I would say about 90% of the

breakthroughs that underpin the modern

AI industry were done either by Google

brain or Google research or deep mind.

So one of our groups the returns are

kind of still very substantial although

they're a bit less than they were

obviously at the start of all of this

scaling. We have amazing guests on the

show but very few honestly will be

considered in the same realm as Newton

Turing Einstein. Our guest today is one

of the greatest minds on the planet and

I consider myself incredibly lucky to

have had the chance to sit down with

him. Those labs that have capability to

invent new algorithmic ideas are going

to start having bigger advantage over

the next few years as the last set of

ideas all the juices being rung out of

them.

>> This is a truly special one and one that

I'll remember for a very long time.

>> I think we could probably get 30 40%

more efficiency out of our national

grids.

>> Enjoy the episode and I so appreciate

the time we had with a very special

human being.

>> I sometimes quantify the coming of AGI

is 10 times the industrial revolution at

10 times the speed. Thrilled to welcome

Damis Albus at Deep Mind. Ready to go.

>> Deis, I'm so excited to be doing this.

Thank you so much for joining me today.

>> Great to be here.

>> Now, there are many places that we could

have start, but I was watching actually

the documentary that you did, which was

fantastic, and I actually wanted to

start on AGI. Mhm.

>> Definitions are very varying. You've

been very thoughtful about what it means

to you.

>> And so I wanted to start, can you

explain to me how you think about it

today so we get that as a kind of ground

center?

>> Yeah. Uh well, we've we've always

defined we've been very consistent how

we define AGI as basically a system that

exhibits all the cognitive capabilities

the human mind has. And that's important

because the brain is the only existence

proof we have that we know of in maybe

in the universe uh that general

intelligence is possible. So that for me

is the bar for what AGI should be.

>> It's the worst question. How close are

we?

Everyone everyone says different things

and it's very difficult when you have

you know very prominent figures saying

it could be as early as you know 2026

2027.

>> Yeah I mean I think look I've got a

probability distribution around um the

timings but I I would say there's a very

good chance of it being within the next

5 years. So that's not long at all.

>> Is that closer than you thought? Has

that changed over time?

>> Not really. I mean actually when you

when you uh it's funny um my co-founder

Shane Le who's chief scientist here um

uh when we started out Deep Mind back in

2010 he used to write blog posts sort of

predicting about uh when AGI would

happen. And bearing in mind in 2010 when

we started almost nobody was working in

AI and everyone thought AI

no one was reading it was a dead end.

No. And but they're still there on the

internet for people to check. And uh we

used to do this extrapolation of compute

and algorithmic uh progress. And

basically we predicted around 20 years

it would take from when we started out

and I think we're pretty much on track.

>> What are the biggest bottlenecks when

you look today? You know in in the

documentary you said you just never have

enough compute.

>> What are the biggest bottlenecks when

you look at where we are today?

>> I think compute is the big one. Not just

for the obvious reason of scaling up uh

your ideas and your systems as as you

know the scaling laws as they're called

you know keeping on building bigger and

bigger um architectures with more and

more parameters. Um and as you do that

you get more intelligent systems but the

other thing you need a lot of compute

for is for doing experiments. So um the

computers the cloud is our workbench

basically. So if you have a new idea, a

new algorithmic idea, but you want to

test it, you kind of got to test it at a

reasonable scale, otherwise it won't

hold when you actually put it into the

main system. So um you need quite a lot

of compute if you have a lot of

researchers with lots of new ideas.

>> You mentioned the word scaling laws.

>> A lot of people suggest that we're

hitting scaling laws and we're starting

to see that plateauing effect.

>> Yeah.

>> Do you think that's true?

>> No, I don't think so. I think it's a bit

more nuanced than that. So um of course

when uh the leading companies all

started building these large language

models you're getting enormous jumps

with each generation of new system. Um

you know maybe they're almost like

doubling in performance. Uh at some

point that had to slow down. So it's not

kind of continuing to be exponential but

that doesn't mean there isn't great

returns uh still for scaling the

existing you know systems up further.

