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>> I want to talk about AI and I would

really want to because I think this is

on everybody's mind more than almost any

other subject today um related to

intersection of business, technology,

society. Um, so Satia, um,

you know, we're we're moving AI from

something that was experimental,

something that we always talked about in

the future, and now it's it's today. Um,

and it's now more foundational. [snorts]

And it's not just foundational for

companies, but it it really is becoming

now foundational

for countries and throughout society and

I think you know you have an advantage

over so many other people you know being

at the forefront of this technology

transition. Um so um with that I wanted

to ask a few questions related to that.

You have described that AI is a plat

plat uh platform shift

and what does what does that mean?

Question one, where do you see that

shift going in the next few years? And

importantly, the third part of my

question would be fast forwarding a few

years, five years,

what's going to seem obvious in

hindsight that feels less clear today?

You know, first of all, um it's great uh

to be back here, uh Larry, and it's um I

had a chance, in fact, yesterday when

you put out the letter, uh to kick off

the forum and read it. And um and in

there, you sort of had this one line of

uh really I think when it comes to AI,

the real question in front of all of us

is how do you ensure that the diffusion

of AI happens and happens fast? I mean I

think you had that line of how do the

models, the data and the infrastructure

[clears throat]

spread more evenly to create surplus

everywhere. If you sort of think about

it, the the way I come at this is not

that um this has always been the arc of

computation, right? You can sort of take

it in the last 30 years or the last 70

years. It's always been about can you

digitize

artifacts on about people, places, and

things and then build analytical and

predictive power. Right? That's what the

mainframes did. That's what the mini

computer did. That's what the client

server error did. That's what the web

error did. The mobile cloud error did.

So it like it it depend irrespective of

which paradigm or platform it has been

one continuous arc of saying let's make

better sense of this world um by

reasoning about it in digital form

because in some sense once you have

these artifacts in digital form you can

use a more malleable resource like

software

>> right

>> um which doesn't have the same type of

you know I'll call it [clears throat]

marginal cost economics associated with

it that allows us to then build more

insight and more uh more capability and

in that context AI I would say is of the

same class at least like the web or the

internet um or mobile or PC or the cloud

or and maybe even greater and so to me

right now where we are is you know let's

take just what's happened with software

engineering, right? Which is one, you

know, is knowledge work. Um, you know,

you could say it's elite knowledge work.

>> It started off, uh, you know, in fact,

my own belief in this generation of AI

and its capability. Uh, really got built

up when I first saw GitHub copilot do

code completions, right? So for the

longest time we had the dream that if

you're a software developer can you

predict the next sort of word or the

next uh line of code uh and suddenly it

started working with these models uh

then you said okay if I can do that then

can I actually go and bring back you

know the flow for a software developer

by going to a chat session and asking

any question and it comes back with

answers that then uh you can use in your

coding flow right so that was the next

thing then you said Well, if that's

working, can I assign it small tasks?

That was the agent mode. Uh, now you

have complete autonomous agents where

you can give it your entire project,

right? It can work uh, you know,

>> 24/7.

>> It can work for 24/7. I mean, it's still

we've got some ways to go for these

things to remain coherent long time. But

nevertheless, it's getting better and

better. And interestingly enough, you

look at it, uh, the software developer

still is got a lot of agency in it,

right? So that's why I kind of still

think that you know going and thinking

of these as somehow living outside of

the realm of human agency is probably

not the right way to think about it. In

fact, the way to perhaps conceive it

like if let's say in early 80s if

somebody had come to us and said what

four billion people are going to wake up

every morning and start typing you would

have said why right you know we have a

like we have a typist pool that's good

enough we don't need four billion people

but we that's what happened right we

invented this entire class of thing

called knowledge work where people

started really using computers uh to go

amplify what we were trying to achieve

uh using software. I think in the

context of AI that same thing is going

to happen.

>> Um it's not like you know what is

hardcore coding is going to remain

hardcore coding forever. It's just that

the levels of abstraction are going to

change. Uh but we also are going to have

code as output just like documents. In

fact, one of Bill's things at Microsoft

from the day I joined in '92 always was

what's the real difference between a

document, a website, and an application,

right? It's the lack of sort of software

that can transform itself. Interestingly

enough, AI finally gives us that, right?

