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Good Morning. [applause]

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Good morning. It's fantastic uh to be in

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Madrid uh and in Spain and uh to be in a
theater. In fact, I was just thinking of

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the last time I was in a theater like

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this this big was uh growing up in India

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watching some Bollywood flick uh
as a middle schooler and being you know

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completely taken aback by the size of

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the theater. Man, this is huge. Uh so

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it's fantastic uh to be with all of you

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um especially at a time like this right

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when there is so much you know talking

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about you know um popular culture who

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would have thought uh that you pick up

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any paper you pick up any news feed and

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all you hear about is AI everywhere um

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and it's exciting uh and so what I

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wanted to do today was to spend some

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time perhaps trying to give our point of

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view on what I think collectively what

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this technology means to us and what we

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can do with it. Uh to us it always

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starts with our mission. Uh our mission

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as a company is about empowering people

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and organizations to achieve more.
Right? That's where we

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start from. And whenever there's been a

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new technology, however powerful, we

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have always tried to focus on can that

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technology realize that mission. uh

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which is can we put it in the hands of

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people and in the institutions that they

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build and can that organization that

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person uh really change the frontier of

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what they do right in fact we have
this entire description of what a frontier

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firm is right because if you think about

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it right any technology is an input uh

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ultimately what we try to do with it is

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change some output um and that's what

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the movement of the frontier is uh and

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in practice practical terms for us u you

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know in the past we talked about digital

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transformation and now we're thinking

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and talking about uh AI transformation

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how can an organization use AI to change

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the customer experience the employee

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experience the internal operations bend

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the curve of innovation right whatever

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goods or services you're producing can

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you now do it with more throughput a

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much more expans with much better speed.

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Uh that really is the bending of the uh

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frontier. Now the interesting thing with

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AI is that you now have to perhaps you

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know take a step back and think a little

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bit on how to structure yourself for

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this new world. And I think it starts

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with a mind uh mindset shift because AI

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in some sense is a little bit of a
horizontal general purpose technology

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that cuts across roles, cuts across

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functions. Uh so you want to think end

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toend workflows, end-to-end process,
end-to-end task trajectories, right?

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That mindset is slightly different
because we've built up all of what we

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have done in our institution in our

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organizations over the decades on a

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particular workflow. In fact, the last
time you could say the workflow really

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changed especially in the enterprise or

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in the in the at the work setting uh was

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when the PCs became standard issue.

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Right? Sometimes I always describe this.

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Imagine in the 80s uh you were a company
trying to do a forecast. Uh how would

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you do it? Right? you would sort of have
faxes going around and you would have

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inter office memos and then uh and then

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ultimately there would be some kind of
a paper forecast and then suddenly email

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became standard issues. Excel was there
and you would send an Excel spreadsheet

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and email and people would enter numbers
and you would have a forecast, right?

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They just changed the work the workflow

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and the work artifact all

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simultaneously. And that's what I mean

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by really rethinking how the work
workflow and the work artifact change

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and the mindset shift. So that means you

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need to be able to think about new

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skills that you need uh to do that new

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tools to do that just like you know

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standard issue in the past uh was all

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these office tools right I mean I one of

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the other things I think about is if
somebody had come to us in the 80s and

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said you know three billion people are
going to get up or four billion people

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are going to get up every morning and
start typing and we would have said why

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like why would we need so many typists

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uh except That's what we do and we
describe it as knowledge work. uh right

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and that's what now the new skill set

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with the new tools is going to allow us

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to do and one thing that I think we all

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are going to learn is how to measure the

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quality of the outputs we create right

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so this eval is something that AI
people talk about right whenever you're

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building a new model what you're
doing is hill climbing on an eval or an

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evaluation criteria and all of us so

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this is not just for labs or the hypers

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scalers or product makers but it's for

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every one of us in some sense to have

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that high taste high bar for what is the

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evaluation of what we are creating as an

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organization and to get clear about it

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and then use AI to transform that eval

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to a new frontier or hill climb. So

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that's a bit of how we think uh one has

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to think about AI transformation.

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And to that end, what we're doing is

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building a full stack. And I'm going to

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spend the rest of my time uh today just

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walking through uh this stack. Um but

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before I do that, I wanted to mention

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that perhaps it's important to think

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about two very important criteria.

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uh when you think about AI since it's a

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learning system of course it means it's

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about intelligence and the intelligence

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that you as a company you as a person

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are going to build with AI right so

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that's clearly uh going to be the case

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but one of the things that you want to

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make sure is top of mind for all of us

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is that it's our intelligence whether

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it's as an individual level or it's
an organization. Right? This is not about

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celebrating some intelligence that's

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outside.

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It is about us being able to take the

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latest model but turning that into more

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tacit knowledge, more knowhow inside the

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company. In fact, I'll talk about
sovereignty at the very end. Of course,

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everybody's talking about sovereignty at

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the comp country level, but I would say

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one of the most important topics is

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perhaps sovereignty of a firm. Right?

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After all, what is a firm? A firm is

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about the tacet knowledge a company has

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thanks to the human capital that is
there uh and even the digital systems.

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And so in the AI age, you want to think

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even more carefully that you are

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building that tacet knowledge and even

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embedding that tacet knowledge
and perhaps a set of weights [snorts] that

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you control, a set of agents or models
that you control. Right? So it's not

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about celebrating anybody else's model.

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It is about your model that you have in

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your firm that makes you a unique

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participant in the economy. Right?

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That's at the end of the day uh the real

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currency for all of us and that comes

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with this second consideration that means
trust. Trust everywhere. There's a

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technical aspect to it, right?
For example, how do you make sure that

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there's real governance around your

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data? Uh how do you make sure that uh

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you have security around anything that's

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happening which agents are accessing. Uh

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but the biggest trust is going to be a

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trust in ensuring that your enterprise

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value that you have built over the

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decades inside of your firm doesn't leak

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out uh to somebody else's uh model or or

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somebody else's business and that I
think is going to be the two things that

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are going to become very very
important as you think through your AI

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transformation journey. So with that as

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a bit of a backdrop I want to start with

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uh the first uh part of our sort of AI

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stack at the experience layer. In fact

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if you look at the best way for us to

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introduce these powerful new AI tools is

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to put them where you already are

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working and then transform that work and

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that workflow. Right? So that's the
approach we have taken with all of this

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co-pilot layer. I describe copilot as

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the UI for AI. Um, it's about really

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taking the human and the human agency

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and the human ambition

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and really amplifying it uh by stitching

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in AI uh and then letting you then use

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AI to do things that you never imagined

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even that you could do before, right? Um

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in fact the the way I think about

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sometimes is we've had these very

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powerful metaphors uh that have been

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there with computing right Jobs had this

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of the bicycle for the mind which I I

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think was a very powerful one. Bill
in the mid '9s came up with this metaphor

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where he talks about he talked about
information at your fingertips. Uh that

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was another very sort of approachable

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way to think about computers especially
with the internet. uh and now I feel

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like we're at that a you know
the beginning of a new era where the

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metaphor could be that we have infinite

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minds with all these agents that each of

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us can use to shape what we're doing the

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artifacts we create the work we do and

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so on and so that's what we are trying
to get done whether it's for you know

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information work whether it's in coding
whether it's in security whether it's

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even in science and so that's sort of

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the co-pilot so If you take information

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work, what we've done is built this

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co-pilot layer right into Outlook into

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teams into word, excel, powerpoint and

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also built the copilot app, right?

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Effectively think of it as a new
module even and this is a graph that's

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connected. It's stateful. So that means

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you can start one place and go to

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another place and that intelligence
layer continues to compound build uh so

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that it helps you with what you're going
to do next. Uh right it doesn't stop in

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any one app. It's not captive.
In fact that's perhaps the biggest sort of

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misnomer. It's not about any one

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application. It is intelligence that is

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sort of ambient across all applications.

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Right? In fact, you can even think about
like one of the metaphors we're playing

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with is think of it as a composer that

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sits above all apps uh right and it's

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there everywhere and that's really
literally what at least copilot uh is in

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its first version of it right so you can

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imagine uh where the the direction of

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travel there is so and and this these

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agentic workflows are already there
right we started with chat right the

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first couple of years of uh copilot
was all about chat chat chat but Now

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increasingly if I look at even my own

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daily usage it's got many agents that

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I'm using all the time and the most
popular one that at least all of us I

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think have gotten used to in the last

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what 12 months or so is researcher. So

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that ambient video shows you I go t you

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know essentially assign a research task

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in this case I think it's some kind of a

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product strategy uh document I want the

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researcher to create it goes off and by

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the way it's not just doing reasoning
over all the data but it's also using

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the computer in fact it has a computer

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use agent um that it's using as a tool

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uh in order to complete the task I
assigned it right so that's kind of an

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example uh of researcher we built

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Another agent called analyst, right?

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This is like having a data science

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analyst with you 24 by7. Um, in fact,

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this one I think is doing um some kind

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of a campaign that I'm u analyzing and I

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load up a bunch of spreadsheets and it
goes and does all of the analysis that

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is needed and gives me insights uh like

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a data scientist would do. Uh and the

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third one is uh the Excel uh agent. Uh

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so we've not only uh built these agents

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in copilot but we have also built agent

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mode into word, excel, powerpoint so

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that you can create these new artifacts.

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In fact you know artifact creation

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has become just easier because you can

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single shot it. But the more interesting

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thing is what you do with the artifact

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by using this agent mode and iterating

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on it continuously refining it. Right?

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when I talked about that eval right you

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want like like a great DCF uh you want a

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spreadsheet that does a fantastic cash

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flow uh or what have you and you can get

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that out and then iterate on it refine
on uh refine it and that's sort of the

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new way to think about even the workflow
because if you you you know previously

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spent a lot of the time building
the spreadsheet you now spend a lot more

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time doing the analysis with the
spreadsheet uh with agent mode and so

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this is the transformation information.

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And to show you some of this at work, I

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wanted to invite up on stage uh my

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colleague Anne. Anne, take it away.

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[applause]

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>> Thank you, Satia. Hi, everyone. I'm so

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excited to show you how the entire

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Copilot stack is transforming the way
people work. I start my day in Copilot,

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and the first thing I'm going to do
is look at all of these models that I have

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to choose from. I'm going to pick GPT

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5.2 into quick response because I've got

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a live demo and a lot to show you. Then

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I'll ask Co-Pilot to prep me for my day.

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Co-pilot uses Work IQ to look at my
meetings, emails, and chats. And it

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looks at its previous history
of interaction with me to know what I

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prioritize the most and come out with a

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customized tailored response. So, let's

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see what's going on today. First up,
I have a time block for this demo here.