So, and we and the other frontier labs

are getting uh a lot of great returns on

on that kind of compute expansion. Um,

so I would say the returns are kind of

um still very substantial, although

they're a bit less than they were

obviously at the start of all of this

scaling.

>> Where are we behind where you thought we

would be?

>> Um, I think actually in most areas we

are ahead of where I thought we would

be. If you think about things like um

the video models or um even now with our

newest systems like Genie, they're

interactive world models. Um which I

think is kind of incredible if you sort

of step back and think about it. I think

if you'd shown me that 5 10 years ago, I

would have been pretty amazed. Um so I

think in most domains we're we we are

ahead of where um the field thought. Um

there's still some big things missing

though like continual learning. These

systems don't learn uh after you finish

training them, after you put them out

into the into the world. You know,

they're not very good at learning

further things. And I think some

critical capabilities that I'm sorry to

ask blunt and basic questions. Why do we

not have continuous learning today?

>> Um well, people haven't quite figured

out yet and all the leading labs are

working on this like how to integrate

new learning into the existing systems

that you know you spent months training.

Um so of course the brain does this very

elegantly, right? And um probably

through things like sleep reinforcement

learning. So you know you just kind of

get consolidation it's called in the

brain where you know your memories

during the day are replayed and then

some of that information is elegantly

incorporated into your existing

knowledge base and perhaps we I thought

for a while maybe we need something like

that uh to incorporate new information

along with uh uh the existing

information base. You mentioned video

models, you mentioned kind of media and

image. It seems that DeepMind has

progressed very quickly and caught up

slashovertaken other providers.

>> I think I've tweeted I think you liked

it, but I basically tweeted um what I

used and how it's changed over time and

Deep Mind Now is my number one for

research for new shows.

>> It wasn't that way before. what has led

to the acceleration and progression of

deep mind in a way that it wasn't maybe

there 2 to 3 years ago?

>> Yeah. Well, we made some organizational

changes. So, I think we've always had

the deepest and broadest research bench

at Google and at DeepMind. I mean, if

you look at the last decade uh or plus,

you know, 15 years, but I would say

about 90% of the breakthroughs that

underpin the modern AI industry were

done by either by Google Brain or uh

Google research or deep mind. one of our

groups um if you think of like Alph Go

and reinforcement learning and of course

transformers you know these are all the

key breakthroughs so I would back us to

sort of um make those breakthroughs in

the future uh if there are any missing

ones um and I think we've basically

helped put together all the talent from

around the company sort of pushing in

one direction uh and then we talked

earlier just about you know compute

resources it was also about combining

all of our resources together so we

could build the biggest models rather

than having two or three versions uh

around the the company. So I think a lot

of it was assembling together all the

ingredients we already had and then kind

of pushing with relentless sort of focus

and and and pace um acting almost like a

startup really uh to get back to the the

frontier and and be ahead in in many

areas.

>> You say if anyone's going to do the

breakthrough it could and should be us.

When you think about that is continuous

learning the next breakthrough that

you're most excited by?

>> I think there's quite a few things that

are missing. There's there's continual

learning. I think there's a lot of uh I

think a lot of mileage in looking at

different memory systems. Um at the

moment we have these long context

windows which are kind of a bit brute

force. You just put everything in them.

Um I think there's there's there's a lot

of uh uh interesting probably

architectures to be invented there. Um

and then there's stuff like uh long-term

planning, you know, hierarchical

planning. These systems are not very

good at planning at long time horizons,

you know, many years into the future. uh

which we as you know with our minds we

can do so um there's quite a lot of uh

uh problems I think that's still left to

overcome maybe one of the biggest is

consistency so you know the I sometimes

call these systems jagged intelligences

because they're really amazing at

certain things uh when you pose the

question in a certain way but in if you

pose a question in a slightly different

way they can actually still fail at

quite elementary things so a general

intelligence shouldn't be that sort of

jagged

>> when you reposition files and you set up

agents to perform in certain ways and

then the files fall over configure it

completely falls over.

>> Exactly.

>> 100%.

>> That's a disaster.

>> Yeah. Well, I mean the general

intelligence, you know, if you think

about how our minds work, it shouldn't

have those kinds of holes in it.

