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[Music]

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SPEAKER 1: We find ourselves
in a new era, one where access

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to information is access
to expertise, from the farm

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to the lab,
from the boardroom to the classroom.

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Now anyone can save time
with a personal assistant.

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SPEAKER 2: By our measurements,
the introduction

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of the AI has meant that it now takes
about 40% less time to access data.

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SPEAKER 3: We've had operational staff
that write policies and procedures,

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and that normally takes
between two and three weeks,

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and they've found a way to do
that within two to three days.

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SPEAKER 1: This is a new way
to analyze with a personal coach.

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SPEAKER 4: AI Insights
gives us the ability

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to supercharge someone's
knowledge of the game,

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make them feel part of a community
and part of the sport.

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SPEAKER 5: Cognates is a Socratic
tutoring support system

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that allows us to have
a 24-hour tutor in every subject.

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SPEAKER 1: AI is unlocking
creativity for us all.

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SPEAKER 6: Descriptions are so detailed in
my imagination, I can paint the artwork.

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SPEAKER 7: One of the biggest pain points

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about the creative process
is just getting those ideas

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out of your head, and it gave
me four different examples.

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I was able to draw inspiration from that
and create an embroidered product based

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on these initial prompts. SPEAKER 1:
And we are just getting started.

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[Music]

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SATYA NADELLA: Right. Good morning.

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Good morning.

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It's fantastic to be back
in Sydney and in Australia.

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This morning, I got up,
and then I tried to sort of go

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down memory lane, thanks
to Copilot, and try and get back

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to the first time I showed up in Sydney
for one of the developer conferences.

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And it was, I think, in 1993,

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and I remember distinctly
getting through the airport

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with my luggable Compaq computers,

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and the conference was all about
-- we had introduced, I think,

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for the first time back then,
Visual Basic for Applications.

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That was like we replaced
the macros in Excel 4.

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Excel 5 had come out,
and we had Visual Basic.

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And I distinctly remember,
in fact, having a session.

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This time around, there a few
more people for my session,

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and then I remember
going to one of the banks

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and having a big discussion
on going from macros

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to Visual Basic -- is it
a good thing, or what have you?

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But little did I know that
whatever 30 odd, 35 years nearly

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after that, the hardest thing
in tech is, again, guess what?

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Agents in Excel and or
command line interfaces

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for coding agents. I mean, it's
fascinating that as the frontier moves,

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so does the arrival back
to where you started,

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to sort of use that T.S. Eliot metaphor.

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To me, I want to spend most
of my conversation today

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and time today to talk a little
bit about that new frontier,

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and how are we all collectively
going to push that forward

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with essentially what is a new input?

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The outcomes that matter still
remain very much the same, right?

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When we think about any
organization, any institution,

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even individually, what we
care about is the excellence

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in outcomes, right, which
is how do we show up and change

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that engagement with customers,
or the employee experience,

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or the efficiency with which
any given business process gets

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completed, or even bending
the curve in innovation, right?

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Whether it's software
development or drug discovery,

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how are we going to truly change that?

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And that's sort of the frontier
that we are trying to move.

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And if you look back at it, right,

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throughout these transitions
that we've had in technology,

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a lot of this has come down
to us changing the way we work

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with technology, but more than
the way we work with technology,

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how technology reshapes the work we do.

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That's sort of been the story
of sort of the movement

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on the frontier,
and it starts, quite frankly,

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with that mindset, right, that curiosity,

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the posture that we need to have
that when there's something new,

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you go all in; you learn it.

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That means you really need
to think about your skills.

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You need the new tools. And perhaps
one of the most interesting things

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and important things that we'll talk about
in the AI era is codifying the outcome,

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perhaps even more robustly going
forward into these evals, right?

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If you think about AI, anyone who's
sort of doing applied AI starts by sort

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of with this mantra called
the product is the eval

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and that hill-climbing on evals, right?

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And that's that subtle, nuanced way all

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of us judge what great looks
like, good looks like inside

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of your organization for any
outcome becomes the real

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currency through which
we can then change the use

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of technology and have it reshape
our organizational frontier.

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And so to that end, we are building
this end-to-end tech stack, right?

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It's got three parts to it,
and for the rest of my talk,

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I want to sort of unpack
each of these in some detail,

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and the true line across all
of this is going to be two things:

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intelligence and trust.

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Because ultimately, you really
want the intelligence to show

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through across each layer of the stack

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and amplify what you're
doing, move that frontier,

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move that outcome,
but you really need trust

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because it quickly can become
vicious cycle versus a virtuous cycle

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if you really don't have
intelligence and trust.

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And so we'll sort of talk through
how those all come together.

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So let's start with sort
of the top of the stack,

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which is around these high-value scenarios

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and high-value applications
or agentic systems.

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And for us, it starts with the way
we are architecting Copilot.

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If you think about Copilot,
whatever, three years ago or so,

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we started with mostly
a chat-based response,

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request-response type
of pattern, and especially

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with GPT-4, it became a new way for us

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to discover knowledge inside
the organization or on the web.

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And then subsequently,
we had reasoning models come

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out that sort of helped us
not just have essentially new

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search, but we had
reasoning plus search, right?

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Even the chat interface started to pretty
drastically change with reasoning models.

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Then we started getting better
at sort of long-run agents

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that you could send tool-calling.

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And so that started sort
of really the beginning of agents

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like Researcher and Deep
Research and what have you.

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So the first thing we started
doing was to say, okay,

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let's take even chat
and that reasoning and make sure

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that you have access
to all the models, right?

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So in fact, when I use
Copilot today, I'm mostly sort

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of doing Auto, and Auto
is picking the best model

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for my intent, and then I have
a dropdown with all the models available.

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And that is sort of how the chat
interface itself has evolved, right?

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You used to be, in the beginning,
you used to have a router,

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but now the router
is becoming smarter and smarter

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in being able to sort of do
the intent classification,

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pick the right models for the intents.

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Next, we've now taken that idea
of having multiple models

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and put them even
in the context of agents.

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So if you go to Researcher,
you now have these new modalities.

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For example, you have
a modality called Critique,

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and I love that a lot because,
after all, how do we work with people?

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We critique each other and our ideas.

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That's the way you work with even models.

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In fact, another mode
that I like a lot is Counsel,

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which is essentially
assigning a particular task

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or a research topic to a group,
and then you get the best

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of all the models come together.

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It's fantastic to see it if you
have not used it to sort of look

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at both the models,
not only doing critique,

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but also finding biases in each
model, and then, in fact,

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insights from each model, right?

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So when even the research
report comes back,

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you gain deeper insights because that you
have multiple models at your disposal.

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Now, the thing that I'm really excited

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about is this next big thing
that we've done, and in fact,

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today's the general availability
of agent mode across Word,

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Excel, and PowerPoint. Yeah. [Cheers]

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Yeah, it's kind of like 1993 again.

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And it's actually, in some sense,
all of my life's work is right here.

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And the reason I say
that is because if you think

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about what's happened is Excel
became a super popular tool,

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because it helped us intuitively
get number sense, right?

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It had representational
power in the canvas, right?

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The rows and columns have
information content, right?

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As soon as you see a spreadsheet,

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you kind of make sense of what
that spreadsheet is, right?

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That's why the canvas has lasted
this many decades, in fact.

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And so now think about
what agent mode does.

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What agent mode does is it takes
that information in the sheet,

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the positional or spatial information
representation, and then mixes it

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with essentially these powerful
reasoning models and the ability

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to take action -- not just
reason, but take action.

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And so when you put that together,
it's magic, right,

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which is you now are creating a very
-- you're instructing the model

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to create a very sophisticated
model, an Excel model.

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And then when it creates
the model, it's represented

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in this canvas, which you know
how to even spatially inspect,

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and you know what's driving what.

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What's next to what? And then you
can then further reason about it.

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And in fact, you can take
that entire reasoning trace,

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plus the sheet, plus the model,
and share it, right?

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I mean, think about
the cognitive work inside the

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company and how it's getting reshaped.

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It has familiarity of a spreadsheet,

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but everything around it is changed.

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And that, I think, is when you know you
have a powerful sort of set of technology.

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And so we're really, really
excited about this agent mode.

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And of course, we're not
stopping there, right?

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The next thing that is also
built into Copilot now is Cowork,

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the ability to now assign
a synchronous task in a queue

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to Cowork, which, by the way,

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could be creating a Word
document or an Excel spreadsheet

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or what have you, and that then
you can open up in agent mode

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and round trip this entire way of working.

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It does feel like when we move
from faxes and interoffice memos

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to emails and spreadsheets and documents
floating around the enterprise.

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It feels like that time
again where the way we work,

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the artifacts we create,
the workflows that we are involved in,

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are fundamentally going
through a sea change.

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And to kind of give you a little bit of a
flavor for this Copilot ecosystem at work,

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I wanted to introduce my colleague, Nirja.

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Nirja, take it over.
NIRJA TRIVEDI: Thanks, Satya.

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[Applause] Hello, everyone.

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I am so excited to be here with you
all in Sydney, and I'm even more excited

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to show you the latest
Microsoft 365 capabilities

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that Satya was just talking
about, including Cowork,

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multimodality,
and using agentic experiences

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in the apps you use every single day.

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We have a lot to show you. So I'm going
to get started, starting with Cowork.

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So Cowork lets you build out assets

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in parallel using Work IQ data.

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Work IQ is more than just work content.

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It is the intelligence layer
that powers Copilot and the apps

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that you use every single day.

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It makes sure that it
understands your reasoning

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and understands your decisions,
the workflows,

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the people that you talk to, so your work
is tailored and personalized to you.

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With Cowork, you also
have access to a breadth

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of different prompts,
like organize my inbox,

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organize my week, prep for a meeting,
and research a company.

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So really a range of different prompts.

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If I scroll down even more,
you see that I have access

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to all the lists of tasks that are running
as well, the ones that have been completed

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and that I can reference the assets being
made, like this Excel doc, as well as ones

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that need user input for me to continue.

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And then finally,
the ones that are in progress

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that I can slam my laptop
shut and then come back,

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and Cowork will still keep working.

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So for this demo, I'm going
to use Cowork to help me build

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out the version 2 of my product launch.

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To do that, Cowork is going
to look through some of my assets.

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It's going to analyze a recap
email that was sent earlier,

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and then build out four
different assets in parallel.

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The first one that it's going to build out
is a launch budget using a previous launch

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that I already had. It's going to take
some assumptions from that launch as well.

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The second thing it's going
to do is that it's going

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to create a new product pitch deck for me,

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using a technical
specification doc that I have.

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And then I also want it to include
that second version launch budget as well.

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Lastly, I wanted to mobilize
my team by sending a chat message

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to my v-Team with highlights
of the news and schedule a meeting

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00:15:22,740 --> 00:15:25,327
with two of my stakeholders
this afternoon.

231
00:15:25,410 --> 00:15:29,617
Okay, that was quite a bit
of different things I want Cowork to do.

232
00:15:29,700 --> 00:15:32,977
Awesome. It hasn't even started yet.

233
00:15:33,060 --> 00:15:37,267
But I wanted to do four different things,
requiring many different skillsets.

234
00:15:37,350 --> 00:15:40,597
To get us all on the same page,
it's going to build me a launch budget.

235
00:15:40,680 --> 00:15:43,357
It's going to create a product
pitch deck, send a message

236
00:15:43,440 --> 00:15:45,547
to my team, and schedule a meeting.

237
00:15:45,630 --> 00:15:52,147
All right, let's get started. As Cowork
gets to work, you'll see on the right side

238
00:15:52,230 --> 00:15:55,057
that I can track its
progress as it continues,

239
00:15:55,140 --> 00:15:57,787
as well as all the tasks
that it has as well.

240
00:15:57,870 --> 00:16:01,777
So the ones that I went over,
like the email recap as well

241
00:16:01,860 --> 00:16:05,497
as the budget,
the pitch deck, all of those.

242
00:16:05,580 --> 00:16:08,407
And then on the chat pane,
you can also see its chain

243
00:16:08,490 --> 00:16:11,077
of thought as it goes
through each of the tasks.

244
00:16:11,160 --> 00:16:13,867
And then finally,
it tells me exactly the summary

245
00:16:13,950 --> 00:16:17,947
of all four tasks, again,
that are running in parallel.

246
00:16:18,030 --> 00:16:19,477
Now, this is sped up for the sake of time,

247
00:16:19,560 --> 00:16:23,197
but you could see here that it
also includes the first action

248
00:16:23,280 --> 00:16:28,267
item, and this is the message, the Teams
message that will be sent to my team.

