WEBVTT

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[music]

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[applause]

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>> Good morning. It's

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It's uh wonderful uh to be back in India

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and here in Mumbai and have a chance uh

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this morning to talk about uh AI and

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mostly to celebrate uh all the AI and

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the AI solutions uh that are being built

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uh by people in this country and this

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state and everywhere. And it's just

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exciting to be able to come back and see

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uh the progress even year on year uh

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that um I you know see
uh you know in action.

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To us, it's always about empowering
people and organizations. So, when we

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talk about even new technology um

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you know platforms, it comes down to how

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is it being really used to empower an

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institution, an organization.

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Um I'm going to talk about in fact Crime
OS that the Chief Minister was here, you

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know, really sponsored and made happen

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uh in this state so that even uh every

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citizen of this uh state can benefit.

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And that's the type of innovation we

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want to see um in any place.

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And but the key thing for us is to be

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grounded in the fact that the
frontier of what is possible

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is changing, right? I mean, that's
the exciting part for any of us is when we

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talk about technology, it allows us to

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reimagine what we can now do uh whether

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it's in our state, whether it is in our
organization, whether it's in our lives,

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right? So, the ability
to move the frontier.

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So, we always start there, right?
With the customer experience. Now, you can

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think about and reimagine it. Whether
it's the employee experience, whether

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it's the operational efficiency, uh

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whether it's the rate of innovation.
If you're a drug company, can you really

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get through clinical trials faster? Can

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we get more new drugs out there? Uh that

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to me is ultimately moving of the
frontier that we are all chasing.

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And to do that though,

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you kind of have to embrace a new

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approach even, right?
You What worked previously

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in some sense, you have to do this hard
job of unlearning and learning what's

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possible, right? Sometimes we sort of

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pattern match too much uh from what

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worked in the previous era uh may not
necessarily work. In fact, there's a new

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frontier. So, when I think about that,
it starts with that mindset, freeing

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ourselves uh that there's a new approach.
For example, in when it comes

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to AI, right? If you look at even
a building of an AI solution,

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you don't start with a spec.
You actually start with the test, right?

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I mean, when people they have fancy
new words talk people talk about

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evaluations, right? They you create
create these rubrics to do evals on a

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particular outcome, then you create
a learning system, then you hill climb on

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that learning system.

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Guess what? That's kind of literally

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inverting the process of building a an

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an information system. You start from

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the outcome, you create a robust eval,

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and then you spec versus
going left to right.

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But the key thing is that means you got

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to pick up new skills, all of us. Uh and

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and in order to pick up the new skills,
you kind of need a new tool chain that

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you are using every day. Uh and

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and to be able to then have all of your

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data that is being brought uh to harness

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this new tool chain, this new skill set
with the new mindset. That's really uh

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the way we want to approach it.

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So, to facilitate this, right?
This can't just be an abstract framework.

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This has got to be an everyday
practice for all of us.

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Um you know, it's an information
worker today or a software developer.

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Um all of us are going to be using

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essentially the same approach. To

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realize that, we've built a tech stack.

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And I want to just literally walk up
this stack or walk down this uh tech

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stack. So, those starting
with the experience layer

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where we have taken this idea that, you

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know, let's bring this AI copilot

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right where you work, right? You may
be you may be in Outlook, you may be in

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Word, you may be in Excel, you may be in

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Teams, you may whatever you are doing

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your work, how do we bring

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the copilot there?
And think of copilot though

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as not just a request response, right?

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That's what we've been doing now nearly
for 2 years, right? You you know, we

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know now that hey, we can ask it any
question, it'll come back with a better

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answer than anything that we have
previously experienced uh in let's call

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it an information retrieval search type

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experience. But now, we have agents. In

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fact, think of copilot as your browser

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for the agentic web.

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In fact, right inside a copilot, you
have very powerful agents built in. Like

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one such uh agent is your researcher.

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And researcher with computer use

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is a real breakthrough.
It's literally like having 24/7

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someone very sophisticated who is your

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research assistant who can go take any

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topic and then because they have
a computer, right? So, they just are not

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like think of it as not a single shot
I go and bring back information after

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thinking. They can the researcher can

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use a computer to do research. Right?

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So, it's not only you have a computer,
your researcher has a computer. So,

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that's the way to conceptualize. Same

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thing with analyst. So, I can load a

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whole bunch of Excel spreadsheets, you
give it to an analyst, and it's kind of

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like giving it to your data science
department, right? Or the data

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scientist. So, it's again having a 24/7

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data analyst who can
then go give you insights.

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And lastly, it's like one of the things

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that I'm very very excited about is what

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happened in software development is

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going to happen with Excel and Excel

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agent, right? So, I in fact had a chance
to participate recently in the Excel

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world championship. By the way, ESPN

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even broadcast Excel championships. We

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have an honest-to-god uh great way to

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sort of celebrate the Excel prowess. And

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so, I was able to participate in it you

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the digital challenge using this Excel
agent. It is so cool to be able to see

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and I worked on Excel all my life

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essentially. And to be able to sort of

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see how the agent mode understands

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deeply the entire semantics of a

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spreadsheet, right? So, it's not just a

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one shot I created a great model. But

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it's the ability to iterate on a pretty

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sophisticated model continuously using
this agent mode just like how we do in

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software engineering. So, it's just
exciting to see this. And also, it's

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really to me at the end of the day, all

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these tools are like the new Word,

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Excel, PowerPoint, Office, right? That's
kind of they're as simple as that. Uh

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and they're in the hands of everyone

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here. And I see it being used like that,

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right? So, no one is sort of you know

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praying at the altar of these tools.

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They're just using them. Uh and and to

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be to see the proliferation,
the diffusion is probably faster than

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anything I've seen, right? I've come
to back to India, I've come back to Mumbai

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throughout every generational shift in

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technology starting in the early '90s.

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And but when I see now the rate of pace
of change, there's no difference quite

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frankly. I could be in any city anywhere
in the world uh and to be able to see uh

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all of this uh applications just being
broadly diffused. To show you some of

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this in action, let me invite up on stage
my colleague Zoe. Zoe, take it away.

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>> [applause] >> Thanks,
Satya. I'm Zoe, a product

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marketer on the Microsoft 365 Copilot
team. And like all of you, I start my

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day commuting in and preparing for the

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day ahead. For that, I'm going
to use Copilot on my mobile.

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Hey Copilot, what's
on my agenda for today?

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You've got a few things lined up today.

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First, there's an AI Tour India meeting

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that started earlier this morning. Then

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at 11:00 a.m., there's a Contoso sales

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call. Later in the afternoon, you've got

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a team sync at 2:00 p.m.

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Thanks, Copilot.
And has my manager sent me any emails?

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Yes, you've got a few emails from Nicole.
Uh just yesterday, she reminded

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the team about a Friday mixer happening
in the common area. She also mentioned

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that finalizing the monthly business
review should be a high priority. She

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also Thanks, Copilot.

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Copilot doesn't just have my work

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context, it knows who I am, what I work

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on, and who I work with. It's why I use

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Copilot. It's a true AI assistant. Now,

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I wanted to take a look at my manager's
ask around that MBR updates for my

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V-team.

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And you'll see I'm actually using GPT

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5.2, which we released yesterday and

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it's immediately available to all

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Microsoft 365 customers. Do you Yeah,

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it's exciting. I know. >> [applause]

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>> What I love about GPT 5.2 is it's

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allowing Copilot to be even better at

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finding the most relevant resources. So,

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my responses are very contextually

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relevant and I'm able to take quick
action. And it makes a very visual

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output. Now,
only Copilot is able to combine

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this constant model innovation with your
work data to provide the most relevant

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response.

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Now, I want to go and update my existing

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monthly business review document, but I

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want to change it with my November data.

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For that, I'm going
to use agent mode in Word.

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Agent mode allows the agent to edit, to

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draft, to format the document, all with

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natural language, but it still
keeps me in complete control.

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And I can see it's starting to look

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across all of that work data, right? My

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emails, my meetings, my team chats, my

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files. Now, that normally takes me hours

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to do. I have to find the right

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information, I have to sift through it,

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but now you'll see the agent is actually
actively applying it on the left-hand

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side. And you'll see that it's actually

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leaving parts alone that haven't changed
month over month over month, like our

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mission, but rather using this to update

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everything and keep me on par with

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exactly the November updates.

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Now, last, I want to make sure I
understand our team budget for the next

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fiscal year. I'm going to again go
to Excel, and I'm going to turn on agent

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mode and ask it to get me a little bit

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more aware of what's been spent, what's

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uncommitted, what's at risk, and to bid

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build a visual dashboard so it's easy

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for me to understand my data.

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Agent mode in Excel natively speaks
Excel just like Satya has said. So, it's

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not going to create a random number that I
don't know where it came from. It will

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actually build formulas right in my

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spreadsheet, right where my budget

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already exists. And it will even create

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native objects like charts and tables,
things that I can continue to

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manipulate. It also walks
through everything it's

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doing on the right-hand side. So,
that way, if say my manager Nicole

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comes to me and asks, "How did I come to

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my conclusion?" Well, Copilot actually

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helps me understand what it did. That

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way, I can continue to work with it.

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I can also ask agent mode to do simple
things like conditional formatting,

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which you'll see, highlighting things in

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red that maybe are at risk. All of these

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things to continue to work with Copilot

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and agent mode in order to get a better

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understanding. And you can see it's

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created this dashboard live. I can If I
click in, I can see these rich formulas

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it's actually built to give me an answer.
The conditional formatting to

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highlight what's at risk in red. And a

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native graph that actually I'm able to

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manipulate even further, should I want.

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Now, Copilot, it hasn't just made me

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better and faster at my job, it's helped

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me make more impact. And I can't imagine

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working without it. Back to you, Satya.