>> We said about a plateauing of scaling

was everyone talks about a

commoditization of models in terms of

capabilities. Do you think we see that

or do you think we see one to two

continuously accelerate ahead of the

others? Yeah, I feel like uh maybe you

know the the the the three or four

leading labs now of which we're one I

think the gap is sort of um starting to

pull away because uh a lot of these

tools also of course help you build the

next generation. So things like coding

tools, math tools and it's getting

harder and harder I would say to kind of

ek out the same uh gains from just the

same ideas. So I think those labs that

have capability to you know invent new

algorithmic ideas are going to start

having bigger advantage over the next

few years as as the the last set of

ideas are sort of um you know all the

juices being rung out of them.

>> I mean you know you were very open with

a lot of your research for years and we

see many very good quality open models.

How do you think about the future of

open? I have many portfolio companies

that kind of use frontier models and

then they use that to set a benchmark

and then they use open models to kind of

get as close as possible but with more

cost effectiveness.

>> What does that future look like?

>> Yeah, I think it's probably similar to

what we're seeing today. I mean we're

we're big supporters of of open science

and and open models and we've done many

many things obviously from from the

original Transformers to to AlphaFold

you know these are all uh things we sort

of given out into the world and to help

the the the research community and we

plan to continue to do that especially

in applied domains you know scientific

domains applying AI to science which is

obviously my passion um but uh I I think

increasingly um you know what you're

going to see is the open source models

probably one step back from the absolute

frontier. Um you know it usually takes

about 6 months for the open source

community to sort of reimplement and

figure out what those ideas are. Um but

we are also uh pushing hard on a kind of

suite of open source models called Gemma

which are you know we're determined to

kind of make best-in-class for their

sizes. So specifically for small

developers or um academics uh or or you

know the beginnings of a startup I think

they're perfect for that and also edge

computing too. So we're very interested

in open source models for certain types

of um applications.

>> How do you think about a world post

LLMs? You have different people with

different views. You Yan Lun with very

different views.

>> For me, I don't think it's uh you know,

I kind of disagree with Yan on a few

things in terms of um I think there

might be there's a 50/50 chance there's

some things maybe missing that we still

need to make breakthroughs in perhaps

their world models. um uh these kinds of

uh approaches. But my betting is uh

pretty strongly is we've seen how

successful these foundation models have

been. They can do incredibly impressive

things. I don't think that's going to go

away. We're still seeing seeing you know

gains from the returns from the scaling

laws. Um so my I think the only question

really is when you think about a future

AGI system is you know is an LLM

foundation model going to be the key

component only or is it the total system

right so I just think it's it's a

question of um uh you know is there

anything else needed not is it not I

don't think it's going to get replaced I

think it's going to get built on top of

these foundation models just like the

way we do with our world models

>> when we think about that future 5 years

out as you said potentially with AGI

what does that world look like? Many

people have different concerns.

>> Yeah.

>> If we just start generally, what does

that world look like to you?

>> I think on the positive side and the

things obviously I've I've spent my

whole career in life building towards

AGI is I think it will be the ultimate

tool for science and medicine. So in

terms of advancing scientific discovery

um finding cures to diseases, I think we

need that kind of technology. And so I'm

hoping um in 5 years plus time we'll be

sort of entering a new golden era,

golden age of scientific discovery.

>> Uh so my mother's got multiple cerosis.

So it's like something it's the thing

that I'm always most excited about. The

thing I worry about is actually kind of

drug discovery, the process of getting

it through all the trials and knowing

that it takes a decade before my mother

will actually get any benefits from it.

>> How do we solve that?

>> I think we'll get to that point soon.

First of all, what we're doing is, you

know, after we did the alpha fold

project to do protein folding, um, then

we spun out a company called Isomorphic

Labs, which is doing extremely well. And

that is supposed to, you know, the idea

there is we're focusing on solving the

rest of the drug discovery process,

which is a lot of chemistry, designing

the compounds, uh, checking it's not

toxic and all the different properties

you need for for drugs to be safe. Um, I

think we'll have that whole drug design

engine ready in, you know, the next 5

plus 5 to 10 years. then you're right.