249
00:16:28,350 --> 00:16:32,617
Again, I always have access
to review before anything is sent.

250
00:16:32,700 --> 00:16:38,137
Be it a Teams message, an invite,
an email -- I can always review.

251
00:16:38,220 --> 00:16:40,357
All right, this looks good to me.

252
00:16:40,440 --> 00:16:44,887
I can also interrupt Cowork.
So let's say, like we always do,

253
00:16:44,970 --> 00:16:47,587
we think of a last-minute
task as we're working.

254
00:16:47,670 --> 00:16:51,967
I can send that to Cowork. For example, I
want to send an email with an exec summary

255
00:16:52,050 --> 00:16:53,827
with the budget recommendations.

256
00:16:53,910 --> 00:16:55,687
I realize that I've sent many messages,

257
00:16:55,770 --> 00:16:58,927
but I haven't really
included that net new asset.

258
00:16:59,010 --> 00:17:01,207
Cowork will work on sending that email,

259
00:17:01,290 --> 00:17:03,727
but it won't stop
the previous list of tasks.

260
00:17:03,810 --> 00:17:06,457
Everything will continue moving forward.

261
00:17:06,540 --> 00:17:10,297
And then you can see here that
that next step is already ready.

262
00:17:10,380 --> 00:17:13,087
It's found some time for me
to meet with my stakeholders,

263
00:17:13,170 --> 00:17:16,297
and this looks great to me,
so I'm going to hit "Create" there,

264
00:17:16,380 --> 00:17:19,687
and I just have a few
more assets to continue.

265
00:17:19,770 --> 00:17:23,167
On the right side, you can see
that my progress bar is continuing.

266
00:17:23,250 --> 00:17:29,947
I have four out of six items done, as well
as different output folders as well.

267
00:17:30,030 --> 00:17:32,797
Finally, the last task is ready.

268
00:17:32,880 --> 00:17:37,987
It has that email, again with
that launch budget attached to,

269
00:17:38,070 --> 00:17:39,667
and that looks good for me to send.

270
00:17:39,750 --> 00:17:45,247
That's the last thing. And then just
a few moments, the task is complete.

271
00:17:45,330 --> 00:17:49,147
So you'll see here on the right
side, I can verify all the tasks

272
00:17:49,230 --> 00:17:52,507
that are complete,
all the folders that have the output

273
00:17:52,590 --> 00:17:57,847
of all the tasks that I have,
finally the input folders as well.

274
00:17:57,930 --> 00:18:01,957
Let's dive in to the first
asset and see how it looks.

275
00:18:02,040 --> 00:18:08,227
All right, not bad. This is the pitch
deck that was ingesting that tech-spec doc

276
00:18:08,310 --> 00:18:12,247
as well as the launch budget. So you
could see here, there's an exec summary.

277
00:18:12,330 --> 00:18:14,577
It's really beautifully designed.

278
00:18:14,660 --> 00:18:21,237
It also includes my brand colors as well
as the version 2 opportunity as well.

279
00:18:21,320 --> 00:18:24,267
And then finally, it includes
a bit of that launch budget as well.

280
00:18:24,350 --> 00:18:27,817
So this is a net new creation by Cowork.

281
00:18:27,900 --> 00:18:31,177
All right, that looks good.
Now that I have the first asset,

282
00:18:31,260 --> 00:18:35,347
let me run over to my Teams
meeting to see if any

283
00:18:35,430 --> 00:18:39,577
of my stakeholders have
responded to my chat.

284
00:18:39,660 --> 00:18:41,407
And I see a couple of them
have started to respond.

285
00:18:41,490 --> 00:18:45,367
So here's that chat message.
I see a few of them are responding,

286
00:18:45,450 --> 00:18:48,637
and let's check this one out. So one
of my team members said this is great,

287
00:18:48,720 --> 00:18:53,017
but I want to know more about
the improvements of v1 versus v2.

288
00:18:53,100 --> 00:18:57,277
Makes sense. Good question.
So I could either go to Copilot,

289
00:18:57,360 --> 00:18:59,827
compare the two technical
specification docs,

290
00:18:59,910 --> 00:19:03,427
copy and paste it back into
Teams, or I can use Teams

291
00:19:03,510 --> 00:19:06,307
for what it's used for, a canvas to work

292
00:19:06,390 --> 00:19:08,947
with both my collaborators
as well as my agents.

293
00:19:09,030 --> 00:19:14,287
So I'm going to do just that. I'm going
to go up, and I'm going to add agents.

294
00:19:14,370 --> 00:19:19,657
And so I want to add Copilot
to this response, and I'm going

295
00:19:19,740 --> 00:19:22,327
to add Copilot to this chat.

296
00:19:22,410 --> 00:19:24,817
I can at mention Copilot
as well as any of the agents

297
00:19:24,900 --> 00:19:31,687
that you saw up there, and I can ground it
on the specification doc and click "Send."

298
00:19:31,770 --> 00:19:36,607
Within a few moments, Copilot will
pull in the right responses a lot richer

299
00:19:36,690 --> 00:19:38,767
than if I quickly gave the answer myself,

300
00:19:38,850 --> 00:19:42,187
or if I had to go back
and copy and paste it from chat.

301
00:19:42,270 --> 00:19:46,687
So you can see here, it's a great
response, and this looks good to me.

302
00:19:46,770 --> 00:19:51,727
I did notice there was one last
stakeholder that had some news,

303
00:19:51,810 --> 00:19:53,737
and this is actually
really good news for me.

304
00:19:53,820 --> 00:19:56,677
She says there's about 100k
underspend this quarter,

305
00:19:56,760 --> 00:19:59,197
and she wants to put that
towards the marketing budget.

306
00:19:59,280 --> 00:20:02,737
You don't see that a lot,
100 extra k, so this is awesome.

307
00:20:02,820 --> 00:20:05,517
So I'm going to go back to Cowork,
and I'm going to figure

308
00:20:05,600 --> 00:20:08,187
out what am I going to do
with this extra budget?

309
00:20:08,270 --> 00:20:12,777
So here I am back at Cowork with that
first task that Cowork completed for me.

310
00:20:12,860 --> 00:20:17,937
That was the Excel doc. So I'm going
to open that up, that Excel sheet,

311
00:20:18,020 --> 00:20:21,687
and right when I open it up,
I want to call it a couple things.

312
00:20:21,770 --> 00:20:24,387
The first thing is right
when I open up the sheet,

313
00:20:24,470 --> 00:20:26,937
I see that it's automatically
saved into my OneDrive,

314
00:20:27,020 --> 00:20:29,907
so I don't need to go and find
it in the task if I need to.

315
00:20:29,990 --> 00:20:31,857
The second thing that I
want to call out is

316
00:20:31,940 --> 00:20:35,947
that it's also applying
the security label as well.

317
00:20:36,030 --> 00:20:39,517
So here I can see that it
has the right security label

318
00:20:39,600 --> 00:20:41,587
that is using the underlying assets.

319
00:20:41,670 --> 00:20:44,887
So Satya mentioned trust
and security are part of everything

320
00:20:44,970 --> 00:20:46,747
that we do, and you see that here as well,

321
00:20:46,830 --> 00:20:51,127
using that underlying
confidentiality label.

322
00:20:51,210 --> 00:20:53,437
And now I'm going to go
and activate Copilot

323
00:20:53,520 --> 00:20:56,047
so we could use it in the
agentic apps that we use.

324
00:20:56,130 --> 00:20:59,347
So here I have access
to multimodal functionality.

325
00:20:59,430 --> 00:21:04,447
I could choose whichever model I want for
the job, including our latest GPT models,

326
00:21:04,530 --> 00:21:07,357
as well as our latest Claude models.

327
00:21:07,440 --> 00:21:09,157
And then I'm going to have
Copilot help me figure

328
00:21:09,240 --> 00:21:12,337
out what am I going to do
with that extra 100k?

329
00:21:12,420 --> 00:21:15,697
So as I click "Send,"
Copilot is going to help me build

330
00:21:15,780 --> 00:21:18,517
out a dashboard with clear
charts and visuals,

331
00:21:18,600 --> 00:21:21,847
and it's also going to show me
clear recommendations about how

332
00:21:21,930 --> 00:21:26,167
to distribute across
the marketing tactics as well.

333
00:21:26,250 --> 00:21:29,287
And as it starts building out,
I can see on the right side

334
00:21:29,370 --> 00:21:31,957
that I can build the dashboard,
creating new sheets

335
00:21:32,040 --> 00:21:33,817
with recommendations and charts.

336
00:21:33,900 --> 00:21:36,577
And I can also see its
chain-of-thought reasoning

337
00:21:36,660 --> 00:21:39,817
as it goes through this analysis.

338
00:21:39,900 --> 00:21:42,667
And then finally,
I get this beautiful image

339
00:21:42,750 --> 00:21:49,207
with pivot tables, Excel native charts, as
well as a full in-depth analysis here too.

340
00:21:49,290 --> 00:21:56,137
So if I scroll down, I can see here
a rich analysis, including key numbers.

341
00:21:56,220 --> 00:21:59,497
It tells me exactly what
each dashboard contains.

342
00:21:59,580 --> 00:22:03,967
And then finally, it tells me
what the core strategy is as well.

343
00:22:04,050 --> 00:22:08,167
And if I go back to the grid,
I can click on any single cell,

344
00:22:08,250 --> 00:22:10,597
and I have the ability to edit a formula

345
00:22:10,680 --> 00:22:15,007
or change any single formula
as well without breaking any

346
00:22:15,090 --> 00:22:18,067
of the grid because it's Excel native.

347
00:22:18,150 --> 00:22:22,927
So we went through quite a few different
things today, four different things.

348
00:22:23,010 --> 00:22:27,727
We were able to build a completely new
pitch deck with using Excel imagery.

349
00:22:27,810 --> 00:22:30,697
We were also able to build
a completely new Excel sheet

350
00:22:30,780 --> 00:22:33,937
and then finally mobilize
my team as well through a series

351
00:22:34,020 --> 00:22:36,457
of Teams messages as well as Outlook.

352
00:22:36,540 --> 00:22:38,437
Now, I hope this gets you excited

353
00:22:38,520 --> 00:22:41,347
because you could see this is
just one way that Copilot fits

354
00:22:41,430 --> 00:22:46,087
into your workflow, but I hope you can see
how it fits into your workflow as well.

355
00:22:46,170 --> 00:22:48,967
Now, I hope this gets you
excited to use Cowork right

356
00:22:49,050 --> 00:22:53,377
in the Copilot app and Copilot
in the apps that you use every single day.

357
00:22:53,460 --> 00:22:54,437
Thank you.

358
00:22:54,520 --> 00:23:01,337
[Applause]

359
00:23:01,420 --> 00:23:02,447
SATYA NADELLA: Thank you so much, Nirja.

360
00:23:02,530 --> 00:23:06,817
It's so wonderful to see
even the evolution of Copilot

361
00:23:06,900 --> 00:23:08,977
in just the last few years, right?

362
00:23:09,060 --> 00:23:13,507
I mean, in the first instance,
it was a bit of a UI on a model,

363
00:23:13,590 --> 00:23:17,347
but now you can think
of even the Copilot system

364
00:23:17,430 --> 00:23:24,430
as essentially a cognitive loop
that or a harness that you have

365
00:23:24,810 --> 00:23:29,677
at your disposal for you
to be able to have more agency,

366
00:23:29,760 --> 00:23:33,127
more ambition on what you want
to create, how you want to,

367
00:23:33,210 --> 00:23:37,897
in some sense, initiate
the trajectory of a task.

368
00:23:37,980 --> 00:23:42,637
And that, I think, is what you
see in all of those features,

369
00:23:42,720 --> 00:23:45,607
and then it's also becoming
quickly multiplayer, right?

370
00:23:45,690 --> 00:23:50,077
So you're not just single-player
mode, but when you're in Teams,

371
00:23:50,160 --> 00:23:53,587
you're interacting with not just Copilot,
but all the other agents as well.

372
00:23:53,670 --> 00:23:56,017
And that, I think, is what is going

373
00:23:56,100 --> 00:24:00,907
to probably unfold very rapidly
throughout this calendar year.

374
00:24:00,990 --> 00:24:03,967
Now, we are seeing fantastic momentum.

375
00:24:04,050 --> 00:24:07,087
The great news is this is not
about sort of this rate

376
00:24:07,170 --> 00:24:09,787
of diffusion here happening slowly.

377
00:24:09,870 --> 00:24:15,127
It's in some sense happening all very
fast, everywhere, at the same time.