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>> [applause]

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>> Thank you so much, Zoe. Whenever I
see that Excel demo, I like to think about

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it that, you know, when I had hair,
I loved Excel, and when I don't have hair,

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I love Excel. Uh, I'll tell you,
it's one of those generational things that

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just gets better and better in spite of

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my follicle challenges. Um,

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you know, to me, uh, if you
sort of take that experience

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layer, one of the things
that's super important

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in the age of AI is to think about
your data as one of the most strategic

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assets. But you got to bring that data

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contextually to the AI.
In fact, people talk about it

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as context engineering. Uh, and I think

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it's a good term for it because in fact,

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a lot of what needs to be done,

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both at this engineering level and even,

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I would say, business decision-making

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level, is to think about not the silos

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of the previous generation, but the

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contextual way you can feed intelligence

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with data. So, to that end,
for example, underneath

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Zoe's demo,

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is perhaps the most important database

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in any company that uses Microsoft 365

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today, right? If you think about it, the

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tacit knowledge inside any corporation,

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any organization, is the knowledge of

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people, their relationships with other

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people, their work artifacts, whether

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they're spreadsheets, documents, their

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conversations in email, meetings in

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Teams. To be able to relate all that,

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because that's where the tacit knowledge
of an organization is. Uh, and it's in

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that database. And so, fundamentally,
what we have transformed that is into

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work IQ, right? So, when we say work IQ,

00:14:36.520 --> 00:14:41.517
it's the stateful part underneath M365

00:14:41.600 --> 00:14:44.117
that now can be brought to bear in the

00:14:44.200 --> 00:14:46.877
context of any AI solution you want to

00:14:46.960 --> 00:14:49.197
build. Not just Copilot, any agent you

00:14:49.280 --> 00:14:51.997
build out there, you can now use work

00:14:52.080 --> 00:14:56.277
IQ. Think about that.
The second thing we've done is, in fact,

00:14:56.360 --> 00:14:58.557
one of the other ubiquitous tools all

00:14:58.640 --> 00:15:01.797
over is Excel and Power BI.

00:15:01.880 --> 00:15:05.157
So, that means you've taken real great

00:15:05.240 --> 00:15:07.757
pain over decades building these

00:15:07.840 --> 00:15:10.917
semantic models to do analysis.

00:15:11.000 --> 00:15:15.197
And now, with Fabric and Fabric IQ,

00:15:15.280 --> 00:15:17.637
you can take all of that data, along

00:15:17.720 --> 00:15:22.757
with all the operational data,
and make it available to your AI.

00:15:22.840 --> 00:15:26.197
And then the last one sort
of really makes it all come

00:15:26.280 --> 00:15:31.117
together. So, what we've done with
Foundry IQ is said what people describe

00:15:31.200 --> 00:15:34.117
as retrieval augmented generation,
right? So, one of the biggest things

00:15:34.200 --> 00:15:35.357
that we have done in the last couple of

00:15:35.440 --> 00:15:37.437
years as we build these AI solutions is

00:15:37.520 --> 00:15:41.197
to do retrieval of data. And we've now

00:15:41.280 --> 00:15:45.237
built the next generation retrieval
system, essentially. It does more than

00:15:45.320 --> 00:15:48.037
retrieval. It in fact plans

00:15:48.120 --> 00:15:51.117
and executes and reasons that plan all

00:15:51.200 --> 00:15:54.517
that plan on how to retrieve relevant

00:15:54.600 --> 00:15:59.477
data from work IQ, Fabric IQ, and any

00:15:59.560 --> 00:16:01.757
other blob storage, unstructured

00:16:01.840 --> 00:16:04.397
storage, or what have you, and bring it

00:16:04.480 --> 00:16:06.917
in the context of your AI application.

00:16:07.000 --> 00:16:09.117
So, that's what this IQ layer is. In

00:16:09.200 --> 00:16:10.877
fact, it's probably one of the more

00:16:10.960 --> 00:16:14.797
salient, yet important pieces on how

00:16:14.880 --> 00:16:17.157
anyone who builds any AI solution going

00:16:17.240 --> 00:16:19.517
forward will be more successful, right?

00:16:19.600 --> 00:16:22.277
In fact, direct correlation between the

00:16:22.360 --> 00:16:25.037
evaluations or the sort of performance

00:16:25.120 --> 00:16:30.037
of your models will be how good is your
data being brought. Just because in some

00:16:30.120 --> 00:16:32.717
sense, models are becoming, quite
frankly, a commodity, right? So, there

00:16:32.800 --> 00:16:36.317
is lots and lots of models out there
which are very, very capable. The

00:16:36.400 --> 00:16:40.477
question is, how do you marshal that
capability? I think it'll come down to

00:16:40.560 --> 00:16:44.237
having a great experience layer where
people can discover the agents you're

00:16:44.320 --> 00:16:46.917
building, and having a fantastic sort of

00:16:47.000 --> 00:16:49.837
capability around context engineering.

00:16:49.920 --> 00:16:53.037
So, the next layer for us

00:16:53.120 --> 00:16:57.677
uh, is really about now that we have
an experience layer, we have the data set

00:16:57.760 --> 00:17:01.637
up, let's build agents, right?
And the beauty of building agents, quite

00:17:01.720 --> 00:17:05.717
frankly, is like building spreadsheets
or writing a document. It's not more

00:17:05.800 --> 00:17:07.717
mystical than that. In fact, agent

00:17:07.800 --> 00:17:11.077
builder that's built right into Copilot

00:17:11.160 --> 00:17:13.837
is like saying, "Hey, I doc new," right?

00:17:13.920 --> 00:17:16.797
Or spreadsheet new. Uh, and so we start

00:17:16.880 --> 00:17:21.157
there. In fact, now we have an app
builder. So, in fact, Bill's big thing

00:17:21.240 --> 00:17:23.757
always, for the from ever since I joined

00:17:23.840 --> 00:17:28.677
Microsoft in the early '90s, was, "Why
the heck do I have a difference between

00:17:28.760 --> 00:17:32.517
a document, a website, and an
application?" Like, in you know, the his

00:17:32.600 --> 00:17:36.357
concept was there should just be one
thing, and it should I should be able to

00:17:36.440 --> 00:17:40.197
transform. And guess what? We finally
have it. Uh, which is app builder can

00:17:40.280 --> 00:17:44.037
you can go in and just specify the app
you want to build, and it'll build it.

00:17:44.120 --> 00:17:47.117
It's kind of like a document, it's kind
of like a website, it's kind of like an

00:17:47.200 --> 00:17:49.357
app, right? So, that's the power. Then

00:17:49.440 --> 00:17:54.477
Copilot Studio is another way to think
of it as build out of your agent. So,

00:17:54.560 --> 00:17:58.157
again, as a natural you're using
a natural language prompt, you can go

00:17:58.240 --> 00:17:59.957
build an agent, you can ground it in

00:18:00.040 --> 00:18:02.237
some data, uh, and you there you have

00:18:02.320 --> 00:18:05.277
it. You have an agent. Um, and so we're

00:18:05.360 --> 00:18:08.237
building out Foundry, in fact, without

00:18:08.320 --> 00:18:12.517
any cliffs to be able to then say,
"Okay, we now have agents. Can I even

00:18:12.600 --> 00:18:17.437
orchestrate multiple agents and build
fully agentic systems?" Microsoft

00:18:17.520 --> 00:18:20.917
Foundry allows you to do that as well,
because the multi-agent frameworks are

00:18:21.000 --> 00:18:25.357
there. All of the tools, uh,
that you need in order to be able to build

00:18:25.440 --> 00:18:27.717
multi-agentic systems are there.

00:18:27.800 --> 00:18:32.477
We also now, of course,
have all these models. We have 11,000

00:18:32.560 --> 00:18:36.557
models. You say, "Why Why do I need 11,000

00:18:36.640 --> 00:18:40.237
models?" In fact,
even for me this week, it is a

00:18:40.320 --> 00:18:43.077
great reminder of why having this

00:18:43.160 --> 00:18:45.797
diversity of models is going to really

00:18:45.880 --> 00:18:48.477
make this world a better place. In fact,

00:18:48.560 --> 00:18:51.317
we launched, uh, and we announced, uh,

00:18:51.400 --> 00:18:54.477
in Foundry, uh, as an open weight, uh,

00:18:54.560 --> 00:18:57.877
model, uh, a model called Giga Time. It

00:18:57.960 --> 00:19:02.317
There was an article published in Cell,
and quite frankly, it gave me goosebumps

00:19:02.400 --> 00:19:05.837
when I sort of read about
it and learned about it,

00:19:05.920 --> 00:19:09.557
uh, because it's work Microsoft did
with both the University of Washington as

00:19:09.640 --> 00:19:12.557
well as Providence Hospitals. Um, and

00:19:12.640 --> 00:19:15.117
what it does is, if you think about the

00:19:15.200 --> 00:19:18.477
challenge of someone with cancer,

00:19:18.560 --> 00:19:21.237
um, today, when you sort of try to

00:19:21.320 --> 00:19:23.117
understand whether immunotherapy will

00:19:23.200 --> 00:19:26.597
work, you kind of have to model the

00:19:26.680 --> 00:19:30.477
immune system, essentially.
And so, there's a test for that.

00:19:30.560 --> 00:19:33.117
The problem with that test is,

00:19:33.200 --> 00:19:35.917
uh, it's a complex test, it takes a long

00:19:36.000 --> 00:19:38.917
time, and it costs a lot of money.

00:19:39.000 --> 00:19:42.397
And so, what this particular model does

00:19:42.480 --> 00:19:46.637
is, it does a simulation of the test.