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So, I'm glad I'm in the right place with
you all. And then if we keep going, we

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see at the top of my inbox, I have an

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ongoing customer conversation about a

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pending contract. And part of my job
as an accounts receivable manager is to

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make sure our customers pay their bills
on time. And to incentivize them to do

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so, I need to offer potentially a cash

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discount for early payments. And again,

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I have lots of options I can offer for
such a cash discount, but I want to use

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co-pilot to make sure that I'm following
a policy compliant approach. So, I'll

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ask Copilot, what are my options
to offer this customer a discount? Now,

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this is where the power of work IQ
really shines. Copilot's looking at our

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policy documentation. It's looking at
the context and history of that customer

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and this particular contract.
And it's applying my role as an accounts

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receivable manager to drive what it is

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I'm specifically entitled to author.
And here we go. We see how all of these

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things come through in the response.

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First up, I can see that we did ini
indeed find that policy documentation. I

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can verify that the source is our finance
SharePoint. So, we're accurately

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pulling those details. And then if I
look through copilot's given me this

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great summary of all the different
options, I can provide anything that

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requires additional approval beyond
my manager level remit. And it ultimately

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summarizes we should start with our

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standard 2% cash discount for any

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payments made within 10 days of invoice.

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Now, that makes sense to me, but before
I go off and add this to the contract, I

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need to assess the impact to our
profitability. And what better place to

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00:16:38,640 --> 00:16:42,797
do that than Excel? Here in Excel,
I have a workbook that I've already built

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with agent mode that models the revenue

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on the contract as it currently is

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stated. And I want to add this nuance
of how a discount impacts it. Right from

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00:16:52,400 --> 00:16:56,477
here, I'll go to co-pilot and turn
on agent mode. So, I don't have to do all

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00:16:56,560 --> 00:17:00,797
this work manually. I'll have the agent
take care of it for me. I'll ask it to

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create a new sheet that models
the impact of offering that 210 cash

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00:17:04,320 --> 00:17:08,797
discount. And because the agent's
grounded in the same work context I am,

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we'll give it access to that policy
documentation file. And it can

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extrapolate additional details from that

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context. Then we'll kick it off. What

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00:17:17,360 --> 00:17:19,757
the agent is going to do here is work

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directly inside my workbook just like I
might or a co-orker might. Unlike us, it

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00:17:24,400 --> 00:17:27,917
goes quite a bit faster, but it still
takes a little bit of time, which means

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now I have time I can
use for other activities.

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00:17:31,760 --> 00:17:35,437
All these insights that are helping
drive business decision making, whether

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it's in Copilot, in Outlook or Excel,
they come from our underlying financial

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data estate. So for our business
insights to be valuable, our data needs

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to be accurate and up-to-date. Dynamics
365 is our platform for intelligent

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00:17:48,560 --> 00:17:53,197
business applications, which enables us
to do just that. Here in Dynamics, when

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I log in, I see I have a notification
from the account reconciliation agent.

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That's a pre-built agent that comes out
of the box in Dynamics, and it's letting

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me know there's a couple discrepancies
in our books that need my attention. So,

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00:18:04,160 --> 00:18:08,717
let's take a look. For each of our
reconciliation exceptions, I get a

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00:18:08,800 --> 00:18:12,157
natural language summary that
immediately tells me exactly what's

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00:18:12,240 --> 00:18:16,477
going on. In this case, a transaction
was posted directly to our ledger, and I

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00:18:16,560 --> 00:18:20,557
can see those posting details here, and
it missed our subleddger. And if you're

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00:18:20,640 --> 00:18:24,957
not a finance person, don't worry.
Just know this is not ideal. We need

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00:18:25,040 --> 00:18:29,277
transactions in both places. And it's
not just insight that the agent is

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00:18:29,360 --> 00:18:33,517
giving me, but it's also recommending
actions. It knows finance best

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00:18:33,600 --> 00:18:35,917
practices. It knows our best practices.

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And in this case, it recommends we
reverse the transaction so we can post

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it properly through the subleddger.

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00:18:42,720 --> 00:18:47,277
From there, all I have to do is confirm
and commit the transaction. And I want

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to highlight what's happening here
because it's so important. This fix is

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being logged directly in our financials.

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So our source of truth can remain just
that. There we go. We got a confirmation

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00:18:57,920 --> 00:19:02,317
the reversal was completed. And for any
of my audit folks out there today,

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everything the agent does, everything I
do gets logged in our audit trail. So we

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00:19:06,160 --> 00:19:09,837
have that critical accuracy
and accountability we need in finance

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00:19:09,920 --> 00:19:13,597
processes. Okay, we took
care of that discrepancy.

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00:19:13,680 --> 00:19:15,357
Let's go back and check on our co-worker

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00:19:15,440 --> 00:19:18,077
agent in Excel.

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And what we can see already is how we

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are on a brand new sheet that the agent
created and it's making edits live right

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00:19:25,840 --> 00:19:27,917
in front of us. We can even follow its

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00:19:28,000 --> 00:19:30,317
thought process as it goes step by step

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00:19:30,400 --> 00:19:35,437
in the pane over here. It looks like
it's reassessing. And because we're

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00:19:35,520 --> 00:19:39,037
doing a live demo and we've only got so
much time left, I'm going to jump ahead

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to the same prompt that I ran earlier
just to show you that output of what it

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looks like. So, we can see we have our

356
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discount situation broken out. It's

357
00:19:48,400 --> 00:19:53,197
pulled that detail of the 2% 10 days
from the prompt and additional context

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from the document that I attached to it.

359
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It's looked at our monthly impact to

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00:19:57,280 --> 00:20:02,397
revenue and ultimately we should have

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00:20:02,480 --> 00:20:06,717
the chart I asked for as well over here
on the side. What I want to stress here,

362
00:20:06,800 --> 00:20:10,557
it's not just putting static text
in cells. These are dynamic formulas and

363
00:20:10,640 --> 00:20:12,637
visuals that work with me and the agent

364
00:20:12,720 --> 00:20:14,717
as we continue to work together and

365
00:20:14,800 --> 00:20:17,837
refine. And most importantly over here I

366
00:20:17,920 --> 00:20:21,917
have full visibility into all the data
sources, calculations and assumptions

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00:20:22,000 --> 00:20:24,157
the agent used to build out this

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00:20:24,240 --> 00:20:27,437
context. So to recap, what did we just

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00:20:27,520 --> 00:20:31,997
see? We saw how co-pilot with work IQ
helps me work not just more efficiently

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00:20:32,080 --> 00:20:36,637
but more effectively. We saw how agent
mode in Excel is helping me very quickly

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00:20:36,720 --> 00:20:40,957
strategize and make decisions that drive
profitability of a contract. And we saw

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00:20:41,040 --> 00:20:45,837
how agents in Dynamics 365 are helping
us create a financial data estate that

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00:20:45,920 --> 00:20:49,837
enables the whole business to make
great decisions. And this is not just for

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00:20:49,920 --> 00:20:54,317
finance personas. Everything I showed
you today is 100% available. I hope you

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00:20:54,400 --> 00:20:56,557
go home and try it out in Copilot, in

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00:20:56,640 --> 00:21:03,160
Excel, and in Dynamics 365.
Thanks. And back to you, Satia.

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00:21:05,470 --> 00:21:08,877
>> [applause] >> you know,
I was uh when I was seeing the

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00:21:08,960 --> 00:21:13,117
demo, I was thinking, God, I started,
you know, my career at Microsoft doing a

379
00:21:13,200 --> 00:21:14,797
lot of Excel demos. I had a lot more

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00:21:14,880 --> 00:21:17,917
hair then and here I am um you know, in

381
00:21:18,000 --> 00:21:20,557
2026 still looking at Excel demos. You

382
00:21:20,640 --> 00:21:24,077
know, one thing that hopefully you capt,

383
00:21:24,160 --> 00:21:26,317
you know, you got out of that what an

384
00:21:26,400 --> 00:21:29,837
showed was, you know, the design of AI

385
00:21:29,920 --> 00:21:34,717
systems, I think requires a particular
thought that it doesn't add more

386
00:21:34,800 --> 00:21:38,637
cognitive load to you, right? I mean,
one of the things that could easily

387
00:21:38,720 --> 00:21:41,037
happen is because you have this powerful

388
00:21:41,120 --> 00:21:43,357
tool that can do things like create an

389
00:21:43,440 --> 00:21:45,917
unbelievable spreadsheet or go off and

390
00:21:46,000 --> 00:21:48,077
reason for hours and come back with some

391
00:21:48,160 --> 00:21:51,997
answer. And if all it does is cause you

392
00:21:52,080 --> 00:21:54,477
to then spend more time understanding

393
00:21:54,560 --> 00:21:56,637
what it did, then that's not great

394
00:21:56,720 --> 00:21:59,757
design. So one of the subtle things that

395
00:21:59,840 --> 00:22:03,917
we are trying to make sure happens uh is

396
00:22:04,000 --> 00:22:06,557
as we build out this co-pilot layer, how

397
00:22:06,640 --> 00:22:09,357
it works with work IQ and how you

398
00:22:09,440 --> 00:22:12,077
traverse uh your job and your task

399
00:22:12,160 --> 00:22:16,957
trajectory, the cognitive load has
to come down. uh even the aesthetic like

400
00:22:17,040 --> 00:22:18,557
one of the things that an was showing in

401
00:22:18,640 --> 00:22:22,157
Excel in agent mode uh right

402
00:22:22,240 --> 00:22:24,477
spreadsheets the reason why spreadsheets

403
00:22:24,560 --> 00:22:27,997
have st stood the test of time uh from

404
00:22:28,080 --> 00:22:29,757
whatever 40 I think we just celebrated

405
00:22:29,840 --> 00:22:33,117
the 40th year of Excel uh is because it

406
00:22:33,200 --> 00:22:36,957
has it communicates a lot cognitively

407
00:22:37,040 --> 00:22:38,877
right the the fact that you have a

408
00:22:38,960 --> 00:22:42,157
tabular data form um you can sort of

409
00:22:42,240 --> 00:22:46,157
look at the formulas in fact I so I
remember the first time I sat next to

410
00:22:46,240 --> 00:22:50,157
somebody you know one of these Excel
ma maestros was like sitting next to a

411
00:22:50,240 --> 00:22:52,477
piano player uh because the way they

412
00:22:52,560 --> 00:22:55,517
built the model uh it made sense and

413
00:22:55,600 --> 00:22:58,157
when you see the output it made sense to

414
00:22:58,240 --> 00:23:02,717
you as a consumer of that model uh and
that needs to be true even when it comes

415
00:23:02,800 --> 00:23:06,637
to AI right so it can't be I single
shot at a model that nobody else can

416
00:23:06,720 --> 00:23:10,877
understand but it has the answer and then
you're looking for hours as to how

417
00:23:10,960 --> 00:23:12,797
the heck did they find the answer and so

418
00:23:12,880 --> 00:23:17,997
that's the type of stuff uh that
matters in how AI gets designed into

419
00:23:18,080 --> 00:23:21,917
experiences. So we it's
great to see the diffusion of

420
00:23:22,000 --> 00:23:25,677
all of this technology right here
in Spain. I had a chance to meet with many

421
00:23:25,760 --> 00:23:27,437
customers who are deploying you know

422
00:23:27,520 --> 00:23:30,797
co-pilot uh using C-pilot. Um I had a

423
00:23:30,880 --> 00:23:32,877
chance to meet this one you know uh

424
00:23:32,960 --> 00:23:36,157
partner of ours uh illunion uh which is

425
00:23:36,240 --> 00:23:38,637
you know doing it's a doing a fantastic

426
00:23:38,720 --> 00:23:40,877
job of not only using this technology

427
00:23:40,960 --> 00:23:43,757
but the social good uh you know how they

428
00:23:43,840 --> 00:23:48,237
employ people with disabilities in
their workforce and empower them uh with

429
00:23:48,320 --> 00:23:52,477
technology is fantastic to see they
built an agent in fact using an agent

430
00:23:52,560 --> 00:23:55,037
builder inside a co-pilot uh for their

431
00:23:55,120 --> 00:23:57,357
the law department to be able to reason

432
00:23:57,440 --> 00:24:00,477
over complex PDFs Uh in fact that's kind

433
00:24:00,560 --> 00:24:05,277
of one of the most favorite features
of copilot is man PDFs are tough to parse

434
00:24:05,360 --> 00:24:09,677
uh and you can go you know assign them
to copilot and it can come back uh and

435
00:24:09,760 --> 00:24:14,237
extract the right sort of uh reasoning
on top of it and give you information.