The next problem is the clinical trials

still take uh many many years, right? Um

and but I think AI can help there in

terms of um maybe simulating uh parts of

the human uh metabolism. Um also

stratifying patients to make sure that

certain patients get exactly the right

type of drug that's suitable for their

uh genomic makeup. Um and so I think AI

can help there too. But I think the real

revolution will come when a few maybe a

dozen or so AI drugs get through the

whole process. Uh and then the

government and the regulatory bodies see

that and they have enough data to sort

of uh back test the predictions of those

models and then maybe what we can do

will be in the future where maybe 10

further years where um we can really

just trust the predictions uh that the

models are making and actually then

maybe skip out some steps perhaps like

the animal testing is not needed

anymore. maybe we can go up the dosage

uh uh uh ladder quicker um because you

can rely on these models. So I think we

got to do in two steps. Solve the drug

design problem first and then look at

the regulatory uh length of time it

takes.

>> Speaking of regulatory AI safety is a

big topic and a big concern. I think it

was again I watched it last night over

dinner which was a great watch which is

obviously the documentary and I think it

was Stephen Hawking who said we must get

it right because we might not get

another chance.

Do you think that's right?

>> Yeah, I do think that's right. I think

that is the the the the stakes uh that

that uh you know we have to deal with

and um you know there's two things I

worry about. One is the misuse of these

systems by bad actors and they can be

repurposed. These are dualpurpose

technologies. They can be used for

incredible good in science and health as

we've just discussed but they can also

be repurposed for harmful ends by a bad

actor. So that's one issue. Second issue

is a technical one. Making sure these

systems as they get more powerful, not

today's systems, but maybe in a year or

two's time when they become more

agentic, more autonomous, as we get

towards AGI, um can they be kept on the

guardrails that we want. Um, and I think

regulation, the right kind of regulation

could help here in terms of making sure

there's at least sort of minimum

standards from all of the uh uh leading

providers, but it needs to ideally be a

kind of international uh standards.

>> What is the right kind of regulation?

And again, I'm kind of quoting yourself

back from this documentary. You're like,

I think we need more global

coordination, which worries me because

we're getting worse at it.

>> Yes.

>> Which I think would be an unwavering

truth.

>> Yes, for sure. I mean, that's it's sort

of crazy the timing that we're in,

right, with this most consequential

maybe technology the world's ever seen.

Um, at the same time as a very

fragmented sort of international uh uh

system and uh it's not ideal, but I

think we're going to have to try and do

the best we can to at least come up with

a sort of set of min maybe minimum

standards, some benchmarks that test for

undesirable properties. For example,

deception. you don't you know nobody

wants should be building systems that

are capable of deception because then um

they could be getting around other

safeguards u and then I imagine you know

if things go well some kind of

certification process that basically

it's almost like a kite mark of you know

quality that this model um has certain

uh uh safeguards and certain guarantees

uh and so therefore um consumers and

companies can safely sort of build on

top of it and I think that is how it

should go ideally. Um but it does have

to be international because of course

these systems are crossborder and you

know they're they're cross territory.

>> Who is that like ultimate verification

system? I you know you obviously started

with theme park.

>> Yes.

>> Uh

yeah brilliant. Don't put the burgers

down too close to the roller coaster. Um

but you know obviously as a media

company I go through any media platform

saying I don't know what's real or fake.

I'm always having to ask what's real or

fake. Who is that arbiter of

verification?

>> Yeah. Well, I think there I mean

ultimately it's got to be government I

think but um you know the kind of

technical bodies that would um be able

to do the technical work would be like

maybe the AI safety institutes you know

there's a very good one in the UK that

uh uh you know was set up under Prime

Minister Sak and I think is doing great

work and there's one in the US and maybe

some of the leading countries that have

the best research should also have an

equivalent body that is staffed with

highquality researchers too um that can

actually evaluate and audit these kinds

of systems uh against certain benchmarks

and um I kind of like independently

check whether they are meeting the right

standards.