00:19:46.720 --> 00:19:49.117
So, you can literally take a simple

00:19:49.200 --> 00:19:54.597
pathology of the tumor, right? So,
literally like a microscope image of a a

00:19:54.680 --> 00:19:59.157
tumor. And then use
essentially GenAI to fill

00:19:59.240 --> 00:20:03.117
in. So, it will do the simulation

00:20:03.200 --> 00:20:05.797
of what is the

00:20:05.880 --> 00:20:08.437
basically spatial proteomics of the

00:20:08.520 --> 00:20:12.397
immune system and say, you now can have

00:20:12.480 --> 00:20:16.517
a test. So, I was thinking about it,
right? With that model,

00:20:16.600 --> 00:20:21.157
that means some entrepreneur
in Mumbai is going to pick it up

00:20:21.240 --> 00:20:22.437
and say, you know what? I'm going to

00:20:22.520 --> 00:20:27.077
build an entire sort of simulation lab

00:20:27.160 --> 00:20:28.837
in silico

00:20:28.920 --> 00:20:31.397
and make it available to every city in

00:20:31.480 --> 00:20:34.277
Maharashtra and in this country, right?

00:20:34.360 --> 00:20:37.397
So, every hospital can start doing what

00:20:37.480 --> 00:20:41.597
now today is done in the few tier one
hospitals and cost a lot of money. I

00:20:41.680 --> 00:20:44.997
mean, that's in some sense, when you're
playing the game, sometimes you forget

00:20:45.080 --> 00:20:48.797
why you're playing it. This is why we
are playing it. We are playing it so

00:20:48.880 --> 00:20:51.637
that we can actually use this innovation

00:20:51.720 --> 00:20:53.877
to make a real difference uh in the

00:20:53.960 --> 00:20:56.597
world. So, it's exciting to see uh how

00:20:56.680 --> 00:20:59.357
this is all coming together. Now, we're

00:20:59.440 --> 00:21:01.357
bringing, as I said, all this together

00:21:01.440 --> 00:21:03.797
in this tool chain. This tool chain is

00:21:03.880 --> 00:21:06.757
going to become what you will use. Uh in

00:21:06.840 --> 00:21:11.597
fact, GitHub is where you would start
interfacing with all of this app stack,

00:21:11.680 --> 00:21:13.037
right? So, if you think of the new app

00:21:13.120 --> 00:21:15.877
server as Foundry, uh the tools are

00:21:15.960 --> 00:21:19.077
Visual Studio and VS Code and GitHub.

00:21:19.160 --> 00:21:23.757
And and it Talking about GitHub,
it's really exciting to see the growth

00:21:23.840 --> 00:21:28.517
of GitHub in India. It's I mean, it's
tremendous. By 2030, India is expected

00:21:28.600 --> 00:21:34.350
to be the number one country in terms
of GitHub participation. [applause]

00:21:35.040 --> 00:21:36.837
You know, it's it's tremendous to see

00:21:36.920 --> 00:21:38.877
not only just the participation, but the

00:21:38.960 --> 00:21:42.317
but the ambition level of the projects

00:21:42.400 --> 00:21:46.157
uh coming out of India are just
continuing to improve and, you know, get

00:21:46.240 --> 00:21:48.717
better. And so,
it's fantastic to see that.

00:21:48.800 --> 00:21:53.557
Now, we're building on GitHub, by the way,
as Agent HQ. So, what I mean by

00:21:53.640 --> 00:21:55.437
that is you go to GitHub and you will

00:21:55.520 --> 00:21:58.117
have access to all the coding models

00:21:58.200 --> 00:22:03.197
that you can use in the context of your
repo. Uh and so, that's the idea. It's

00:22:03.280 --> 00:22:05.117
not about sort of having one model, but

00:22:05.200 --> 00:22:10.397
you have your code repo, just like Work
IQ, think of this as Code IQ, right? So,

00:22:10.480 --> 00:22:14.277
that's kind of really it. Like,
your code is your most important resource.

00:22:14.360 --> 00:22:18.757
Yeah, you want that. And then you want
many many models over the years to sort

00:22:18.840 --> 00:22:23.357
of keep working and creating more
of that repo uh and the repo features. And

00:22:23.440 --> 00:22:27.157
so, that's what And by the way, the form
factors, right? If people say, oh, wow,

00:22:27.240 --> 00:22:30.037
will I only use autonomous agents? Will

00:22:30.120 --> 00:22:35.237
I use only an IDE with agent mode? Will
I use a command line? Guess what? I use

00:22:35.320 --> 00:22:36.997
all three all day long, right? I mean,

00:22:37.080 --> 00:22:39.597
that's it. Like, the form factors

00:22:39.680 --> 00:22:42.477
uh of how I interface with AI are going

00:22:42.560 --> 00:22:45.477
to remain essentially always uh some

00:22:45.560 --> 00:22:50.277
IDE, some uh CLI interface, uh and a a

00:22:50.360 --> 00:22:53.637
new chat interface, if you will. And
that I think is the way you'll interact

00:22:53.720 --> 00:22:58.277
with it. But to show you all of this in
its full glory, I wanted to invite up on

00:22:58.360 --> 00:23:02.637
stage my colleague Karan.
Karan, take it away.

00:23:02.720 --> 00:23:06.117
>> [applause] >> Thanks, Satya.

00:23:06.200 --> 00:23:08.677
A few weeks ago at GitHub Universe, we

00:23:08.760 --> 00:23:11.757
announced Agent HQ, which is an agentic

00:23:11.840 --> 00:23:16.557
platform that unites every agent, making
them native to the GitHub flow you're

00:23:16.640 --> 00:23:18.477
already familiar with. So, let me show

00:23:18.560 --> 00:23:21.117
you a demo of what that looks like.

00:23:21.200 --> 00:23:26.117
All right. So, I have a demo app here
that's a storefront for some of the

00:23:26.200 --> 00:23:30.477
nostalgic toys, which might have
been common in many of our households.

00:23:30.560 --> 00:23:33.117
However, as you can see, the app doesn't

00:23:33.200 --> 00:23:35.757
really have any search or filter options

00:23:35.840 --> 00:23:38.357
and I want to add that functionality.

00:23:38.440 --> 00:23:41.077
So, I have my code repo here on GitHub.

00:23:41.160 --> 00:23:43.477
With the new agents panel, I can now

00:23:43.560 --> 00:23:46.277
kick off an agentic task from anywhere

00:23:46.360 --> 00:23:49.117
on GitHub by just describing the task

00:23:49.200 --> 00:23:53.917
and delegating it to the Copilot coding
agent. So, Copilot starts working on it

00:23:54.000 --> 00:23:55.557
in the background.

00:23:55.640 --> 00:23:57.717
I can assign a task to Copilot from

00:23:57.800 --> 00:24:01.277
anywhere I work on github.com, in VS

00:24:01.360 --> 00:24:05.717
Code, in the CLI, or even on my mobile.

00:24:05.800 --> 00:24:07.517
All right. While working across multiple

00:24:07.600 --> 00:24:10.757
repositories and tasks, I have delegated

00:24:10.840 --> 00:24:13.597
many of these tasks to Copilot and I

00:24:13.680 --> 00:24:15.757
want to know what's going on with it.

00:24:15.840 --> 00:24:18.677
So, now I can see all of my agent

00:24:18.760 --> 00:24:21.077
sessions in a single mission control

00:24:21.160 --> 00:24:25.717
view, both ongoing as well
as completed ones over here.

00:24:25.800 --> 00:24:27.597
Not just that, I can also start off a

00:24:27.680 --> 00:24:30.837
task from right here while using custom

00:24:30.920 --> 00:24:33.797
agents that I have created or have been

00:24:33.880 --> 00:24:36.637
built by partners. So, these custom

00:24:36.720 --> 00:24:39.757
agents are domain experts, which extend

00:24:39.840 --> 00:24:44.637
the Copilot coding agent across
all of your tools and workflows.

00:24:44.720 --> 00:24:48.837
All right. So, let's dive into the uh
session that we just kicked off and see

00:24:48.920 --> 00:24:51.437
what Copilot is working on. So, I can

00:24:51.520 --> 00:24:53.717
see the progress of Copilot here on the

00:24:53.800 --> 00:24:57.477
left and also the draft PR on the right.

00:24:57.560 --> 00:25:00.317
So, I'm a developer, so which means I am

00:25:00.400 --> 00:25:04.597
still in control. So, I can even steer a

00:25:04.680 --> 00:25:08.397
ongoing session midway to have Copilot

00:25:08.480 --> 00:25:12.037
work in a slightly different direction.

00:25:12.120 --> 00:25:15.917
So, this will take a few minutes,
so I'll let Copilot continue working on

00:25:16.000 --> 00:25:17.797
that in the background, but I want to

00:25:17.880 --> 00:25:20.477
show you one of the uh agent sessions I

00:25:20.560 --> 00:25:23.157
kicked off earlier for the same feature.

00:25:23.240 --> 00:25:25.077
So, I can see it's completed and then

00:25:25.160 --> 00:25:28.157
there's a summary as well. And oh, nice

00:25:28.240 --> 00:25:30.317
screenshots as well, thanks to the

00:25:30.400 --> 00:25:33.437
Playwright uh MCP. I can also take a

00:25:33.520 --> 00:25:36.477
look at uh the files it has changed over

00:25:36.560 --> 00:25:40.037
here and also ask it for any change, if

00:25:40.120 --> 00:25:42.237
I need, right over here.

00:25:42.320 --> 00:25:44.517
All right. Amidst all of this, if I want

00:25:44.600 --> 00:25:47.317
to have uh you know, a chat with Copilot

00:25:47.400 --> 00:25:49.957
to ask it something or so, I can jump

00:25:50.040 --> 00:25:52.717
into Copilot chat right here as well.

