, but you can imagine

and I hopefully you understood from the

keynote what the ingredients are, what

it's going to come together with. And

then I think we really need a step

change in research on analysis and

understanding controllability. But the

other key thing is it's got to be

international. You know, that's pretty

difficult and I've been very consistent

on that because it's an inter it's it's

a technology going to fit everyone in

the world. It's been built by different

countries and different companies in

different countries. So you got to get

some international kind of norm I think

around uh uh what we want to use these

systems for and and what are the kinds

of benchmarks that we want to test

safety and reliability on. Um but

there's plenty of work to get on with

now like we don't have those benchmarks.

We should we and the industry and

academia should be agreeing to consensus

of what those are. What role do you want

to see export controls play in doing

what you just said? Well, export

controls is a very complicated issue and

and obviously geopolitics today is

extremely complicated. Um, and there

you know, I can I see both sides of the

arguments on that. You know, there's

proliferation uncontrolled

proliferation of these technologies. Uh

do you want different places to have

frontier modeling uh training

capability? Uh, I'm not sure that's a

good idea. But on the other hand, um

you want western technology to be to be

uh the thing that's adopted uh around

the world. So, it's a complicated

trade-off. Like, if there was an easy

answer, I think we'd all, you know, I

would be, you know, shouting it from the

rooftops. But I think there's it's it's

nuance like most real world problems

are. Do you think we're heading into a

bipolar conflict with China over AI if

we aren't in one already? I just

recently saw the Trump administration

making a big push to uh make the Middle

East uh countries in the Gulf like Saudi

Arabia and the UAE into AI powerhouses.

have them, you know, use American chips

to to train models that will not be sort

of accessible to to China and its AI

powers. Do you see that becoming sort of

the foundations of a new global

conflict? Well, I hope not, but I I

think uh short term, you know, I feel

like AI is getting caught up in the in

the bigger g geopolitical shifts that

are going on. So, I think it's just part

of that and it happens to be one of the

most uh topical new things that's

appearing. But on the other hand, what

I'm hoping is as people as these

technologies get more and more powerful

the world will realize we're all in this

together because we are. And so, uh, you

know, and the the the the last few steps

towards AGI, um, hopefully we're on the

longer timelines actually, right? Um

the more the timelines I'm thinking

about, then we get time to sort of get

the the the the collaboration we need

at least on a scientific level, um

before before then would be good. Do you

feel like you're in sort of the the

final home stretch to AGI? I mean

Sergey Brin, uh, Google's co-founder

had a a memo that was reported on by my

my colleague at the New York Times

earlier this year that went out to

Google employees and said, you know

we're in the sort of the home stretch

and everyone needs to get back to the

office and be working all the time uh

because this this is when it really

matters. Do you have that sense of like

of of finality or or sort of entering a

new phase or an end game? I think we are

past the middle game, that's for sure.

But I've been working every hour there

is for the last 20 years because I felt

the how important and momentous this

this technology would be and we've

thought it was possible for 20 years and

I think it's coming into view now. I

agree with that and um whether it's 5

years or 10 years or 2 years that

they're all actually quite short

timelines when you're talking discussing

what the the enormity of the

transformation of this techn you know

this technology is going to bring uh

that none of those timelines are very

long. When we come back, more from

Dennis Assabus about the strange futures

that lie ahead.

[Music]

We're going to switch to some more

general questions about the AI future.

Sure. A lot of people now are starting

to, at least in conversations that I'm

involved in, think about what the world

might look like after AGI. Um, the

context in which I actually hear the

most about this is from parents who want

to know um what their kids should be

doing, studying, will they go to

college? Um, you have kids that are

older than than my kid. Um, how are you

thinking about that? So I think that the

when it comes to kids and I get asked

this quite a lot is is u university

students um I think first of all I

wouldn't dramatically uh change some of

the basic advice on STEM uh getting good

at even for things like coding I would

still recommend because I think whatever

happens with these AI tools you'll be

better off understanding how they work

and how they function and you know what

you can do with them. Um, I would also

say immerse yourself now. That's what I

would be doing as a teenager today in in

trying to become a sort of ninja at

using the the the latest tools. I think

you can almost be sort of superhuman in

some ways if you got really good at

using uh all the latest uh coolest AI

tools. Um, but don't neglect the basics

too because you need the fundamentals.