436
00:24:14,320 --> 00:24:16,237
It's just great to see uh this kind of

437
00:24:16,320 --> 00:24:19,357
diffusion. So that was the first layer

438
00:24:19,440 --> 00:24:22,477
of the stack which is about building in

439
00:24:22,560 --> 00:24:26,957
AI experience into the flow uh of human

440
00:24:27,040 --> 00:24:29,117
activity and then really helping us

441
00:24:29,200 --> 00:24:32,637
raise the human ambition. Uh the next

442
00:24:32,720 --> 00:24:35,037
piece of the stack is the agent

443
00:24:35,120 --> 00:24:37,757
platform. Um and when it comes to agent

444
00:24:37,840 --> 00:24:42,877
platform the first place to start is the

445
00:24:42,960 --> 00:24:45,437
data layer but more importantly not just

446
00:24:45,520 --> 00:24:48,797
raw data but uplifting the semantics of

447
00:24:48,880 --> 00:24:51,437
this data into what we describe as the

448
00:24:51,520 --> 00:24:54,237
IQ layer right and an talked about this

449
00:24:54,320 --> 00:24:56,477
right when when she was using co-pilot

450
00:24:56,560 --> 00:24:59,437
she was essentially interfacing with

451
00:24:59,520 --> 00:25:03,037
this thing that we describe as work IQ

452
00:25:03,120 --> 00:25:05,197
uh and and and I would claim that one of

453
00:25:05,280 --> 00:25:07,997
the most important databases inside of a

454
00:25:08,080 --> 00:25:11,517
commercial entity is the information

455
00:25:11,600 --> 00:25:14,077
underneath their Microsoft 365 tenant. I

456
00:25:14,160 --> 00:25:16,237
mean think about it right inside of

457
00:25:16,320 --> 00:25:19,997
Microsoft 365 you have all this very

458
00:25:20,080 --> 00:25:22,717
rich information people their

459
00:25:22,800 --> 00:25:24,877
relationships to other people like your

460
00:25:24,960 --> 00:25:28,397
org chart uh not just org chart in its

461
00:25:28,480 --> 00:25:32,557
formality but even who is working with
whom on what projects right I mean think

462
00:25:32,640 --> 00:25:37,597
about how important that is for any
company uh you have all their calendars

463
00:25:37,680 --> 00:25:41,437
you have all of their communications
in teams and outlook you have all their

464
00:25:41,520 --> 00:25:43,517
documents in one drive and sharepoint

465
00:25:43,600 --> 00:25:46,797
you have projects and project schedules.

466
00:25:46,880 --> 00:25:49,437
So that is a very rich set of

467
00:25:49,520 --> 00:25:52,557
relationship and entities uh that now

468
00:25:52,640 --> 00:25:55,837
you can in fact use AI models to reason

469
00:25:55,920 --> 00:25:58,157
over. That's what work IQ is. It's a

470
00:25:58,240 --> 00:26:01,277
stateful reasoning agent essentially on

471
00:26:01,360 --> 00:26:04,957
top of a very rich data model uh which

472
00:26:05,040 --> 00:26:07,277
captures all this tacet knowledge of an

473
00:26:07,360 --> 00:26:09,997
organization. The other place where

474
00:26:10,080 --> 00:26:12,157
we've done in fact you all have created

475
00:26:12,240 --> 00:26:14,957
a tremendous amount of semantic data

476
00:26:15,040 --> 00:26:19,517
semantic information is PowerBI right
I mean if you think about the number of

477
00:26:19,600 --> 00:26:21,917
dashboards over the years that have been

478
00:26:22,000 --> 00:26:24,957
created someone took the pains to make

479
00:26:25,040 --> 00:26:27,677
sure that all that data was annotated

480
00:26:27,760 --> 00:26:30,877
with the right semantic um annotation so

481
00:26:30,960 --> 00:26:35,677
that you could make sense of it right uh
this is some sales aggregate information

482
00:26:35,760 --> 00:26:37,837
it is some forecast information it's

483
00:26:37,920 --> 00:26:40,797
some supply chain information. Now all

484
00:26:40,880 --> 00:26:43,917
that can be brought together uh in

485
00:26:44,000 --> 00:26:48,877
fabric IQ. Uh and then foundry IQ
is another service we have built in to be

486
00:26:48,960 --> 00:26:52,317
able to in fact have an outer
orchestrator of all these IQs.

487
00:26:52,400 --> 00:26:54,877
Essentially you can have a plan that

488
00:26:54,960 --> 00:26:59,757
gets created that orchestrates work IQ,
fabric IQ and any other document

489
00:26:59,840 --> 00:27:04,477
database or any other thing that you have.
Uh and now you can build

490
00:27:04,560 --> 00:27:09,037
essentially agents on top of all of
this IQ layer. Right? So data is the best

491
00:27:09,120 --> 00:27:11,117
bootstrap but more importantly not just

492
00:27:11,200 --> 00:27:13,117
raw data that you build a forward index

493
00:27:13,200 --> 00:27:16,397
on or you do a search on but literally

494
00:27:16,480 --> 00:27:19,357
have a reasoning layer plus data

495
00:27:19,440 --> 00:27:22,397
available for any agentic system. So

496
00:27:22,480 --> 00:27:24,797
that's sort of the first layer of the uh

497
00:27:24,880 --> 00:27:27,757
the agent platform. Then once you have

498
00:27:27,840 --> 00:27:32,077
this, the thing that you need is an app

499
00:27:32,160 --> 00:27:34,077
server, right? I mean with any

500
00:27:34,160 --> 00:27:37,437
generation of technology,

501
00:27:37,520 --> 00:27:41,677
you always built a middle tier
app server so that you could build

502
00:27:41,760 --> 00:27:43,757
applications on that technology, right?

503
00:27:43,840 --> 00:27:45,757
It happened with the web, right? We had

504
00:27:45,840 --> 00:27:48,877
web servers. Uh we had it with.NET and

505
00:27:48,960 --> 00:27:51,917
Java. Uh we had that with cloud, right?

506
00:27:52,000 --> 00:27:54,157
We had cloudnative app servers. So this

507
00:27:54,240 --> 00:27:56,877
time around again for us it's foundry

508
00:27:56,960 --> 00:27:59,837
where you have all the services to build

509
00:27:59,920 --> 00:28:02,317
agent and multi- aent systems starting

510
00:28:02,400 --> 00:28:05,117
with frameworks uh with you know

511
00:28:05,200 --> 00:28:09,117
services for AI safety like guard rails

512
00:28:09,200 --> 00:28:12,077
services for doing these eval services

513
00:28:12,160 --> 00:28:14,237
to do fine-tuning what have you. So

514
00:28:14,320 --> 00:28:17,837
these are a complete list of runtime

515
00:28:17,920 --> 00:28:20,157
services that you need in order to build

516
00:28:20,240 --> 00:28:23,037
agents and multi- aent systems. And on

517
00:28:23,120 --> 00:28:26,237
top of that, you have models. In fact,

518
00:28:26,320 --> 00:28:30,157
Foundry comes with 11,000 models.
And you would say, "Wow, why do I need

519
00:28:30,240 --> 00:28:33,277
11,000 models?" In fact, it comes
with all the popular ones, right? You have

520
00:28:33,360 --> 00:28:37,117
all the GPT family, you have the Claude
family, you have grot, you, you know,

521
00:28:37,200 --> 00:28:39,037
you pick your, you know, frontier model

522
00:28:39,120 --> 00:28:40,957
that you want to use. It's there. But it

523
00:28:41,040 --> 00:28:44,397
also has in fact all the models in all

524
00:28:44,480 --> 00:28:48,717
the domains that you want to be able
to access and use as part of your

525
00:28:48,800 --> 00:28:51,917
applications. And in fact just to kind
of give you an intuition for why the

526
00:28:52,000 --> 00:28:54,477
world needs more models not just one

527
00:28:54,560 --> 00:28:57,037
model it, you know, we launched this

528
00:28:57,120 --> 00:28:58,957
thing called gigat time I think a few

529
00:28:59,040 --> 00:29:03,197
months back. Um and you know it was done

530
00:29:03,280 --> 00:29:06,557
in in cooperation with Providence um and

531
00:29:06,640 --> 00:29:10,957
a couple of other partners of our
University of Washington. Um and the

532
00:29:11,040 --> 00:29:13,517
idea was if you look at cancer treatment

533
00:29:13,600 --> 00:29:18,237
and imunotherapy, one of the challenges
is you never know if a particular

534
00:29:18,320 --> 00:29:22,237
imunotherapy would work on a particular
tumor. And so in order to figure that

535
00:29:22,320 --> 00:29:26,717
out, there's a test that one does.
And that test, you know, costs a lot of

536
00:29:26,800 --> 00:29:29,037
money, takes a lot of time. And so the

537
00:29:29,120 --> 00:29:32,077
researchers put together essentially a

538
00:29:32,160 --> 00:29:35,517
spatial proteomics um solution. So

539
00:29:35,600 --> 00:29:40,397
essentially using generative AI to
simulate that test, right? So instead of

540
00:29:40,480 --> 00:29:44,237
doing the test, what if we could do
a simulation of that test and that way

541
00:29:44,320 --> 00:29:47,837
then you can reduce the cost of it all
and make it available to every secondary

542
00:29:47,920 --> 00:29:50,237
hospital in every uh country so that

543
00:29:50,320 --> 00:29:55,037
they can now do that same test and you
know make a difference in oncology. So

544
00:29:55,120 --> 00:29:56,717
to give you a flavor for this, let's

545
00:29:56,800 --> 00:29:59,597
roll the video.