00:25:52.800 --> 00:25:57.037
So, Copilot provides me a choice
of various different models from different

00:25:57.120 --> 00:26:01.077
providers, including GPT-5.2,

00:26:01.160 --> 00:26:05.997
which just launched last night and already
available on GitHub Copilot. See

00:26:06.080 --> 00:26:08.517
how fast it is. And

00:26:08.600 --> 00:26:11.077
yeah. All right. Not just that, I can

00:26:11.160 --> 00:26:14.037
also bring in my own models from

00:26:14.120 --> 00:26:17.637
Microsoft Foundry. So, I have a few

00:26:17.720 --> 00:26:20.037
models that I've deployed on Microsoft

00:26:20.120 --> 00:26:22.797
Foundry, including a couple of them that

00:26:22.880 --> 00:26:25.597
I had fine-tuned myself and I have

00:26:25.680 --> 00:26:27.837
brought in all of these models to

00:26:27.920 --> 00:26:30.557
Copilot by connecting them to my

00:26:30.640 --> 00:26:33.117
deployments in Foundry.

00:26:33.200 --> 00:26:36.597
So, now I can actually use all of these

00:26:36.680 --> 00:26:39.837
models directly within Copilot, be it on

00:26:39.920 --> 00:26:41.917
GitHub or VS Code.

00:26:42.000 --> 00:26:44.517
Now, I was just doing something and I

00:26:44.600 --> 00:26:46.877
fine-tuned one of the model

00:26:46.960 --> 00:26:50.077
uh just for fun to see if I can converse

00:26:50.160 --> 00:26:53.077
almost like a developer, you know, in

00:26:53.160 --> 00:26:57.957
Mumbai. So, I just asked it something
simple saying, what is Kubernetes? Your

00:26:58.040 --> 00:27:02.477
pods, nodes, kuch samajh
mein nahi aata hai. And this

00:27:02.560 --> 00:27:06.757
is coming and it says, simple language
mein samjho. This is what containers

00:27:06.840 --> 00:27:11.797
are. This is what Kubernetes is.
And it's so awesome, right? Because this

00:27:11.880 --> 00:27:14.437
Yeah. This

00:27:14.520 --> 00:27:17.517
is coming from my own model that's

00:27:17.600 --> 00:27:19.517
deployed on Foundry, which I can use in

00:27:19.600 --> 00:27:22.277
Copilot, right? That's pretty awesome.

00:27:22.360 --> 00:27:24.557
All right. So, speaking of deployments

00:27:24.640 --> 00:27:29.837
and everything, um I had also created
a PR to implement a new product detail

00:27:29.920 --> 00:27:33.237
page uh you know,
sometime earlier and I can

00:27:33.320 --> 00:27:35.957
see that Copilot has already reviewed my

00:27:36.040 --> 00:27:39.597
PR and also left a few comments on it.

00:27:39.680 --> 00:27:41.517
So, let's take a look at some of the

00:27:41.600 --> 00:27:44.597
suggestions it has left, like this one

00:27:44.680 --> 00:27:47.237
here, which speaks about uh DB

00:27:47.320 --> 00:27:49.717
connection pool exhaustion or, you know,

00:27:49.800 --> 00:27:52.477
this one uh right here, which speaks

00:27:52.560 --> 00:27:54.717
about type safety.

00:27:54.800 --> 00:27:58.197
So, this is the new agentic Copilot code

00:27:58.280 --> 00:28:01.757
review, which combines LLM detections,

00:28:01.840 --> 00:28:06.277
tool calling, and also deterministic
scans that help you with smarter

00:28:06.360 --> 00:28:09.837
reviews. Not just that, I can actually ask

00:28:09.920 --> 00:28:12.837
Copilot to help me implement all of

00:28:12.920 --> 00:28:18.077
these suggestions in a single PR, which
will again kick off an agentic task for

00:28:18.160 --> 00:28:21.397
me. All right. You all know how important

00:28:21.480 --> 00:28:24.477
security is, right? So, thankfully, I'm

00:28:24.560 --> 00:28:26.917
also a security admin for this account

00:28:27.000 --> 00:28:30.197
on GitHub. So, with the new integration

00:28:30.280 --> 00:28:33.277
between Microsoft Defender for Cloud and

00:28:33.360 --> 00:28:35.917
GitHub Advanced Security, I can now

00:28:36.000 --> 00:28:38.037
specifically filter

00:28:38.120 --> 00:28:41.597
for runtime risk issues in production

00:28:41.680 --> 00:28:44.277
that have been identified by Defender.

00:28:44.360 --> 00:28:47.077
All right, right here. So, that's really

00:28:47.160 --> 00:28:49.917
nice and a campaign to address all of

00:28:50.000 --> 00:28:53.717
these alerts was created and oops, I see

00:28:53.800 --> 00:28:56.797
some of them in my repo as well, but not

00:28:56.880 --> 00:28:59.597
to worry because Copilot can also help

00:28:59.680 --> 00:29:02.877
me fix these security issues. So, I can

00:29:02.960 --> 00:29:06.517
just select all of these and then say,

00:29:06.600 --> 00:29:09.037
assign to Copilot. And then Copilot will

00:29:09.120 --> 00:29:12.397
go off and then create a PR to address

00:29:12.480 --> 00:29:15.157
all of these security issues.

00:29:15.240 --> 00:29:17.597
All right. You know, so now we have

00:29:17.680 --> 00:29:19.757
multiple different agents working on

00:29:19.840 --> 00:29:22.517
different tasks in the background, but I

00:29:22.600 --> 00:29:25.157
want to write some code myself as well.

00:29:25.240 --> 00:29:27.757
So, here is uh the agent session for the

00:29:27.840 --> 00:29:30.037
search and filter function that we were

00:29:30.120 --> 00:29:32.917
looking at earlier and um I want to make

00:29:33.000 --> 00:29:36.237
some changes to it. So, I can continue

00:29:36.320 --> 00:29:38.717
iterating with this by opening this

00:29:38.800 --> 00:29:42.597
directly in VS Code, which will open up

00:29:42.680 --> 00:29:45.197
the session for me. So, I can either

00:29:45.280 --> 00:29:48.237
check out the branch or even apply the

00:29:48.320 --> 00:29:51.197
changes directly in my current branch

00:29:51.280 --> 00:29:53.637
and I can continue working. So, I will

00:29:53.720 --> 00:29:56.997
open up the familiar chat interface here

00:29:57.080 --> 00:30:01.757
in VS code and I'll
switch to my agent mode

00:30:01.840 --> 00:30:03.757
probably use one of the models I

00:30:03.840 --> 00:30:06.917
deployed from foundry and I will ask it

00:30:07.000 --> 00:30:09.837
to make one small change, which is I

00:30:09.920 --> 00:30:14.437
also want an auto complete
feature for the search bar as well.

00:30:14.520 --> 00:30:16.357
So, this can continue working in the

00:30:16.440 --> 00:30:19.437
background and you would have seen that

00:30:19.520 --> 00:30:23.717
just like on github.com, I also have a

00:30:23.800 --> 00:30:27.477
mission control view in VS code as well,

00:30:27.560 --> 00:30:30.077
where I can see all of my local agents,

00:30:30.160 --> 00:30:35.357
my cloud agents, my background
agents, which is running as well.

00:30:35.440 --> 00:30:39.197
All right. So, I want
to implement a new feature.

00:30:39.280 --> 00:30:41.477
The toys are great, but I want to make a

00:30:41.560 --> 00:30:44.237
bundle of it and almost gift them as a

00:30:44.320 --> 00:30:46.437
play box. I know many of you would love

00:30:46.520 --> 00:30:48.917
that as well, right? But, I need to plan

00:30:49.000 --> 00:30:51.837
how to go about implementing it. So,

00:30:51.920 --> 00:30:54.997
with the new plan agent in VS code, I

00:30:55.080 --> 00:30:57.917
can ask Copilot to create an

00:30:58.000 --> 00:31:00.357
implementation plan and also keep

00:31:00.440 --> 00:31:03.797
iterating with it until I am confident

00:31:03.880 --> 00:31:07.477
and ready with it.
So, I had created one of

00:31:07.560 --> 00:31:11.957
you know, this plan earlier as well,
which I'll just open up right over here.

00:31:12.040 --> 00:31:14.357
So, you can see it gives me a detailed

00:31:14.440 --> 00:31:16.517
plan for all the steps that needs to be

00:31:16.600 --> 00:31:19.717
accomplished. So, whenever I'm ready, I

00:31:19.800 --> 00:31:23.837
can kind of continue coding this or I

00:31:23.920 --> 00:31:26.877
can hand it off to an agent as well to

00:31:26.960 --> 00:31:30.037
work on it in the background. So, I can

00:31:30.120 --> 00:31:33.277
hand it off to the CLI agent and I will

00:31:33.360 --> 00:31:35.957
say include the changes. That's fine and

00:31:36.040 --> 00:31:39.477
this will start working on this agent in

00:31:39.560 --> 00:31:43.277
the background. All right.
So, some of this will take a

00:31:43.360 --> 00:31:48.037
bit of time to complete. So, meanwhile,
if when all of this task would have been

00:31:48.120 --> 00:31:51.557
completed, let's see how
our app would look like.

00:31:51.640 --> 00:31:54.917
So, it would look like something like
that with all of the filters that are

00:31:55.000 --> 00:32:00.157
there and also my search and auto
complete feature and I can go start

00:32:00.240 --> 00:32:04.327
playing my brick game. All right.

00:32:04.410 --> 00:32:07.717
>> [applause] >> So,

00:32:07.800 --> 00:32:10.117
in the past few minutes, we went from so

00:32:10.200 --> 00:32:13.797
many different ideas, planned them and

00:32:13.880 --> 00:32:16.757
also coded them using Copilot coding

00:32:16.840 --> 00:32:20.317
agents in the cloud on github.com, in VS

00:32:20.400 --> 00:32:23.677
code, in background using CLI agents,

00:32:23.760 --> 00:32:26.357
had that code reviewed with the Copilot

00:32:26.440 --> 00:32:29.557
code review agent and also implemented

00:32:29.640 --> 00:32:33.157
the security fixes using Copilot auto

00:32:33.240 --> 00:32:36.597
fix agent. All the while having

00:32:36.680 --> 00:32:38.917
ability to look at all of my agent

00:32:39.000 --> 00:32:41.677
sessions, steer them in the single

00:32:41.760 --> 00:32:44.917
mission control view on GitHub or even

00:32:45.000 --> 00:32:48.517
in VS code. And that is Agent HQ. Thank

00:32:48.600 --> 00:32:52.280
you. Back to you, Satya. >> [applause]

00:32:54.280 --> 00:32:58.237
>> Thank you so much, Karan. And so,
you know, when you look at all of these

00:32:58.320 --> 00:33:00.557
tools that Karan showed, you know, what

00:33:00.640 --> 00:33:02.477
does it do? It inspires you. So,

00:33:02.560 --> 00:33:05.277
recently we had Thanksgiving in the

00:33:05.360 --> 00:33:07.837
United States. So, we had some time off.