and then I think uh teach sort of meta

skills really of um like learning to

learn and the only thing we know for

sure is there's going to be a lot of

change over the next 10 years right so

how does one get ready for that what

kind of skills are useful for that

creativity skills um

adaptability resilience I think all of

these sort of you know meta skills is

what will be important uh for the next

generation um and I think it'll be very

interesting to see what they do because

they're going to grow up AI native just

like the last generation

grew up mobile and and iPad and you know

sort of that that kind of you know

tablet native and then previously

internet and computers which was my era

and um you know they always I think the

kids of that era always seem to adapt to

uh make use of the latest coolest tools

and I think there's more we can do on

the AI side to make the tools actually

um if people are going to use them for

school and education let's make them

really good for that and sort of

provably good and I'm very excited about

bringing it to education in a big way

and also to you know if you had an AI

tutor uh to bring it to poor parts of

the world that don't have good

educational systems. Um so I think

there's a lot of upside there too.

Another thing that kids are doing with

AI is chatting a lot with digital

companions. Um Google DeepMind doesn't

make any of these companions yet. Um

some of what I've seen so far seems

pretty worrying. It seems pretty easy to

create a chatbot that just does nothing

but tell you how wonderful you are and

that can sort of like lead into some

dark and weird places. So, I'm curious

what observations you've had as you like

look at this uh market for AI companions

and whether you think I I might want to

build this someday or I'm going to leave

that to other people. Yeah, I think we

got to be very careful as we as we start

entering that domain and and that's why

we we haven't yet and we're being very

thoughtful about that. My my view on

this is um more through the lens of uh

the universal assistant that we talked

about yesterday, which is something

that's incredibly useful for your

everyday productivity. You know, gets

rid of the boring, mundane tasks that we

all hate doing to give you more time to

do the things that you love doing. I

also really um hope that they're going

to enrich your lives by giving you

incredible recommendations, for example

on all sorts of amazing things that um

you didn't realize you would enjoy. you

know, sort of the delight you with

surprising things. Um, so I think these

are the the ways I'm hoping that uh

these systems will go and actually on

the positive side, I feel like um we if

this assistant becomes really useful and

knows you well, you could sort of

program it with you obviously with

natural language to protect your

attention. So you could almost think of

it as a system that works for you, you

know, as an individual. it's yours and

um it protects your attention from being

assaulted by other algorithms that want

your attention which is actually nothing

to do with AI. Most most social media

sites that's what they're doing

effectively. Their algorithms are trying

to gain your attention and I think

that's actually the worst thing and it

be great to to protect that so we can be

more in you know creative flow or

whatever it is that you want you want to

do. That's how I would want these

systems to be useful to people. If if

you could build a system like that, I

think people would be so incredibly

happy. I think right now people feel

assailed by the algorithms in their life

and they don't know what to do about it.

Well, the reason is is because you have

to use your you've got one brain and you

have to let's say whatever it is a

social media stream, you have to dip

into that torrent to then get the piece

of information you want. But then you've

already but you're doing it with the

same brain. So you've already affected

your mind and your mood and other things

by dipping into that torrent and you

know to find the valuable you know the

piece of information that you wanted.

But if if an assistant dig digital

assistant did that for you, you would

you know you'd only get the useful

nugget and you wouldn't need to um break

your you know your your mood or what it

is that you're doing the day or your

concentration with your family, whatever

it is. Um I think that would be

wonderful. Yeah, Casey loves that idea.

You love that idea. I love this idea of

an AI agent that protects your attention

from all the forces trying to assault

it. I'm not sure how the the ads team at

Google is going to feel about this. Um

but we can ask them when the show comes.

