this is the greatest force of reducing

the technology divide the world's ever

known it will have an impact on GDP of

every country uh in the double digits in

the coming years nobody's going to do

this for you you've got to do it

yourself it's up to organizations to

Enterprises to countries to to to build

what they need the stakes at play are

basically the equivalent of modern

digital colonialization AI isn't just

Computing infrastructure it's also

cultural infrastructure

today we're talking about Sovereign AI

all things National infrastructure um

and open

source and so let's let's just start

with the first question I usually get

from nation state leaders which is is AI

actually a general purpose technology

right so in the in the history of

humanity we've had maybe a handful of

these 22 24 economists call these

specific technologies that accelerate

economic progress broadly across Society

right electricity the printing press and

the question everybody's asking right

now is is that the right way to think

about AI or why isn't AI just another

important but ultimately narrow

technology it is a general purpose

technology uh because uh it basically

revisit entirely the way we are building

software and the way we are using

machines uh and so in the same way that

internet was a general purpose

technology AI is a general purpose

technology here it allows to build

agents that are doing things on your

behalf and in that respect it can be

used in any vertical uh of the industry

it can be used for services for public

services uh it can be used to change the

life of citizens uh it can be used for

agriculture it can obviously be used for

defense purposes so it covers everything

that a state needs to worry about and so

in that respect it's very natural that

uh any state makes it a priority and

makes it uh and find a dedicated

National AI strategy

by the way everything Arthur said is

100% correct it is also exactly the

reason why everybody's given

up and it's precisely

wrong and the reason for that is this um

if it's a general purpose technology and

one company can build the ultimate

general purpose technology then why

should anybody else do it and that is

the flaw right but that's also the mind

trick to convince everyone that this is

a technology that intelligence is only

you know something that a few people

ought to go build everybody ought to sit

back and wait for it I would

advise that everybody engage

Ai and that this is not not for the

privileged uh intelligence is for

everyone and it is not just a few

companies in the world who should build

it everybody should build it nobody's

going to care more about uh the Swedish

culture and the Swedish language and and

the Swedish people and the Swedish

ecosystem more than Sweden nobody's

going to care about the the ecosystem of

Saud Arabia more than Saudi Arabia and

nobody's going to care about it about

Israel more than Israel despite the fact

that the technology is general purpose

and absolutely true how could

intelligence not be general purpose um

it is also it is also hyp specialized

and the reason for that is because let's

face it I don't think I'm waiting around

for a general purpose chatbot uh to be

an expert in a particular area of

disease I still think that I would

prefer to have somebody who is hyp

specialized in that field uh to F

fine-tune to train you know and post

train if you will right an AI model

that's going to be specialized in that

it's the general purpose technology the

same way your programming language is

the general purpose

technology uh and in addition to that

also a culture uh carrying technology so

I think what that means is that there's

an infrastructure there are chips that

obviously not every country are going to

build there are uh general purpose

models like base models compression of

the web uh that are eventually going to

be open source and that can serve as the

the right basis for constructing uh

specialized systems uh but beyond that

uh I think it's up to organization to

Enterprises to countries to to to build

what they need so the way to make it

work is to take a general purpose model

uh like an open source model for

instance and to get the knowledge you

have specifically or ask uh your

citizens or ask your employees to

distill their knowledge into the systems

into the agents that are going to be

working on your behalf so that do that

progressively those agents become more

accurate and more following the

instructions uh and the the the

specifications that the country or an

Enterprise may have when it's building

uh AI systems so you need vertical

experts or you need cultural experts or

you need people with a certain uh um

National agenda to partner with uh

technological companies that can expose

the open source infrastructure in a way

that is easy to use and in a way that is

easy to specialize so I think that's

where the the frontier lies it's a very

horizontal technology but to make

anything useful out of it you need a

partnership between the horizontal

providers and the vertical experts but

unlike previous general purpose

technology waves in history like

electricity or the or the printing press

how is this one different if I'm a if

I'm a nation state leader and I'm trying

to understand what the right framework

is for me to think about AI in my

country should I think about it like

digital labor should I think think about

it as as uh again to Bridges I think

it's similar to electricity in the sense

that it will have an impact on GDP of

every country uh in the double digits in

the coming years so that means that from

an economical point of view uh Every

Nation needs to worry about it because

if they don't manage to set up

infrastructure to set up their own

Sovereign capacities at the right place

that means that this is money that might

flow back to other countries so that's

kind of changing the economic

equilibrium across the world

in the sense that's not very different

from electricity which 100 years ago if

you weren't building electricity

factories you were you you you were

preparing yourself to buy it from your

neighbors uh which at the end of the of

the day isn't great because it creates

some uh some dependencies so that's I

think in that sense it is similar what

is fairly different I think there's two

things first of all it's a it's kind of

an amorphic technology uh if you want to

create digital labor with it you you

need to shape it you need to have

infrastructure talent and software and

the talent needs to be created locally I

think this is quite important and the

reason for that is that in contrast with

electricity this is a Content producing

technology so you have uh agents that

are producing content that are producing

text producing images producing voice

interacting with with people and when

you're producing content and interacting

with the society you become a social

construct and in that respect uh social

construct Theory cultures and and carry

the values of of either an Enterprise or

a country and so if you want those