00:33:07.920 --> 00:33:12.637
And so, what does one do when you have
time off? You build. And so, I built an

00:33:12.720 --> 00:33:15.717
app. And so,
this is by the way, this is my

00:33:15.800 --> 00:33:19.157
regular laptop you are seeing. So, this

00:33:19.240 --> 00:33:22.917
is um in fact, my Azure subscription.

00:33:23.000 --> 00:33:24.317
And

00:33:24.400 --> 00:33:27.597
this is in fact my app that's deployed

00:33:27.680 --> 00:33:31.517
right now, I think in in Canada Central.

00:33:31.600 --> 00:33:33.717
This is my repo. This is my GitHub repo.

00:33:33.800 --> 00:33:37.197
So, this is my code.

00:33:37.280 --> 00:33:39.157
In fact, the way my standard sort of way

00:33:39.240 --> 00:33:43.197
I work is I have a Windows 365

00:33:43.280 --> 00:33:46.957
setup. So, it travels with me everywhere.
And then, of course, I use

00:33:47.040 --> 00:33:50.677
GitHub. And then I use code spaces.
So, so this is I think it'll show up with

00:33:50.760 --> 00:33:54.197
the code space here. So, it's kind
of like turtles all the way down, right?

00:33:54.280 --> 00:33:57.837
So, I have my code space running
on GitHub, running on Windows 365. So,

00:33:57.920 --> 00:34:00.837
that's kind of my virtual
environment that I travel with.

00:34:00.920 --> 00:34:04.677
And of course, like as Karan
was showing, every morning I get up

00:34:04.760 --> 00:34:08.197
and my favorite place to go is here,
right? Which is I go to my repo and I go

00:34:08.280 --> 00:34:12.317
to my you know, Copilot
and start assigning it

00:34:12.400 --> 00:34:17.637
tasks. Like every day there's some
multiple agents I kick off. Like this

00:34:17.720 --> 00:34:21.317
morning I said, "Please upgrade this
entire app to GPT-52." Right? So, and

00:34:21.400 --> 00:34:24.277
it's actually done and when I'm done
with my keynote and I'm on a flight

00:34:24.360 --> 00:34:26.437
back, I'll kind of get there

00:34:26.520 --> 00:34:28.877
on my flight Wi-Fi and be able to

00:34:28.960 --> 00:34:31.957
actually look at the PR and push it.

00:34:32.040 --> 00:34:35.477
If you will. In fact, I'll test it
once with my code spaces and then I'll

00:34:35.560 --> 00:34:38.237
commit. But, that's in fact, it shows

00:34:38.320 --> 00:34:39.437
you

00:34:39.520 --> 00:34:43.517
the power of how you can literally start

00:34:43.600 --> 00:34:46.077
really taking simple tasks to big

00:34:46.160 --> 00:34:48.717
updates and start powering them powering

00:34:48.800 --> 00:34:51.917
through them. So, what did I build? Um

00:34:52.000 --> 00:34:53.677
So, what I built

00:34:53.760 --> 00:34:58.677
is essentially a new deep research tool.

00:34:58.760 --> 00:35:02.957
You know, one of my life's ambition
is to figure out how to get a job in the

00:35:03.040 --> 00:35:06.837
Copilot team. And so,
I'm preparing for it. I'm sort

00:35:06.920 --> 00:35:11.357
of desperately trying how
to get competent enough to get

00:35:11.440 --> 00:35:15.717
hired. And so, therefore, I said, "Okay,
let me build a new more sophisticated

00:35:15.800 --> 00:35:18.957
deep research application."
And so, the what the thing

00:35:19.040 --> 00:35:22.837
that I did is you know,
I said, "Look, there are all

00:35:22.920 --> 00:35:25.717
these models available. So, why don't I

00:35:25.800 --> 00:35:29.917
create decision frameworks that work
on top of the model?" And one of those

00:35:30.000 --> 00:35:32.357
decision frameworks is Andrej Karpathy,

00:35:32.440 --> 00:35:37.077
who was a famous AI researcher,
came up with this LLM council, he called

00:35:37.160 --> 00:35:41.077
it. Right? So, the idea is you
will have like like a selection

00:35:41.160 --> 00:35:42.597
committee, you will have multiple

00:35:42.680 --> 00:35:44.837
members of the committee, right? So, in

00:35:44.920 --> 00:35:48.517
this case, I can have GPT-51 or 52

00:35:48.600 --> 00:35:53.077
today, Claude, Gemini, Lama, you know,
Grok, whatever, right? Pick your

00:35:53.160 --> 00:35:57.677
favorite selection committee. Then you
elect even a chairman. Like you can say,

00:35:57.760 --> 00:36:01.797
"Who is the chairman?
Is it Claude or Gemini or

00:36:01.880 --> 00:36:03.797
you know, GPT or what have you?" Pick

00:36:03.880 --> 00:36:06.557
your chair and then go ahead and issue a

00:36:06.640 --> 00:36:09.197
query and ask it to do stuff.

00:36:09.280 --> 00:36:12.077
And then another one I did was

00:36:12.160 --> 00:36:14.437
DXO. This is a pretty cool decision

00:36:14.520 --> 00:36:19.717
framework. This is comes out of health
care. In fact, we first implemented In

00:36:19.800 --> 00:36:24.317
fact, we've implemented it in health
care, where you have just different

00:36:24.400 --> 00:36:26.437
roles. So, for example, one role is a

00:36:26.520 --> 00:36:29.197
lead researcher, who does Let think of

00:36:29.280 --> 00:36:32.597
it as exhaustive breadth first research.

00:36:32.680 --> 00:36:36.117
There's a critical reviewer. And the
And by the way, you can assign different

00:36:36.200 --> 00:36:39.197
models to these different roles. So,
I have a lead researcher that happens to

00:36:39.280 --> 00:36:42.917
be Claude, a critical reviewer that
happens to be a GPT. And what does a

00:36:43.000 --> 00:36:45.677
critical reviewer do? They

00:36:45.760 --> 00:36:47.917
That particular role finds method

00:36:48.000 --> 00:36:51.677
problems, right? Say, recency bias or

00:36:51.760 --> 00:36:56.397
period bias, whatever are the issues
which are cognitive biases.

00:36:56.480 --> 00:37:00.357
Then you can have domain expert,
data analyst, so different roles. And In

00:37:00.440 --> 00:37:05.157
fact, when we applied DXO in health care,
it turned out that having multiple

00:37:05.240 --> 00:37:09.797
roles work together in a multi-agent
system outperformed any single model.

00:37:09.880 --> 00:37:13.037
Right? It's intuitive, right? Why not
have all the smart people working with

00:37:13.120 --> 00:37:16.037
you on a particular decision versus one.

00:37:16.120 --> 00:37:18.997
And so, that was DXO. Then I implemented

00:37:19.080 --> 00:37:21.477
even another one called ensemble.

00:37:21.560 --> 00:37:23.637
And the idea with ensemble is you sort

00:37:23.720 --> 00:37:26.277
of fire off parallel queries,

00:37:26.360 --> 00:37:28.637
anonymize the responses, so there is no

00:37:28.720 --> 00:37:31.157
bias even in selection

00:37:31.240 --> 00:37:33.797
and then synthesize, right? In fact,

00:37:33.880 --> 00:37:35.797
last night I said, "Well, let's even

00:37:35.880 --> 00:37:38.837
implement a regular old debate." So,

00:37:38.920 --> 00:37:42.967
this is a new feature I added
and thank God it's showed up here.

00:37:43.050 --> 00:37:46.077
>> [laughter] >> And basically,
think of it as chain of

00:37:46.160 --> 00:37:50.037
debate. In fact, I I think of all
of these as instead of thinking of them as

00:37:50.120 --> 00:37:53.477
chain of thought, thinking of all
of this as chain of debate. And I said,

00:37:53.560 --> 00:37:56.917
"Let's actually

00:37:57.000 --> 00:38:00.597
have a full-on debate." So, the bottom
line here is you can have different

00:38:00.680 --> 00:38:02.917
formats of you know, even debates. You

00:38:03.000 --> 00:38:07.757
can have pros and cons, SWOT. You can
have risk versus impact, counterfactual

00:38:07.840 --> 00:38:11.877
versus devil's advocate, right? So,
you can even pick your form framework. You

00:38:11.960 --> 00:38:15.957
can have critiques. So, each sort of each
person makes an argument, everybody

00:38:16.040 --> 00:38:20.037
else critiques. You can have multiple
rounds of it. So, I was just kind of I

00:38:20.120 --> 00:38:23.117
had a lot of time, I guess,
in my hotel room yesterday. But,

00:38:23.200 --> 00:38:26.917
so I went on and added features.
Like So, I said, "Okay, let's have" Oh, by

00:38:27.000 --> 00:38:30.277
the way, I even added because I was
spending so many tokens, I said, "Man, I

00:38:30.360 --> 00:38:33.837
might as well actually add cost control
to my thing." And so, I said, "Do I need

00:38:33.920 --> 00:38:36.437
fast lower cost or deep higher cost?"