546
00:29:59,680 --> 00:30:02,157
Imunotherapy is really the most

547
00:30:02,240 --> 00:30:04,557
promising direction for taming cancer

548
00:30:04,640 --> 00:30:09,757
once and for all. The whole idea is using
the immune system to fight cancer.

549
00:30:09,840 --> 00:30:12,797
>> The big challenges we have in the
field right now is to understand which

550
00:30:12,880 --> 00:30:16,877
patients will respond.
>> Facial proteomics can simultaneously

551
00:30:16,960 --> 00:30:19,357
measure multiple protein and create

552
00:30:19,440 --> 00:30:22,477
multiplex imunofllororescent images. It

553
00:30:22,560 --> 00:30:26,637
can tell you whether the patient might
respond to imunotherapy or not. But

554
00:30:26,720 --> 00:30:28,797
unfortunately to generate this kind of

555
00:30:28,880 --> 00:30:34,760
images takes days and cost
thousands [music] of dollars.

556
00:30:35,520 --> 00:30:39,437
In collaboration with Providence
and University of Washington, we developed

557
00:30:39,520 --> 00:30:42,077
this multimodel AI called gigat time to

558
00:30:42,160 --> 00:30:45,277
generate spatial proteomics at scale. So

559
00:30:45,360 --> 00:30:46,877
we have trained the model using large

560
00:30:46,960 --> 00:30:50,157
scale data analyzing millions of cells.

561
00:30:50,240 --> 00:30:52,957
The input image has limited data. We

562
00:30:53,040 --> 00:30:55,597
don't know where the protein is active.

563
00:30:55,680 --> 00:30:57,437
Gigat time can convert [music] this

564
00:30:57,520 --> 00:31:00,957
image to a colorful multiplex imunosence

565
00:31:01,040 --> 00:31:03,437
image. The color is the protein

566
00:31:03,520 --> 00:31:08,317
activation. They are the key factor
drive diseases especially in cancer.

567
00:31:08,400 --> 00:31:12,637
This can help us to predict patient
drug response. We now taking the first

568
00:31:12,720 --> 00:31:16,397
meaningful step to be able to generate
this kind of data for everyone in the

569
00:31:16,480 --> 00:31:18,797
globe.

570
00:31:18,880 --> 00:31:23,677
The gigat time model is now available
to everyone through Microsoft Foundry. Our

571
00:31:23,760 --> 00:31:25,677
hope is to [music] empower every

572
00:31:25,760 --> 00:31:28,317
secondary hospital in every tertiary

573
00:31:28,400 --> 00:31:32,717
city to be able to now
conduct their own research.

574
00:31:32,800 --> 00:31:37,037
>> We look forward to a future where we
can actually bring this to the clinic,

575
00:31:37,120 --> 00:31:38,877
impact patients lives and bring the next

576
00:31:38,960 --> 00:31:40,877
generation of therapies to places where

577
00:31:40,960 --> 00:31:44,557
this would be otherwise impossible.

578
00:31:44,640 --> 00:31:48,077
You know, it's a great example of um how

579
00:31:48,160 --> 00:31:50,957
you translate sort of unique knowledge

580
00:31:51,040 --> 00:31:55,837
that a institution in this case the
combination of quite frankly Providence

581
00:31:55,920 --> 00:31:59,437
and University of Washington had into
something that now of course is going to

582
00:31:59,520 --> 00:32:03,517
be a powerful tool and a powerful model
for the rest of the world to use. Uh but

583
00:32:03,600 --> 00:32:08,237
when I talked earlier about that
intelligence in every company, that's

584
00:32:08,320 --> 00:32:11,597
what it is. In fact, if some someone
asks me like how many models should

585
00:32:11,680 --> 00:32:15,997
there be in the world, I would say the
number of the companies out there are in

586
00:32:16,080 --> 00:32:17,997
the world because after all, every

587
00:32:18,080 --> 00:32:21,277
company is effectively a model of some

588
00:32:21,360 --> 00:32:25,277
unique tacet knowledge. Uh sometimes
it's proprietary. You don't want to put

589
00:32:25,360 --> 00:32:29,037
it in foundry, but it'll be in your
company. But sometimes it may make sense

590
00:32:29,120 --> 00:32:33,117
for you to then expose it to even the
world. And that to me is uh how to think

591
00:32:33,200 --> 00:32:36,477
about models. is not about just one
model but it's about sort of the models

592
00:32:36,560 --> 00:32:40,797
all of us build uh that truly increase
not only the intelligence in your

593
00:32:40,880 --> 00:32:43,037
company but the collective intelligence.

594
00:32:43,120 --> 00:32:45,277
So now we talked about two things,

595
00:32:45,360 --> 00:32:47,597
right? We talked about that IQ layer. We

596
00:32:47,680 --> 00:32:50,157
talked about the app server with

597
00:32:50,240 --> 00:32:54,077
Foundry. And now it's time to build uh

598
00:32:54,160 --> 00:32:57,517
agents. And to do that, we have a a full

599
00:32:57,600 --> 00:33:01,757
tool chain, right? Starting with App
Builder. Uh for those of you who used

600
00:33:01,840 --> 00:33:03,997
things like Power Apps before, App

601
00:33:04,080 --> 00:33:06,957
Builder is the AI sort of era version of

602
00:33:07,040 --> 00:33:09,437
it where I can now go in and just use

603
00:33:09,520 --> 00:33:11,757
natural language to essentially build a

604
00:33:11,840 --> 00:33:14,877
fullscale app. Um and in this case, I

605
00:33:14,960 --> 00:33:18,957
think it's sort of um an app that
was builting building for a person a

606
00:33:19,040 --> 00:33:21,677
personalized career path uh application.

607
00:33:21,760 --> 00:33:25,277
Um and so they were able to just simply
use natural language to build a full

608
00:33:25,360 --> 00:33:28,317
stack application that was generated. Um

609
00:33:28,400 --> 00:33:31,597
a second uh solution we have is copilot

610
00:33:31,680 --> 00:33:33,517
studio right so copilot studio allows

611
00:33:33,600 --> 00:33:36,477
you to build agents uh so you can again

612
00:33:36,560 --> 00:33:39,517
uh take any workflow and make it agentic

613
00:33:39,600 --> 00:33:42,157
by grounding it in some knowledge uh and

614
00:33:42,240 --> 00:33:45,037
again using uh this builder tool uh to

615
00:33:45,120 --> 00:33:47,197
build a pretty sophisticated um

616
00:33:47,280 --> 00:33:49,437
application for employee onboarding for

617
00:33:49,520 --> 00:33:53,517
example um and so this is a second tool

618
00:33:53,600 --> 00:33:55,837
that's available uh for us and of course

619
00:33:55,920 --> 00:34:00,717
if you're a professional developer We
now have GitHub and Visual Studio and uh

620
00:34:00,800 --> 00:34:04,477
you're able to use the sophestication
of those tools to build agent and

621
00:34:04,560 --> 00:34:07,277
multi-agent systems. Uh in fact, it's

622
00:34:07,360 --> 00:34:12,077
fantastic to see the growth of GitHub
as a community in uh uh is Spain. We have

623
00:34:12,160 --> 00:34:13,837
two and a half million developers on

624
00:34:13,920 --> 00:34:17,677
GitHub. It's grown 22% over year. Um and

625
00:34:17,760 --> 00:34:21,357
it's just unbelievable like what's
happening uh in coding and the number of

626
00:34:21,440 --> 00:34:25,517
repos that are getting created in public
and of course a lot more in private. uh

627
00:34:25,600 --> 00:34:29,677
just given the power of these coding
agents and so uh it's great to see even

628
00:34:29,760 --> 00:34:31,997
uh Spain participate in it. So one of

629
00:34:32,080 --> 00:34:34,397
the core goals we have with GitHub is

630
00:34:34,480 --> 00:34:37,037
not just to have your repo or your CI/CD

631
00:34:37,120 --> 00:34:40,157
pipelines and all the other core

632
00:34:40,240 --> 00:34:42,557
primitives of GitHub but it's also

633
00:34:42,640 --> 00:34:45,197
becomes the organizing layer for all of

634
00:34:45,280 --> 00:34:48,237
the coding agents. So we're designing in

635
00:34:48,320 --> 00:34:51,117
uh right into GitHub uh you know

636
00:34:51,200 --> 00:34:55,357
whatever your favorite coding agent
is is available right there. In fact, you

637
00:34:55,440 --> 00:35:00,077
can go to your mobile app in the morning
and you essentially have a new inbox uh

638
00:35:00,160 --> 00:35:01,837
which is your coding agents and their

639
00:35:01,920 --> 00:35:05,357
work uh and you can go and keep track of

640
00:35:05,440 --> 00:35:08,477
what's happening on your repo uh you

641
00:35:08,560 --> 00:35:11,357
know by these coding agents. Uh so

642
00:35:11,440 --> 00:35:14,477
that's what we describe as agent HQ. Now

643
00:35:14,560 --> 00:35:19,357
the other thing that we've also sort
of um made sure is we build all the

644
00:35:19,440 --> 00:35:23,677
different form factors right because
if you look at how coding has changed uh

645
00:35:23,760 --> 00:35:27,997
the IDE still remains a very important
one in fact if you look back uh the

646
00:35:28,080 --> 00:35:30,397
evolution the first thing where we used

647
00:35:30,480 --> 00:35:32,877
AI was in the next that it suggests

648
00:35:32,960 --> 00:35:35,437
right so you're in an IDE it's doing

649
00:35:35,520 --> 00:35:38,797
essentially uh completions uh then we

650
00:35:38,880 --> 00:35:41,597
put chat uh so that you could stay in

651
00:35:41,680 --> 00:35:43,837
flow and you didn't have to go outside

652
00:35:43,920 --> 00:35:48,797
to get answers and you could continue
to code. Uh then we had agent mode where

653
00:35:48,880 --> 00:35:53,277
you could assign the agent a set of tasks.
Um and it could complete that. Uh

654
00:35:53,360 --> 00:35:57,677
and then you have these fully autonomous
agents as well. Now so in fact putting

655
00:35:57,760 --> 00:36:01,677
all of these form factors together,
right? The CLI or the command line

656
00:36:01,760 --> 00:36:04,477
matters, the IDE matters, the autonomous

657
00:36:04,560 --> 00:36:09,437
agent matters. The other thing we are
also making sure we do is all the models

658
00:36:09,520 --> 00:36:11,117
are available to you. Right? It's not

659
00:36:11,200 --> 00:36:15,117
it's multi-form factor multimodel uh and

660
00:36:15,200 --> 00:36:17,917
in fact many many sub aents uh that you

661
00:36:18,000 --> 00:36:19,757
may build for a variety of reasons for

662
00:36:19,840 --> 00:36:23,757
profiling for security all are part of

663
00:36:23,840 --> 00:36:28,237
the GitHub copilot ecosystem and to show
you all of this in action I wanted to

664
00:36:28,320 --> 00:36:30,237
invite up on stage my colleague Marlene

665
00:36:30,320 --> 00:36:33,537
Marlene take it away

666
00:36:33,620 --> 00:36:35,437
[applause]

667
00:36:35,520 --> 00:36:39,277
>> thanks Aia and hi
everyone let's say I'm

668
00:36:39,360 --> 00:36:44,077
a developer working at a toy company
called Tailspin Toys. A few days ago, I

669
00:36:44,160 --> 00:36:48,157
got an email from the search product
management team asking me to add some

670
00:36:48,240 --> 00:36:52,557
new search and filter features to the
site. They've given me a short checklist

671
00:36:52,640 --> 00:36:56,797
of what I should build, including
a search bar with text search for simple

672
00:36:56,880 --> 00:36:59,037
searches and Azure search for more

673
00:36:59,120 --> 00:37:01,277
complex ones. They've also asked me to

674
00:37:01,360 --> 00:37:03,997
add in a sidebar so customers can filter

675
00:37:04,080 --> 00:37:09,037
by both category and price. I'd like
for Copilot to help me with this task. And

676
00:37:09,120 --> 00:37:11,917
so to do that, I'm going
to head to my terminal

677
00:37:12,000 --> 00:37:13,677
and I'm going to run the command to get

678
00:37:13,760 --> 00:37:17,757
GitHub Copilot CLI started. With Copilot

679
00:37:17,840 --> 00:37:21,117
CLI, I can now kick off any agentic task

680
00:37:21,200 --> 00:37:23,997
directly in my terminal.