values not to disappear and not to

depend on on a central provider you need

to engage with it more profoundly that

you would be to engage with electricity

for instance would you agree with that

Jensen couple of ways to think about it

your

country's digital

intelligence is not likely something you

would want to

Outsource to a third party

without some

consideration your digital intelligence

is just now a new infrastructure for you

your telecommunications your health care

your education your highways your

electricity now there's a new layer this

new layer is your digital

intelligence it's your responsibility to

decide how you want this digital

intelligence to

evolve and whether you want to Outsource

it so that you could never have to worry

about intelligence again uh or this is

something that you feel you want to

engage uh maybe even control and shape

uh into into a national infrastructure

of course it has all the things that

author said AI factories infrastructure

etc

etc there's another way you could think

about it is your digital

Workforce now this is a new

layer and you've got to decide whether

the digital Workforce

of your country or your company is

something that you decide to

Outsource hope it evolves the way that

you would like it to or is it something

that you want to

engage maybe even decide to control and

nurture and make better we hire general

purpose employees all the time we hire

them out of school some of them are more

general purpose than others some of them

are more intelligent than others

but once they become our employees we

decide to onboard

them train them guard rail them evaluate

them continuously improve them we make

the investment necessary to make general

purpose intelligence right into Super

intelligence that we could benefit from

and so I think that that second layer

thinking about it as digal Workforce in

both cases uh it contributes to the

National economy in both cases it

contributes to social advance in both

cases that it contributes to the culture

and I think that in both cases a country

needs to play a very active role in it

and so I I think it it it's back to your

original question about about Sovereign

AI how to think about it yes it is

definitely a general purpose technology

but you have to decide how to shapee it

your

country's Digital Data belongs to you

your uh national library right your

history for so long as you want to

digitize it you could make it available

to everybody in the world you could also

make it available to companies or

researchers and institutions in your own

country it belongs to you of course

these are all vaporous things they're

very soft ideas

um but it it does belong to you and you

could decide it belongs to you in the

sense that that this is where you came

from it belongs you belongs to you in

the sense that you could decide um how

to put it to use for the benefit of your

uh your people and it belongs to you in

the sense that uh it's your

responsibility to shape its future you

know Sovereign AI it's your

responsibility there are several other

types of assets that nation states fund

and protect the

military um your your electricity grid

if let's say I I I've understood not the

criticality of AI

infrastructure and Sovereign AI do I

have to

now take control of every part of the

stack Jensen mentions uh I guess digital

Workforce right and I think it's a very

good uh analogy that you need an

onboarding platform for your AI

Workforce which means you need to be

able to customize the models and pour

the knowledge that are sitting uh in

your National libraries into the model

so that suddenly speaks better your

language right uh you need uh to be able

to put uh to to get your systems to know

about your lows right so that suddenly

the guard rails that are set when you're

deploying an AI software uh are uh

compliant with uh everything that you've

done before and so that onboarding

platform that requires to customize to

gu to evaluate uh and then when noticing

that certain things needs to be improved

to fix things to debug things uh that's

the platform that we are building right

and once the custom systems are made uh

it's important to be able to maintain

them yourselves so that means being able

to deploy them on your own

infrastructure being able to ask your uh

technological Partners to potentially

disappear from the loop your it

department is going to become the HR

department Department of your digital

Workforce and they're going to use these

tools that Arthur describes to onboard

AIS fine-tune AIS guard rail them

evaluate them continuously improve them

right and that flywheel will be managed

by the the new the modern version of the

IT department right and we'll have

biological Workforce and we'll have a

digital Workforce and it's fantastic and

so so nobody's gonna do this for you

you've got to do it yourself right

that's why even though we have so many

technology companies in the world every

Company still has their own it

Department I've got my own it Department

I'm not going to out Outsource it to

somebody else in the future they'll be

even more important to me because

they'll be you know helping us manage

these digital workforces you're going to

do this in every country you're going to

do this in every company within those

countries and so so the space for what

Arthur is

describing uh to take this general

purpose technology

but to really fine-tune it right into

domain experts they're National experts

or they're industrial experts or they're

corporate experts these or functional

experts this is the future the giant

future space of AI well so you both said

something that I just want to make sure

I'm understanding correctly you you

called it uh soft a soft concept like

your culture and you said there are a

bunch of norms that the training data

has that you customize the models on it

sounds like you're saying unlike compute

storage networking which have by the way

you said Norms that exactly means it's

soft versus rules which are more hard

yeah or or algorithms and laws which are

very

specific yeah there's different things

that you want to incorporate into your

AI systems right there are some elements

of style and of um of knowledge that

you're not going to to enforce through

strict gws right that you can enforce

through continuous training of models

for instance you take uh the National

Library yeah you take preferences and

you you you distill it into the models

themselves and then you have a set of

flows uh you have a set of policies if

you're in the company and those are

strict and so usually the way you build

it is that you connect the models to the

strict rules and you make sure that

every time it answers it you verify that

the rules are respected so there's

really two things so on one side you're

pouring and compressing knowledge in a

soft way into the models and on the

other side you're making sure that you

have a certain number of policies and

and rules that are strictly enforced and

that have 100% accuracy so on one side

this is soft this is preference this is