Um, some people are starting to look at

the job market, especially for recent

college graduates, and uh worry that

there we're already starting to see

signs of AI power job loss. Um

anecdotally, I talked to young people

who, uh, you know, a couple years ago

might have been interested in going into

fields like tech or consulting or

finance or law who are just saying like

I don't know that these jobs are going

to be around much longer. Um, a recent

article in the Atlantic wondered if

we're starting to see AI competing with

college graduates for these entry- level

positions. Do you have a view on that? I

haven't looked at that. You know, I

don't know. I haven't seen the studies

on that, but um, you know, maybe it's

starting to appear now. I I don't think

there's any hard numbers on that yet. At

least I haven't seen it. Um I think for

now I mostly see these as tools that

augmenting what you can do and what you

can achieve. Um I think like with most I

think the next era I mean maybe after

AGI things will be different again but

over the next five to 10 years I think

we're going to find uh what normally

happens with with big sort of new

technology shifts which is that some

jobs get disrupted but then new um you

know more valuable usually more

interesting jobs get created. So I do

think that's what's going to happen in

the in the nearer term. Um so you know

today's graduates and the next you know

next five years let's say I think it's

very difficult to predict after that um

that's part of this sort of more

societal change that we need to get

ready for. I mean I think the the

tension there is that you're right these

tools do give people so much more

leverage. Um but they also like reduce

the need for big teams of people doing

certain things. I was talking to someone

recently who said, you know, they had

been at a data science uh company in

their previous job that had 75 people

working on some kind of data science

tasks and now they're at a startup that

has one person doing the work that used

to require 75 people. And so I guess the

question I'd be curious to get your view

on is what are the other 74 people

supposed to do? Well, look, I think um

uh these tools are going to unlock uh uh

the ability to create things much more

quickly. So, you know, I think there'll

be more people that will do startup

things. I mean, there's a lot more

surface area one could attack and try

with these tools um that was possible

before. So, let's take programming for

example. um you know so obviously these

these systems are getting better at

coding but the best coders I think are

getting differential value out of it

because they still understand how to

pose the question and architect the

whole codebase and and check what the

coding does but simultaneously at the

hobbyist end it's allowing designers and

maybe nontechnical people to vibe code

some things you know whether that's

prototyping games or or websites or uh

movie ideas so in in theory it should be

those 70 people or whatever should could

be creating new startup ideas. Maybe

it's going to be less of these bigger

teams and more smaller teams or very

empower empowered by AI tools. Um but it

but that goes back to the education

thing then which skills are now

important. It might be different skills

like creativity sort of vision and uh

design sensibility um you know could

become increasingly important. Do you

think you'll hire as many engineers next

year as you hire this year? I think so.

Yeah, that's that's the I mean there's

no plan to to hire less, but you know

we again you have we have to see how

fast the the coding uh agents improve um

today. They they're not, you know, they

can't do things on their own. They need

to they need uh they're just helpful for

for the best, you know, for the best

human coders. Last time we talked to

you, we asked you about some of the more

pessimistic views about AI in the

public. And one of the things you said

to us was that the field needed to

demonstrate concrete use cases that were

just clearly beneficial to people to

kind of shift this. My observation is

that I think there are even more people

now who are like actively antagonistic

toward AI. And I think maybe one reason

is they hear folks at the big labs

saying pretty loudly eventually this is

going to replace your job. And most

people just think well I don't want that

you know. So I'm curious like looking on

from that past conversation if you feel

like we have seen some use cases enough

use cases to start to shift public

opinion or if not what some of those

things might be that actually change

views here. Well I think we're we're

we're working on those things. They take

time to develop. Um I think the a kind

of universal assistant would be one of

those things if it was uh kind of really

yours and working for you effectively.

So technology that works for you. Um, I

think that this is what economists and

other experts should be working on is do

you have uh does everyone have manage

a a suite of of you know fleet of agents

that are doing things for you and you

know including potentially earning you

money or building you things? Um, you

know, does that become part of the

normal job process? I could imagine that

in the next four or five years. I also

think that as we get closer to AGI and

we make breakthroughs in we probably

talked about last time material

sciences, energy fusion, these sorts of

things helped by AI um uh we should have

we should start getting to a position in

society where we're getting towards what

I would call radical abundance where

there's a lot of resources u to go

around and then again it's more of a

political question of how would you

distribute that in a fair way right so

I've heard this term like universal high

income something like that uh I think is

going to probably be you know good and

necessary but obviously there's a lot of

uh complications there that need to be

thought through. Um so and and then in

between there's this transition period

you know between now and whenever we we

have that sort of situation where what

what do we do about the change in in the

in the interim and depends on how long

that is too. What part of the economy do

you think AGI will transform last? Well

I mean I think the parts of the economy

where you know involves humanto human

interaction and emotion um and those

things I think uh you know will probably

be the hardest things for for AI to do.