681
00:37:24,080 --> 00:37:28,477
For C-pilot to know how it should help
me with my task, it needs to somehow be

682
00:37:28,560 --> 00:37:30,317
able to get the information from the

683
00:37:30,400 --> 00:37:32,797
email we got from the PM team and bring

684
00:37:32,880 --> 00:37:35,597
that information into the terminal.

685
00:37:35,680 --> 00:37:37,997
Earlier, Satia talked about work IQ,

686
00:37:38,080 --> 00:37:40,557
which allows us to connect to the M365

687
00:37:40,640 --> 00:37:43,757
suite and to bring that information here

688
00:37:43,840 --> 00:37:49,197
into the CLI so Copilot can work with it.
You'll see at the top here. It's

689
00:37:49,280 --> 00:37:50,637
doing a lot of thinking, but you'll see

690
00:37:50,720 --> 00:37:52,957
that Copilot has already connected to

691
00:37:53,040 --> 00:37:56,237
the work IQ MCP server. In my prompt,

692
00:37:56,320 --> 00:37:58,397
I've asked Copilot to look for the PM

693
00:37:58,480 --> 00:38:01,757
team's email and then to return a list

694
00:38:01,840 --> 00:38:03,837
of the things we need to build based off

695
00:38:03,920 --> 00:38:05,997
of that email. You'll also see that

696
00:38:06,080 --> 00:38:09,437
copilot has invoked the work IQ skill

697
00:38:09,520 --> 00:38:12,557
which under the hood uses the graph API

698
00:38:12,640 --> 00:38:14,957
to be able to triage through my emails.

699
00:38:15,040 --> 00:38:17,757
It's going to look through and hopefully

700
00:38:17,840 --> 00:38:20,237
if the demo gods are on my side this

701
00:38:20,320 --> 00:38:22,797
morning, it's going to find the email

702
00:38:22,880 --> 00:38:25,997
that the PM team sent us and then we'll

703
00:38:26,080 --> 00:38:29,197
list out the things we need to build.

704
00:38:29,280 --> 00:38:31,037
While it's doing that, I'll also point

705
00:38:31,120 --> 00:38:33,757
out that I'm currently connected to

706
00:38:33,840 --> 00:38:38,557
Claude Opus 4.6. So, this is one of the

707
00:38:38,640 --> 00:38:40,717
most popular coding models that we have

708
00:38:40,800 --> 00:38:43,997
available, but we have a couple more

709
00:38:44,080 --> 00:38:46,957
um as well. Okay, great. I can see that

710
00:38:47,040 --> 00:38:49,757
Copilot has used Worth Q to find the

711
00:38:49,840 --> 00:38:52,077
email and it's list out listed out the

712
00:38:52,160 --> 00:38:54,797
checklist that was in the email we saw

713
00:38:54,880 --> 00:38:57,117
earlier before. So, we're almost ready

714
00:38:57,200 --> 00:38:59,997
to build. And like I said, I'm connected

715
00:39:00,080 --> 00:39:03,277
to Claude Opus 4.6, but I'm not sure if

716
00:39:03,360 --> 00:39:08,077
this is the right model for the job.
So, let's see what options we have. With a

717
00:39:08,160 --> 00:39:10,237
single GitHub copilot subscription, I

718
00:39:10,320 --> 00:39:12,237
now have access to all the latest

719
00:39:12,320 --> 00:39:15,917
models, including like I can see here,

720
00:39:16,000 --> 00:39:18,877
GPT 5.3 codecs. Now, I'm not sure how

721
00:39:18,960 --> 00:39:21,117
many of you are chronically on social

722
00:39:21,200 --> 00:39:23,837
media online like I am, but if you are,

723
00:39:23,920 --> 00:39:26,317
you might have already heard about this

724
00:39:26,400 --> 00:39:28,637
model. It just was released a few days

725
00:39:28,720 --> 00:39:30,957
ago and it's already available for us to

726
00:39:31,040 --> 00:39:33,837
use in GitHub Copilot today. So, this is

727
00:39:33,920 --> 00:39:35,757
super exciting. I'm going to choose this

728
00:39:35,840 --> 00:39:38,957
model and then I'm going to send through

729
00:39:39,040 --> 00:39:41,117
and then I'm going to send through a new

730
00:39:41,200 --> 00:39:44,797
prompt to be able to ask co-pilots to go

731
00:39:44,880 --> 00:39:47,677
ahead and get started with building that

732
00:39:47,760 --> 00:39:52,077
search and filter feature.
You'll see that the model begins by

733
00:39:52,160 --> 00:39:54,237
thinking. It's going to analyze both my

734
00:39:54,320 --> 00:39:57,597
prompt and the codebase around it. And

735
00:39:57,680 --> 00:40:01,837
after that, it's going to create a plan
and then implement on that plan. This

736
00:40:01,920 --> 00:40:03,517
process is going to take a few minutes.

737
00:40:03,600 --> 00:40:07,197
And so while Copilot works in the
background, let's switch over to a new

738
00:40:07,280 --> 00:40:09,757
terminal. Earlier today, I gave Copilot

739
00:40:09,840 --> 00:40:12,477
the same prompt asking you to build that

740
00:40:12,560 --> 00:40:14,557
search and filter feature, but I asked

741
00:40:14,640 --> 00:40:17,757
it to add the changes to a PR. You'll

742
00:40:17,840 --> 00:40:20,477
see that we are also connected to the

743
00:40:20,560 --> 00:40:23,677
Playright MCP server. I'd like for

744
00:40:23,760 --> 00:40:26,877
Copilot to use this MCP server to test

745
00:40:26,960 --> 00:40:29,677
the search bar and the filter just like

746
00:40:29,760 --> 00:40:32,397
a human user would.

747
00:40:32,480 --> 00:40:34,717
Playright is an open-source testing

748
00:40:34,800 --> 00:40:36,797
framework built by Microsoft that allows

749
00:40:36,880 --> 00:40:39,997
us to be able to automate browser

750
00:40:40,080 --> 00:40:42,797
interactions. So to be able to test an

751
00:40:42,880 --> 00:40:46,237
app, Playright will begin by opening up

752
00:40:46,320 --> 00:40:49,997
a browser and it will start running an

753
00:40:50,080 --> 00:40:54,397
automated script. It's going to start
typing in inputs, navigating to

754
00:40:54,480 --> 00:40:56,877
different pages, and all along the way

755
00:40:56,960 --> 00:40:59,277
it's going to take screenshots. You can

756
00:40:59,360 --> 00:41:01,757
see my hands are not on the keyboard.

757
00:41:01,840 --> 00:41:04,717
And this is all co-pilot and playright

758
00:41:04,800 --> 00:41:07,197
doing this work for us and testing that

759
00:41:07,280 --> 00:41:09,837
our features are working as expected. We

760
00:41:09,920 --> 00:41:12,717
just saw Copilot literally working

761
00:41:12,800 --> 00:41:17,437
through four different functional tests
of our application. We can see that our

762
00:41:17,520 --> 00:41:19,117
search bar and our filters are working

763
00:41:19,200 --> 00:41:21,517
as expected. And this is perfect. Our

764
00:41:21,600 --> 00:41:24,717
tests have passed. Now that we know that

765
00:41:24,800 --> 00:41:27,117
the app is working, we're almost ready

766
00:41:27,200 --> 00:41:29,997
to merge the PR. But before we do, let's

767
00:41:30,080 --> 00:41:34,477
add in some documentation
and head over to VS Code for that.

768
00:41:34,560 --> 00:41:36,957
For developers, good documentation has

769
00:41:37,040 --> 00:41:39,437
always been important, but I would say

770
00:41:39,520 --> 00:41:42,157
it's even more so now that we can

771
00:41:42,240 --> 00:41:45,517
generate code faster than ever with AI.

772
00:41:45,600 --> 00:41:49,757
To be able to automate the process
of creating better documentation, I've

773
00:41:49,840 --> 00:41:52,157
created a PR visualizer app and I've

774
00:41:52,240 --> 00:41:55,037
used the GitHub Copilot SDK to do this.

775
00:41:55,120 --> 00:41:57,757
Copilot SDK gives developers the power

776
00:41:57,840 --> 00:42:00,557
of GitHub copilot directly in their

777
00:42:00,640 --> 00:42:02,877
apps. The Python code that you can see

778
00:42:02,960 --> 00:42:05,757
on the screen creates a co-pilot agent

779
00:42:05,840 --> 00:42:08,077
session. And in this session, I'm

780
00:42:08,160 --> 00:42:11,437
passing through this PR data tool set.

781
00:42:11,520 --> 00:42:15,837
And that set will be used to pull
in data from the PR we saw uh in the

782
00:42:15,920 --> 00:42:18,077
terminal. And then we'll pass that data,

783
00:42:18,160 --> 00:42:20,717
the key points to Excaliraw, which we've

784
00:42:20,800 --> 00:42:23,517
connected to Copilot using MCP. And then

785
00:42:23,600 --> 00:42:25,917
Excaliraw is going to generate a diagram

786
00:42:26,000 --> 00:42:30,637
for us that will show us which changes
have been made to our code in that PR.