culture preference somebody's preference

is

multi-dimensional you know you know what

you prefer it's implicit many times in

well there's so many there's so many

features that that defines my preference

it takes AI to be able to

precisely um comply with the description

that Arthur was describing just now

could you imagine if a human had to

write this in Python you know describe

every one of these capture every one of

these things uh in C+ plus you know

based on this I prefer that but if you

did that I prefer that other thing and I

mean the number of rules would be in

insane right which is the reason why AI

has the ability to kind of codify all of

this it's a it's a it's a new

programming model that can deal with the

ambiguity of life well it sounds like

you're saying AI isn't just computing

infrastructure it's also cultural

infrastructure yes it is is that right

and it's about making sure that your

cultural infrastructure and the human

experties that are uh in your in your

company or in your country makes it to

the AI systems right uh culture

reflection values we we were just

talking about how uh each one of these

AI models AI

Services um respond differently to the

type of questions you're asked right

because they they codified the values of

their service or the values of their com

company into each one of their services

could you imagine this now Amplified you

know at a international scale for me

this is an inherent limitation of

centralized AI models where you're

thinking that you can encode uh some

Universal values and some Universal

expertise into uh a general purpose

model uh at some point you need to take

the general purpose model and ask a

specific population of employees or of

citizens what what are their preferences

and what are their expectations and you

need to make sure that you're

specializing the model in a soft way and

in a hard way uh through rules and

through culture and preferences and so

that part is not something that you can

Outsource as a country it's not

something that you can Outsource as an

Enterprise you need to own it well then

is it an exaggeration to say if it is

cultural infrastructure and I don't own

sovereignty of it I'm the stakes at play

are basically the equivalent of modern

digital

colonialization if you're saying a

you've got to think about AI is almost

like your digital Workforce and another

country or somebody I who's not my

sovereign uh Nation can decide what my

Workforce can and can't do oh that's a

problem some of it is is universal for

example um it is possible for for

certain companies to serve um Nations

and and and society and companies around

the world because you know it's

basically Universal um but it cannot be

the only it can't can't be the only

social fabric it cannot be the only

layer it it can't be the only digital

intelligence layer it has to be

augmented by something Regional there's

hey you know I think McDonald's is

pretty good everywhere all right

Kentucky Fried Chicken's pretty good

everywhere I I think it's Starbucks is

fine everywhere um but you still want

the local style local taste that

augments on top of that that's right

local cafes you know the mom and pop

restaurants and because it it defines

the culture right it defines Society it

defines us and and so I think it's

terrific that you have Walmart

everywhere that you can count on

everywhere you know I think it's fine

but but there's you need to have local

taste local style local preference local

Excellence local you know Services maybe

let let me swing in another way the the

um uh it is very likely that in the

context of our digital Workforce in the

future uh we will have some digital

workers which are generic right they're

just really good at doing maybe basic

research or you know something basic

good college level graduate or they're

useful for every company it's

unnecessary for me to create something

new right I think Excel is pretty good

Microsoft Office is universally

excellent right I'm perfectly fine with

it reference good reference architecture

base that's right right then there's

then there's industry specific tools and

Industry specific expertise that is

really important like for example we use

synopsis and Cadence Arthur doesn't have

to because it's specific to our industry

not his we probably both use Excel you

know and so we both probably both used

PDFs we both used browsers you know and

so there's some Universal things that we

can all take advantage of and there'll

be universal digital workers that we can

take advantage of and then there'll be

industry specific and then they'll be

company specific right inside our

company we have some special skills that

are very important to us that defines us

you know it's highly biased if you will

okay they're biased to doing the things

that I need them to do very guard rail

to doing very specific work highly

biased to the needs and the Specialties

of our company right and and so we

become super hum in those areas well

your digital Workforce is going to be

the same and AI is going to be the same

there'll be some that

that you just kind of take off the off

the shelf you know the new search will

likely be you know some AI right the new

research will probably be some AI but

then there'll be industrial versions of

AIS that we'll maybe get from Cadence

and others and then we'll have to groom

our own using Arthur's tools right and

we'll have to fine-tune them we'll

onboard them we'll you know make them

incredible I very much agree with this

vision of having like a general purpose

model and then some layer of of um of

specializ for Industries and then an

extra layer of specialization for

companies and for countries you will

have a tree of AI systems that are more

and more specialized and maybe to give a

concrete example with what we recently

did uh so we released in January a model

called mistal small and it's a general

purpose model so so it speaks all of the

languages uh it knows mostly about most

things uh but then what we did is that

uh we took it and and we started a new

family of specialized models that were

specialized in languages

uh so we took uh more languages in

Arabic more languages in Indian in

Indian languages uh and we retrained the

model and so we distill this extra

knowledge that the initial model hadn't

seen and so in doing that we actually

made it much much better in being

idiomatic when it speaks Arabic uh and

when it speaks uh languages from the

Indian Peninsula and so that's language

is actually very it's probably like the

first thing you can do when you're

specializing a model the good thing is

that for for a for a given size of model

you can get a model that is much better

if you choose to specialize it in a

language so today uh our model it's the

24b it's called M saaba it's a model

tune on Arabic um is outperforming every

other Lang language model that are like

five times larger and the reason for

that is that we did the specialization

and so that's the first layer and then

if you think of like a second layer you