00:38:36.520 --> 00:38:40.117
I'm picking balanced here.
I can pick the different research

00:38:40.200 --> 00:38:44.477
analyst, system architect, risk officer,
three types of roles, and then have

00:38:44.560 --> 00:38:47.077
multiple rounds.
And so, you get the point.

00:38:47.160 --> 00:38:49.797
So, of course, you build all this,

00:38:49.880 --> 00:38:52.557
what does one good old South Asian do?

00:38:52.640 --> 00:38:55.877
the best ever Indian test cricket team.

00:38:55.960 --> 00:38:59.997
I think we need one. And so,
if I go to my history, you'll

00:39:00.080 --> 00:39:03.317
see it. Like I've been at it.
I've been working at it. In fact, I have a

00:39:03.400 --> 00:39:08.917
fantastic MLB lineup as well.
But, since we are here in Mumbai,

00:39:09.000 --> 00:39:10.117
I'll show you some of the stuff, right?

00:39:10.200 --> 00:39:14.597
So, I'll show you the council one.
I'll show you

00:39:14.680 --> 00:39:17.557
what it came back with.
In fact, let me kind of

00:39:17.640 --> 00:39:21.797
increase the font here. So, this is the
chairman's synthesis. It shows me, you

00:39:21.880 --> 00:39:23.917
know, obviously Sunil opens with Sehwag,

00:39:24.000 --> 00:39:27.277
Rahul, Sachin, Virat's A man, it picked

00:39:27.360 --> 00:39:31.157
So, this is the synthesis and it shows
VVS at six and seven and then it goes

00:39:31.240 --> 00:39:32.837
on. Here's the interesting thing, right?

00:39:32.920 --> 00:39:37.437
The chairman's sort of report says
that there was unanimous essentially

00:39:37.520 --> 00:39:40.997
consensus, right? Gavaskar, Sehwag,
Dravid, Tendulkar, Kohli, Kapil Dev,

00:39:41.080 --> 00:39:43.277
Ashwin, Bumrah. But, the big debate, of

00:39:43.360 --> 00:39:48.797
course, was, you know, do you need an
additional batsman or not? And thank God

00:39:48.880 --> 00:39:51.677
they selected, at least being
a Hyderabadi, I'm really proud that they

00:39:51.760 --> 00:39:53.197
made

00:39:53.280 --> 00:39:57.077
VVS Laxman in there. Um and here's the

00:39:57.160 --> 00:39:59.677
interesting thing. So, he waited 5-1 and

00:39:59.760 --> 00:40:01.597
Claude's inclusion. I mean, think about

00:40:01.680 --> 00:40:04.757
this. These two models bid for my man

00:40:04.840 --> 00:40:07.637
Laxman, right? Because uh why? Of his

00:40:07.720 --> 00:40:09.917
crisis management. And sort of made the

00:40:10.000 --> 00:40:14.917
point about why. Uh and then the other
debate was who's the captain? Um in

00:40:15.000 --> 00:40:18.357
fact, I think Oh, by the way, the Kumble
versus Zaheer was also a very good one,

00:40:18.440 --> 00:40:20.997
right? Which is uh it sort of chose Anil

00:40:21.080 --> 00:40:25.477
versus uh Zaheer. Zaheer is going to be
12th man. Um you know, depends, right? I

00:40:25.560 --> 00:40:28.637
I guess that's where I think there's
more work to be done. I'll pick Zaheer

00:40:28.720 --> 00:40:32.437
some days and sometimes um uh
depending on where where you're

00:40:32.520 --> 00:40:34.637
playing. Captaincy debate, you know, it

00:40:34.720 --> 00:40:36.797
picked uh Kohli. So, this

00:40:36.880 --> 00:40:41.157
the key thing is I see the annotations

00:40:41.240 --> 00:40:43.797
uh of every the chain of debate and it

00:40:43.880 --> 00:40:46.597
gives me insight. Um in fact, if I go

00:40:46.680 --> 00:40:50.317
back um to show you one other case, let

00:40:50.400 --> 00:40:53.157
me go back to my history again.

00:40:53.240 --> 00:40:56.837
Uh let's see. Let me go to the

00:40:56.920 --> 00:41:01.037
uh the DXO view. I think this is
interesting because the DXO view is

00:41:01.120 --> 00:41:03.477
slightly different, right? Uh it says

00:41:03.560 --> 00:41:06.717
here is what the lead researcher did. Um

00:41:06.800 --> 00:41:09.117
then it says here's what the critical

00:41:09.200 --> 00:41:11.357
reviewer did. But here's the cool thing.

00:41:11.440 --> 00:41:14.557
Here Look at the way it found the

00:41:14.640 --> 00:41:17.717
biases, the error bias. Right? So, the

00:41:17.800 --> 00:41:20.597
critical reviewer's job was to say,

00:41:20.680 --> 00:41:25.797
"Okay, when I'm selecting something,
are you mixing up your stats?" Right? I

00:41:25.880 --> 00:41:28.597
mean, like I always say, like, you know,
sometimes you could say, "Wow, the

00:41:28.680 --> 00:41:32.277
fitness levels of the modern teams are
so great and wonderful and what have

00:41:32.360 --> 00:41:36.717
you?" But man, like to watch Gavaskar
go and bat without a helmet or you know,

00:41:36.800 --> 00:41:40.397
even a I don't know
um Hazare go and uh you

00:41:40.480 --> 00:41:45.317
know, uncovered wicket in play uh
in England. Like, how do you really

00:41:45.400 --> 00:41:49.557
equate for that, right? So, that's the
error bias. And so, to me, to be able to

00:41:49.640 --> 00:41:54.917
kind of have all these models debate
that issue uh is what this is all about.

00:41:55.000 --> 00:41:56.397
So, anyway, so you get the point, right?

00:41:56.480 --> 00:41:57.357
Which is

00:41:57.440 --> 00:42:02.157
I You can use these decision frameworks

00:42:02.240 --> 00:42:06.997
as the new form of meta cognition,
right? So, if you have all this

00:42:07.080 --> 00:42:08.557
abundance

00:42:08.640 --> 00:42:12.077
of models, the ability to literally do

00:42:12.160 --> 00:42:14.877
what I did, which is just build your own

00:42:14.960 --> 00:42:17.037
multi-agent system, right? That's the

00:42:17.120 --> 00:42:22.117
new commodity. I mean, the reality is,
you know, this is lit- all of us are

00:42:22.200 --> 00:42:26.277
going to be doing this work just like
how we do spreadsheets or documents.

00:42:26.360 --> 00:42:30.397
And if we have that power, then the
question is what are we going to do with

00:42:30.480 --> 00:42:34.317
it? And one of the things I think
is exercise better judgment, better

00:42:34.400 --> 00:42:37.317
decision. And in high-stakes situations,

00:42:37.400 --> 00:42:39.717
in health care, uh in financial

00:42:39.800 --> 00:42:43.477
services, in insurance, in supply chain.

00:42:43.560 --> 00:42:47.077
And that's the beauty of it, right?
To be able to think about these as meta

00:42:47.160 --> 00:42:49.997
cognition frameworks for us to apply

00:42:50.080 --> 00:42:54.237
better judgment uh in a world where
we have all of this. One of the other

00:42:54.320 --> 00:42:58.077
things I should mention is I built all
this. I even made it in fact a co-pilot.

00:42:58.160 --> 00:43:00.277
So, this is all in my Azure private

00:43:00.360 --> 00:43:04.077
tenant. Um and I So, I then did a you

00:43:04.160 --> 00:43:07.237
know, basically an uh an open API uh to

00:43:07.320 --> 00:43:09.357
it. Uh and so then I made it into a

00:43:09.440 --> 00:43:11.677
co-pilot agent. And then I said, "Great,

00:43:11.760 --> 00:43:14.317
let me go deploy." And guess what? You

00:43:14.400 --> 00:43:19.517
know, I was caught. So, in other words,
the Microsoft DLP data you know, loss

00:43:19.600 --> 00:43:23.437
prevention came in and said,
"You can't deploy it uh because

00:43:23.520 --> 00:43:25.997
uh you this has to be deployed inside

00:43:26.080 --> 00:43:27.837
the Microsoft tenant. You just can't

00:43:27.920 --> 00:43:30.517
point it to an external agent uh because

00:43:30.600 --> 00:43:33.037
that's a massive exfil problem." And

00:43:33.120 --> 00:43:35.757
that is thanks to this other part, which

00:43:35.840 --> 00:43:38.157
I think is the runtime of agents we have

00:43:38.240 --> 00:43:41.197
built called Agent 365. And Agent 365

00:43:41.280 --> 00:43:45.357
takes our identity
management, our Defender,

00:43:45.440 --> 00:43:50.077
our things like Purview and data loss
prevention, all of that and brings it to

00:43:50.160 --> 00:43:53.637
the agent world. Just like what we
have done for end-user computing, if you

00:43:53.720 --> 00:43:55.357
will, we're now doing it for agents.

00:43:55.440 --> 00:43:58.717
Because if people are going to build
powerful agents like the one I built,

00:43:58.800 --> 00:44:02.917
you want full compliance, you want full
visibility, you want full governance of

00:44:03.000 --> 00:44:06.637
it. And that's what the Agent 365 does,
right? Because you can't just run around

00:44:06.720 --> 00:44:11.037
and say, "I have thousands and hundreds
of thousands of agents without that

00:44:11.120 --> 00:44:13.437
security and governance and visibility."

00:44:13.520 --> 00:44:15.077
And that's really

00:44:15.160 --> 00:44:18.277
uh what uh Agent 365 delivers.