Which is I can write a document. I can

just say, "No, I don't want it as a

document. I want it as a website." It'll

just transform that document using code

into a website. I say, "Oh, I don't like

the website. I want an app." it'll write

more code to transform it. So that

reasoning and cap reasoning capabilities

that prediction capabilities that

ability to take action remain long-term

coherent is all improving. Um and our

job though is to parlay this like take

even what you at BlackRock are doing

right when you're bring taking something

like say co-pilot plus Aladdin and

bringing those things [clears throat]

together

>> to improve the productivity in the firm

for the decisions you want to make right

with your data

>> I could just tell you from at our firm

things that would take 12 hours to

compute now takes minutes for us

processing $14 trillion of other

people's money with hundreds of

thousands of different um mandates um we

could do that instantaneous and we you

know that to me if it wasn't for the

technology and AI today we would not be

able to function to the scale that we're

operating

>> that's right and so to me that one firm

at a time one country at a time if we

can really take these tokens and bend

the curve of productivity then there is

surplus everywhere and that's really the

goal

>> well surplus could be scary too does

does surplus mean fewer workers? What do

we mean by surplus? And so, you know,

the I'm going to tie that into my second

question about AI diffusion.

>> Yeah.

>> To me, the the whole realization of AI

for any society and also for a more

balanced world is making sure that it's

diffused and accessible and available

across the world. So what you know can

you describe how this process of

diffusion uh across economies across

companies across people and countries

how does that play out?

>> Yeah I think

that this is the real question right

because one of the uh things right now

the zeitgeist is a little bit about the

admiration for AI in its abstract form

or as as technology.

>> [clears throat]

>> uh but I think we as even a global

community um have to get to a point

where we're using this to do something

useful uh that changes uh the outcomes

of people and communities and countries

and industries right otherwise I don't

think uh this makes much sense right in

fact I would say we will quickly lose

even the social permission uh to

actually take something like uh energy

which is a scarce resource and use it to

generate these tokens. If these tokens

are not improving health outcomes,

education outcomes, public sector

efficiency, private sector

competitiveness across all sectors,

small and large, right? And that to me

is ultimately the goal. So therefore I

think really diffusion is everything.

And so the way it happens is let's sort

of unpack this uh on the supply side

what needs to happen in each country is

the tokens per dollar per watt have to

sort of monotonically get more efficient

and better right so to some degree even

what we're trying to do with the

investments the two firms are doing uh

around the world is to just say that

like let's make sure that the supply is

there which is uh everything from the

chips on down ultimately ely to these

token factories that get deployed

everywhere. By the way, there's not one

token factory. This token factory is the

first thing that's going to be diffused

all around the world. It's just like

electricity, right? You just need a

ubiquitous grid of uh energy and tokens

that then will power the rest of the

economy. Right? So that's I think one

side of it. Then the demand side of this

is a little bit like every firm has to

start by using it. If I look back even

you know when the PCs first came out or

the personal computing era started I I

loved you know I think Jobs had a nice

metaphor he called it the bicycle for

the mind uh Bill had a metaphor which I

remember was like information at your

fingertips right these two metaphors

were great like which allowed us to say

that's what it is it's a tool that I

will use to get information at my

fingertips I'll use it as a cognitive

amplifier

now I think that's what we have

you know 10x 100x right so in some sense

you as a as every knowledge worker you

now have access to infinite minds that's

the way I think about it right so

there's a [clears throat] cheering award

winner uh called Raj ready who had this

nice metaphor of AI and he had this long

before uh even generative AI he said

either either it's a cognitive amplifier

or it's a guardian angel right so if you

think of AI as that um then that in the

global workforce

Right? When a doctor can get to a

patient, spend more time with the

patient because the AI is doing the

transcription and entering the records

in the EMR system entering the right

billing code so that the health care uh

you know industry is better served

across the payer, the provider and uh

and the patient ultimately right that's

an an outcome that I think all of us can

benefit from. So I feel ultimately it's

going to require real leadership on the

private sector and the public sector to

ensure that diffusion happens and the

one thing other thing I'll mention Larry

is skilling right so in some sense the

thing that diffusion is very strongly

correlated to one thing alone which is

how broadly are people skilled in using

this um and interestingly enough I Think

if mobile has taught us one thing is it

it's actually distinct from what

happened in the PC right uh I remember

even growing up in the global south uh

there used to be a real relationship

between learning excel skills or word

skills and getting a job um you know

right now um what's the model in in

mobile it's kind of created the same

opportunity but it's been a lot more

consumptionled it's these creator

economy and what have But it has not

been about sort of oh wow here is how

you get a healthare job or here is how

you get a finance job or here is how you

get you know you get ahead

professionally

um and that needs to come back right

people need to say I pick up this AI

skill and now I'm a better provider of

some product or service in the real

economy

>> so it's it's very [clears throat] easy

to see how mobile and the diffusion of

mobile how it transformed economies

especially in the global South. How does

how do this you know to me I I I just

read a research report that said

the applications for AI so far are

heavily weighted towards those who are

educated or educated economies.