can think of verticals uh so if you want

to build a model which is not only good

at Arabic but also good at uh handling

legal cases in Saudi Arabia for instance

uh well you need to specializ specialize

it again so there's some extra work that

needs to be done in Partnership uh with

companies to make sure that now you not

only your system is good at at speaking

a certain language but it's good at

speaking a certain language and

understanding the Lego work that is done

in this language uh and so it's true for

any combination that you can think of of

vertical and language I see you want to

have like a medical diagnosis assistant

in French well you need to be good at

French but you also need to understand

how to be good at at uh speaking the

French language of Physicians right and

so those two things it's very hard to do

as a general purpose model provider if

this is true and what you're describing

is is real that I need the capabilities

to customize this AI layer on my local

Norms my local data which is fairly

sophisticated from a from a technical

capability perspective how would you

advise a big nation to think about this

the stack we're talking about the chips

the compute the data center the models

that sit on top the applications and

then ultimately the um what you were

describing as as the AI nurse or the AI

doctor and how how would you advise

someone that's a smaller Nation

differently I would say you need to own

the you need to I guess to buy and to

set up the horizontal part of the stack

so you need the infrastructure uh you

need the inference Primitives uh you

need the customization Primitives uh you

need the observability you need uh the

ability to connect GS to models uh to

connect models to sources of information

of real-time information those are

Primitives that are fairly well

factorized across uh the different

countries across the different

Enterprises and once you have that so

you these are things that can be bought

then you can start working then you can

start building you build from these

Primitives according to your values

according to your expertise and thanks

to your local talents the question is

where is the frontier in between what is

horizontal and horizontal if you're a

small Enterprise or a small uh country

you should probably buy and what is

vertical and specific to you and that's

definitely something that you need to

build you have to get it in your head

that it's not as hard as you think it is

first of all um because because the

technolog is getting better it's easier

could you imagine doing this 5 years ago

it's impossible could you imagine doing

this 5 years from now it'll be

trivial and so we're somewhere in that

middle the only question is do you have

to do it right the truth of it matter is

I hate onboarding employees and the

reason for that is because it takes a

lot of work but once you set up an HR

organization and Leadership mentoring

organization and

cesses then your ability to onboard

employees is easier and is

systematically more enjoy enjoyable for

everybody involved but in the very

beginning is hard setup is always hard

setup is always hard this is no

different um the only question is do you

need to do it if you if you want to be

part of the future and this is the most

consequential

technology of all time right not just

our time of all time digital

intelligence how much more valuable can

how much more important can it be and so

if you come to the conclusion this is

important to you then you have to engage

it as soon as you can learn it learn

along the way and just know that it's

getting easier and easier all the time

the fact of the matter is if we try to

do agentic systems even three years ago

it was incredibly hard right but agentic

systems are a lot easier today and all

of the tools necessary for cure rating

data sets for onboarding the digital

employees to evaluating the employees to

guard railing all of digital employees

all all of those are getting better all

the time the other thing about

technology is when it becomes faster

it's easier right you know could you

imagine back in the old days of course

you know I had the benefit of seeing

computers from its earliest days and the

the performance of the computers were so

frustratingly slow everything you did

was hard right but these days you know

the type of things we do is just it's

it's magical because it's also fast you

know and so whether it's it's um

uh motivated by your your institutional

need to engage the most consequential

technology of all time or the fact that

it's getting better all the time so it's

not that hard I think the number of

excuses is running out so let's talk

about that for a second because change

is hard I've got an endless list if I'm

a nation state leader I'm facing

increasing amounts of geopolitical risk

my my I don't know who my allies are

elections are coming there's any number

of things I've got to deal with but now

I let's say I understand that that this

is

important you guys spend so much time

talking to nation state leaders who are

thinking about what are the risks of

adopting AI too fast and and you're

right the Zeitgeist has shifted based on

the Paris action Summit it seemed like

people are there's there's a more a tone

of optimism more than there was a tone

of pessimism a year ago but what are the

most common questions you get from

nation state leaders when they're asking

you about risks and how to think about

them so I've heard several questions uh

but one of the risk is to see your

population start uh getting afraid of

the technology for fear of it replacing

them and and that that is something that

can actually be prevented uh if uh we

collectively make sure that everybody

get access to the technology and is

trained in using it so the the Skilling

of of the of the various citizens of the

population

is extremely important and stating AI as

an opportunity for them to actually work

better and showing actually the the

purpose of it through applications

through things that they can actually

install on their smartphone uh through

Public Services uh we're working for

instance with the French unemployment

system uh to actually connect

opportunities of jobs to an unemployed

unemployed people through AI agents that

are being actioned by obviously human

operators within the within the agency

and that is one opportunity that's a

very palatable opportunity for people to

find a job better right and so that's

part of the thing that uh can make sure

that population understand the

opportunity and the fact that AI is

really just a new change for them to

adopt just the same way they had to

adopt personal computers in the 90s and

internet in the in the two 2000s the

common aspect with these changes is that

you need people to uh embrace the

technology and I think the the biggest

problem that nation states may have is

to see AI increase the digital divide uh

that is already relatively big but if

you work together and if if done in this

in the right way we can make sure that