787
00:42:30,720 --> 00:42:34,477
So I have the app running locally
in simple browser and I'm going to pass

788
00:42:34,560 --> 00:42:36,237
through the link to the PR that I got

789
00:42:36,320 --> 00:42:40,877
from Copilot and it's going to start off

790
00:42:40,960 --> 00:42:45,597
by calling that tool we passed through
in the code and we'll use that tool to

791
00:42:45,680 --> 00:42:49,197
pull in the information and then it's
going to combine that with the excal

792
00:42:49,280 --> 00:42:52,477
draw format to understand how to then

793
00:42:52,560 --> 00:42:57,677
generate a diagram like you can see
on the screen there to show us what has

794
00:42:57,760 --> 00:43:01,357
been added to our code. So we can see
which parts the search bar the filter

795
00:43:01,440 --> 00:43:05,037
have been added to the code and which
parts of our code base have been

796
00:43:05,120 --> 00:43:07,677
modified as well. So in the past few

797
00:43:07,760 --> 00:43:11,277
minutes we've seen Copilot with work IQ

798
00:43:11,360 --> 00:43:13,677
pull in a spec from an email and build

799
00:43:13,760 --> 00:43:16,237
it. We've also seen Copilot work with

800
00:43:16,320 --> 00:43:18,797
Playright to be able to test an app

801
00:43:18,880 --> 00:43:20,957
functionally. And finally we've just

802
00:43:21,040 --> 00:43:24,957
seen Copilot SDK with MCP generate an

803
00:43:25,040 --> 00:43:27,517
architecture diagram for us. So whether

804
00:43:27,600 --> 00:43:30,637
you're working in the CLI, in the IDE,

805
00:43:30,720 --> 00:43:33,597
or adding AI to your applications, give

806
00:43:33,680 --> 00:43:36,397
GitHub copilot a try today. Over to you,

807
00:43:36,480 --> 00:43:40,920
Charles. Thanks.

808
00:43:42,240 --> 00:43:47,437
>> No, thanks. Thanks, Marlene. And um
you know, that kind of gives you a flavor

809
00:43:47,520 --> 00:43:49,037
for

810
00:43:49,120 --> 00:43:53,277
um you know, what's possible uh with

811
00:43:53,360 --> 00:43:57,037
GitHub Copilot. And if you look at the,
you know, who would have thought again,

812
00:43:57,120 --> 00:43:59,357
you know, you're back at the terminal

813
00:43:59,440 --> 00:44:01,917
doing all these powerful things. And

814
00:44:02,000 --> 00:44:06,637
that's because you can through natural
language get your tasks done, right? I

815
00:44:06,720 --> 00:44:08,397
mean, you're not just writing code, but

816
00:44:08,480 --> 00:44:10,877
you're able to literally reference a

817
00:44:10,960 --> 00:44:13,757
team's meeting, your email discussions,

818
00:44:13,840 --> 00:44:17,437
and have work IQ surface the plan that

819
00:44:17,520 --> 00:44:21,357
you then go execute through the coding
agent and change your repo. I mean

820
00:44:21,440 --> 00:44:23,997
that's the power uh of some of these AI

821
00:44:24,080 --> 00:44:27,277
capabilities all compounding. Uh so work

822
00:44:27,360 --> 00:44:30,957
IQ doesn't just remain in uh Office 365

823
00:44:31,040 --> 00:44:34,317
or Microsoft 365. It now is available to

824
00:44:34,400 --> 00:44:39,597
you even when uh you're coding and that I
think is a great example. The other

825
00:44:39,680 --> 00:44:43,117
thing that Marlene showed which I
think is another very powerful thing for

826
00:44:43,200 --> 00:44:46,397
developers is the entire platform or the

827
00:44:46,480 --> 00:44:49,037
runtime underneath GitHub copilot is now

828
00:44:49,120 --> 00:44:51,197
available for anybody to build their own

829
00:44:51,280 --> 00:44:54,157
agentic system. Uh so you can now take

830
00:44:54,240 --> 00:44:56,237
that access to multiple models. Right?

831
00:44:56,320 --> 00:45:01,357
So when she did the PR visualizer
that was built using that SDK uh where

832
00:45:01,440 --> 00:45:04,877
literally you have access to all the
models that GitHub copilot has. So your

833
00:45:04,960 --> 00:45:07,837
app has that the skills uh and its

834
00:45:07,920 --> 00:45:12,637
ability to call MCP servers.
It has that. So that way you can now build

835
00:45:12,720 --> 00:45:16,877
applications that are you know maybe
in core banking or in insurance or in

836
00:45:16,960 --> 00:45:21,357
healthcare that literally are also going
to be like GitHub copilot with variety

837
00:45:21,440 --> 00:45:25,677
of different form factors multiple
models and access to all the MCP servers

838
00:45:25,760 --> 00:45:30,477
and skills. So that's just a great
example. So that was the stanza where we

839
00:45:30,560 --> 00:45:35,117
now said we can you can build you can
build all these agents and multi- aent

840
00:45:35,200 --> 00:45:37,437
systems. In fact you can take what is

841
00:45:37,520 --> 00:45:39,597
underneath even GitHub copilot and build

842
00:45:39,680 --> 00:45:42,717
your own version of it. Now if you have

843
00:45:42,800 --> 00:45:45,677
all these agents, the next big issue

844
00:45:45,760 --> 00:45:48,157
that one has to deal with is how do you

845
00:45:48,240 --> 00:45:51,597
govern them? And really to ensure that

846
00:45:51,680 --> 00:45:55,357
you have a robust solution uh for both

847
00:45:55,440 --> 00:45:58,877
observability and governance of AI

848
00:45:58,960 --> 00:46:01,517
agents, we built agent 365. And to talk

849
00:46:01,600 --> 00:46:06,637
about this very important initiative,
uh I wanted to introduce up on stage my

850
00:46:06,720 --> 00:46:09,947
colleague Charles Lammana.
Charles, come on up.

851
00:46:10,030 --> 00:46:12,050
>> [applause]

852
00:46:12,640 --> 00:46:15,437
>> Thanks.

853
00:46:15,520 --> 00:46:18,237
And like Satia said, we're going to have

854
00:46:18,320 --> 00:46:22,557
a lot of agents moving forward. What
we're going to see is a need to have a

855
00:46:22,640 --> 00:46:26,957
control plane to go manage and sit
on top of all of these different agents.

856
00:46:27,040 --> 00:46:29,677
That way, we're going to be able
to get the right level of visibility and

857
00:46:29,760 --> 00:46:33,437
understanding about what people are
creating and how those agents are

858
00:46:33,520 --> 00:46:37,197
interacting with other people,
with data, and all the different systems

859
00:46:37,280 --> 00:46:39,837
inside the enterprise. And this control

860
00:46:39,920 --> 00:46:42,717
plane really enables three core things

861
00:46:42,800 --> 00:46:45,917
across all of your agents. First, it

862
00:46:46,000 --> 00:46:50,077
helps you with governance. You can
create policies and workflows so you can

863
00:46:50,160 --> 00:46:52,557
manage exactly who creates your agents,

864
00:46:52,640 --> 00:46:54,717
who consumes them, how they're shared,

865
00:46:54,800 --> 00:46:57,597
and what systems they can work with.

866
00:46:57,680 --> 00:47:01,757
Second, you can have perfect
observability. So, you can understand

867
00:47:01,840 --> 00:47:06,397
what these agents are doing on a regular
basis. That way, if there's a risk or a

868
00:47:06,480 --> 00:47:10,557
potential security problem or just
errors or cost issues, you can start to

869
00:47:10,640 --> 00:47:14,877
have an understanding across all
of your different agents. And last but not

870
00:47:14,960 --> 00:47:18,797
least, you can enforce security policies.
So no matter what these agents

871
00:47:18,880 --> 00:47:23,437
are doing in terms of connecting
or authenticating or authorizing or even

872
00:47:23,520 --> 00:47:28,077
having real-time protection for things
like jailbreaks and XPIA, you're able to

873
00:47:28,160 --> 00:47:31,997
protect these agents across your entire
estate. And as all of this starts to

874
00:47:32,080 --> 00:47:35,437
come together, we need to have a really
integrated experience. So, I'm going to

875
00:47:35,520 --> 00:47:39,757
walk you through some of the new
Agent 365 capabilities that we've been

876
00:47:39,840 --> 00:47:43,917
releasing. If we look
at what Agent 365 enables you

877
00:47:44,000 --> 00:47:47,677
to do is it's a one-stop shop to get
a view of all of those agents that are

878
00:47:47,760 --> 00:47:51,917
created, it's available right inside
the Microsoft Admin Center. So the same

879
00:47:52,000 --> 00:47:56,477
place where you already go today to do
things like manage your users or devices

880
00:47:56,560 --> 00:47:58,877
or even your co-pilot rollout, you're

881
00:47:58,960 --> 00:48:01,117
now able to see all of the different

882
00:48:01,200 --> 00:48:04,877
agents that are active. And if I look at

883
00:48:04,960 --> 00:48:07,437
some of this top level dashboard view,

884
00:48:07,520 --> 00:48:09,357
you can get an idea of how many agents

885
00:48:09,440 --> 00:48:12,877
there are. In this example, 128,000, as

886
00:48:12,960 --> 00:48:16,797
well as how many users are interacting
with those agents, in this case, over

887
00:48:16,880 --> 00:48:19,677
58,000. And we chose these numbers for

888
00:48:19,760 --> 00:48:21,517
the demo to really highlight that ratio

889
00:48:21,600 --> 00:48:24,077
that we expect. There are going to be a

890
00:48:24,160 --> 00:48:28,797
lot of agents and every user is going to
need to interact with many agents to get

891
00:48:28,880 --> 00:48:32,717
their job done most efficiently. Whether
it's inside of GitHub copilot or agents

892
00:48:32,800 --> 00:48:37,757
that your IT team or dev teams have
created or even from third parties. And

893
00:48:37,840 --> 00:48:39,277
that's one of the important parts of

894
00:48:39,360 --> 00:48:43,517
agent 365 is it's not just for Microsoft

895
00:48:43,600 --> 00:48:47,917
platforms or Microsoft agents. We
of course have registrations for Copilot

896
00:48:48,000 --> 00:48:49,997
Studio and Foundry, but if your agents

897
00:48:50,080 --> 00:48:54,957
are built and integrated with Workday
or Google or Service Now or Manis, you're

898
00:48:55,040 --> 00:48:58,957
able to see these agents and what
they're doing. And you can track things

899
00:48:59,040 --> 00:49:03,997
like trends as well as potential risks
as they show up. And the risk part is an

900
00:49:04,080 --> 00:49:07,197
important item because if you have
over a 100,000 agents, you don't want to

901
00:49:07,280 --> 00:49:10,077
click through each one of them
to understand what the problems are. So,

902
00:49:10,160 --> 00:49:13,997
we're able to give you the top actions
as an admin. So, you can go and manage

903
00:49:14,080 --> 00:49:17,757
things like which agents require
review or approval, where you have those

904
00:49:17,840 --> 00:49:20,717
potential problems or agents that have
been orphaned and no longer have an

905
00:49:20,800 --> 00:49:25,277
owner. All available inside that console.
And if I say click through into

906
00:49:25,360 --> 00:49:28,557
agent risk, you can see here are some of
them that that have been identified. And

907
00:49:28,640 --> 00:49:32,557
if I look at the comm's agent, I can
start to get a quick view of what its