378
00:24:15,210 --> 00:24:18,757
In Australia, we've now
got some fantastic momentum

379
00:24:18,840 --> 00:24:23,107
around Copilot usage across
all of the top companies

380
00:24:23,190 --> 00:24:24,637
that are represented on this slide.

381
00:24:24,720 --> 00:24:30,877
I think 17 of the 20 top companies in the
exchange here are using Copilot deeply.

382
00:24:30,960 --> 00:24:32,647
All the banks are using it.

383
00:24:32,730 --> 00:24:36,937
Multiple. I think there is 57
government agencies using it.

384
00:24:37,020 --> 00:24:38,887
So it's fantastic to see the momentum,

385
00:24:38,970 --> 00:24:43,237
and I had a chance yesterday
to meet with this team

386
00:24:43,320 --> 00:24:48,337
at Australia Post that is
sort of looking at Copilot

387
00:24:48,420 --> 00:24:51,967
and the adoption of Copilot,
and it's a complete bottoms-up way.

388
00:24:52,050 --> 00:24:54,937
They have all these Copilot
champions and team captains

389
00:24:55,020 --> 00:25:00,007
who are really making sure
that everyone is trained on it

390
00:25:00,090 --> 00:25:01,897
and is getting the best out of it.

391
00:25:01,980 --> 00:25:07,357
But the thing that stood out
for me is how much care they're sort

392
00:25:07,440 --> 00:25:11,317
of taking in making accessibility one

393
00:25:11,400 --> 00:25:15,217
of the big beneficiaries of Copilot.

394
00:25:15,300 --> 00:25:17,617
In other words, they've used Copilot

395
00:25:17,700 --> 00:25:22,327
and its native technology,
but it's really driving everyone

396
00:25:22,410 --> 00:25:25,987
with all the abilities to be
able to fully participate

397
00:25:26,070 --> 00:25:28,567
and give them more agency
inside of the enterprise,

398
00:25:28,650 --> 00:25:31,807
inside of a Teams
conversation, in a meeting,

399
00:25:31,890 --> 00:25:35,317
inside of being able
to sort of use Copilot

400
00:25:35,400 --> 00:25:39,127
to really get things done
in a much more seamless way,

401
00:25:39,210 --> 00:25:41,197
and it was fantastic to see that.

402
00:25:41,280 --> 00:25:43,957
So that was the first part of the stack.

403
00:25:44,040 --> 00:25:49,570
So we go to the next stage,
which is the AI platform

404
00:25:50,760 --> 00:25:57,577
or the agent platform. Now,
the best way to think about this is

405
00:25:57,660 --> 00:25:59,557
in a couple of different layers.

406
00:25:59,640 --> 00:26:02,767
There is models. There's context.

407
00:26:02,850 --> 00:26:07,567
There is agents. There's harness, and
then there's security around it, right?

408
00:26:07,650 --> 00:26:10,507
So you want to have
access to all the models.

409
00:26:10,590 --> 00:26:14,677
You want to be able to have
access to all of the rich context.

410
00:26:14,760 --> 00:26:18,277
You then want the agents to be
built on top of that, right,

411
00:26:18,360 --> 00:26:22,657
using any model with all the context, and
then you want the harness that knows how

412
00:26:22,740 --> 00:26:24,337
to orchestrate all of it, right?

413
00:26:24,420 --> 00:26:26,527
That's sort of the core of it,
and then, of course,

414
00:26:26,610 --> 00:26:29,407
you need the security
and compliance around it.

415
00:26:29,490 --> 00:26:33,697
So I want to unpack a little
bit of this platform.

416
00:26:33,780 --> 00:26:39,757
And for us, the organizing
sort of piece here is Foundry,

417
00:26:39,840 --> 00:26:44,497
and Foundry brings all
of these things together,

418
00:26:44,580 --> 00:26:47,707
whether it's the agent
and the multiagent run times,

419
00:26:47,790 --> 00:26:50,527
whether it's models,
whether it's the context layer.

420
00:26:50,610 --> 00:26:54,937
And so I kind of want to walk through
each one of these pieces in some detail.

421
00:26:55,020 --> 00:26:57,877
The first is the models.

422
00:26:57,960 --> 00:27:01,987
Of course, you have the frontier
models, but the reality is,

423
00:27:02,070 --> 00:27:09,070
we have 11,000 models in Foundry
today, and this is sort

424
00:27:09,210 --> 00:27:14,707
of the base material for you to be able to
build your own agent in agentic systems.

425
00:27:14,790 --> 00:27:17,587
In fact, all the frontier
models, as I said, are there,

426
00:27:17,670 --> 00:27:19,717
and they're rapidly evolving.

427
00:27:19,800 --> 00:27:22,207
We ourselves, with MAI have
launched, even in the last,

428
00:27:22,290 --> 00:27:26,107
I think, month and a half,
a fantastic transcription model,

429
00:27:26,190 --> 00:27:28,177
an image model in two varieties.

430
00:27:28,260 --> 00:27:33,067
One is a very high-performance
and low-cost image model,

431
00:27:33,150 --> 00:27:39,487
a new voice model, again.
And so it's fantastic to see the, really,

432
00:27:39,570 --> 00:27:44,707
not just the big frontier models, but even
the helper models we use all the time

433
00:27:44,790 --> 00:27:49,807
for different modalities inside of our
applications are all rapidly evolving.

434
00:27:49,890 --> 00:27:54,157
But I wanted to talk about
this one model that struck a chord

435
00:27:54,240 --> 00:28:00,637
for me because what is the
innovation agenda around models,

436
00:28:00,720 --> 00:28:04,867
and how does one go about it
and build this as a capability

437
00:28:04,950 --> 00:28:08,497
that truly empowers
the next level of innovation?

438
00:28:08,580 --> 00:28:13,537
So to showcase that, let me
just roll the video on GigaTIME.

439
00:28:13,620 --> 00:28:16,597
HOIFUNG POON: Immunotherapy
is really the most promising

440
00:28:16,680 --> 00:28:19,657
direction for taming cancer,
once and for all.

441
00:28:19,740 --> 00:28:23,647
The whole idea is using
the immune system to fight cancer.

442
00:28:23,730 --> 00:28:26,317
CARLO BIFULCO: The big challenge we have
in the field right now is to understand

443
00:28:26,400 --> 00:28:30,697
which patients will respond. HOIFUNG POON:
Spatial proteomics can simultaneously

444
00:28:30,780 --> 00:28:36,097
measure multiple protein and create
multiplex immunofluorescent images.

445
00:28:36,180 --> 00:28:38,827
They can tell you whether
the patient might respond

446
00:28:38,910 --> 00:28:40,987
to immunotherapy or not,
but unfortunately,

447
00:28:41,070 --> 00:28:46,990
to generate this kind of images,
takes days and costs thousands of dollars.

448
00:28:49,500 --> 00:28:52,567
In collaboration with Providence
and University of Washington,

449
00:28:52,650 --> 00:28:55,717
we developed this multimodal
AI called GigaTIME

450
00:28:55,800 --> 00:28:59,047
to generate spatial proteomics at scale.

451
00:28:59,130 --> 00:29:00,517
SHENG WANG: So we have
trained the model using

452
00:29:00,600 --> 00:29:04,147
large-scale data,
analyzing millions of cells.

453
00:29:04,230 --> 00:29:06,607
The input image has limited data.

454
00:29:06,690 --> 00:29:09,547
We don't know where the protein is active.

455
00:29:09,630 --> 00:29:11,887
GigaTIME can convert this image

456
00:29:11,970 --> 00:29:15,757
to a colorful multiplex
immunofluorescent image.

457
00:29:15,840 --> 00:29:18,337
The color is the protein activation.

458
00:29:18,420 --> 00:29:22,237
They are the key factor drives
diseases, especially in cancer.

459
00:29:22,320 --> 00:29:25,147
This can help us to predict
patient drug response.

460
00:29:25,230 --> 00:29:27,427
HOIFUNG POON: We are now
taking the first meaningful step

461
00:29:27,510 --> 00:29:31,540
to be able to generate this kind
of data for everyone in the globe.

462
00:29:32,850 --> 00:29:37,297
The GigaTIME model is now available
to everyone through Microsoft Foundry.

463
00:29:37,380 --> 00:29:43,387
Our hope is to empower every secondary
hospital in every tertiary city to be able

464
00:29:43,470 --> 00:29:46,537
to now conduct their own research.

465
00:29:46,620 --> 00:29:48,427
CARLO BIFULCO: We look forward to a future

466
00:29:48,510 --> 00:29:50,857
where we can actually
bring this to the clinic,

467
00:29:50,940 --> 00:29:54,397
impact patients' lives, and bring the next
generation of immunotherapies to places

468
00:29:54,480 --> 00:29:57,070
where this would be otherwise impossible.

469
00:29:58,520 --> 00:30:03,907
[Applause]

470
00:30:03,990 --> 00:30:08,317
SATYA NADELLA: What was said
in the video sort of makes a ton

471
00:30:08,400 --> 00:30:14,887
of sense, that you can
take a technology like this,

472
00:30:14,970 --> 00:30:18,037
take what is a complex test
that costs a lot of money,

473
00:30:18,120 --> 00:30:20,737
takes a lot of time,
and truly democratize it, right,

474
00:30:20,820 --> 00:30:24,397
that point about every tertiary hospital

475
00:30:24,480 --> 00:30:28,117
in every secondary city
now with just a pathology,

476
00:30:28,200 --> 00:30:32,677
a regular slide pathology can
get this test done, essentially,

477
00:30:32,760 --> 00:30:38,917
is a pretty big breakthrough.
But the question is how that came about.

478
00:30:39,000 --> 00:30:41,587
The way it came about
is two research institutions

479
00:30:41,670 --> 00:30:45,037
that have had this knowledge,
right, this ability.

480
00:30:45,120 --> 00:30:48,577
They've obviously had all this data,
and they were able to take

481
00:30:48,660 --> 00:30:53,497
that data, train a model,
a generative model,

482
00:30:53,580 --> 00:30:58,657
to essentially do simulation
of a test using compute

483
00:30:58,740 --> 00:31:00,967
and share it with the world, right?

484
00:31:01,050 --> 00:31:03,397
That, to me, is what's happening, right?

485
00:31:03,480 --> 00:31:06,127
This is not about one frontier
model company doing things.

486
00:31:06,210 --> 00:31:09,007
It's about all of us as an ecosystem

487
00:31:09,090 --> 00:31:14,917
at the frontier have the ability
to use these new techniques,

488
00:31:15,000 --> 00:31:17,287
like generative AI, these new algorithms,

489
00:31:17,370 --> 00:31:20,917
this new compute
infrastructure that's there,

490
00:31:21,000 --> 00:31:25,387
and turn what is essentially
the tacit knowledge we have,

491
00:31:25,470 --> 00:31:29,167
the information we have,
the data edge we have,

492
00:31:29,250 --> 00:31:31,777
and turn that not only
to move the frontier inside the

493
00:31:31,860 --> 00:31:37,087
organization, but you can move
the frontier for the society at large.

494
00:31:37,170 --> 00:31:39,727
And that, I think,
is what's exciting about sort

495
00:31:39,810 --> 00:31:44,287
of the true democratic force
that's underlying all this.

496
00:31:44,370 --> 00:31:49,267
The next thing that I want to talk
about beyond the models is context.

497
00:31:49,350 --> 00:31:53,737
In some sense context around
the models is perhaps one

498
00:31:53,820 --> 00:31:58,207
of the core assets that's,
quite frankly, distributed, right?

499
00:31:58,290 --> 00:32:03,127
Every organization has unique
context, and you want to be able

500
00:32:03,210 --> 00:32:07,207
to organize that context such
that you can put it together

501
00:32:07,290 --> 00:32:11,377
with these models to both
reason and take action.

502
00:32:11,460 --> 00:32:15,277
And that's sort of one of the
top engineering considerations

503
00:32:15,360 --> 00:32:18,697
and architectural considerations,
Work IQ, right?

504
00:32:18,780 --> 00:32:25,780
So Work IQ is that rich database
underneath Microsoft 365, right?

505
00:32:27,000 --> 00:32:29,647
Everything Nirja just showed
around Copilot was tapping

506
00:32:29,730 --> 00:32:34,627
into Work IQ, and that Work
IQ is available headless

507
00:32:34,710 --> 00:32:39,157
through an MCP interface
for any app developer to be able

508
00:32:39,240 --> 00:32:43,177
to build an agentic system around
it, and this -- think about this.