>> If I could give you like a magic wand

that was only applicable to AI safety

sav uh what would be your implementation

idea program that you would put in place

with this magic wand? Yeah, I think we

need some kind of um uh international

body maybe similar to the atomic agency

something like that that perhaps the the

AI safety institutes sort of feed into

and the research community has to also

do this and be involved in like what are

the right set of benchmarks to check

what types of traits what types of

capabilities uh maybe there are other

safeguards too like um you know it's it

wouldn't be desirable to have uh AI

systems um output tokens that are not

human readable. So you know in some kind

of machine language that we couldn't

understand. I think that would in you

know introduce a new vulnerability. So

there's quite a few sort of things like

that which I think most of the leading

labs uh would agree are probably not

best to do. Um and then these uh these

bodies would uh you know these

institutions would test against those

things and I think that would give the

public confidence and um and you know

academia could be involved as well as

well as civil society that these uh

systems which are going to get

incredibly powerful um have been

independently uh checked and audited.

>> That's it. Your magic wand's done now.

That was the one.

>> Maybe I used on the wrong thing but

>> time will tell.

>> Yes. Exactly. you said there about um

science being one of the most exciting

areas in five years time.

>> I have to ask it because it's one of the

biggest concerns is the labor

displacement problem. I just had Mark

Andre on the show actually and he said

that I was a he said I was a Marxist for

I know which I was like for bringing

Yeah. Mark's wonderful so I'm not

blaming him but he was like it's

completely rubbish.

>> Yeah.

>> I don't agree with it at all. We've

always over pass overcome it. M

>> how do you think about the labor

displacement problem when you look at

how truly capable these systems are

>> and what that does to labor markets?

>> Well, certainly you know in the past uh

with every new revolutionary technology

there's been a lot of uh jobs uh

disruption. So that's for sure and I

think that's definitely going to happen.

So a lot of old jobs you know go away or

not viable anymore but then actually uh

the history of it is that um a whole set

of new jobs arrive that maybe one can't

even imagine before and those are high

quality higher paying. So that's the

normal course. Of course you have to be

very careful to say this time is

different and um I guess that's what

people like Mark are claiming is like

you know it's the same as as as the last

sort of you know 10 massive

breakthroughs like the internet, mobile

and so on. Um I do think this is going

to be bigger uh than all of those

previous uh uh breakthroughs uh

technological breakthroughs. I mean I

sometimes quantify like AGI at the

coming of AGI as like 10 times the

industrial revolution uh at 10 times the

speed. So unfolding over a decade

instead of a century. So if you you know

I've been reading a lot about the

industrial revolution. There's a lot of

great books about it and um that caused

a huge amount of upheaval as well as a

lot of advances. I mean we wouldn't have

modern medicine today. Child mortality

was at 40% back in back pre-industrial

revolution. So things things you

wouldn't want it not to have happened

but ideally this time around we uh

mitigate some of the downsides a bit

better than we did during the industrial

revolution. I often listen to amazing

voices like yours and I get very excited

by how fast it's coming. Yeah. And then

I try and stop myself from being too

useful and think ah I should be more

wise and I'm told that you know we

always overestimate what can be done in

a year and underestimate what can be

done in 10.

>> Is that the truth here or is it actually

coming faster than we

>> No, I I think that's still the truth. I

mean maybe all the both time scales of

short-term and long-term are nearer than

than than other technologies. But I do

think like literally today as of today

and in the next year things are a bit

overhyped in AI. I mean there couldn't

be any more hyped in some ways. Uh but

on the other hand interestingly I still

think it's still very underappreciated

how revolutionary this is going to be in

the in the sort of time scale of about

10 years. So we could call that long

term. So there's still that dichotomy

even even today with AI

>> with the concern around labor markets.

There's also a concern around income

inequality and the concentration of

wealth to few players.

>> How do you see that shaping out with the

comment on industrial revolution and

what happens there?