And so does that create that you know

more of a bifurcation a more

polarization? How do we ensure that that

that diffusion is spread evenly? How do

we make sure that [clears throat] we're

not leaving major portions of society or

the world behind? Because I think that's

that's [clears throat] going to be the

big issue for us going forward.

>> Yeah. So, it's it's it's interesting,

right? This is one of those times when

uh by definition and because of the

rails that have been established you

know as you said right which is

>> uh what's happened with mobile as well

as what's happened with um uh you know

essentially connectivity

>> right

>> you have the ability [clears throat] to

sort of deliver the tokens pretty evenly

around the world

>> right

>> a lot more so than let's say uh the PC

era or even the beginning of the mobile

era, right? Because it took a long time

for even the mo smartphone in particular

to penetrate um all of the world.

Whereas now it's not the case, right?

These models and their outputs are

pretty much available everywhere. And so

the question to me is

what's the use cases that make sense,

right? It's one in fact one of the demos

I always go back to. I think this was

even in the beginning of 23 was a rural

Indian farmer was able to use a bot

built on I think a very early GPT3 or 25

even uh essentially to reason over some

farm subsidies that he had heard about

in a local language and had it even in

that very early days uh have it even

show some agentic behavior right like go

complete a form for me. So in some sense

it took you know it brought back agency

to someone uh who perhaps didn't have

that because the technology was so much

more accessible. So I I do think it's in

our hands even in the global south to

use it uh to create I would say more of

that opportunity where there isn't one.

Um but I think what the [clears throat]

necessary conditions still are do you

have uh the capital investment being put

in uh do you even have an environment

for capital because in an interesting

way we are for example as hyperscalers

investing all over right including the

global south uh so as long as there's an

environment which attracts the capital

investment

>> and you see the demand

>> and you see and then yeah the demand is

there yeah

>> um and so the question is how do you

have a set of policies that allow for

both the capital to come in for it to

find nexus with there are certain things

by the way private capital can do

certain things that public capital only

can do for example the grid right it's

not I mean grid in most countries is

sort of fundamentally driven by

governments

>> public

>> and public and so if [clears throat] you

don't have a sophisticated sort or

rather if you don't have a a real

approach to modernizing the grid uh that

will hold things back I mean there's a

lot of talk about behind the meter and

so on and Yes, there's some amount of

that we can do ourselves.

>> We can do that in the US. Many countries

can't.

>> Exactly. And it's not long-term

scalable, right? I mean, like to me, a

long-term scalable solution is to have

uh you know, all of these token

factories, part of the real economy,

connected to the grid, connected to the

telco network, delivering uh just like

we delivered bits, you have to deliver

tokens plus bits. Um and that's kind of

what's going to drive an at scale

whether it's in the global south or in

uh on the developed world.

>> So so many people talk about there may

be an AI bubble. I mean the most

important thing that we see as an

investor is the the democratization of

technology is and the diffusion of that

technology really does then transform

the demand and the the companies or the

countries that diffuse it fastest are

going to be the ultimate winners. not

the technology creator.

>> That's that's you know it's it's it's

for this not to be a bubble

>> y

>> by definition it requires that the

benefits of this are much more evenly

spread. I mean I think a telltel sign of

if it's a bubble would be if all we're

talking about are the tech firms. Uh

right? If uh all we talk about is what's

happening to the technology side that

then that's by you know it's just purely

supply side

>> right

>> uh ultimately if we are not talking

about wow here is a drug comp you know

drug that was sort of uh brought into

the market that's super successful

because it was uh AI accelerated the

clinical trial it's not even the magical

molecule right it's kind of even the

rest of what is uh needed in order to

make something much more relevant right

um and So the more we [clears throat]

have uh and by the way it's happening

right. So I'm not sort of saying that

that's why I'm much more confident that

this is a technology that will in fact

build on the rails of cloud and mobile

diffuse faster and bend the productivity

curve and bring local surplus and

economic growth all around the world.