509
00:32:43,260 --> 00:32:47,167
Up to now, I've always said that one
of the most important databases inside

510
00:32:47,250 --> 00:32:49,477
of any organization is the
database we don't talk about,

511
00:32:49,560 --> 00:32:53,977
right, which is the database
that contains all of the people

512
00:32:54,060 --> 00:32:57,487
and their relationship to other
people inside the organizations,

513
00:32:57,570 --> 00:33:02,797
the communications graph and data,
the document libraries,

514
00:33:02,880 --> 00:33:07,747
project files -- all that is in Work IQ.

515
00:33:07,830 --> 00:33:11,467
Similarly, another very
important database when it comes

516
00:33:11,550 --> 00:33:16,027
to thinking about outcomes
is what's in Fabric, right?

517
00:33:16,110 --> 00:33:19,327
If you think about all
the various dashboards

518
00:33:19,410 --> 00:33:23,587
that have gotten created over
the decade, and if you think

519
00:33:23,670 --> 00:33:28,447
of any dashboard is some outcome,
some metric, someone's measuring.

520
00:33:28,530 --> 00:33:32,677
So there's a rich semantic
model that was created.

521
00:33:32,760 --> 00:33:37,267
That's, again, super
important context to be able

522
00:33:37,350 --> 00:33:44,350
to give these LLMs and models
as you try to drive your workflows.

523
00:33:45,360 --> 00:33:49,087
And by the way, Fabric now
has the ability to reach

524
00:33:49,170 --> 00:33:53,377
out to any data, not just in Azure,
but everywhere, right?

525
00:33:53,460 --> 00:33:54,937
You can connect to AWS.

526
00:33:55,020 --> 00:33:58,897
You can connect to GCP, bring
all of the enterprise data asset

527
00:33:58,980 --> 00:34:02,917
as context inside of this Fabric IQ.

528
00:34:03,000 --> 00:34:06,307
And then, in fact, the outer loop
of this can all be Foundry IQ.

529
00:34:06,390 --> 00:34:11,857
What Foundry IQ does is essentially even
not only gets you that document database.

530
00:34:11,940 --> 00:34:13,957
So if you have a lot
of unstructured information,

531
00:34:14,040 --> 00:34:19,237
you can then vectorize it,
make it a part of your context.

532
00:34:19,320 --> 00:34:24,387
But you can also use it as an
outer planner to be able to plan

533
00:34:24,470 --> 00:34:28,077
across Work IQ, Fabric,
and Foundry, right?

534
00:34:28,160 --> 00:34:31,477
That's sort of how these
things come together.

535
00:34:31,560 --> 00:34:37,087
So context and this IQ layer
beyond the models becomes the

536
00:34:37,170 --> 00:34:39,757
other second important piece.

537
00:34:39,840 --> 00:34:43,027
Now the third thing I want
to talk about when it comes

538
00:34:43,110 --> 00:34:48,337
to agent platform is the agent
service itself, right?

539
00:34:48,420 --> 00:34:54,957
With every shift in platforms,
you kind of need a runtime,

540
00:34:55,040 --> 00:34:56,287
and that's what this is.

541
00:34:56,370 --> 00:35:01,987
The new runtime for AI agents
is this Foundry agent service,

542
00:35:02,070 --> 00:35:05,827
and really exciting announcements
today up and down the stack, right?

543
00:35:05,910 --> 00:35:08,647
We have now persistent memory.

544
00:35:08,730 --> 00:35:15,577
We have tools use. So basically models
that then can be attached to knowledge

545
00:35:15,660 --> 00:35:20,137
that can then also have attached to tools.

546
00:35:20,220 --> 00:35:23,707
You can then, on top of it,
build these multiagent

547
00:35:23,790 --> 00:35:27,357
and agentic systems that you
can orchestrate using any

548
00:35:27,440 --> 00:35:32,247
orchestrator you choose.
You can publish them with one click.

549
00:35:32,330 --> 00:35:35,337
And then, as I said,
you have memory that's persistent

550
00:35:35,420 --> 00:35:38,157
across all of this,
and they're long-living agents,

551
00:35:38,240 --> 00:35:43,107
and they're stateful agents.
And so the one thing that I'm most excited

552
00:35:43,190 --> 00:35:48,777
about is the hosted agent service
that now goes into preview mode as well.

553
00:35:48,860 --> 00:35:51,327
So it's really exciting to see
this because now what do you have?

554
00:35:51,410 --> 00:35:55,887
Okay, all right. [Cheers]

555
00:35:55,970 --> 00:36:00,927
In fact, this is like that day
we started the cloud journey the

556
00:36:01,010 --> 00:36:03,747
first time we talked about
containers and said, oh,

557
00:36:03,830 --> 00:36:06,327
there's a new type of virtual
machine, effectively,

558
00:36:06,410 --> 00:36:07,977
for cloud-native applications.

559
00:36:08,060 --> 00:36:13,717
So think of this as the birth
of this new generation

560
00:36:13,800 --> 00:36:18,487
of AI-native applications
with a hosted service.

561
00:36:18,570 --> 00:36:22,117
So literally, you have
a sandbox per session

562
00:36:22,200 --> 00:36:24,997
that can bootstrap itself fast, right?

563
00:36:25,080 --> 00:36:28,087
So it's instant on. So in fact,
when you look at the service,

564
00:36:28,170 --> 00:36:30,637
you will instantly be
able to bring up a sandbox.

565
00:36:30,720 --> 00:36:33,217
The sandboxes are isolated.

566
00:36:33,300 --> 00:36:38,377
You have persistent memory that
is session state is persistent.

567
00:36:38,460 --> 00:36:41,887
And then, of course, in like
any other service in the cloud,

568
00:36:41,970 --> 00:36:44,047
you can scale up and down
elastically, right?

569
00:36:44,130 --> 00:36:47,677
So this becomes, I think,
a massive boost because one

570
00:36:47,760 --> 00:36:50,317
of the things I've been
talking to a lot of developers,

571
00:36:50,400 --> 00:36:55,657
and they come and say, look, when can
we have a hosted service for agents?

572
00:36:55,740 --> 00:36:57,937
Because agents are new software, right?

573
00:36:58,020 --> 00:36:59,557
People talk about what's
happening to software.

574
00:36:59,640 --> 00:37:05,137
Guess what? All software is going agentic,
and you need to host these agents.

575
00:37:05,220 --> 00:37:08,707
You need to have them have
their own computer, essentially,

576
00:37:08,790 --> 00:37:13,887
and that's what this hosted
container sandbox is.

577
00:37:13,970 --> 00:37:17,337
And then it needs to have state, so that's
kind of where the persistence comes.

578
00:37:17,420 --> 00:37:19,827
You need to be able
to scale them up and down.

579
00:37:19,910 --> 00:37:23,667
And so we are very, very excited
about this new Foundry service

580
00:37:23,750 --> 00:37:25,857
with hosted agents and what you all can do

581
00:37:25,940 --> 00:37:29,047
to scale the agentic
systems going forward.

582
00:37:29,130 --> 00:37:31,717
Now then you have the runtimes,
you have the models,

583
00:37:31,800 --> 00:37:36,937
you have the context -- you want
to start building these applications,

584
00:37:37,020 --> 00:37:41,257
and that's where we have the richness
of multiple different tools.

585
00:37:41,340 --> 00:37:44,017
You can start right inside
of Copilot with something

586
00:37:44,100 --> 00:37:49,207
like App Builder, hit a prompt, and here's
a career path app that you just built

587
00:37:49,290 --> 00:37:52,027
with a single prompt
inside of a chat session.

588
00:37:52,110 --> 00:37:54,937
It's a full stack app that gets generated.

589
00:37:55,020 --> 00:37:59,347
You can share it, or you can
go to Copilot Studio, again,

590
00:37:59,430 --> 00:38:02,707
build an agent using
a simple set of prompts.

591
00:38:02,790 --> 00:38:05,497
In this case,
it's an onboarding app that got built

592
00:38:05,580 --> 00:38:10,237
as our onboarding agent that got
built using Copilot studio.

593
00:38:10,320 --> 00:38:13,957
And then, of course,
you have the full richness

594
00:38:14,040 --> 00:38:17,947
of the professional developer
tools, right, with VS Code

595
00:38:18,030 --> 00:38:22,177
and GitHub and now all the agentic tools.

596
00:38:22,260 --> 00:38:25,027
In fact, one of the most
exciting things is right here

597
00:38:25,110 --> 00:38:28,807
in Australia, we now have, in GitHub,

598
00:38:28,890 --> 00:38:34,747
2.3 million strong community. [Applause]

599
00:38:34,830 --> 00:38:36,217
Yeah, it's and it's growing, right?

600
00:38:36,300 --> 00:38:38,377
It's 26% growth year over year.

601
00:38:38,460 --> 00:38:43,857
It's great to see the community
continuing to scale.

602
00:38:43,940 --> 00:38:48,457
And GitHub is not just
about the code repo,

603
00:38:48,540 --> 00:38:54,787
but it's also now becoming the place,
the control plane for all the agents.

604
00:38:54,870 --> 00:38:59,977
So we described it as Agent HQ,
so you have every coding agent

605
00:39:00,060 --> 00:39:05,227
that that you use will be available
to you right next to your repo, right?

606
00:39:05,310 --> 00:39:08,677
So you have your code.
You have your coding agents.

607
00:39:08,760 --> 00:39:11,767
And in fact, one of my
favorite new features is,

608
00:39:11,850 --> 00:39:16,527
not only do I have my source code,
but I have my session traces

609
00:39:16,610 --> 00:39:19,767
as a first-class thing
inside of GitHub, right?

610
00:39:19,850 --> 00:39:21,627
So because, after all, going forward,

611
00:39:21,710 --> 00:39:26,157
it becomes very important
for us to not only have the code,

612
00:39:26,240 --> 00:39:29,347
but also the traces
that generated the code

613
00:39:29,430 --> 00:39:31,747
because of the coding
agents right with the code.

614
00:39:31,830 --> 00:39:36,607
And I think this become, in fact,
for true provenance of code going forward,

615
00:39:36,690 --> 00:39:39,667
that becomes a pretty important attribute.

616
00:39:39,750 --> 00:39:45,937
And then we're also exposing the tooling
through all the form factors, right?

617
00:39:46,020 --> 00:39:49,777
As I said, CLI is back.
Everybody loves it.

618
00:39:49,860 --> 00:39:51,487
And the reason why is not just

619
00:39:51,570 --> 00:39:54,367
because a command line is powerful,
but it's command line

620
00:39:54,450 --> 00:40:00,357
when coupled with rich agentic
models and natural language

621
00:40:00,440 --> 00:40:01,827
that have access to everything.

622
00:40:01,910 --> 00:40:04,237
In fact, one of the coolest things

623
00:40:04,320 --> 00:40:08,557
that I found myself doing is you
can literally go to your repo

624
00:40:08,640 --> 00:40:12,727
in GitHub CLI and then
you can connect to Work IQ

625
00:40:12,810 --> 00:40:17,767
through something like an MCP and say,
hey, I was in a design meeting yesterday.

626
00:40:17,850 --> 00:40:20,257
We had a discussion on this topic.

627
00:40:20,340 --> 00:40:23,617
Can you make sure that my repo
is in sync with the discussion?

628
00:40:23,700 --> 00:40:25,507
I mean, think about that.
How crazy is it, right?

629
00:40:25,590 --> 00:40:28,477
With a natural language
utterance, essentially,

630
00:40:28,560 --> 00:40:31,657
you're able to relate a design meeting

631
00:40:31,740 --> 00:40:36,937
that was mostly a transcription
-- that to source code,

632
00:40:37,020 --> 00:40:39,127
all being done in a command line, right?

633
00:40:39,210 --> 00:40:44,227
That's the type of richness
in that form factor that you can bring.

634
00:40:44,310 --> 00:40:47,047
And then, of course, IDEs still matter.

635
00:40:47,130 --> 00:40:49,387
In fact, IDEs are becoming ADEs, right?

636
00:40:49,470 --> 00:40:52,027
So they're now evolving to, basically,

637
00:40:52,110 --> 00:40:57,097
now the sessions app inside
of VS Code is basically an ADE.

638
00:40:57,180 --> 00:41:01,297
And sometimes when you
go into an agent mode and

639
00:41:01,380 --> 00:41:04,267
or the agentic output,
it says please open up in VS Code.

640
00:41:04,350 --> 00:41:07,177
And if you want to inspect any code,
you want that profiler there.

641
00:41:07,260 --> 00:41:09,397
You want the debugger there.

642
00:41:09,480 --> 00:41:14,377
And so we have all the form
factors that developers need

643
00:41:14,460 --> 00:41:19,777
and want and want to use as part
of a workflow all available to them.