00:44:18.360 --> 00:44:20.677
You know, the the beauty of all this is

00:44:20.760 --> 00:44:23.557
there is significant momentum,

00:44:23.640 --> 00:44:26.717
significant ambition level to translate

00:44:26.800 --> 00:44:30.957
this ultimately into people doing
unbelievable things. So, this morning I

00:44:31.040 --> 00:44:32.797
had a chance to meet with, for example,

00:44:32.880 --> 00:44:35.117
the folks from Adani Cement uh who are

00:44:35.200 --> 00:44:38.037
really doing this invoice to payment uh

00:44:38.120 --> 00:44:40.277
and reduce something that took 20 plus

00:44:40.360 --> 00:44:45.077
days to 4 hours. Uh I had a chance
to meet the team from Aditya Birla where

00:44:45.160 --> 00:44:48.957
they took their mobile app that's
doing complex products and sort of said,

00:44:49.040 --> 00:44:52.917
"Okay, here's a conversational interface
that just makes it simpler for them to

00:44:53.000 --> 00:44:56.917
be able to interact." I had a chance
to meet with the folks at LTI Mindtree.

00:44:57.000 --> 00:44:58.717
They're doing a complete wall-to-wall

00:44:58.800 --> 00:45:01.197
transformation using things from

00:45:01.280 --> 00:45:04.557
co-pilot to co-pilot studio to a foundry

00:45:04.640 --> 00:45:07.117
to do every business process and relook

00:45:07.200 --> 00:45:09.277
at it from an AI-first perspective.

00:45:09.360 --> 00:45:11.957
Mankind, it's a beautiful use case of

00:45:12.040 --> 00:45:15.237
really putting that a power of AI and

00:45:15.320 --> 00:45:20.077
the expertise in the hands of the people
who are use the med- medical reps who

00:45:20.160 --> 00:45:23.517
are visiting doctors
in rural areas to be able to

00:45:23.600 --> 00:45:25.357
make sure that you can the medical rep

00:45:25.440 --> 00:45:28.397
is confident in answering the questions

00:45:28.480 --> 00:45:31.117
and then improving care outcomes.

00:45:31.200 --> 00:45:35.837
Yes Bank has done a fantastic job
of really the invoice turnaround times,

00:45:35.920 --> 00:45:38.597
right? So, they're also doing
a wall-to-wall transformation with 40 plus

00:45:38.680 --> 00:45:41.677
use cases. But really seeing the ROI in

00:45:41.760 --> 00:45:46.157
each one of these examples. Now,
one of the chances I had again was

00:45:46.240 --> 00:45:47.757
to meet

00:45:47.840 --> 00:45:52.037
uh with the team that built this new

00:45:52.120 --> 00:45:56.077
agentic system called the Maha Crime OS.

00:45:56.160 --> 00:46:00.117
Uh so, this is was sponsored by uh
the Chief Minister of Maharashtra who is

00:46:00.200 --> 00:46:03.960
here with us. >> [applause] >> And

00:46:06.480 --> 00:46:11.197
And it was really great to meet
uh both the SP as well as

00:46:11.280 --> 00:46:15.557
uh the people in the team uh
and in fact, the investigative team.

00:46:15.640 --> 00:46:17.957
Uh and the pride they had

00:46:18.040 --> 00:46:21.157
in in being able to use this system

00:46:21.240 --> 00:46:24.997
to deliver sort of justice to a citizen

00:46:25.080 --> 00:46:27.837
in Nagpur who was

00:46:27.920 --> 00:46:30.197
uh unfortunately a victim of a crime.

00:46:30.280 --> 00:46:35.037
But the ability for them to use this
agentic system to speed up the time to

00:46:35.120 --> 00:46:40.640
justice was just fantastic to see.
So, let's go ahead and play the video.

00:46:40.810 --> 00:46:42.830
>> [music]

00:46:45.200 --> 00:46:51.207
>> Today, policing is all about cyber
crime. It is all about financial frauds.

00:46:51.290 --> 00:46:54.557
>> [music] >> And it is global.

00:46:54.640 --> 00:46:57.077
Nagpur rural receives about 30 cyber

00:46:57.160 --> 00:47:00.077
crime reports every single day. So, you

00:47:00.160 --> 00:47:04.237
can imagine the kind of influx of cyber
crime reporting that that's taking

00:47:04.320 --> 00:47:08.380
place. >> [music]

00:47:12.910 --> 00:47:14.930
[music]

00:47:17.490 --> 00:47:19.510
[music]

00:47:29.280 --> 00:47:31.300
[music]

00:47:32.280 --> 00:47:34.877
>> Maha Crime OS serves
as an investigation

00:47:34.960 --> 00:47:38.117
co-pilot. You're able to have an agent

00:47:38.200 --> 00:47:42.677
that is able to detect the patterns,
able to make sense of it, and have it

00:47:42.760 --> 00:47:46.797
speak to the direction
of the investigation itself.

00:47:46.880 --> 00:47:49.677
The Maharashtra government through
Marvels has collaborated with both

00:47:49.760 --> 00:47:51.957
Microsoft as well as Cyber Eye in terms

00:47:52.040 --> 00:47:55.877
of adapting Crime OS for the needs of

00:47:55.960 --> 00:47:57.997
Maharashtra. Ethical [music] and

00:47:58.080 --> 00:48:01.197
responsible AI for public good is our

00:48:01.280 --> 00:48:04.597
motto. And Microsoft has provided us a

00:48:04.680 --> 00:48:09.360
very safe and advanced platform.

00:48:13.820 --> 00:48:15.840
>> [music]

00:48:19.060 --> 00:48:21.080
[music]

00:48:23.000 --> 00:48:25.917
>> Maha Crime OS AI is built using you

00:48:26.000 --> 00:48:31.117
know, multiple technologies. Underlying
this is Azure and AI services through

00:48:31.200 --> 00:48:34.697
Microsoft Foundry. We're also using
Defender for for cloud and Microsoft

00:48:34.780 --> 00:48:37.437
[music] Fabric.

00:48:37.520 --> 00:48:41.907
With Maha Crime OS AI,
the turnaround times of investigations are

00:48:41.990 --> 00:48:44.957
>> [music] >> decreased
by 80%. Almost 100% of

00:48:45.040 --> 00:48:49.317
investigations are getting digitally
registered and investigators are acting

00:48:49.400 --> 00:48:53.180
upon. >> [music]

00:48:54.320 --> 00:48:55.997
>> So, that makes me really proud that

00:48:56.080 --> 00:48:58.957
we're able to adapt emerging technology

00:48:59.040 --> 00:49:03.517
to fulfill the needs of several
millions of of people across

00:49:03.600 --> 00:49:05.997
>> [music] >> the state
and make government function

00:49:06.080 --> 00:49:08.517
much more effectively and transparently

00:49:08.600 --> 00:49:11.797
for them. If AI is properly used in

00:49:11.880 --> 00:49:17.640
governance, we can make
the lives of people easier.

00:49:25.220 --> 00:49:27.240
>> [music]

00:49:33.730 --> 00:49:35.750
[music]

00:49:40.000 --> 00:49:43.317
>> It's [applause] It's a great
example, quite frankly, of

00:49:43.400 --> 00:49:46.437
using the power of all this technology

00:49:46.520 --> 00:49:48.997
to help citizens of this country.

00:49:49.080 --> 00:49:53.157
And more importantly, it also sort
of is a great example of how quickly this

00:49:53.240 --> 00:49:56.517
technology is diffusing, right? I mean,
if you would have thought about any

00:49:56.600 --> 00:50:00.717
previous era of tech and the
rate at which it would come to

00:50:00.800 --> 00:50:04.877
sort of impact in a context like this
versus where we are. And that I think is

00:50:04.960 --> 00:50:08.397
the opportunity in front of us.
So, it's fantastic to

00:50:08.480 --> 00:50:10.637
see. So, the last piece I want to talk

00:50:10.720 --> 00:50:13.117
about is powering all of this, of

00:50:13.200 --> 00:50:16.077
course, ultimately is the

00:50:16.160 --> 00:50:18.317
infrastructure, right? You have got to

00:50:18.400 --> 00:50:21.557
run essentially a token factory.

00:50:21.640 --> 00:50:25.157
And when it comes to token factory, one
of the most important things is this one

00:50:25.240 --> 00:50:28.797
simple equation, yet very
very important, which is

00:50:28.880 --> 00:50:31.677
performance per tokens

00:50:31.760 --> 00:50:36.717
is all about tokens
per dollar or per rupee

00:50:36.800 --> 00:50:41.477
per watt. Right? You have to optimize.

00:50:41.560 --> 00:50:43.237
And And this, by the way, is going to be

00:50:43.320 --> 00:50:45.837
the most important thing, right? Whether

00:50:45.920 --> 00:50:49.197
any country, any community, any state

00:50:49.280 --> 00:50:51.517
that is able to sort of really have the

00:50:51.600 --> 00:50:56.357
infrastructure that delivers
this efficient frontier of the token

00:50:56.440 --> 00:50:58.397
factory is just going to have more

00:50:58.480 --> 00:51:01.037
abundance of tokens that can then be

00:51:01.120 --> 00:51:05.717
deployed to drive the productivity,
which directly is going to be correlated

00:51:05.800 --> 00:51:08.757
to the GDP growth in the economy. And so,

00:51:08.840 --> 00:51:11.157
to that end, we are all in, right? We

00:51:11.240 --> 00:51:13.477
are building out Azure as the world's

00:51:13.560 --> 00:51:16.317
computer. We are investing in building

00:51:16.400 --> 00:51:19.157
out Azure in India. We now have multiple

00:51:19.240 --> 00:51:23.557
regions. In fact, we right in in in
Maharashtra, we have a Central India

00:51:23.640 --> 00:51:25.157
region in Pune.

00:51:25.240 --> 00:51:28.237
We have West India in Mumbai. We have

00:51:28.320 --> 00:51:30.837
South India in Chennai. We have

00:51:30.920 --> 00:51:34.477
a DC with in partnership with Jio.