So um you know aren't already aren't

people already doing AI therapy and

talking with chat bots for things that

they might have paid someone you know

$100 an hour for? Well therapy is a very

narrow domain and I'm not sure exactly

there's a lot of you know hype about

those things. I'm not actually sure how

many uh of those things are really going

on in terms of actually affecting the

real economy and rather than just sort

of more toy things. Um and I don't think

the AI systems are like capable of doing

that properly yet. Um but just the kind

of emotional connection uh and uh that

we get from talking to each other and um

doing things in nature in the real

world. Uh I don't think that AI can

really replicate all of those things. So

if you lead hikes that' be a good job.

Yeah. Yeah, climb Everest. My intuition

on this is that it's going to be some

heavily regulated industry where there

will just be like a a massive push back

on the use of AI to displace labor or or

take people's jobs like healthcare or or

education or something like that. Um

but you think it's going to be an easier

lift in those heavily regulated

industries? I don't know. I mean, it

might be, but then we have to weigh that

up as society whether we want all the

all the all the the positives of that.

for example, you know, curing all

diseases or or um you know, I think

there's a lot of uh finding new energy

sources. So, I think these things would

be clearly very beneficial for society

and I think we need um to for our other

big challenges. It's not like there's no

challenges in society other than uh AI.

But I think AI can be a solution to a

lot of those uh other challenges be that

energy, resource constraints, uh aging

disease, um you know, you name it and

water access etc. a ton of problems

facing us today. Climate um I think AI

can potentially help with all of those.

And I agree with you, society will need

to decide what um it wants to use this

these technologies for. And um but then

you know what's also changing is what we

discussed earlier with products is the

technology is going to continue

advancing um and that will open up new

possibilities like uh kind of radical

abundance space travel these things um

which are a little bit out of scope

today unless you read a lot of sci-fi

but I think rapidly becoming uh real.

During the industrial revolution, there

were lots of people who embraced new

technologies, moved from farms to cities

to work in the new factories. Uh were

sort of early adopters on that curve. Um

but that was also when the

transcendentalists started retreating

into nature and rejecting technology.

That's when thorough went to Walden Pond

and there was a big movement of

Americans who just saw the new

technology and said, "I don't think so.

Not for me." Do you think there will be

a similar movement around rejection of

AI? And if so, how how big do you think

it'll be? Um, I don't know if it'll be I

mean there could be a get back to nature

and I mean I think a lot of people will

want to do that and and I think this

potentially will give them the room and

space to do it right. And if you're in a

world of radical abundance, I fully

expect that's what what a lot of us will

want to do is use it to, you know, I

think again I'm thinking about it sort

of space fairing and and and and more

you know, kind of um maximum human

flourishing, but uh I think there will

be that will be exactly some of the

things that a lot of us will choose to

do and but have time and the space and

the the resources to do it. Are there

parts of your life where you say, "I'm

not going to use AI for that," even

though it might be pretty good at it for

some sort of reason, wanting to protect

your creativity or your thought process

or something else. Um, I don't think AI

is good enough yet to have impinged on

any of those sorts of areas where I

would, you know, it's mostly I'm using

it for, you know, things like you did

with Notebook LM, which I feel find

great, like breaking the ice on a new

topic, scientific topic, and then

deciding if I want to get more deep into

it. That's one of my main use cases.

summarization, those things. I think

those are all just helpful. Um, but you

know, we'll see. I haven't got any

examples of what of what you suggested

yet, but maybe as AI gets more powerful

there will be. When we talked to Dario

Amade of Anthropic uh recently, he

talked about this feeling of excitement

mixed with a kind of melancholy about

the progress that AI was making in

domains where he had spent a lot of try

time trying to be very good like coding.

Yes. um where it was like you see a new

coding system that comes out, it's

better than you, you think that's

amazing, and then your second thought is

like, "Ooh, that stings a little bit."

Have you had any experiences like that?

So maybe maybe one reason it doesn't

sting me so much is I've had that

experience when I was very young with

chess. So you know um chess was going to

be my first career and you know I was

playing pretty professionally when I was

a kid for the England junior teams and

then Deep Blue came along, right? And

clearly uh the computers were going to

be much more powerful than the world

champion forever after that. And so, but

yet I still enjoy playing chess. Um

people still do. It's different, you

know, but it's a bit like I can, you

know, Usain Bolt, we celebrate him for

for running the 100 meters incredibly

fast, but we've got cars, but we don't

care about that, right? Like it's we

we're interested in other humans doing

it. And um I think that'll be the same

with robotic football and all of these

other things. So, um and that maybe goes

back to what we discussed earlier about

what I think in the end we're interested

in in other human beings. That's why

even like a novel, maybe it maybe AI

could write one day a novel that's sort

of technically good, but I don't think

it would have the same soul or

connection to the reader that um uh if

you knew it was written by an AI, at

least as far as I can see for now. You

mentioned robotic football. Is that a

real thing? We're not sports fans, so I

just want to make sure I haven't missed

something. I was meaning soccer. Yeah.