644
00:41:19,860 --> 00:41:22,957
And so to show you all
of this developer richness,

645
00:41:23,040 --> 00:41:26,677
I wanted to introduce Australia's
own Damien to the stage.

646
00:41:26,760 --> 00:41:33,337
Damian, take it away.
[Applause] DAMIAN BRADY: Thanks, Satya.

647
00:41:33,420 --> 00:41:37,417
Thanks, Satya. My name is Damian,
as Satya mentioned, and I am one

648
00:41:37,500 --> 00:41:41,677
of the team at GitHub, and also one
of the 2.3 million developers right here

649
00:41:41,760 --> 00:41:45,727
in Australia, from Brisbane.
Yeah, thank you.

650
00:41:45,810 --> 00:41:48,547
All right.
One of the projects I've been working

651
00:41:48,630 --> 00:41:52,687
on lately is Tailspin Transport,
and it is a little tool.

652
00:41:52,770 --> 00:41:56,257
It's our EV company, and so I'm going
to be working on the website today.

653
00:41:56,340 --> 00:41:58,137
I'm going to show you
what that looks like.

654
00:41:58,220 --> 00:42:00,777
So let's have a look at the website.

655
00:42:00,860 --> 00:42:03,567
This is the Tailspin Transport website.

656
00:42:03,650 --> 00:42:05,397
We have our EV vehicles.

657
00:42:05,480 --> 00:42:08,637
We've got a Vehicles page as well,
very comprehensive FAQ,

658
00:42:08,720 --> 00:42:10,237
which I'll get to in a second.

659
00:42:10,320 --> 00:42:14,437
And I'm going to do some work today to add
a search feature to the Vehicles page.

660
00:42:14,520 --> 00:42:17,047
And the reason I'm doing that now
is because I've got an email

661
00:42:17,130 --> 00:42:19,927
from one of our product managers
giving me some information

662
00:42:20,010 --> 00:42:24,187
that I've been waiting for about
exactly how they want this to behave.

663
00:42:24,270 --> 00:42:27,097
So I use Visual Studio Code,
and I use Visual Studio,

664
00:42:27,180 --> 00:42:29,977
but lately I've been using
the GitHub Copilot CLI,

665
00:42:30,060 --> 00:42:32,107
so that's what I'm going to use today.

666
00:42:32,190 --> 00:42:35,707
So let's kick off the CLI,
and the first thing I want to do

667
00:42:35,790 --> 00:42:38,677
when I get in is have a look
at the model that I'm using.

668
00:42:38,760 --> 00:42:43,567
So I have been playing with GPT-5.4,
you can see right down the bottom.

669
00:42:43,650 --> 00:42:45,727
But this is a relatively
straightforward task.

670
00:42:45,810 --> 00:42:49,317
So I'm actually going to use
a different model, and I'm going

671
00:42:49,400 --> 00:42:53,937
to use Claude Sonnet 4.6. Now, you can
see just how many models I have available

672
00:42:54,020 --> 00:42:58,647
to me just with the one Copilot
subscription, even Opus 4.7,

673
00:42:58,730 --> 00:43:00,727
which came out incredibly recently.

674
00:43:00,810 --> 00:43:03,847
So we use 4.6,
and then I'm going to ask it

675
00:43:03,930 --> 00:43:05,887
to gather some information together for me

676
00:43:05,970 --> 00:43:08,617
so I know I'm doing
exactly the right work.

677
00:43:08,700 --> 00:43:10,507
Now, here's a prompt I prepared earlier.

678
00:43:10,590 --> 00:43:12,337
You don't want to see me type that.

679
00:43:12,420 --> 00:43:15,937
And what I'm doing is I'm
asking Work IQ to find that email

680
00:43:16,020 --> 00:43:18,907
from Evie Smith, the PM,
and pull that information

681
00:43:18,990 --> 00:43:22,537
in together with information
from the GitHub issue

682
00:43:22,620 --> 00:43:25,377
and build a single set of requirements.

683
00:43:25,460 --> 00:43:27,417
And so let's have a quick
look at that email.

684
00:43:27,500 --> 00:43:30,027
We can see that there's
a requirement for a search bar

685
00:43:30,110 --> 00:43:33,727
with simple search,
and then Azure AI Search,

686
00:43:33,810 --> 00:43:36,397
the category sidebar,
the price range filter.

687
00:43:36,480 --> 00:43:39,667
And as much as a developer I
would love Evie to have put this

688
00:43:39,750 --> 00:43:43,717
in our GitHub issue, which is right
here, I realized that that's not

689
00:43:43,800 --> 00:43:46,567
where communication always
happens in our organization.

690
00:43:46,650 --> 00:43:50,737
So I could copy and paste this,
but instead, I'm using MCP to go

691
00:43:50,820 --> 00:43:55,057
and reach those two surfaces
to pull the information together.

692
00:43:55,140 --> 00:43:57,937
So Work IQ allows me to reach
into the M suite and pull

693
00:43:58,020 --> 00:44:01,507
that information out,
and the GitHub MCP server also lets me

694
00:44:01,590 --> 00:44:04,237
reach into GitHub to pull
that information out.

695
00:44:04,320 --> 00:44:07,267
And what's really cool is it's
worked out it can do both

696
00:44:07,350 --> 00:44:12,427
of those things in parallel, which
is something that I am incapable of doing.

697
00:44:12,510 --> 00:44:15,847
I am decidedly single-threaded,
unfortunately.

698
00:44:15,930 --> 00:44:19,477
So as that's coming back,
it's going to build this one set

699
00:44:19,560 --> 00:44:24,217
of requirements for me, after asking
Work IQ and getting that information back.

700
00:44:24,300 --> 00:44:26,677
And I could continue
with that set of requirements

701
00:44:26,760 --> 00:44:32,677
and work synchronously here in the CLI
or switch to the Visual Studio Code

702
00:44:32,760 --> 00:44:35,107
and keep working synchronously there.

703
00:44:35,190 --> 00:44:38,017
Here we go. Starting
to get the information back.

704
00:44:38,100 --> 00:44:39,217
And if I wanted to do that,

705
00:44:39,300 --> 00:44:44,437
I could also control it remotely using
this new remote command in the CLI.

706
00:44:44,520 --> 00:44:46,927
So even though the work
is happening on my machine,

707
00:44:47,010 --> 00:44:51,457
I could adjust it and make
changes using my mobile or the web.

708
00:44:51,540 --> 00:44:54,817
But instead, I want
to show you a new feature,

709
00:44:54,900 --> 00:44:58,417
a new experimental feature
called the rubber duck agent.

710
00:44:58,500 --> 00:45:02,527
So this will take a sec, so I'm going
to go to one I've already done earlier.

711
00:45:02,610 --> 00:45:05,617
And all I'm doing here is asking
can you give me a rubber duck

712
00:45:05,700 --> 00:45:07,987
review of these requirements?

713
00:45:08,070 --> 00:45:12,037
And what the CLA is doing is it's
kicking off the rubber duck

714
00:45:12,120 --> 00:45:16,957
agent here, and it's reviewing
what work has just been done.

715
00:45:17,040 --> 00:45:19,627
Now, the cool thing about
this is I was using Claude Sonnet

716
00:45:19,710 --> 00:45:23,107
4.6, but to do the review,
it's going to choose a model

717
00:45:23,190 --> 00:45:27,427
from a different model family,
so we get a real second opinion on this.

718
00:45:27,510 --> 00:45:29,557
And it's lucky we did.
We found a few issues here.

719
00:45:29,640 --> 00:45:32,587
Range is ambiguous, type versus category.

720
00:45:32,670 --> 00:45:34,927
The fallback spec is too vague,
and a few other things.

721
00:45:35,010 --> 00:45:38,677
So I would obviously want
to clarify some of these

722
00:45:38,760 --> 00:45:40,627
for it before I started
work, but this is a demo,

723
00:45:40,710 --> 00:45:42,697
so let's just pretend it's perfect.

724
00:45:42,780 --> 00:45:44,047
I'm going to continue with this.

725
00:45:44,130 --> 00:45:48,217
I could do it locally, but instead,
I'm going to delegate this to GitHub,

726
00:45:48,300 --> 00:45:51,487
and that will send this
information to the cloud,

727
00:45:51,570 --> 00:45:54,457
and the Copilot cloud
agent will pick up that work

728
00:45:54,540 --> 00:45:58,027
and start working on that, while
I can move on to something else.

729
00:45:58,110 --> 00:46:00,637
So the thing I'm going to move
on to is the other thing

730
00:46:00,720 --> 00:46:03,247
that we do as developers,
which is a code review.

731
00:46:03,330 --> 00:46:06,037
Now, I mentioned that FAQ
page was really comprehensive.

732
00:46:06,120 --> 00:46:08,647
The downside of that is
sometimes it's hard to scroll

733
00:46:08,730 --> 00:46:11,677
through all that information,
and so it'd be great

734
00:46:11,760 --> 00:46:14,707
if we had an agent that we
could just ask questions of,

735
00:46:14,790 --> 00:46:17,437
and that's what my colleague
has put together.

736
00:46:17,520 --> 00:46:21,397
So he's used Microsoft Foundry
and created a custom agent.

737
00:46:21,480 --> 00:46:24,637
It's based on GPT 5.4 mini,
which means that it's going

738
00:46:24,720 --> 00:46:27,637
to be responsive and relatively cheap.

739
00:46:27,720 --> 00:46:33,637
We've got a set of instructions here, and
importantly, we also have a file search.

740
00:46:33,720 --> 00:46:36,817
Now, this is one of many ways
you can insert real information

741
00:46:36,900 --> 00:46:40,717
in, but he just uploaded a markdown
document with our FAQ page.

742
00:46:40,800 --> 00:46:45,367
So that means when we ask
a question of the agent here,

743
00:46:45,450 --> 00:46:48,427
we get real answers, grounded in truth.

744
00:46:48,510 --> 00:46:50,497
So they're grounded in that FAQ document,

745
00:46:50,580 --> 00:46:53,797
rather than potentially
hallucinating them.

746
00:46:53,880 --> 00:46:55,987
But we can't just trust
that that's going to work.

747
00:46:56,070 --> 00:46:57,757
So he's also done some evaluation

748
00:46:57,840 --> 00:47:01,837
and used automatic evaluation
to have a look at how successful

749
00:47:01,920 --> 00:47:04,597
that agent is across
a number of different areas,

750
00:47:04,680 --> 00:47:08,287
so things like tool selection,
output utilization,

751
00:47:08,370 --> 00:47:11,647
even things like relevance
and at task adherence.

752
00:47:11,730 --> 00:47:14,077
So we've really validated
whether that agent is going

753
00:47:14,160 --> 00:47:15,637
to do the job we want it to do.

754
00:47:15,720 --> 00:47:17,437
Okay, that's the agent. That looks great.

755
00:47:17,520 --> 00:47:19,537
Let's look at the actual pull request.

756
00:47:19,620 --> 00:47:20,977
Now, because I've been doing other work,

757
00:47:21,060 --> 00:47:24,307
the first thing I did was assign
this to GitHub Copilot.

758
00:47:24,390 --> 00:47:28,597
And Copilot Code Review has looked through
this pull request, knowing the context

759
00:47:28,680 --> 00:47:32,047
of our entire repo,
and it's made some suggestions.

760
00:47:32,130 --> 00:47:34,087
It's made quite a few
suggestions, actually.

761
00:47:34,170 --> 00:47:36,517
We've got nine comments
generated, and I could scroll

762
00:47:36,600 --> 00:47:39,217
through and fix them
individually if I wanted,

763
00:47:39,300 --> 00:47:42,877
or I could just ask Copilot
to fix them all in one batch.

764
00:47:42,960 --> 00:47:45,727
And in fact, you can
send a message to Copilot

765
00:47:45,810 --> 00:47:49,417
from any pull request,
whether it was created by a human

766
00:47:49,500 --> 00:47:55,147
or by the cloud agent, and ask
it to do some additional work.

767
00:47:55,230 --> 00:47:59,437
So in this case, that FAQ
page involves a UI change,

768
00:47:59,520 --> 00:48:01,897
but I didn't see any
screenshots in the pull request,

769
00:48:01,980 --> 00:48:03,487
which would have been really nice.

770
00:48:03,570 --> 00:48:08,857
So I asked Copilot to use Playwright
to take before-and-after screenshots

771
00:48:08,940 --> 00:48:10,807
so I can see the UI difference.

772
00:48:10,890 --> 00:48:13,057
And this is another MCP
server we have access to.

773
00:48:13,140 --> 00:48:15,727
And sure enough, there we go.
We've got a before and an

774
00:48:15,810 --> 00:48:18,097
after with our fields
to ask the questions.