>> Well, I think there's different ways

that could play out. So, um, you know,

maybe pension funds should be buying

into all the big AI companies and making

sure that everyone has a piece of that

or sovereign funds, maybe everyone,

every country should have a sovereign

wealth fund that does that. that would

be the sort of um uh investment way of

doing it. I think also there needs to be

thinking thought about if there is uh

this massive uh productivity gain but

it's sort of narrow where that occurs

you know how do we redistribute and and

how do we distribute that um so that

everyone benefits from uh uh these huge

gains and I can see all sorts of ways

that could be done including like

providing sort of infrastructure and

other things um with that additional

productivity gain I mean there could be

unbelievable things happening in the 5

to 10 year time scale including like a

breakthrough through in some kind of

renewable free energy. You know, maybe

we sold fusion. Uh we're working on

that, right, with with with our partners

at Commonwealth Fusion. Um uh I think AI

is going to usher in, you know, maybe we

have amazing new superconductors, better

batteries, you know, material science.

There's all sorts of ways I could see

that completely changing the nature of

the economy.

>> How how do we solve the energy crisis

that comes with an AI revolution? What

it means in terms of energy requirements

is unprecedented.

I know it's an incredibly hard question

which I'm delving from really hard

question to really but how do we solve

that unprecedented need for new energy?

>> Well, I think actually um AI will in the

in the medium to long run uh more than

pay for itself. I think in terms of

energy costs in so you know we work on

all these projects of like optimizing

existing infrastructure like optimizing

the grid. I think we could probably get

30 40% more efficiency out of our

national grids. Um and then there's like

modeling the climate and weather and we

have all sorts of the best kind of

weather modeling systems in in in the

world. So that helps us work out where

the effects are really happening to

mitigate that. Uh and then finally the

most exciting maybe is like these new

breakthrough technologies like fusion

like new batteries uh superconductors

that I think uh AI will be essential for

helping us reach and then I think we'll

be in a completely new energy situation

than we've ever been as humanity where

uh and then that will of course help

with things like the climate and

environment um and eventually also help

us um get into space much more cheaply

because if you have a you know an

incredible energy source like fusion

um then you have effectively unlimited

rocket fuel because you can just um

distill catalyze sea water.

>> I'm not going to ask you to solve space.

Don't worry then.

>> My my question was on being in the UK.

>> Yeah,

>> you're in London. I'm in London. I'm

very proud to be in the UK.

>> You have been, I'm sure, pushed or

prodded at every turn to move to the US.

>> Why have you stayed?

>> Well, um I should ask you that question,

too. But I think uh I think I saw in

London when we started Deep Mind as a

place that and and the UK in general and

and Europe in some to some degree

there's incredible talent here. You know

we've always had I don't know what it is

three or four of the top 10 universities

in the world with Cambridge and Oxford

Imperial or UCL these kind of

universities. So we're producing um kind

of the envy of the world really these

amazing graduates and PhD students. Um

we have incredible scientists here. We

got rich heritage of that all the way

from you know cheuring and and hawking

and Darwin uh Newton. So you know we

have this incredible history of of of

scientific breakthroughs and having

great thinkers. So I felt we had all the

ingredients uh and the talent and great

engineers here but it just hadn't been

galvanized into uh an ambitious startup

idea deep tech startup idea. and and

that's what I but I I felt it was

possible and I felt that there was

actually less competition here for that

sort of talent and we could even draw in

the best talent from the top uh European

universities and that's what it was like

in the early days of deep mind. So I

think it was a huge structural advantage

for us and then the final thing is maybe

being a bit away from the valley. There

is some disadvantage in that you're not

plugged into the network and the gossip

and the the latest trends and vibes and

all these things. We're a little bit out

of it here, but um it does I think it's

very conducive to to thinking deeply

about things, being more original about

how you think. And I think that's great

for things like deep tech where you know

you don't want to be distracted by the

latest fad. You want to you you know

it's going to be a 20 emission which is

what we knew at the beginning of deep

mind. So I think being a little bit away

from that um maelstrom is quite good. I

mean, Palmer lucky at Angel often talks

about being 400 miles away from the

valley. It's core to his kind of

innovative thinking.

>> Yes, we're a few thousand miles away,

but yeah,

>> terrible question. Will Europe have a

trillion dollar company? You know, you

see the Americans always bash us for our

lack of large companies. I ping Daniel

Ek and be like, "Come on, dude. That's

exactly

>> but we don't have a trillion dollar

company."