AI is actually reducing the digital

divide AI is a new way to program a

computer it is because by typing in some

words you can make the computer do

something just like we did in the past

right we used to type words and we make

computers do something and now you talk

to it yeah right now you can talk to it

you could do you can interact with it in

a whole lot of ways you can make the

computer do things for you a lot easier

today than it was before right the

number of people who could prompt chat

GPT and do productive things right just

from a human potential perspective is

vastly greater than the number of people

who can program C++

ever and therefore we have closed the

technology divide probably the greatest

equalizer we've seen it is by definition

right the greatest equalizer of

Technologies of all time but you still

need to have uh citizens to know about

it I think that's the thing I'm just

describing the fact yes I'm describing a

fact the fact is there are more people

who program computers using chat GPT

today than there are people who program

computers using C++ right that's a

fact in four short years that's a

straight up fact actually three years

yeah a straight up fact and so the fact

is this is the

greatest force of reducing the

technology divide the world's ever known

right it's just perceived and what

Arthur is saying the perception

through you know I don't know who I'm

talking about it and I don't know how

talking about it but the fact of the

matter is it is not stopping right it's

not stopping anything the number of

people who are actively using chat GPT

today is off the charts I think it's

terrific it's completely

terrific anybody who's talking about

anything else apparently isn't working

and so I I think people realize the

incredible capabilities of AI and and

how it's helping them with their work I

use it every single day I used it this

morning and so every single day I use it

and and I think that that uh deep

research is incredible right you know it

my goodness the the work that that um uh

Arthur and and all of the computer

scientists around the world are doing is

incredible and and and people know it

people are picking it up obviously right

just the number of active users um let

let's talk about open source for a bit

because both of you have talked quite

publicly about the importance of open

models in the context of sovereign AI at

Deep Mind you're part of the chinchilla

skating laws which were openly published

your co-founder gam created llama and

then last year Nvidia and mrr worked on

a jointly trained model called mrr Nemo

why are open models such a big part of

your focus because it's an horizontal

technology and um Enterprises and states

are going to be eventually willing to

deploy it on their own infrastructure uh

having this openness is is important

from a sovereignty perspective that

that's the first point and then the

second Point uh of importance is that uh

releasing open source models is a way to

accelerate progress and uh we created

mistal on the basis that what we've seen

during our early career when we were

doing AI in between 2010 and

2020 uh was an acceleration of progress

because every lab was building on top of

each other and that's something that

kind of disappeared uh with the first

large language models

uh from open AI in particular and so

spinning back that open flywheel of I

contribute something and then another

lab is contributing something else right

and then we iterate from that is the

reason why we created mistal and I think

we we did a good job at it because we we

started to release models and then meta

started to release models as well right

uh and then we had Chinese company like

deeps release uh Stronger models and

everybody benefit from it so coming back

to mral Nemo of um when difficulty of

creating AI models in an open way is

that this is more a cathedral than a

Bazar setting when it comes to open

source because you have large span to do

to build a model and so what we did with

Nvidia team is really to to to mix the

two teams together have them work on the

same infrastructure the same code have

the same problems and combine their

expertise to build the same model and

that has been very successful uh because

Nvidia brought a lot of things we didn't

know I think we brought things that

Nvidia didn't know and at the end of the

day we produced with something that was

at the time the best model for its size

right and so we really believe in such

collaborations and we think that we

should do them more and at a higher

scale and not only with only two uh

companies but probably with three or

four and that's the way open source is

going to Prevail yeah I completely agree

the the um benefit of Open

Source uh in addition to accelerating

and elevating the basic science the

basic Endeavor um of all of the general

models and the general capabilities is

the open- source versions also activate

a ton of Niche markets and Niche

Innovation uh all of a sudden Healthcare

Life Sciences physical sciences uh

robotics Transportation the number of

industries that were activated as a

result of Open Source capabilities are

sufficient good is incredible don't

ignore the the incredible capabilities

of Open Source particularly in The

Fringe the niche n but Mission critical

where data might be sensitive yeah it

could be for example in mining

energy right who's going to go create an

AI company to go mine energy energy is

really important but the mining of

energy is not that big of a market and

so open open source activates every

single one of them Financial Services it

turns out activates them healthare

defense you pick your favorites anything

that is Mission critical and that

requires uh to do one's own deployment

uh that potentially requires to do on on

on the edge deployment as well right um

and anything that requires uh some

strong auditing and the ability to do a

thorough evaluation of it you can

evaluate a model much better if you have

access to the weights than if you only

have access to apis and so if you want

to build certainty around the fact that

your system is going to be 100% accurate

I don't think you should be using a

close Source model and you have to

connect it into your flywheel how are

you going to connect your local data

yeah you have to connect it into your

own your local data your own local

experience the more you use it the

better it becomes that flywheel you you

can't do it without open source but

let's say I'm uh I'm a nation state

leader I've I've been considering open

source I'm starting to hear things like

hey open source is a threat to National

Security we should not be exporting our

models because these open models

actually give away a ton of um nation

state Secrets or more importantly the

bad guys can use these open models too

and so this is a threat to security

instead what we should be doing is

locking down maybe development amongst

two or three Labs that have the

infrastructure to to you know get

licenses from the government to do

training to do the right safety and