775
00:48:18,180 --> 00:48:19,837
Now, that wasn't enough for me.

776
00:48:19,920 --> 00:48:24,517
I asked for a couple more screenshots, and
I noticed there's actually an issue here.

777
00:48:24,600 --> 00:48:27,157
We didn't connect
to the Foundry correctly,

778
00:48:27,240 --> 00:48:29,047
so there's something
wrong in the code here,

779
00:48:29,130 --> 00:48:31,297
and this is something I might
not have noticed initially,

780
00:48:31,380 --> 00:48:34,717
if I was just doing a code
review looking at that code.

781
00:48:34,800 --> 00:48:38,857
Okay, I've shown you a few things today,
so I just want to quickly recap.

782
00:48:38,940 --> 00:48:42,367
We looked at using the GitHub Copilot CLI

783
00:48:42,450 --> 00:48:46,687
to consolidate information
using MCP servers like Work IQ

784
00:48:46,770 --> 00:48:51,877
and the GitHub MCP server to start
work on that new search functionality.

785
00:48:51,960 --> 00:48:56,767
We looked at using Microsoft Foundry
to build a custom agent grounded in truth

786
00:48:56,850 --> 00:49:00,307
so that we could really answer
questions that the customers had.

787
00:49:00,390 --> 00:49:04,567
And then we looked at how Copilot Code
Review can help us review a pull request

788
00:49:04,650 --> 00:49:08,467
and how Copilot can even improve
that pull request as we go.

789
00:49:08,550 --> 00:49:11,227
And trust me, I've only
just touched the surface

790
00:49:11,310 --> 00:49:14,767
of what we can do with Copilot
to help enable our teams

791
00:49:14,850 --> 00:49:17,887
to complete more work faster.
Thanks, everyone.

792
00:49:17,970 --> 00:49:19,437
Back to you, Satya.

793
00:49:19,520 --> 00:49:25,197
[Applause]

794
00:49:25,280 --> 00:49:28,687
SATYA NADELLA: I must say
we have come a long way

795
00:49:28,770 --> 00:49:35,017
in our naming as well from Visual
Basic to rubber duck agents.

796
00:49:35,100 --> 00:49:39,787
I love it. It's pushing the frontier.

797
00:49:39,870 --> 00:49:45,937
And one of the other super
important considerations, right,

798
00:49:46,020 --> 00:49:47,677
when you build all these agents, right,

799
00:49:47,760 --> 00:49:50,407
what Damian showed is just
pretty stunning, right,

800
00:49:50,490 --> 00:49:54,847
between Copilot Studio plus what
he showed, you can be assured

801
00:49:54,930 --> 00:49:57,847
of one thing, which is we're
going to have lots and lots

802
00:49:57,930 --> 00:50:02,107
of agents inside of our organization,
in our tenant, in our network.

803
00:50:02,190 --> 00:50:04,597
And so one of the key
considerations is going

804
00:50:04,680 --> 00:50:08,827
to be security and compliance
and observability,

805
00:50:08,910 --> 00:50:12,607
and that's what's the motivation
for Agent 365, right,

806
00:50:12,690 --> 00:50:17,107
which is how do we build a new
control plane, just like we did

807
00:50:17,190 --> 00:50:21,577
for the previous era,
but for the agentic era, right?

808
00:50:21,660 --> 00:50:24,097
We need an identity. So we extended Entra.

809
00:50:24,180 --> 00:50:27,607
We needed security. We extended Defender.

810
00:50:27,690 --> 00:50:33,037
We needed data security.
We extended Purview.

811
00:50:33,120 --> 00:50:39,397
And so fundamentally, now you have in
Agent 365 a platform and a control plane.

812
00:50:39,480 --> 00:50:41,287
And by the way, they don't
need to be hosted on Foundry.

813
00:50:41,370 --> 00:50:45,337
They can be hosted on Azure.
They can be anywhere, again, on AWS, GCP,

814
00:50:45,420 --> 00:50:47,827
anywhere on your private cloud, even,

815
00:50:47,910 --> 00:50:51,997
but you have this one
control plane that you can use

816
00:50:52,080 --> 00:50:54,637
to be able to manage all of these agents

817
00:50:54,720 --> 00:51:00,427
and have real visibility into all of these
agents, and set governance policies

818
00:51:00,510 --> 00:51:03,457
and enforce those governance policies.

819
00:51:03,540 --> 00:51:10,027
Now, one other consideration
that perhaps in the months

820
00:51:10,110 --> 00:51:12,067
to come and definitely in the years

821
00:51:12,150 --> 00:51:17,977
to come will probably become
the top consideration is in this age

822
00:51:18,060 --> 00:51:25,060
of AI where you no longer
just have a platform,

823
00:51:25,470 --> 00:51:32,077
but you have a platform that
extends into a learning system.

824
00:51:32,160 --> 00:51:37,477
So then the question
for any software company,

825
00:51:37,560 --> 00:51:43,567
any new AI startup, or,
for that matter, any bank, insurance,

826
00:51:43,650 --> 00:51:47,917
energy, telco, whatever -- pick
your favorite sort of sector

827
00:51:48,000 --> 00:51:51,187
of the economy -- is, guess what?

828
00:51:51,270 --> 00:51:54,907
There's no limits
to these learning systems.

829
00:51:54,990 --> 00:51:59,437
They can pick up what's novel
and new and interesting to know

830
00:51:59,520 --> 00:52:04,207
about any business and just make
it part of their base weights.

831
00:52:04,290 --> 00:52:07,867
And so this is a pretty new
thing that's never happened

832
00:52:07,950 --> 00:52:13,837
before because it's not like the last
time you thought about a digital system.

833
00:52:13,920 --> 00:52:18,127
That digital system was a
self-improving learning system.

834
00:52:18,210 --> 00:52:22,177
It was a system that you could
sort of use with your context

835
00:52:22,260 --> 00:52:26,947
and your workflows and make
sure that that IP that you create

836
00:52:27,030 --> 00:52:33,067
on top of it is yours. And that
is what you now and all of us have

837
00:52:33,150 --> 00:52:34,927
to do, even in the AI era.

838
00:52:35,010 --> 00:52:37,897
And to do that, that means
the first thing is to really think

839
00:52:37,980 --> 00:52:42,637
about each of these layers
of the agent platform and make sure

840
00:52:42,720 --> 00:52:47,647
that you start with your evals,
right, after all,

841
00:52:47,730 --> 00:52:51,907
your hill-climbing with any
agent system you build

842
00:52:51,990 --> 00:52:55,747
on some eval and making sure
that those evals are private.

843
00:52:55,830 --> 00:52:59,527
In fact, perhaps one of the most
important IP of an organization

844
00:52:59,610 --> 00:53:03,547
or a software company that is
doing something unique is going

845
00:53:03,630 --> 00:53:05,857
to be captured in some
private benchmark, right?

846
00:53:05,940 --> 00:53:07,627
In fact, all the public benchmarks are

847
00:53:07,710 --> 00:53:12,337
so saturated they're more
or less becoming sort of noise.

848
00:53:12,420 --> 00:53:15,697
And so therefore the real
game for anything is

849
00:53:15,780 --> 00:53:20,557
about hill-climbing on benchmarks,
and private benchmarks you create,

850
00:53:20,640 --> 00:53:25,507
private evaluation sets you create,
are going to become the new IP.

851
00:53:25,590 --> 00:53:28,387
The next thing you want to make
sure is you decouple, right?

852
00:53:28,470 --> 00:53:30,607
You want to have access
to multiple models.

853
00:53:30,690 --> 00:53:33,637
Like rubber duck is cool,
or critique is cool,

854
00:53:33,720 --> 00:53:40,177
but you want the harness, the context,
and the models to all be decoupled.

855
00:53:40,260 --> 00:53:43,837
It's tempting to couple them
to get some gain on an eval,

856
00:53:43,920 --> 00:53:48,937
but then you're stuck with that
one particular local maxima.

857
00:53:49,020 --> 00:53:51,547
But you're not going for the
global maxima, where you want

858
00:53:51,630 --> 00:53:56,917
to be able to ride every frontier
gain or your own model development.

859
00:53:57,000 --> 00:54:03,427
You want that context
that's there with your --

860
00:54:03,510 --> 00:54:08,977
you want to externalize
as much of the expertise inside

861
00:54:09,060 --> 00:54:11,887
of your organization into skills, right?

862
00:54:11,970 --> 00:54:14,197
Simple markdown files like in Cowork,

863
00:54:14,280 --> 00:54:17,917
one of the cool things is you
start writing these skills,

864
00:54:18,000 --> 00:54:20,407
and you even compound
the skills individually

865
00:54:20,490 --> 00:54:21,817
and at an organizational level.

866
00:54:21,900 --> 00:54:25,927
So it's kind of externalizing
the expertise of an organization.

867
00:54:26,010 --> 00:54:28,807
You want to keep that,
again, very private.

868
00:54:28,890 --> 00:54:33,727
You want to take all the tacit
knowledge of the organization.

869
00:54:33,810 --> 00:54:35,827
In fact, the tacit knowledge
of the organization is

870
00:54:35,910 --> 00:54:39,487
with the people and the way they
work and the way they work with AI.

871
00:54:39,570 --> 00:54:43,057
Those traces when I talked
about in GitHub, now in sessions,

872
00:54:43,140 --> 00:54:47,947
you have the traces and the
trajectories of all of your agents.

873
00:54:48,030 --> 00:54:54,097
That's super important data. Not just your
source code, but that data is your data.

874
00:54:54,180 --> 00:54:56,587
And you can train your own model.

875
00:54:56,670 --> 00:55:00,727
You can take a base model, which is a
pretrained model an open-weight model,

876
00:55:00,810 --> 00:55:03,847
and train that with these
traces, and then put it back

877
00:55:03,930 --> 00:55:08,257
in Copilot in Auto,
and it'll then route to your model.

878
00:55:08,340 --> 00:55:14,677
So I think that this idea
and this consideration

879
00:55:14,760 --> 00:55:20,107
around sovereignty of a firm because,
ultimately, what is the future of a firm?

880
00:55:20,190 --> 00:55:24,067
The future of a firm is its
ability to continuously,

881
00:55:24,150 --> 00:55:28,267
monotonically get better
at its knowledge creation,

882
00:55:28,350 --> 00:55:32,497
and it's able to capture
its tacit knowledge

883
00:55:32,580 --> 00:55:36,757
that is not only inside
of context or in skills,

884
00:55:36,840 --> 00:55:39,817
but even in the weights
that you control as your own IP.

885
00:55:39,900 --> 00:55:42,457
And so that's what we're
building the agent platform for,

886
00:55:42,540 --> 00:55:43,717
and you'll continue to see us,

887
00:55:43,800 --> 00:55:46,597
even going into our developer
conference in early part

888
00:55:46,680 --> 00:55:50,107
of June, talk about this in
great detail because we think

889
00:55:50,190 --> 00:55:52,867
that this is what, as I said, any startup,

890
00:55:52,950 --> 00:55:55,297
any software company,
and, for that matter,

891
00:55:55,380 --> 00:55:59,227
any part of the economy that now
needs to be thinking of all

892
00:55:59,310 --> 00:56:02,137
of these pretty strategically.

893
00:56:02,220 --> 00:56:06,367
And sort of speaking of that in Australia,
we have fantastic momentum in terms

894
00:56:06,450 --> 00:56:08,977
of people building these agentic systems.

895
00:56:09,060 --> 00:56:13,237
I had a chance yesterday
to meet with many of them.

896
00:56:13,320 --> 00:56:19,927
AEMO has taken Fabric and Foundry and
built out a fantastic system to be able

897
00:56:20,010 --> 00:56:23,137
to improve the signal-to-noise
ratio around alerts, right?

898
00:56:23,220 --> 00:56:28,417
Because how do you really have
AI help them manage, in fact,

899
00:56:28,500 --> 00:56:31,207
some of the most critical
infrastructure around the grid

900
00:56:31,290 --> 00:56:34,957
in a much more streamlined
way and an effective way?

901
00:56:35,040 --> 00:56:38,137
Had a chance to sort of meet
the team at the Commonwealth Bank,

902
00:56:38,220 --> 00:56:42,997
where they have built and extended
both their customer-facing bot

903
00:56:43,080 --> 00:56:46,027
with all the new power of these models,

904
00:56:46,110 --> 00:56:50,377
but also for their agents
in their call centers to be able

905
00:56:50,460 --> 00:56:52,987
to take the escalations
and have the tools.

906
00:56:53,070 --> 00:56:56,647
The productivity gains that
they're seeing is tremendous.