>> Not yet. I mean, Daniel may well get

there with one of his companies. You

know, Spotify, Hell Singh, I think those

are two good options. I think there's no

reason why we can't have that. I'm I'm

going to try and do that with Isomorphic

U which is headquartered here uh and I

think has the potential to be that. But

I think that's one of the disadvantages

of Europe is obviously we're combination

of you know um smaller markets. So

that's one thing we have to kind of

overcome. Maybe this EU inc thing um

could be a good innovation.

>> I'm pulling out the magic wand again.

You can change you've got the magic but

this time applied to European

technology.

>> What would you do to implement a growth

mindset? a ability to build that

trillion dollar company that we don't

have today.

>> I think in the UK, I mean this may apply

to other European countries too. I think

unlocking what pension funds can invest

in or just for the kind of growth stage,

I think we're brilliant at doing the

startup idea and getting it to a certain

level like we did with Deep Mind. But

then if you really want to cross that

sort of chasm into the trillion dollar

uh global, you know, player, then where

are the billion dollar rounds going to

come from? uh where you can really take

on those that the you know the existing

incumbents and I think that certainly

was missing 10 years ago when I was

doing fundraising for Deep Mind and um I

think it's still kind of missing today

just that kind of level of ambition and

and the amount the capital markets can

can support.

>> I read about some of your early rounds

raising in the S Malibu

families kids. Exactly.

>> Um okay we're going to do a quick fire

round meeting Elon for the first time.

>> How was that? Oh yeah, it was amazing.

Um, it was at a it was at a founders

fund because we were both SpaceX and

DeepMind were part of a same portfolio,

a kind of amazing portfolio that Peter

Teal had at Founders Fund and uh I think

we were both invited I think I was

invited to my first portfolio kind of

conference and I think it must be back

in 2011 or 2012 very early days. So we

were the small little upcoming thing and

I had a small speaking slot and then and

then and then and then Elon was the you

know big thing in that portfolio. So, he

had the keynote, but then we met

afterwards. I think it was in Elon says

it was like we were passing each other

in the bathroom or something and uh we

said hi and we both hit off, you know,

immediately like uh as sort of, you

know, people that were uh almost too

ambitious in their thinking perhaps and

love sci-fi and um and and I really

wanted to visit his rocket factory. So,

I was sort of trying to get an Angular

invite to to SpaceX and and uh in LA and

I think I got there a couple, you know,

he invited me at the end of that meeting

and and that was our second meeting in

in the space effects factory.

>> I love it. Not even speaking slots as

big as his.

>> I don't know about that.

>> Healthcare revolution disease

eradication that you're most excited

about. Again, for me it's specifically

with multiple scerosis.

>> Yeah. Well, look, I want to literally

cure cancer. I know people say that's

the cliche, but I actually what we're

building at isomorphic is general

purpose. So we're trying to build a a

platform, a drug design platform that

will be applicable to any therapeutic

area. So ideally it will help with

everything from neurogeneration,

cardiovascular, immunology, cancer.

Those are the ones we're we're focusing

first, but eventually it should be

applicable to every disease area.

>> What are you thinking about that you're

not reading about or seeing anyone talk

about?

Um I think it's more so I think a lot of

people are worrying about the economic

questions around AGI uh that we talked

about earlier but I I worry a lot about

the philosophical questions around it

like when it comes let's say assume we

get the technical right let's assume we

get the economical economics part of it

right both of those are hard then

there's a philosophical question of what

is meaning what is purpose um we'll find

out won't be what consciousness is um

what does it mean to be human I think

that's uh uh what's coming down the road

and I think we need some great new

philosophers to help us to help us uh

navigate that.

>> Hard final question.

>> There are many different ways you could

describe what you do. What would you

most like to be remembered for your

legacy to be?

>> Um I would like uh my legacy to sort of

be remembered for like advancing science

um and doing uh building technologies

that bring incredible benefits into the

world like curing terrible diseases.

Dis, thank you so much for putting up

with my meandering conversation. You've

been fantastic. I really appreciate it.

>> Thank you very much.