Not just economic growth driven by

capital expenses.

uh right because that's it's a narrow

point in time uh calculation is

>> right now that's what we're seeing more

>> that's what we're seeing you know you

know in in the developed world in

particular uh but remember my capital

like that the one thing that you know is

definitely we spending a lot of it in

the United States but 50% of it is also

all over the world

>> right

>> um and so interesting enough it depends

on uh demand all over the world and the

demand all over the world will only be

there if there is local surplus plus all

over the world. And so that's sort of

the way I see the equation.

>> So let's drill down a little more. As AI

diffuses,

obviously organizations, companies,

governments are going to have to evolve.

I'm now getting to the demand side. So

how do you think the structure of

organizations is changing in an AI world

across roles, across teams, management?

I'm I'm sure um Microsoft has evolved

itself. So it probably be good to tell

the audience how do you see this

diffusion occur in the utilization at

the corporate level or at a government

level that which ultimately then creates

that demand which eliminates any fears

and bubbles.

>> Yeah. Now I think it's probably one of

the the the big challenges with all of

these new technologies is when u work

work artifact and workflow changes

uh that means we as firms have to change

how we work. In fact, I remember meeting

um uh the CEO of General Ali, you know,

a few years back and he was describing

he had joined um uh the firm, you know,

prec [clears throat] era and uh uh and

he was describing how for example they

worked with their agents in the field uh

with faxes inter office memos and um and

and suddenly [clears throat] the PC

showed up and people would then put a

spreadsheet in an email and send it

around and the entire workflow and the

work process has changed right so

similarly I think with AI uh you are

going to start seeing uh actual change

in how workflow happens right I mean

even [clears throat]

in fact for me coming to Davos you know

whatever 50 bilateral meetings I have

preparing for those uh had a particular

workflow right which is uh there is to

be my field team would prepare notes and

that would come to my HQ and that would

get further refined and nothing had

really changed right since I joined in

'92 to essentially even a few years

back. Whereas now I just go to co-pilot

and say hey I'm meeting Larry please

give me a brief and it comes back and

gives me by the way the one nice thing

is it gives me a 360 right it knows what

we're doing with you as a client what

we're doing as a client of yours and

everything in [clears throat and cough]

between as an investment it's so it

captures even information unlike

anything else in fact what I do is I

take that and immediately share that

back with all my colleagues across all

the functions right think about it it's

a complete inversion of how information

is flowing in the organization. It's not

like this classic we have an

organization, we have departments, we

have these specializations and the

information trickles up. No, no, no. It

actually it flattens the entire

information flow. So once you start

having that you have to redesign

structurally. Uh so the current

structure may not make sense. um because

you want people to be able to work in a

way that allows them to have this

information flow freely. So what all

this leads me to if I had to sort of say

what's the formula the formula I think

it starts with the mindset. So the

mindset we as leaders should have is we

need to think about changing the work

the workflow with the technology then

that needs skill set. So you can't sort

of talk about this in the abstract. You

got to use it. Like so if I'm not using

the

>> you have to trust it.

>> You have to trust it. You have to use

it. You have to learn even how to put

the guardrails to trust it. Right? So

you can't again you can't just be afraid

of it. Uh it's going to it's going to be

diffused. So the question is as a firm

you have to use it to learn how to even

uh put the guardrails that allow you to

be able to trust it. So skills uh so

mindset skills the other big

consideration uh really is how do you

make sure you have the data set that

you're feeding like context like it's

kind of like you have a new intelligence

layer but the intelligence layer is only

as good as the context you give it. So

people describe it even as context

engineering but that is what firms do

right if you think about what do firms

do it's all about the tacit knowledge we

have by working as people in various

departments and moving paper and

information so the question is how do

you really have this AI also have that

context so these are sort of some of the

new things that have to percolate

throughout an organization uh to take

advantage in fact that's why I think you

you're going to see that challenge of

why am I not seeing immediate results in

productivity because you have to do the

hard work. In fact, that's why it's not

going to be at some you know it's going

to there going to be firmwide

differences. They're going to there

could be sectorwide differences but it's

going to fundamentally be because of the

leadership will in an organization.

>> Do you see the applications being used

across large companies and medium and

small companies or is it still the

domain of mostly the large companies at

this moment? I I think that what you're

seeing is it's easier the because if you

have a green sort of um uh you know if

you start fresh it's easier to adopt

these tools and you construct your

organization knowing that these tools

exist. So

>> is it a barbell then?