907
00:56:56,730 --> 00:57:00,277
I had a chance to meet with
even one student and as well

908
00:57:00,360 --> 00:57:04,597
as a software company
called Cogniti that's working

909
00:57:04,680 --> 00:57:09,127
with the University of Sydney
in enabling, in fact, teachers, right?

910
00:57:09,210 --> 00:57:11,917
We know that a lot of the
students are using these tools

911
00:57:12,000 --> 00:57:16,117
to learn, but teachers being
able to create new course material

912
00:57:16,200 --> 00:57:22,627
and change the way they conduct their
sessions and so on were fantastic to see.

913
00:57:22,710 --> 00:57:26,290
And of course, I had a chance
to meet with Cricket Australia.

914
00:57:27,330 --> 00:57:29,797
And look, I mean, it's always awesome.

915
00:57:29,880 --> 00:57:31,717
I think Australia has been collecting --

916
00:57:31,800 --> 00:57:34,057
and Cricket Australia -- has
been collecting stats from,

917
00:57:34,140 --> 00:57:36,547
I forget, 1896 or something like that.

918
00:57:36,630 --> 00:57:38,677
In fact, the last time I was here,
somebody was telling me,

919
00:57:38,760 --> 00:57:41,557
hey, if you ever wanted to check
the scorecards of Steve Smith

920
00:57:41,640 --> 00:57:44,197
when he was a 15-year-old,
there's no problem.

921
00:57:44,280 --> 00:57:48,907
You have that all data.
And I'm a big cricket stats buff.

922
00:57:48,990 --> 00:57:52,687
And one of the things, in fact,
one of the queries I have,

923
00:57:52,770 --> 00:57:54,427
which I'm going to do this weekend, is --

924
00:57:54,510 --> 00:57:58,957
and in fact, I was asking them
whether they have basically an

925
00:57:59,040 --> 00:58:01,960
MCP server for all of their data.

926
00:58:03,390 --> 00:58:08,827
And it's all in Azure Blob Storage,
so I must assume it is there.

927
00:58:08,910 --> 00:58:12,397
But one of my things I want to check
out is the conversion ratios.

928
00:58:12,480 --> 00:58:16,897
If truly Steve Smith's
under-15 scores are there,

929
00:58:16,980 --> 00:58:20,407
I want to see them next
Vaibhav Suryavanshi's scores

930
00:58:20,490 --> 00:58:25,687
and see how good is this guy
going to be when he grows up?

931
00:58:25,770 --> 00:58:32,317
And so that type of ability
to use data in a rich way

932
00:58:32,400 --> 00:58:35,887
and then translate it
in their case to the outcome

933
00:58:35,970 --> 00:58:40,147
that they desire, which is fan
engagement, is fantastic to see.

934
00:58:40,230 --> 00:58:44,797
So let's roll the video. TODD GREENBERG:
Cricket is our national sport.

935
00:58:44,880 --> 00:58:47,977
It's an Australian pastime.
It brings communities together.

936
00:58:48,060 --> 00:58:50,977
So fan engagement is at the very
core of everything we think

937
00:58:51,060 --> 00:58:52,417
about here at Cricket Australia.

938
00:58:52,500 --> 00:58:55,717
We're engaging with more than a million
fans on the Cricket Australia Live app.

939
00:58:55,800 --> 00:58:58,447
The challenge for us is to
personalize their experience.

940
00:58:58,530 --> 00:58:59,707
LEIGH DAVIES: When I'm
watching a cricket match,

941
00:58:59,790 --> 00:59:02,047
I'm paying attention to the
battle between bat and ball.

942
00:59:02,130 --> 00:59:04,387
It's fascinating. It keeps me involved.

943
00:59:04,470 --> 00:59:06,487
CASSIE GRAY: It's a pretty nuanced sport.

944
00:59:06,570 --> 00:59:10,237
It can speed up or surprise
you at any point in the game,

945
00:59:10,320 --> 00:59:13,807
and I love to follow along
and learn more about the players.

946
00:59:13,890 --> 00:59:16,687
TODD GREENBERG: AI Insights
provides a level of opportunity

947
00:59:16,770 --> 00:59:18,397
for all fans, whether that's engaging

948
00:59:18,480 --> 00:59:20,647
with the rich statistics
and history of the sport

949
00:59:20,730 --> 00:59:23,707
or it's just learning
the simple rules and regulations.

950
00:59:23,790 --> 00:59:25,147
BALAMURUGAN P M: Ease of use and accuracy

951
00:59:25,230 --> 00:59:27,487
to check the player stats,
to check the match stats,

952
00:59:27,570 --> 00:59:30,967
was non-negotiable for us
and Azure Open AI just hit the mark.

953
00:59:31,050 --> 00:59:35,977
Millions of data points that sits
in our Azure Cosmos TV are being served

954
00:59:36,060 --> 00:59:39,280
to our fans in subseconds.

955
00:59:40,950 --> 00:59:42,457
SPEAKER 8: Oh, that's a good strike.

956
00:59:42,540 --> 00:59:45,037
He's a strong boy is Ryan Harris.

957
00:59:45,120 --> 00:59:47,107
CASSIE GRAY: I like that I
can switch between profiles.

958
00:59:47,190 --> 00:59:49,387
So most of the time I'm a newbie.

959
00:59:49,470 --> 00:59:52,057
Sometimes I'll switch to stats guru.

960
00:59:52,140 --> 00:59:54,907
I like to deep dive,
especially on great players,

961
00:59:54,990 --> 00:59:57,877
what their stats are,
what they're known for.

962
00:59:57,960 --> 00:59:59,557
LEIGH DAVIES: I could
really see myself finding

963
00:59:59,640 --> 01:00:01,687
out information about test history.

964
01:00:01,770 --> 01:00:05,797
I noticed that the AI Insights
goes as far back as the 1800s.

965
01:00:05,880 --> 01:00:07,417
BALAMURUGAN P M:
With our Azure infrastructure set

966
01:00:07,500 --> 01:00:11,677
up in a way that is scalable,
it gives us a whole new avenue

967
01:00:11,760 --> 01:00:14,167
to interact with our fans
like never before.

968
01:00:14,250 --> 01:00:18,067
The frequency on which they
use the app, that has increased

969
01:00:18,150 --> 01:00:21,067
by 30% from the past season
to the current season.

970
01:00:21,150 --> 01:00:23,077
LEIGH DAVIES: Cricket is a
game that's in your blood.

971
01:00:23,160 --> 01:00:24,907
We're all from different parts
of life, but we're connected

972
01:00:24,990 --> 01:00:26,497
to cricket and the game that we love.

973
01:00:26,580 --> 01:00:28,567
TODD GREENBERG:
AI Insights gives us the ability

974
01:00:28,650 --> 01:00:30,727
to supercharge someone's
knowledge of the game,

975
01:00:30,810 --> 01:00:34,437
make them feel part of a community
and part of the sport.

976
01:00:34,520 --> 01:00:39,437
[Music] SATYA NADELLA: All right, so cool.

977
01:00:39,520 --> 01:00:43,937
[Applause]

978
01:00:44,020 --> 01:00:48,047
For a stats-rich sport,
this is just game changer.

979
01:00:48,130 --> 01:00:53,887
In fact, they were showing me
how you're following a match,

980
01:00:53,970 --> 01:00:57,937
and it just keeps coming ball by ball
with this unbelievable statistics.

981
01:00:58,020 --> 01:01:02,770
And one of the things that they had
was these conversion ratios, right?

982
01:01:04,170 --> 01:01:06,037
I guess, I don't know,
at least I'm obsessed about it,

983
01:01:06,120 --> 01:01:09,457
which is what's the greatest ever
test team from different eras?

984
01:01:09,540 --> 01:01:13,807
And you can't compare. One of the hard
statistical problems is how do you compare

985
01:01:13,890 --> 01:01:18,037
people across generations?
And it turns out there is causality,

986
01:01:18,120 --> 01:01:19,447
and it's called conversion ratio.

987
01:01:19,530 --> 01:01:23,317
So if you sort of really like -- at least,
that's one of the predictable things.

988
01:01:23,400 --> 01:01:26,590
And so I'm going to have
a lot of fun this weekend.

989
01:01:28,590 --> 01:01:32,317
So the last layer of the
tech stack I want to talk

990
01:01:32,400 --> 01:01:36,847
about is the cloud and AI
token factory, right?

991
01:01:36,930 --> 01:01:40,957
So this is the rich
infrastructure that you get

992
01:01:41,040 --> 01:01:47,947
to build to power all of these agent
systems and applications going forward.

993
01:01:48,030 --> 01:01:52,897
And it comes down to getting
this one super important formula

994
01:01:52,980 --> 01:01:59,197
right, which is performance is really
about tokens per dollar per watt.

995
01:01:59,280 --> 01:02:03,397
And you really want to be best-in-class
at it as a hyperscaler, and you want

996
01:02:03,480 --> 01:02:06,307
to be best-in-class at it as a country.

997
01:02:06,390 --> 01:02:08,617
And that, I think,
is what's going to really create

998
01:02:08,700 --> 01:02:12,787
that abundance of tokens
that then allows the rest

999
01:02:12,870 --> 01:02:15,937
of what we talked about to come alive.

1000
01:02:16,020 --> 01:02:22,657
And so we are building out Azure
as the world's computer with all

1001
01:02:22,740 --> 01:02:25,267
of the regions around the world.

1002
01:02:25,350 --> 01:02:30,277
We have 70 plus data center
regions around the world

1003
01:02:30,360 --> 01:02:32,467
with one fungible fleet.

1004
01:02:32,550 --> 01:02:38,137
And of course, when it comes
to Australia, we have regions

1005
01:02:38,220 --> 01:02:41,077
in Sydney, in Melbourne, and Canberra.

1006
01:02:41,160 --> 01:02:45,217
And it's growing.
It's 100% renewable energy-powered.

1007
01:02:45,300 --> 01:02:48,877
We're very proud of it.
We're very committed to it.

1008
01:02:48,960 --> 01:02:54,227
We're also, yeah. [Applause]

1009
01:02:54,310 --> 01:02:58,547
And one of the ways we
continue to improve performance

1010
01:02:58,630 --> 01:03:03,107
and power performance is by
innovating on the systems level

1011
01:03:03,190 --> 01:03:05,407
and even all the way
to the silicon level, right?

1012
01:03:05,490 --> 01:03:08,197
So we obviously partner
with NVIDIA and AMD,

1013
01:03:08,280 --> 01:03:11,287
but we're thrilled about
the progress Maia is making.

1014
01:03:11,370 --> 01:03:18,127
And Maia 200 now has 30 plus percent over
any leading ASIC out there in the market.

1015
01:03:18,210 --> 01:03:20,917
And we'll continue to innovate
and co-innovate, in fact,

1016
01:03:21,000 --> 01:03:26,977
between those agent run times
as well as the model architecture,

1017
01:03:27,060 --> 01:03:32,047
plus the AI system itself because there
are very efficient ways for us to be able

1018
01:03:32,130 --> 01:03:36,187
to really drive that tokens per
dollar per watt going forward,

1019
01:03:36,270 --> 01:03:39,697
and we want to be able
to have that ability.

1020
01:03:39,780 --> 01:03:46,747
We also are very focused
on our sovereignty considerations,

1021
01:03:46,830 --> 01:03:52,777
and so this is where we are making
sure that you have all of the controls.

1022
01:03:52,860 --> 01:03:57,037
Not only do we have the regions
in country, but we conform

1023
01:03:57,120 --> 01:03:58,837
to all the regulatory requirements.

1024
01:03:58,920 --> 01:04:02,227
You have the ability
to manage your own keys.

1025
01:04:02,310 --> 01:04:08,197
You can encrypt data. You can use
confidential computing such that the data

1026
01:04:08,280 --> 01:04:15,157
in use is also encrypted. So there's tons
of sovereignty control that is built-in.

1027
01:04:15,240 --> 01:04:18,247
And the one other consideration
I would also submit is

1028
01:04:18,330 --> 01:04:21,277
that you want to think about
cyber resilience as well

1029
01:04:21,360 --> 01:04:22,777
as sovereignty together, right?

1030
01:04:22,860 --> 01:04:27,097
Because you want the global
intelligence of signals

1031
01:04:27,180 --> 01:04:33,127
when it comes to your overall
resilience in addition to sovereignty.

1032
01:04:33,210 --> 01:04:35,497
You don't want to sacrifice
one for the other.

1033
01:04:35,580 --> 01:04:40,057
And that's, again, part of the design
of what we have put together.

1034
01:04:40,140 --> 01:04:43,597
And with all of that,
one of the things I'm absolutely thrilled

1035
01:04:43,680 --> 01:04:47,047
to announce is our doubling
down our commitment.