certification i' I've certainly been

hearing that a lot how should I think

about that versus what you're telling me

which is actually open is is better for

Mission critical Industries

collaboration in between Labs is going

to be uh to be to be critical for

Humanity success and if if one state

decides to lck things down what the only

thing that is going to happen is that

another state will take the leadership

uh because uh cutting yourself from the

open fly will is just too high as just

too high of a cost for you to maintain

competitivity if you do that this is a

debate that has occurred in in in the

United States and effectively if um if

there's some export control over weights

uh this is not going to stop any country

uh in Europe any any country in Asia to

to continue its progress right and they

will collaborate uh to actually

accelerate that progress so so I think

we just need to embrace the fact that

this is an horizontal technology uh very

similar to programming languages

programming languages they're all open

source right so I think AI just needs to

be open source in that respect we're

glad to see that at the AI Summit that

occurred last week for instance uh this

was very much on the agenda this

realization that we could accelerate

together by being more open about uh the

way we build the technology uh and and

so it's great to see that open source

has a lot of good days before it it is

impossible to

Control software is impossible to

control if you want to control it then

somebody else's will emerge and become

the

standard just as Arthur

mentioned and the question is is open

source safer open source enables more

transparency more researchers more

people to scrutinize scrutinized to work

the reason why every single company in

the world is built every cloud service

provider is built on open source is

because it is the safest technology of

all

give me an example of a public Cloud

today that's built on an infrastructure

stack that isn't open source you start

from open source you could customize it

right um but the benefit of Open Source

is the contribution of so many people

and the scrutiny you know very

importantly you can't just put any

random stuff into open source you get

laugh off laugh off the internet you've

got to put good stuff on the open source

because the scrutiny is in is intense

and so so I think the the open source

provides all of that great collaboration

to accelerate accelerate Innovation

escalate

Excellence uh ensure

transparency attract scrutiny all of

that improves safety in a sense you're

saying it's partly more secure because

as we've seen with open source databases

storage networking compute you get Mass

red teaming the whole world can help red

team yeah right your technology versus

just a small group of researchers inside

your company is that roughly right right

way to exactly

exactly by pulling a lot of

organizations together to come up with a

technology that they can all use and

specialize on their own domains right

you're forcing the technology to be good

for every one of them and so that means

you're removing biases you're you're

really making sure that the general

purpose models that you're building are

are as good as possible and don't have

failures and I think open source in that

respect is is also a way to reduce the

number of failure points if um as a

company today I decide to rely fully on

a single organization and on its uh

safety principles on its red teaming

organization as well kind of trusting it

a little too much whereas if I'm

building my technology on open source

models I'm trusting the world to make

sure that the basis on which I'm

building is secure uh so that's a

reduction of failure points and that's

obviously something that you need to do

as an Enterprise or as a

country um we're going to transition a

little bit now into company building

which is something a lot of people are

excited to hear from both of you about

so let's start with you Jensen you've

remarked that Nvidia is the smallest big

company in the

world what allow enabled you to operate

that way I mean our our architecture was

designed for several things it was

designed um to uh adapt well in a in a

world of change

either caused by us or affecting us and

the reason for that is because

technology changes fast and and if you

if you over correct on

controllability then you you are you

underserving A System's ability to uh

become agile and to adapt and and so our

company is is uh uses words like aligned

instead of use words like control um you

know I don't I don't know that one time

I've used the word control um in in

talking about the way that the company

Works uh we care about minimum

bureaucracy and uh we want to make our

processes as lightweight as possible now

all of that is so that we can en enhance

efficiency enhance agility and so on so

forth uh we avoid things like

words like division when Nvidia was

first started it was it was it was

Modern to talk about divisions right and

I hated the word divide you know why

would you create an organization that's

fundamentally divided I hated the word

business units the reason for that is

because why should anybody exist as one

uh why don't why don't you leverage as

much of the company's resources as

possible you know I wanted a a a system

that was organized uh much more like a

Computing unit like a computer uh to uh

uh deliver on an output as efficiently

as possible um and so the company's

organization looks a little bit like a

Computing stack and what is this

mechanism that we're trying to create

and in what environment are we trying to

survive in you know is this much more is

it is this much more like a you know

peaceful Countryside or is this like

much more like a Concrete Jungle you

know what kind of environment are you in

because the the type of the type of um

system you want to create should be

consistent with that and the thing that

always strikes me odd is that every

company's orc chart looks very similar

but they're all different things you

know one's a snake the other one's a

elephant the other one's a you know you

know a cheetah and everybody is is

supposed to be some somewhat different

in in that in that Forest but somehow

they all all same exact you know

structure same exact organization

doesn't seem to make sense to

me I agree that uh it feels like

companies have personalities and uh and

despite the fact that they're organized

sometimes similarly I should say that uh

obviously we we have a lot of things to

learn and I mean the company is not not

even two years old to I guess one one

challenge we have with M Tri and I think

our competitors have the same challenge

is that this is the one of the first

time that the software company is

actually a deep tech company uh that is

driven by science science doesn't have

the same time scales as software right

you need to operate uh on a monthly

basis uh sometimes you don't know uh

exactly when the thing will be ready uh

but on the other hand you have customers

that are asking when is the next model