908
00:49:32,640 --> 00:49:34,317
problems are. I can see there are two

909
00:49:34,400 --> 00:49:35,997
security risks that require my

910
00:49:36,080 --> 00:49:39,757
attention. And if I go and look at it,

911
00:49:39,840 --> 00:49:43,437
you can start to see that the agents
are integrating with the same systems we

912
00:49:43,520 --> 00:49:46,797
already use to secure those users
and those devices. In this case, it uses

913
00:49:46,880 --> 00:49:50,877
Entra to let me know that the agent has
an abnormal sign-in frequency. So that's

914
00:49:50,960 --> 00:49:53,197
based on things like location, IP range,

915
00:49:53,280 --> 00:49:57,997
how often it's logging in, it's been
flagged for further review. Or one of

916
00:49:58,080 --> 00:50:00,957
the reasons you want to have your agents
and your users managed in the same

917
00:50:01,040 --> 00:50:04,957
system is we can look and identify
a risky user who may have been compromised

918
00:50:05,040 --> 00:50:09,117
is interacting with this agent in a way
that isn't normal. This can help us

919
00:50:09,200 --> 00:50:13,357
elevate and flag across people
and agents and devices where that risk is

920
00:50:13,440 --> 00:50:18,317
starting to show up. And in addition
to looking at the agents, you have to have

921
00:50:18,400 --> 00:50:22,557
a process by which these agents get
deployed. So we have an experience where

922
00:50:22,640 --> 00:50:26,397
you can approve each agent as it's
getting set up. So if I look at this

923
00:50:26,480 --> 00:50:30,317
staffing agent which someone has
requested me to go provision, I can

924
00:50:30,400 --> 00:50:33,677
start to publish it out to users
and I can manage do I want to do it to

925
00:50:33,760 --> 00:50:37,277
everybody or do I want to do it
to specific users or groups? I can manage

926
00:50:37,360 --> 00:50:41,997
who has access to it and importantly I
have this idea of a template and these

927
00:50:42,080 --> 00:50:45,997
templates have built-in policies around
what the agent can do. So I can just

928
00:50:46,080 --> 00:50:49,357
choose for example the staffing access
governance template to make sure the

929
00:50:49,440 --> 00:50:54,237
agent's going to behave the way I expect
it to. And if I look here, I can also

930
00:50:54,320 --> 00:50:57,197
review the permissions and the access
that this agent is going to take

931
00:50:57,280 --> 00:51:00,957
advantage of. So I can very quickly
click through and get a good idea of

932
00:51:01,040 --> 00:51:04,637
what these agents are doing across
my organization and start to scale that

933
00:51:04,720 --> 00:51:09,597
agent practice. But it's not just
about the agents, it's also about the

934
00:51:09,680 --> 00:51:14,317
intelligence, the systems it connects to.
So if I look here inside of the

935
00:51:14,400 --> 00:51:19,277
agent 365 experience, we have an IQ page.
And this is important because the

936
00:51:19,360 --> 00:51:23,437
thing that makes you unique is your data,
not the model. And here I can

937
00:51:23,520 --> 00:51:26,237
understand things like how many emails

938
00:51:26,320 --> 00:51:30,477
or how much emails or meeting contexts
are being used by agents on a regular

939
00:51:30,560 --> 00:51:34,957
basis. And this lets you know how that IQ
is being put to work to go drive that

940
00:51:35,040 --> 00:51:36,877
business outcome and business process

941
00:51:36,960 --> 00:51:39,197
value. And this goes across all of the

942
00:51:39,280 --> 00:51:42,797
important work IQ data and more. So what

943
00:51:42,880 --> 00:51:45,917
agent 365 really starts to enable is

944
00:51:46,000 --> 00:51:50,637
this idea that you can empower users
to create agents. You can have IT teams

945
00:51:50,720 --> 00:51:54,317
that build their agents and you can
also work with third parties and other

946
00:51:54,400 --> 00:51:56,397
companies to get those agents deployed

947
00:51:56,480 --> 00:51:59,837
at work. This creates an incredible

948
00:51:59,920 --> 00:52:02,557
agent ecosystem. We've already worked

949
00:52:02,640 --> 00:52:06,477
with dozens of other companies like SAP,

950
00:52:06,560 --> 00:52:08,877
Service Now, Workday, as well as

951
00:52:08,960 --> 00:52:11,677
startups like Genpark and Manis or even

952
00:52:11,760 --> 00:52:14,717
the AI labs like Anthropic and OpenAI to

953
00:52:14,800 --> 00:52:17,677
integrate with agent 365. That way, as

954
00:52:17,760 --> 00:52:21,837
you get these things deployed, you can
make sure you have a single simple way

955
00:52:21,920 --> 00:52:24,317
to govern and control them across the

956
00:52:24,400 --> 00:52:29,197
entire company. So, with that, I hope
you give a 365 a try. It's available in

957
00:52:29,280 --> 00:52:31,117
the Microsoft admin center today and

958
00:52:31,200 --> 00:52:34,437
I'll hand it back over to you Satia.

959
00:52:34,520 --> 00:52:38,237
[applause] >> Thanks.

960
00:52:38,320 --> 00:52:40,557
>> Yeah, that it's it's a very important

961
00:52:40,640 --> 00:52:43,677
sort of aspect I think of how the roll

962
00:52:43,760 --> 00:52:45,357
out of these agents will happen. I mean

963
00:52:45,440 --> 00:52:48,557
that thing that Charles showed the ratio

964
00:52:48,640 --> 00:52:51,597
of people to agents by definition is

965
00:52:51,680 --> 00:52:53,677
going to be enormous. uh it may be what

966
00:52:53,760 --> 00:52:58,317
2:1 3:1 who knows uh but you really want

967
00:52:58,400 --> 00:53:01,437
to have this governance system uh this

968
00:53:01,520 --> 00:53:03,517
observability system and the assurance

969
00:53:03,600 --> 00:53:06,317
of security so that you can confidently

970
00:53:06,400 --> 00:53:10,797
deploy these agents inside of the
enterprise. You know the good news in

971
00:53:10,880 --> 00:53:15,997
all of this again is there's fantastic
traction in building out these agentic

972
00:53:16,080 --> 00:53:20,397
systems right here in Spain. And I had
this morning a chance to meet uh you

973
00:53:20,480 --> 00:53:23,757
know a bunch of developers. Uh I had
a chance to meet the developers at

974
00:53:23,840 --> 00:53:27,357
Factorial who are building out a full HR

975
00:53:27,440 --> 00:53:29,997
agentic system. Uh it's fantastic to see

976
00:53:30,080 --> 00:53:32,397
the success they're having uh their

977
00:53:32,480 --> 00:53:34,637
design uh of how they're building the

978
00:53:34,720 --> 00:53:39,517
system out. Um also had a chance to meet
with Ferovial. In fact obviously they're

979
00:53:39,600 --> 00:53:42,797
some of the largest infrastructure
builders. They're building in fact our

980
00:53:42,880 --> 00:53:45,757
Madrid data center expansion as well.

981
00:53:45,840 --> 00:53:47,597
Um, and they were showing me how they've

982
00:53:47,680 --> 00:53:50,077
built an agentic system that literally

983
00:53:50,160 --> 00:53:53,597
allows you to effectively apply uh,

984
00:53:53,680 --> 00:53:56,877
intelligence in real time to any project

985
00:53:56,960 --> 00:53:58,957
of theirs, whether it's a data center

986
00:53:59,040 --> 00:54:01,997
buildout, a highway, an airport. Um, had

987
00:54:02,080 --> 00:54:04,157
a chance to meet with the Repol folks

988
00:54:04,240 --> 00:54:07,357
who are deploying um, uh, these IT

989
00:54:07,440 --> 00:54:11,837
workflows. Uh in fact they showed me
an agent that was doing automatic conver I

990
00:54:11,920 --> 00:54:13,917
mean basically was able to connect a

991
00:54:14,000 --> 00:54:16,477
front end using ABBA to their SAP and

992
00:54:16,560 --> 00:54:19,757
doing the automation uh to of a workflow

993
00:54:19,840 --> 00:54:22,317
and now they're scaling it beyond it. um

994
00:54:22,400 --> 00:54:24,957
had a chance to meet with uh Sanitas

995
00:54:25,040 --> 00:54:28,237
which is really built a voice system um

996
00:54:28,320 --> 00:54:32,237
the which is a front end to how their co

997
00:54:32,320 --> 00:54:35,597
customers are interfacing uh with uh

998
00:54:35,680 --> 00:54:37,917
their uh their entire sort of mobile

999
00:54:38,000 --> 00:54:40,797
application. Uh and so it's great to see

1000
00:54:40,880 --> 00:54:45,437
uh the level of innovation and the
diffusion of all of these technologies

1001
00:54:45,520 --> 00:54:49,117
uh and the developers right here in Spain
uh not only building for Spain but

1002
00:54:49,200 --> 00:54:51,677
for the world uh and uh and it just

1003
00:54:51,760 --> 00:54:54,157
showcases the power uh of the human

1004
00:54:54,240 --> 00:54:57,277
capital uh here and then the and in

1005
00:54:57,360 --> 00:54:59,197
their hands these tools get translated

1006
00:54:59,280 --> 00:55:03,117
into an unbelievable uh value. The last

1007
00:55:03,200 --> 00:55:06,317
sort of part of the talk is going to be

1008
00:55:06,400 --> 00:55:08,237
about the token factory. So, we talked

1009
00:55:08,320 --> 00:55:12,717
about AI um the experience layer, we

1010
00:55:12,800 --> 00:55:16,477
talked about the agent platform, and of
course, if you're going to have lots of

1011
00:55:16,560 --> 00:55:19,197
agents, you need infrastructure that

1012
00:55:19,280 --> 00:55:22,397
generates tokens most efficiently. Um

1013
00:55:22,480 --> 00:55:24,157
and it's all about this particular

1014
00:55:24,240 --> 00:55:26,557
formula, right? It's about performance

1015
00:55:26,640 --> 00:55:29,837
uh of tokens per dollar per watt. Um and

1016
00:55:29,920 --> 00:55:34,557
the goal is how do you really make sure
um uh you have the most efficient system

1017
00:55:34,640 --> 00:55:36,477
for it? and we're building out Azure as

1018
00:55:36,560 --> 00:55:40,077
the world's computer. Um we have uh 70

1019
00:55:40,160 --> 00:55:42,797
plus data center regions, 400 plus data

1020
00:55:42,880 --> 00:55:45,197
centers. We continuing to expand that.