00:51:34.560 --> 00:51:39.277
We are also very very excited about sort
of our new South Central region that is

00:51:39.360 --> 00:51:44.077
going to come out in Hyderabad next year,
which is going to be on the next big

00:51:44.160 --> 00:51:47.997
data center region.
And we keep expanding. And to that end,

00:51:48.080 --> 00:51:52.077
I'm really By the way, the the region in
Hyderabad is going to be 100% renewable,

00:51:52.160 --> 00:51:57.037
right? So, that's the other cool thing is
we are really focusing on sustainability

00:51:57.120 --> 00:51:59.997
as well. >> [applause]

00:52:00.080 --> 00:52:05.117
>> And And to us,
we are also building all of

00:52:05.200 --> 00:52:09.637
this with the making
sure that there's real

00:52:09.720 --> 00:52:13.717
sovereignty. Because after all, at the
end of the day, this is not just about

00:52:13.800 --> 00:52:18.757
bringing the capabilities, but it's also
ensuring that there is real sovereignty

00:52:18.840 --> 00:52:22.477
controls. And so, for that, we have
a full set of options, right? You can use

00:52:22.560 --> 00:52:26.277
public cloud. You can use public cloud
with sovereign controls. That means you

00:52:26.360 --> 00:52:30.517
have you know, you're the key management
locally. You can use confidential

00:52:30.600 --> 00:52:34.357
computing in concert with it. So, that
means when the data is being processed,

00:52:34.440 --> 00:52:37.357
even in use, at rest, in flight, it's

00:52:37.440 --> 00:52:39.877
encrypted and the keys are with you.

00:52:39.960 --> 00:52:42.757
We also have the private cloud option.

00:52:42.840 --> 00:52:44.637
We also obviously have a region that's

00:52:44.720 --> 00:52:47.197
operated by local partners like Jio or

00:52:47.280 --> 00:52:50.397
NIC and others. And so, we have a full

00:52:50.480 --> 00:52:55.157
set of options. So, think of it
as a portfolio that you can then

00:52:55.240 --> 00:52:57.317
use that portfolio for the different

00:52:57.400 --> 00:52:59.557
workloads you have and have the

00:52:59.640 --> 00:53:02.677
assurance of your own sovereignty and

00:53:02.760 --> 00:53:06.637
control over it. We are also doing,
in fact, sovereign data control. So,

00:53:06.720 --> 00:53:10.717
Copilot and all of the things we talked
about are all being locally processed.

00:53:10.800 --> 00:53:15.597
So, none of the data is going
outside of the the data boundary of

00:53:15.680 --> 00:53:18.637
India. And so, we're
very excited about sort of

00:53:18.720 --> 00:53:21.397
all of these options being available.

00:53:21.480 --> 00:53:23.877
Now, one other point I would also make,

00:53:23.960 --> 00:53:25.997
since we talked about sort of crime OS

00:53:26.080 --> 00:53:28.517
and and what have you, is

00:53:28.600 --> 00:53:31.877
one of the biggest challenges is for any

00:53:31.960 --> 00:53:36.237
online infrastructure
is cyber and cyber risk

00:53:36.320 --> 00:53:40.917
and cyber security. And it's
an intelligence game, right?

00:53:41.000 --> 00:53:44.597
And ultimately, whoever has the best
intelligence is going to be able to

00:53:44.680 --> 00:53:48.677
defend you the best. And so,
Microsoft sees trillions of

00:53:48.760 --> 00:53:52.437
signals every day. We see every
nation-state and every cybercriminal

00:53:52.520 --> 00:53:56.597
everywhere in the world.
And we are able to harness that to

00:53:56.680 --> 00:53:59.877
protect. In fact, in the demo current
showed was pretty cool, right? Which is

00:53:59.960 --> 00:54:03.917
he basically was able to take
your production sort of instance, have

00:54:04.000 --> 00:54:08.237
Defender check in real time what are all
the vulnerabilities, and then have you

00:54:08.320 --> 00:54:10.037
even go back and fix those

00:54:10.120 --> 00:54:11.997
vulnerabilities. And that's not a static

00:54:12.080 --> 00:54:15.397
list. That is a dynamic list that is

00:54:15.480 --> 00:54:17.757
sort of being monitoring essentially

00:54:17.840 --> 00:54:19.757
what is the cyber activity and the cyber

00:54:19.840 --> 00:54:22.837
attack sort of graph. And so, to me,

00:54:22.920 --> 00:54:25.717
really thinking about sovereignty

00:54:25.800 --> 00:54:28.917
and cyber resilience are two sides of

00:54:29.000 --> 00:54:33.077
risk, right? So, when you think about
even you managing your risk, you want to

00:54:33.160 --> 00:54:35.797
think about it. Because if you get
disconnected, you have a sovereign

00:54:35.880 --> 00:54:40.157
everything, except it doesn't
have the best intelligence,

00:54:40.240 --> 00:54:41.957
it's sort of you're going to be very

00:54:42.040 --> 00:54:44.157
very vulnerable. And so, therefore, it's

00:54:44.240 --> 00:54:47.397
a much more nuanced discussion that

00:54:47.480 --> 00:54:51.437
needs real thought. And then, at the end
end of the day, it's not a one-time

00:54:51.520 --> 00:54:53.997
decision. It's a continuous monitoring

00:54:54.080 --> 00:54:58.677
of that portfolio of risk and opportunity.
So, to that end, we are

00:54:58.760 --> 00:55:03.077
very very committed to this country. And
therefore, one of the things that I was

00:55:03.160 --> 00:55:06.757
excited to have a chance is meet
the Prime Minister earlier this week and

00:55:06.840 --> 00:55:12.040
even announce the biggest announcement.

00:55:13.030 --> 00:55:16.437
>> [applause] >> It's It's
our biggest investment in

00:55:16.520 --> 00:55:20.077
Asia, 17 and a half billion,
which builds on top of the 3 billion we

00:55:20.160 --> 00:55:22.717
announced earlier in the year, in fact.

00:55:22.800 --> 00:55:24.837
And so, we are obviously very excited to

00:55:24.920 --> 00:55:27.437
bring all of this infrastructure to

00:55:27.520 --> 00:55:30.797
empower ultimately all the citizens of

00:55:30.880 --> 00:55:34.557
this country, all the developers of this
country. And so, that's where I want to

00:55:34.640 --> 00:55:37.277
close out with, which is to us,

00:55:37.360 --> 00:55:41.917
all this is about empowering every
person and every organization in India

00:55:42.000 --> 00:55:43.677
to achieve more.

00:55:43.760 --> 00:55:46.437
To us, you know, whether it is

00:55:46.520 --> 00:55:48.997
you know, things like gigatime,

00:55:49.080 --> 00:55:52.237
which allow us to think about

00:55:52.320 --> 00:55:57.677
making what is personalized care or even

00:55:57.760 --> 00:56:00.477
precision oncology possible in every

00:56:00.560 --> 00:56:03.397
tier two city in this country because

00:56:03.480 --> 00:56:06.077
they can an entrepreneur here will take

00:56:06.160 --> 00:56:11.197
an advance like that and parlay it
into health outcomes that matter.

00:56:11.280 --> 00:56:14.717
Or the Maha crime OS that's going to

00:56:14.800 --> 00:56:16.477
deliver

00:56:16.560 --> 00:56:19.717
the justice to someone who was a victim

00:56:19.800 --> 00:56:22.957
of some crime in Nagpur. To us,

00:56:23.040 --> 00:56:26.277
ultimately, that's why we do what we do.

00:56:26.360 --> 00:56:28.677
All of us working together to realize

00:56:28.760 --> 00:56:33.357
the opportunity ahead of us in this age
of AI is an exciting time. I hope you

00:56:33.440 --> 00:56:37.957
all have high ambition for what you can
do. We sure do for you all. Thank you

00:56:38.040 --> 00:56:41.510
all very very much. >> [applause]

00:56:45.800 --> 00:56:49.757
>> India has more than 400 million
unorganized workers. They're earning on

00:56:49.840 --> 00:56:53.517
the day-to-day wages and not
getting any [music] benefits.

00:56:53.600 --> 00:56:56.797
We need to shift the people from the
informal to the formal sector. [music]

00:56:56.880 --> 00:56:59.957
That would help us to develop our economy.
Through the initiative of

00:57:00.040 --> 00:57:04.517
e-Shram, the national unorganized
workers database was developed and we

00:57:04.600 --> 00:57:08.237
are able to provide those workers
[music] with social security benefits,

00:57:08.320 --> 00:57:10.397
also giving them access to the national

00:57:10.480 --> 00:57:12.997
career service portal. NCS is a one-stop

00:57:13.080 --> 00:57:17.677
solution for job seekers, employers,
training providers, and counselors. We

00:57:17.760 --> 00:57:21.157
have AI-based matching between
[music] the job seekers' skills and the

00:57:21.240 --> 00:57:23.957
employers. So, any informal worker can

00:57:24.040 --> 00:57:28.677
go to the NCS [music] portal and match
with the right employer. NCS has an AI

00:57:28.760 --> 00:57:32.237
resume builder. And if a person
is lacking certain skills, the [music] AI

00:57:32.320 --> 00:57:36.717
tool helps him to develop those
skills by giving them recommendations.

00:57:36.800 --> 00:57:39.997
We were expecting millions
of unorganized workers to register in this

00:57:40.080 --> 00:57:44.677
system. [music] So, we wanted to have
scalability, reliability, and security.

00:57:44.760 --> 00:57:48.357
Microsoft Azure met all these
qualifications. In addition [music] to

00:57:48.440 --> 00:57:54.640
that, Azure Open AI has become
a game-changer for unorganized workers.

00:58:00.050 --> 00:58:02.070
>> [music]

00:58:11.000 --> 00:58:13.237
>> Around 150 million workers are now on

00:58:13.320 --> 00:58:15.557
the NCS portal. We are also [music] now

00:58:15.640 --> 00:58:20.277
trying to bring in more and more
schemes to benefit these workers. AI is

00:58:20.360 --> 00:58:23.997
supporting us to provide opportunities
for the individuals and youth of the

00:58:24.080 --> 00:58:26.310
country. [music]