coming up when is this capability going

to be available Etc so you need to

manage expectation and I think for um

like the the biggest challenge and I

think we're starting to do a good job at

it is to manage the the hinge in between

uh the product requirements and what the

science is able to do research and yes

and product research and product and you

don't want the research team to be fully

dedicated to making the product work you

need to work and I think we we we've

started to do it on making sure that you

have several frequencies in your company

you have fast frequencies on the produ

side iterating every week uh and you

have slow frequencies on the on the

science side that are looking at why

profoundly the product is selling on

certain domains and how they could fix

it through research through new data

through new architecture through new

paradigm um and that's I think that's

fairly new this is not something that

you would find in a typical SAS company

uh because because this is inherently a

science problem I mean Nvidia is one of

the most successful companies that have

over 30-year timeline has figured out a

way to keep science and research ahead

of the rest of the world whether it was

Cuda back in 2012 where that was

fundamental systems research or Cosmos

today which is now saying you know is

definitely state-ofthe-art on how

simulation should work out we've

harmonized exactly what Arthur just said

is is that is that fistic right for you

yeah we harmonized that inside our

company we have we have we have basic

research applied research and then we

have architecture and then we have

development and we have multiple layers

of it and and these layers are all

essential right and they all have their

own time clock in the case in the case

of basic research the the time the

frequency is could be quite low and yet

on the other hand all the way to the

product side we have a whole industry of

customers who are counting on us and so

we have to be very precise and somewhere

between basic research and discovering

discovering hopefully uh surprises that

nobody expects right on the one hand on

the other hand uh to be to be able to

deliver on what everyone expects

predictably okay um these two extremes

uh we manage uh harmoniously inside our

company there's so many fascinating

things about this Market but there's one

in particular that I want to call out

both of you have customers that are also

your

competitors right so and and those

competitors are huge and highly

capitalized Tech Giants Nvidia sells

gpus to AWS which is building its own

chips trainum and Arthur you're you know

your training models that you sell

through AWS and Azure who have funded

Labs like anthropic and open AI so how

do you win an environment like this and

how do you manage those relationships

because we talked about company building

internally but now I'm curious

externally how do you survive in this in

in this situation Jensen said it well

you give up control but you work on

alignment and uh despite the fact that

sometimes you have um uh like certain

companies can be competitors you may

have a line interest and you can work on

on specific agendas that are shared you

have to have your own

place um obviously

obviously these cloud service providers

aren't working with Arthur because they

already have the same thing they just

want two of the same things it's because

Arthur and mrol has a position in the

world that is unique to mrol and they

add value in a particular place that

that is unique a lot of the conversation

we've had today um are are areas that

that mstr and the work and and their

position in the world um makes them

uniquely good at and uh we are different

you know we're not just

another um Asic or and we we can do

things for the csps and do things with

the csps that are not possible for them

to do themselves for example nvidia's

architecture in every is in every cloud

and in a lot of ways we have the first

onboarding for uh amazing future

startups and the reason for that is

because by onboarding to Nvidia they're

they don't have to make a strategic or

business or otherwise commitment to a ma

major Cloud they could go into every

cloud and they could even decide to

build their own system if they like uh

because the economics turns out to be

better for them at some point or they

would like access to capabilities that

we have right that are some more

protective within the clouds and so so

whatever whatever the reasons are um in

order to be to be a good partner to

somebody you still have to have a unique

position you need to have a unique

offering and and I think Mr ra has a has

a very unique offering we have a very

unique offering and and our position in

the world is is um uh uh important to uh

even the people we compete against and

so so I I think when we when we are

comfortable within that and comfortable

with our own skin then we can be

excellent Partners to all of the all of

the csps and and then we want to see

them succeed um I know that it's a weird

thing to say when you see them as a

competitor which is the reason we don't

see them as a competitor we see them as

a collaborator who happens to compete

with us as well and so uh you know

probably the single most important thing

that we do for all the csps is bring

them business right and and that's what

a great Computing platform does we bring

people business I remember when Arthur

and I first met we sat down in London at

a late night restaurant and kind of

sketched out the plan for his series a

and we were kind of figuring out what he

would why he needed so much capital for

the series a which in hindsight was

remarkably efficient I think uh the mrr

series a we put together was half a

billion relative to other folks who had

to spend multiple billions to get to the

same place but I asked him you know what

chips would you like to run on and I

don't even think you you looked at me so

absurdly as if I had asked you a

question that how could it be an answer

other than Nvidia other than h100s and I

think that

ecosystem has been the startup ecosystem

that Nvidia has invested in creates so

much business for the clouds yeah what

is the philosophy that led you to invest

so deeply in startups and

Founders so early on even before anybody

knew about

them um two reasons I would say one the

first reason is that I rarely call us as

a GPU company what we make is a GPU but

I I think of Nvidia as a Computing

company if you're a Computing company

the most important thing you think about

is developers right if you're a chip

company the most important you think the

thing you think about is a chip and and

uh all of our all of our strategies all

of our actions all of our priorities all

of our Focus all of our investments 100%

of it is aligned with the attitude that

is developer first it's about the

Computing platform first another way of

saying ecosystem right and and so so uh

everything starts uh there everything

ends there GTC is a developers

conference right um and uh all of our

initiatives inside the company is