1021
00:55:45,280 --> 00:55:50,317
Uh right here in Spain, we have two
regions uh both in central and north. Uh

1022
00:55:50,400 --> 00:55:54,877
the other thing that we have remained
very committed to is renewable. Uh and

1023
00:55:54,960 --> 00:55:56,877
we are 100% in fact we've achieved that

1024
00:55:56,960 --> 00:55:59,837
milestone where all of our data centers

1025
00:55:59,920 --> 00:56:02,637
have 100% of renewable power. In fact,

1026
00:56:02,720 --> 00:56:05,277
you can even think about our spend in

1027
00:56:05,360 --> 00:56:08,557
capital and our procurement and PPAs are

1028
00:56:08,640 --> 00:56:11,597
a great stimulant uh for essentially any

1029
00:56:11,680 --> 00:56:14,237
renewable financing uh and project

1030
00:56:14,320 --> 00:56:18,957
financing. And so it's great to see
this uh continue to be a focus for us and

1031
00:56:19,040 --> 00:56:20,397
everyone else who are our partners

1032
00:56:20,480 --> 00:56:25,437
there. The other aspect uh is the the

1033
00:56:25,520 --> 00:56:29,197
systems themselves. In fact, we are
at a fascinating time, right? We are at a

1034
00:56:29,280 --> 00:56:32,397
time where you are starting to see the

1035
00:56:32,480 --> 00:56:35,357
workloads somewhat get stable, right?

1036
00:56:35,440 --> 00:56:40,557
These atcale training workloads
or inference workloads. They're unlike

1037
00:56:40,640 --> 00:56:41,917
anything we've seen in the past, right?

1038
00:56:42,000 --> 00:56:45,037
These are large sync data parallel

1039
00:56:45,120 --> 00:56:49,117
synchronous um you know GPU workloads.

1040
00:56:49,200 --> 00:56:51,357
Uh but you're now learning that for

1041
00:56:51,440 --> 00:56:54,237
example take even an inference workload

1042
00:56:54,320 --> 00:56:56,957
that you can optimize it. uh you can

1043
00:56:57,040 --> 00:57:00,477
disagregate the prefill and the decode.

1044
00:57:00,560 --> 00:57:04,637
um you could in fact take even the how

1045
00:57:04,720 --> 00:57:08,957
you access memory whether it's high
bandwidth memory or SRAMM how do you

1046
00:57:09,040 --> 00:57:11,357
think about uh the dist like you can

1047
00:57:11,440 --> 00:57:14,237
have a draft model completely in SRAMM

1048
00:57:14,320 --> 00:57:18,717
so there's a complete renaissance
of systems in fact so you know starting in

1049
00:57:18,800 --> 00:57:20,637
December to now there has been a

1050
00:57:20,720 --> 00:57:22,957
renaissance uh in how you think about

1051
00:57:23,040 --> 00:57:25,917
even the systems for inference uh and so

1052
00:57:26,000 --> 00:57:28,237
the Microsoft approach is going to be to

1053
00:57:28,320 --> 00:57:31,437
have a a fleet that takes advantage of

1054
00:57:31,520 --> 00:57:35,997
the best technology and uses the power
of software to make it available to so

1055
00:57:36,080 --> 00:57:38,637
that you can have the best token factory.
Right? We're going to partner

1056
00:57:38,720 --> 00:57:42,637
with Nvidia. We're going to partner with
AMD. We're building our own systems. In

1057
00:57:42,720 --> 00:57:45,197
fact, Maya 200, which we just disclosed,

1058
00:57:45,280 --> 00:57:47,677
which is taking real traffic today, uh

1059
00:57:47,760 --> 00:57:50,397
is around 30 plus% better than any

1060
00:57:50,480 --> 00:57:53,197
leading node in any leading system uh in

1061
00:57:53,280 --> 00:57:57,517
Azure today. So we're going to continue
to innovate uh and really take advantage

1062
00:57:57,600 --> 00:57:59,597
of the ecosystem innovation so that we

1063
00:57:59,680 --> 00:58:02,637
can build out the best token factories.

1064
00:58:02,720 --> 00:58:07,277
Now we in addition to doing all
this work around building out the data

1065
00:58:07,360 --> 00:58:09,277
centers making sure that they're you

1066
00:58:09,360 --> 00:58:12,157
know got renewable sources of energy

1067
00:58:12,240 --> 00:58:14,317
innovation in the system side and the

1068
00:58:14,400 --> 00:58:17,837
silicon side. Uh the other aspect is

1069
00:58:17,920 --> 00:58:20,637
making sure we're committed uh to

1070
00:58:20,720 --> 00:58:24,077
Europe. Um and we made in April of last

1071
00:58:24,160 --> 00:58:26,557
year, we had these European commitments

1072
00:58:26,640 --> 00:58:28,957
uh around strengthening the digital

1073
00:58:29,040 --> 00:58:31,357
ecosystems here with local partnerships

1074
00:58:31,440 --> 00:58:35,117
and local innovation, privacy, security

1075
00:58:35,200 --> 00:58:39,597
and continuity uh and digital resilience.
Uh and these are commitments

1076
00:58:39,680 --> 00:58:44,077
we stand by and we've continued to work
and one of the aspects of this is

1077
00:58:44,160 --> 00:58:48,477
sovereignty. uh after all
sovereignty is a top consideration

1078
00:58:48,560 --> 00:58:50,957
uh for any country and uh in order to

1079
00:58:51,040 --> 00:58:53,277
address that what we've done is really

1080
00:58:53,360 --> 00:58:55,837
built a portfolio of solutions. In fact,

1081
00:58:55,920 --> 00:58:57,997
if you think about sovereignty as

1082
00:58:58,080 --> 00:59:01,037
essentially managing even risk uh the

1083
00:59:01,120 --> 00:59:05,997
way you manage risk in any other domain
is to have a portfolio. Um and that is

1084
00:59:06,080 --> 00:59:07,597
essentially what you should even think

1085
00:59:07,680 --> 00:59:09,757
of when it comes to sovereignty which is

1086
00:59:09,840 --> 00:59:13,197
some workloads using sovereign services

1087
00:59:13,280 --> 00:59:16,077
in public cloud. You can for example

1088
00:59:16,160 --> 00:59:18,157
encrypt all your data. You can manage

1089
00:59:18,240 --> 00:59:23,117
the keys that do the encryption. You can
use confidential computing uh in order

1090
00:59:23,200 --> 00:59:27,197
to ensure that the data that's being
used even in a public cloud is never

1091
00:59:27,280 --> 00:59:29,997
outside a confidential chamber. Right?

1092
00:59:30,080 --> 00:59:33,597
So effectively it is fully sovereign. Uh

1093
00:59:33,680 --> 00:59:37,437
so that's sort of one solution. On top
of that, we have a solution which is

1094
00:59:37,520 --> 00:59:40,557
fully local. Um and so when it comes to

1095
00:59:40,640 --> 00:59:45,357
uh we already have Microsoft 365 uh
local, we have Azure local and we're

1096
00:59:45,440 --> 00:59:49,037
very thrilled this week to announce
Foundry local. Right? So when we talked

1097
00:59:49,120 --> 00:59:50,957
about Foundry with all its models,

1098
00:59:51,040 --> 00:59:53,757
they'll be available now even locally uh

1099
00:59:53,840 --> 00:59:58,477
for you to be able to use in a private
uh setting. Uh and not only that, we

1100
00:59:58,560 --> 01:00:00,557
were also thrilled this week to announce

1101
01:00:00,640 --> 01:00:03,197
that we've taken Azure local which up to

1102
01:00:03,280 --> 01:00:07,917
now had a control plane uh which was in
the cloud but even the local with local

1103
01:00:08,000 --> 01:00:10,157
operations but now you can have fully

1104
01:00:10,240 --> 01:00:13,197
disconnected uh um control plane. So

1105
01:00:13,280 --> 01:00:16,157
that means you have a local cloud with a

1106
01:00:16,240 --> 01:00:18,797
local operation control plane versus

1107
01:00:18,880 --> 01:00:21,917
being in any hybrid uh mode uh at all.

1108
01:00:22,000 --> 01:00:25,277
And so these are all again um options

1109
01:00:25,360 --> 01:00:29,037
for you to think about the different
workloads and where you would like to

1110
01:00:29,120 --> 01:00:31,677
put them in order to have the best uh

1111
01:00:31,760 --> 01:00:34,317
efficient use of the cloud resources and

1112
01:00:34,400 --> 01:00:38,717
we also have many sovereign partners
locally uh who are helping us ensure

1113
01:00:38,800 --> 01:00:43,597
that all these portfolio of solutions
are available uh to all of you. The one

1114
01:00:43,680 --> 01:00:45,597
aspect I wanted to uh talk about though

1115
01:00:45,680 --> 01:00:47,437
at the very end of it is as you think

1116
01:00:47,520 --> 01:00:50,877
about sovereignty and risk, it's also

1117
01:00:50,960 --> 01:00:55,357
important especially for policy makers
to think about even cyber and cyber

1118
01:00:55,440 --> 01:00:59,357
security and cyber resilience because
if you think about cyber uh it's

1119
01:00:59,440 --> 01:01:03,277
fundamentally an intelligence game,
right? It's a global intelligence game,

1120
01:01:03,360 --> 01:01:06,317
right? you're only as resilient on cyber

1121
01:01:06,400 --> 01:01:09,037
to be able to sort of make tap into the

1122
01:01:09,120 --> 01:01:12,077
full intelligence the world has to

1123
01:01:12,160 --> 01:01:17,437
protect yourself. Uh because no cyber
attack respects borders. Uh and that I

1124
01:01:17,520 --> 01:01:21,677
think is a very important thing. So when
you think about a sovereign service or a

1125
01:01:21,760 --> 01:01:26,317
sovereign cloud and if for what whatever
reason it doesn't have the best cyber

1126
01:01:26,400 --> 01:01:30,397
protection that sovereignty
unfortunately could get compromised and

1127
01:01:30,480 --> 01:01:32,797
so therefore you really want to think

1128
01:01:32,880 --> 01:01:35,597
about sovereignty and cyber as two parts

1129
01:01:35,680 --> 01:01:38,237
of the risk uh spectrum that you want to

1130
01:01:38,320 --> 01:01:43,437
manage uh simultaneously and that's what
uh we have a commitment to ensuring we

1131
01:01:43,520 --> 01:01:47,437
are partnering with in fact local
providers uh in bringing that global

1132
01:01:47,520 --> 01:01:49,597
intelligence to all of these portfolio

1133
01:01:49,680 --> 01:01:52,477
solutions. Um, so I want to close out

1134
01:01:52,560 --> 01:01:55,757
where I started. Ultimately, to us, it's

1135
01:01:55,840 --> 01:01:58,397
about taking all of what we talked about

1136
01:01:58,480 --> 01:02:00,557
this power of this new technology, but

1137
01:02:00,640 --> 01:02:03,837
putting it in your hands so that you can

1138
01:02:03,920 --> 01:02:08,077
start to transform your organizations,

1139
01:02:08,160 --> 01:02:12,477
uh, your communities and all of Spain,
uh, through the power of these

1140
01:02:12,560 --> 01:02:14,877
technologies and really your ingenuity.

1141
01:02:14,960 --> 01:02:18,797
So, thank you all very very much
for your partnership and I look forward to

1142
01:02:18,880 --> 01:02:22,627
sort of seeing what you all build going
forward. Thank you and have a great day.

1143
01:02:22,710 --> 01:02:24,730
[applause]