>> It is a barbell. So small companies that

are just starting use that platform

>> 100%. And I think in fact I would say

even for large organization there's a

fundamental challenge right because

unless and until your rate of change

keeps up with

>> right

>> uh with with what is possible uh you

you're going to get schooled by someone

small being able to achieve scale

because of these tools. So but I think

scale I mean large organizations have an

inherent strength. You have the

relationships, you have the data, you

have uh um you have knowhow. But the

bottom line is if you don't translate

that with a new production function, uh

then you really will be stuck. And so

therefore the change management

challenge for large organizations is

going to be bigger. The structural

challenge for small organizations of how

to overcome scale issues is going to be

harder. So it's sort of the two sides in

an interesting way. It's going to be a

very competitively intense world uh

where neither side like whether you're a

new entrant or an incumbent can't take

it as like I I can just coast. What

about country to country? Are you seeing

big differences in how the applications

are being used? Is it is AI still the

domain of developed countries or is it

becoming rapidly a domain of all

countries? I I I'm seeing there are two

things [clears throat] I would say Larry

as I travel around the world the quality

of um whether it's the knowhow the

software developers the startups

um or even large or large organizations

it's not that different it's fascinating

you can show up in Jakarta you can show

up in Istanbul you can show up in Mexico

City it's not that different than

showing up even in say Seattle or San

Francisco right it's not uh for the

first time just because access to what's

happening uh is there that said

[clears throat] at scale the commitment

to using this the risk capital being

there the large companies pushing it

hard I mean I you know again the US you

know is in fact if I compare it uh take

financial sector if financial sector's

adoption of the cloud

versus AI night and day right because in

an interesting way it's much faster uh

when it comes to AI versus it was with

the cloud and cloud because for a

variety of reasons

>> regulatory issues too until the

regulators allowed banks to bring their

data off out of campus that was a big

issue.

>> Yeah. So I would say I think wherever

[clears throat] you know so in the west

in particular in the US uh there is

clearly a real I would say more of an

energy around it in terms of going and

using it uh but it's sort of a lot more

uniformly spreading around the world

than any technology at least I've seen

>> but are you are you you [clears throat]

mentioned about the power the grid is

that going to be one of the determinants

of of the accessibility ility if you do

not have cheap power it the demand is

costly

>> 100%. So if you sort of look at the

tokens per dollar per watt right which I

think in [clears throat] some sense I

would claim that GDP growth in any place

will be directly correlated like you if

you sort of buy my entire argument that

look we've got a new commodity its

tokens

>> right

>> and the job of every economy uh and

every firm in the economy is to

translate these tokens into economic

growth then if you have a cheaper

commodity it's better uh And so that's

sort of what why there's tokens per

dollar per watt. And by the way, there

are many many elements to this, right?

Which is uh it's not just um the

production side. That's why I think even

having the grid is important. Um uh

construction costs, right? So if you

like if you think about the total TCO

uh everything it's like the how are you

a cheap producer of energy? Can you

build the data centers? Uh then what's

the cost curve uh of the silicon and the

systems? uh the and by the way look at

the token pricing right token pricing

basically drops by you know a half uh

every 3 months uh I mean this is a so

how so that that's why I think you can

sort of really plot how you use the

tokens to create surplus knowing that

you have a commodity that's whose prices

are just going to monotonically come

down in a pretty fast curve

>> we're sitting in Europe uh and there is

a real fear

because of the co Europe does not have

its own power. It has to import mostly

of its power. Um

do you have any messages for Europe

related to this?

>> Yeah, I mean I think so there are two

sets of things right. one is you know

here we are in in Switzerland and I look

at uh the the pharma or the financial

sector you know obviously Switzer uh

they they do do a big job in in in this

country as in in Europe but they're also

international brands and international

operations. So one thing that uh

whenever I think about Europe is the

Europeans are producing products and

services that actually are going

everywhere in the world and so therefore

uh European competitiveness is about the

competitiveness of their output globally

not just in Europe. I think sometimes

when you come to Europe there's a lot of

conversation about just Europe. Uh but

European economy is thrives and has you

know thrived in the last whatever 200

years 300 years. the miracle of the west

is fundamentally because of what has

happened in Europe uh is because they

were able to produce things that the

world needed and so I would say that's

number one and in order to do that you

again I go back to the human capital

here is just fantastic and world class

uh you have to absolutely invest in uh

producing uh you know having the energy

and the tokens here which again you're

attracting like as I said we're

investing and others are investing uh

the the data centers here so the

question is what's that next generation

of output that comes from here. Right? I

always think about the German middle

star. Whenever I go to a jeweler or a

dentist in the United States, I'm

surrounded by German middle. Right.