developer first so that's number one the

second thing is uh we were pioneering a

new Computing approach that that was

very alien to the world of general

purpose Computing and so this

accelerated Computing approach was

rather rather alien and and counter

intuitive and and rather awkward for a

very long time and and so we're

constantly seeking out looking for uh

the next incredible uh breakthrough the

next impossible thing to do without

accelerated Computing and so it's very

natural that I would find and would seek

out you know researchers and and and uh

uh great thinkers like Arthur uh because

it's you know I'm looking for the next

killer app and so that's kind of a natur

natural intuition Natural Instinct of

somebody who's creating something new

and so you know if if there's an amazing

uh uh computer science thinker that we

haven't engaged with uh you know that's

my bad we got to get on it from a

Computing perspective what are the most

significant Trends you see on the

horizon and in particular if you could

if for for an audience of folks who

might be leading who might be prime

ministers or presidents or Ministers of

it and some of the world's fastest

growing markets trying to understand

where Computing is going how would you

how would you guide them we're moving to

toward uh workloads that are more and

more asynchronous so workloads where you

give a task to uh an AI system and then

you you wait for it to do like 20

minutes of research before returning uh

so that's definitely changing a bit the

way uh you should be looking at um at

infrastructure uh because that creates

more load uh so it's um guess it's a

bull case for data centers and for

NVIDIA uh we

as I've said I guess in the beginning of

this episode um all of this is not going

to happen well if you don't have this

the right onboarding infrastructure for

the agents if you don't have a proper

way for your AI systems to learn about

the people they are interact with and to

learn from the people they interact with

right so that aspect of learning from

human interaction is going to be

extremely important in the coming uh in

the coming years and there's another

aspect which is around personalization

of having I guess models and systems

consolidate the representation of their

users to be as useful as possible I

think we are in the early stage of that

but that's going to change again pretty

profoundly the the interaction we have

with the machines that will know more

about us uh and know more about our

taste and how to be as useful as

possible toward us and as um I guess to

to to to close on that I think as a as a

prime minister or as a as a leader of of

a country I want to think about

education about making sure that I I

have a local talent pool uh that

understands AI enough uh to create

specialized AI systems and I want to

think about infrastructure both on the

physical side but also on the on the

software side so what are the right

Primitives what is the right partner to

work with that is going to provide you

with the platform of onboarding and so

those two things uh are important if you

have this and you have the talent and if

you do deep partnership

the economy uh of your state is going to

be profoundly changed the last 10 years

we've seen extraordinary change in

Computing um from hand coding to machine

learning from CPUs to gpus from software

to AI right we're across the entire

stack the entire industry is has been

completely transformed and we're going

through that still the next 10 years is

going to be incredible um of course you

know we the the industry has been

wrapped up in talking about scaling laws

and pre-training is important of course

and continues to be um now we have

post-training right and posttraining is

is thought experiments and practice and

tutoring and coaching and um all of the

skills that we use uh as humans to learn

you know the the idea that that um uh

thinking and agentic and robotic systems

are now just around the corner is really

quite exciting and so so the amount of

what it means to Computing is is very

profound and uh and that's where we've

spent the last you know people are

surprised that that um uh that Blackwell

is such a great leap over Hopper and the

reason for that is because we built

black wal for

inference and at a just in time because

all of a sudden uh thinking is such a s

such a big Computing

load and and so that's one layer is is a

Computing layer the next layer is the

the type of AIS that we're going to see

um there's the agentic AI the

informational digital worker AIS um but

we now have physics AI That's making

great progress and then there's physical

AI That's making great progress and uh

physics AI is of course uh things that

are that are uh that obey the the uh uh

and understand the the the physical laws

and and the atomic laws and the chemical

laws and you know all of all of the

various physical sciences are going to

see some great breakthroughs and I'm

very excited about that that affects

industry that affects science uh affects

Higher Education and Research and and

then physical ai ai that understand the

nature of the physical world you know

from from from F friction to inertia the

cause and effect you know object

permanence those kind of basic things

that that humans have common sense but

most AIS don't and and so I think that

that going to enable a whole bunch of

robotic systems that are going to have

great implications in manufacturing and

others you know the US economy is is

very heavily weighted on on knowledge

workers and and yet many of the other

countries are very heavily weighted on

on um uh manufacturing right and uh and

so I I think for for many of the prime

ministers and and the leaders of

countries to realize that that uh the

AIS that they need

uh to transform and to revolutionize

their industries that are so vital to

them whether it's energy focused or

manufacturing focused is just around the

corner and they ought to stay very alert

uh to this and I would encourage people

not to not to um

overr uh the technology and and

sometimes when you over admire a

technology overr the technology you

don't end up engaging it you're afraid

of it somehow um don't don't this is you

know the some of the things that we said

today about about AI closing the

technology divide is is Gen is really

something that i' to be recognized this

is of such incredible National interest

that you have the responsibility to

engage it anyhow exciting times

ahead that was incredible thank you both

so much for making time if they want if

they want to go learn more they want to

figure out how to partner with with the

two companies call us you kidding me you

can call us yes am I right the two

start with listening to this podcast and

then giving them a speed dial put their

numbers in the in the show notes Jensen

nvidia.com

job

done you heard it here we're very risk

on

to I can I can attest to that all right

thank you so much all right thank you

all right thank you thank you so much

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