No. Yeah. No, no. Uh I don't know. I I I

there are there are Robocup uh sort of

soccer type little robots trying to kick

balls and things. Uh I'm not sure how

serious it is, but there is a there is a

field of of robotic football. You you

mentioned the you know sometimes a novel

written by a robot might not feel like

it have a soul. I have to say for as

incredible as the technology is in VO or

Imagine. I sort of feel that way with it

where it's like it's beautiful to look

at but I don't know what to do with it.

You know what I mean? Exactly. And

that's that's what I was you know that's

why we work with great artists like

Darren Aronowski and Shanka on the

music. Um is I I totally agree. I think

these are tools and they can come up

with technically good things and I mean

V3 is unbelievable like when I look at

the you know I don't know if you've seen

some of the things that are going viral

being posted at the moment with the

voices actually I didn't realize how big

a difference audio is going to make to

the video I think it just really brings

it to life but it's still not as Darren

would say yesterday when we were

discussing on an interview it it it

doesn't he he brings the storytelling

it's not got deep storytelling like a

master filmmaker would do or a master

novelist you know, the top of their

game. And um it might never do, right?

It's just always going to feel

something's missing. It's a sort of a

soul for a better word of the piece, you

know, the real humanity, the magic, if

you like the the great pieces of art

you know, art too. when you when I see a

Van Go or a Rothco or you know why does

that touch your you know I spill you

know um sort of you know hair's gone the

back of my my my spine because of I

remember you know and you know about

what what they went through and um the

struggle to produce that right in every

brushstroke of Van Go's brush strokes

his his sort of torture and I'm not sure

what that would mean even if the AI

mimicked that and you were told that it

was like so what right and and and so I

think that is the piece that at least as

far as I can see out to 5 10 years um

the the top human creators will always

be bringing and that's why we've done

all of our tools VO lia in in com in

collaboration um with top creative

artists the new pope pope Leo is

reportedly interested in AGI I don't

know if he's AGI pilled or not but uh

that's something that he's spoken about

before um do you think we will have a

religious revival or a renaissance of

interest in faith and spirituality in a

world where AGI is forcing us to think

about what gives our lives meaning. I

think that potentially could be the case

and um I actually did speak to the last

pope about that and and the Vatican's

been interested even prior to this pope

haven't spoken to him yet but on these

these matters how does AI and religion

uh and uh technology in general and

religion uh interact and and what's

interesting about the Catholic churches

and I'm a member of the Pontipical

Academy of Sciences is they've always

had uh which is strange for a religious

body a scientific arm you know which

they like to always say Galileo was the

founder

and and uh those

interest so so but then and and it's

actually really separate and I always

thought that was quite interesting and

people like Steven Hawking and and you

know avowed atheists were part of the

academy and and that's partly why I

agreed to join it is because it's a

fully scientific body and it's very

interesting and I was fascinated they've

been interested in this for 10 plus

years so they they were on on this early

in terms of like how interesting or how

phil from a phil philosophical point I

think um uh this this this technology

will be and I and I I actually think we

need more of that type of thinking and

work from from philosophers and

theologians uh actually would be really

really good. So I hope the new pope is

genuinely interested. Um we'll close on

a question that uh I recently heard

Tyler Cowan ask Jack Clark from

Anthropic that I thought was so good and

decided to just steal it whole cloth. In

the ongoing AI revolution, what is the

worst age to be? Oh wow.

Uh well I I don't I mean you know

um gosh I haven't thought about that but

I mean I think any age uh uh where you

can live to see it is a good age because

I think we are going to make some great

strides uh with things like you know

medicine and so um I think it's going to

be incredible journey. I don't none of

us know you know exactly how it's going

to transpire. It's very difficult to

say, but it's going to be very

interesting to find out. Try to be young

if you can. Yes, young is always better.

I mean, in general, young is always

better. All right, Deis, thanks so much

for coming. Thank you very much.

[Music]