Totally.

>> Um it's just unbelievable engineering

provice of that country. uh and now the

question and by the way that's the point

that they are producing industrial

products which today are built in into

it all the intelligence as well that

data right so I know whenever we come to

Europe everyone's like talking about

sovereignty and data this data that

guess what Europe actually should be

much more concerned about access to

their industrial companies their

financial services companies of data

from US and the rest of the world. Uh as

opposed to just thinking that somehow by

protecting Europe you're going to be

competitive. You are only going to be

competitive if the products coming out

of Europe are globally competitive,

right?

>> Um and so that's I think what needs to

change. Uh you know Europe has led in

privacy that's fantastic has led in many

aspects of even safety around AI and

what have you. And that's a feature uh

that's great. But you also have to

complement it with by building locally

and then also thinking globally what's

the contribution this continent will

make uh to the rest of the world which

it has historically been a leader

>> a leader. So do you think the whole idea

around sovereignty of data is that being

misunderstood?

I I think that the when people talk

about sovereignty

first of all it's very important clearly

and who owns

>> and in a week like this it's more

important

>> uh but that said uh it is you have to

kind of think about how what is

sovereignty mean like for example in the

AIL the topic that's least talked about

but I feel will be most talked about in

in this uh this calendar will be the

sovereignty of a firm. Just imagine if

your firm you're not able to embed the

tacit knowledge of the firm in a set of

weights in a model that you control. By

definition, you have no sovereignty.

That means you're leaking enterprise

value to some model company somewhere.

In fact, that it's sort of fascinating

that nobody's talking about that, right?

It's like everybody's talking about

everything else that is sort of you know

outside of that whereas the most

important thing is it really doesn't

matter if you in fact the data center

where it runs is the least important

thing quite honestly but like even there

first of all the data centers all are

all over just because speed of light is

a real constraint uh and so therefore

the data centers will be spread yes uh

you will have digital you'll be able to

encrypt everything you'll be able to

have the keys with you all of these are

much more techn technically solve

problems but the one problem that will

only be solved is by you having much

more sovereignty over the you know tacid

knowledge uh and control over the models

and it's not a one-way enterprise value

transfer um and so to me I think

sovereignty requires real thought on

what is it um you know control of

destiny means that your your ability to

produce something that is unique is

preserved uh David Ricard was not wrong.

There's comparative advantage in

countries. Uh there is comparative

advantage in firms that needs to be

preserved even in the AI era. That's

what'll give you real sovereignty.

>> One last question. I know we're running

out of time.

Um in 5 years or 10 years, is there

going to be one dominant model that

we're all going to be using or are and

how is Microsoft preparing for this? Are

you going to be are we going to be using

one model for for enterprise, one model

for other other traits?

>> You know, even in the last whatever 3

years, four years that we've been at it,

um the the reality at this point is it's

a multimodel world, right? I mean the in

fact if you think about it the in both

there are going to be multiple models

and the trick is really how do you take

advantage of these multiple models and

in fact build your own model by

distilling these right uh so think of

these models uh that you orchestrate to

build your own model and more

importantly you do what is described as

orchestration or harness engineering. So

the IP of any application or any firm is

how do you use all these models with

context engineering or your data. Yes.

>> Right. So it's that three parts. So can

I bring in all the models by the way uh

which is closed source, open source,

build my own model, orchestrate them and

feed it my data to change the trajectory

of some outcome that I care about.

That's it. That's the entire picture. So

you can do it in like oh I produce a

particular product or service. Uh first

I got to do better better job in sales

or better job in R&D or better job in

finance or what have you. And you take

that outcome and then you say can I use

all the models orchestrate them and feed

it my context and then in as a result of

it the reasoning traces are really

leading to some capability and models

that I control as my IP. As long as

firms can answer that question, they're

going to be getting ahead.

>> Ladies and gentlemen, let's uh thank

Satya, my friend. Thank you [applause]

for

and hopefully this is the beginning of

many great dialogue and conversations

here at the World Academic Forum. Thank

you everyone.

>> Thank you.

Heat. Heat.

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>> [music]

>> Heat. Heat.

>> [music]