1036
01:04:47,130 --> 01:04:50,317
We are making our biggest
commitment in Australia

1037
01:04:50,400 --> 01:04:53,437
of Australian dollars of 25 billion.

1038
01:04:53,520 --> 01:04:57,127
[Applause]

1039
01:04:57,210 --> 01:05:04,057
So this will allow us to expand
this ability to generate the tokens

1040
01:05:04,140 --> 01:05:06,097
and provide the cloud infrastructure.

1041
01:05:06,180 --> 01:05:09,367
I think one of the things that gets
lost is we just think about the tokens.

1042
01:05:09,450 --> 01:05:12,607
But when I talked about
those hosted agents,

1043
01:05:12,690 --> 01:05:17,557
hosted agents need classic
cores, classic storage.

1044
01:05:17,640 --> 01:05:22,657
Same thing. Every Cowork session
needs storage and compute.

1045
01:05:22,740 --> 01:05:24,217
And so one of the things that we are going

1046
01:05:24,300 --> 01:05:30,877
to see is the explosion of both
cloud and AI accelerators,

1047
01:05:30,960 --> 01:05:33,787
and that's one of the things
that we are very excited about,

1048
01:05:33,870 --> 01:05:37,687
making sure we continue
to invest and bring all of that.

1049
01:05:37,770 --> 01:05:41,137
We are also thrilled
to expand our partnership

1050
01:05:41,220 --> 01:05:45,437
with the Signals Directorate and Cyber
Shield because, again, going back to --

1051
01:05:45,520 --> 01:05:51,197
[Applause] -- because one of the
challenges we all collectively face is how

1052
01:05:51,280 --> 01:05:56,227
do we protect ourselves, defend
ourselves and our organizations,

1053
01:05:56,310 --> 01:06:00,157
against cyber threat actors?
And so that's, again, a signals game.

1054
01:06:00,240 --> 01:06:04,717
That's an intelligence game. And we want
to make sure that we are able to extend

1055
01:06:04,800 --> 01:06:08,227
that Cyber Shield arrangement
such that the most vulnerable,

1056
01:06:08,310 --> 01:06:11,617
right -- that is that small
business or the consumer --

1057
01:06:11,700 --> 01:06:16,657
is protected when it
comes to any cyber attack.

1058
01:06:16,740 --> 01:06:20,047
And lastly, we are
really thrilled to double

1059
01:06:20,130 --> 01:06:23,077
down on the skilling
investments we're making.

1060
01:06:23,160 --> 01:06:28,837
We're going to train three plus
million Australians around AI skills.

1061
01:06:28,920 --> 01:06:34,687
And again, there's going to be
a real change in the way we work,

1062
01:06:34,770 --> 01:06:40,477
the tools we use to work. And so to be
able to get ahead of all of the jobs

1063
01:06:40,560 --> 01:06:43,327
and the economic opportunity
that gets created,

1064
01:06:43,410 --> 01:06:45,577
I think it's going to be very,
very important for all of us,

1065
01:06:45,660 --> 01:06:50,107
including Microsoft, to do
the work to bring the skilling

1066
01:06:50,190 --> 01:06:52,447
and the upskilling that is required.

1067
01:06:52,530 --> 01:06:54,517
So I kind of want
to close where I started,

1068
01:06:54,600 --> 01:06:58,327
which is our mission always remains

1069
01:06:58,410 --> 01:07:02,617
to empower every person --
in this case, in Australia

1070
01:07:02,700 --> 01:07:06,967
and every organization --
to be able to achieve more,

1071
01:07:07,050 --> 01:07:09,937
and I want to leave you with, I think,

1072
01:07:10,020 --> 01:07:14,467
a video that perhaps best
captures what this mission means

1073
01:07:14,550 --> 01:07:18,607
to us and what it means to us
collectively to strive for.

1074
01:07:18,690 --> 01:07:19,837
Thank you all very, very much.

1075
01:07:19,920 --> 01:07:21,460
Have a restful day.

1076
01:07:22,520 --> 01:07:27,817
[Music]

1077
01:07:27,900 --> 01:07:32,107
GRANT MAHER: Deadly Coders,
we're there to ignite that spark

1078
01:07:32,190 --> 01:07:33,967
of interest in STEM,
particularly in coding

1079
01:07:34,050 --> 01:07:37,417
and robotics for Indigenous
kids throughout Australia.

1080
01:07:37,500 --> 01:07:39,367
LEVI DUGGER: They need an opportunity,
like everyone else,

1081
01:07:39,450 --> 01:07:42,607
to be able to move with the
ever-changing world and keep

1082
01:07:42,690 --> 01:07:48,340
up with the demands of potential
future careers and educational pathways.

1083
01:07:49,410 --> 01:07:51,997
JACK REIS: Deadly Coders
are here to help bring skills

1084
01:07:52,080 --> 01:07:56,107
such as coding, education
around artificial intelligence,

1085
01:07:56,190 --> 01:08:01,207
and we are doing that in partnership
with organizations such as Microsoft.

1086
01:08:01,290 --> 01:08:04,297
>> Deadly Coders' Minecraft
Education program help break

1087
01:08:04,380 --> 01:08:07,957
down those stereotypes that mob
usually see for themselves.

1088
01:08:08,040 --> 01:08:10,687
The program makes AI and coding fun.

1089
01:08:10,770 --> 01:08:14,317
It gamifies the whole process,
which actually then creates

1090
01:08:14,400 --> 01:08:17,977
that energy and that spark.
EYLANIA NAAWI: It's a new way of learning,

1091
01:08:18,060 --> 01:08:21,877
and I think kids really need
to know how to use it correctly,

1092
01:08:21,960 --> 01:08:25,387
how to use it safely,
how to use it culturally,

1093
01:08:25,470 --> 01:08:30,627
and how we can use it alongside
culture, not replacing culture.

1094
01:08:30,710 --> 01:08:32,547
JACK REIS: When you are
able to impact the lives

1095
01:08:32,630 --> 01:08:37,917
of young kids who typically would not have
access to technology, to be able to bring

1096
01:08:38,000 --> 01:08:41,037
that to their world is priceless.

1097
01:08:41,120 --> 01:08:44,277
EYLANIA NAAWI: Making sure that
younger mob had the opportunity

1098
01:08:44,360 --> 01:08:46,437
to do whatever they wanted.

1099
01:08:46,520 --> 01:08:53,520
[Music]

1100
01:08:55,980 --> 01:08:58,777
SPEAKER 9: Please welcome
the President of Microsoft Australia

1101
01:08:58,860 --> 01:09:01,437
and New Zealand, Jane Livesey.

1102
01:09:01,520 --> 01:09:08,520
[Music]

1103
01:09:08,780 --> 01:09:11,137
JANE LIVESEY: Welcome to the AI tour.

1104
01:09:11,220 --> 01:09:14,317
It's wonderful to see so
many creators and innovators,

1105
01:09:14,400 --> 01:09:19,327
all who are helping shape Australia's
next chapter here with us today.

1106
01:09:19,410 --> 01:09:21,907
Thank you for taking the time.

1107
01:09:21,990 --> 01:09:25,327
This is my first AI tour since
taking on the leadership role

1108
01:09:25,410 --> 01:09:27,487
at the end of last year.

1109
01:09:27,570 --> 01:09:31,867
After three decades in business,
I've been asked why Microsoft?

1110
01:09:31,950 --> 01:09:34,357
And for me, the answer
has always been the same.

1111
01:09:34,440 --> 01:09:38,797
It's two words: trust and safety.

1112
01:09:38,880 --> 01:09:42,937
So much of our world is changing
from healthcare, education,

1113
01:09:43,020 --> 01:09:45,697
business and communications --

1114
01:09:45,780 --> 01:09:49,357
there couldn't be a more
important time to be Microsoft.

1115
01:09:49,440 --> 01:09:52,267
We have the privilege and the obligation

1116
01:09:52,350 --> 01:09:55,297
to help Australia navigate
this change with trust

1117
01:09:55,380 --> 01:09:58,867
and safety embedded in everything we do.

1118
01:09:58,950 --> 01:10:03,817
You just heard Satya outline
Microsoft's vision for an AI era

1119
01:10:03,900 --> 01:10:06,847
where intelligence moves
from experimentation

1120
01:10:06,930 --> 01:10:11,347
into everyday work embedded
in the world's most trusted cloud

1121
01:10:11,430 --> 01:10:15,777
and productivity platforms.
AI is no longer abstract.

1122
01:10:15,860 --> 01:10:20,647
It's here, and it's changing
how organizations operate.

1123
01:10:20,730 --> 01:10:23,707
Across every sector,
we're seeing the appetite

1124
01:10:23,790 --> 01:10:27,427
to adopt AI early and responsibly.

1125
01:10:27,510 --> 01:10:31,297
It's not just about doing things faster.

1126
01:10:31,380 --> 01:10:34,557
It's about unleashing human ambition,

1127
01:10:34,640 --> 01:10:38,517
and that's what we call
frontier transformation.

1128
01:10:38,600 --> 01:10:42,267
It's no longer about
pilots or proof of concepts.

1129
01:10:42,350 --> 01:10:46,017
It's about empowering people,
reinventing business process,

1130
01:10:46,100 --> 01:10:48,717
and unlocking the value of data.

1131
01:10:48,800 --> 01:10:55,167
And this moment isn't just about
technology; it's about shared prosperity.

1132
01:10:55,250 --> 01:10:57,657
There couldn't be a more important time

1133
01:10:57,740 --> 01:11:02,457
to unlock the value that AI
brings to all of Australia.

1134
01:11:02,540 --> 01:11:05,937
Through our local ecosystem,
Microsoft contributes more

1135
01:11:06,020 --> 01:11:11,307
than $36 billion to Australia's
GDP, and we support more

1136
01:11:11,390 --> 01:11:15,547
than 150,000 jobs nationwide.

1137
01:11:15,630 --> 01:11:20,467
But the opportunity ahead
of us is even more enormous,

1138
01:11:20,550 --> 01:11:22,807
but none of this will happen on its own.

1139
01:11:22,890 --> 01:11:28,357
It takes leadership -- leadership that
is bold and curious and people-centered --

1140
01:11:28,440 --> 01:11:34,567
and partnership across industry,
technology, and across all

1141
01:11:34,650 --> 01:11:39,067
of Australia to ensure that we
adopt AI in a way that is safe,

1142
01:11:39,150 --> 01:11:43,567
inclusive, and benefits all Australians.

1143
01:11:43,650 --> 01:11:46,267
And that's why this conversation matters.

1144
01:11:46,350 --> 01:11:49,597
I first want to thank
our partners and our sponsors

1145
01:11:49,680 --> 01:11:52,437
for bringing these moments to life.

1146
01:11:52,520 --> 01:11:57,217
[Applause]

1147
01:11:57,300 --> 01:11:59,997
There are two organizations
that are at the heart

1148
01:12:00,080 --> 01:12:04,407
of Australia's national fabric:
Telstra and Westpac,

1149
01:12:04,490 --> 01:12:06,907
and they're putting this into practice.

1150
01:12:06,990 --> 01:12:09,457
They're not just talking
about what's possible.

1151
01:12:09,540 --> 01:12:15,007
They're doing it. So please join me
in welcoming Vicki Brady, CEO of Telstra,

1152
01:12:15,090 --> 01:12:19,987
and Anthony Miller, CEO of Westpac,
to talk about how AI is transforming their

1153
01:12:20,070 --> 01:12:23,887
organizations, their people,
and Australia's future.

1154
01:12:23,970 --> 01:12:26,437
Thank you. Let's get started.

1155
01:12:26,520 --> 01:12:33,437
[Applause]

1156
01:12:33,520 --> 01:12:38,407
[Music]

1157
01:12:38,490 --> 01:12:41,797
I couldn't be more excited to have you
both here for this conversation today.

1158
01:12:41,880 --> 01:12:45,667
I've been looking forward
to it as part of the AI tour.

1159
01:12:45,750 --> 01:12:50,257
Both Telstra and Westpac are operating
in an environment that's rapidly changing.

1160
01:12:50,340 --> 01:12:52,987
That's something that
we're all experiencing.

1161
01:12:53,070 --> 01:12:56,797
How are you navigating this?
Maybe, Anthony, I'll start with you?

1162
01:12:56,880 --> 01:13:00,067
ANTHONY MILLER: So certainly,
it's one of the most challenging

1163
01:13:00,150 --> 01:13:04,327
and invigorating
environments for a long time.

1164
01:13:04,410 --> 01:13:07,537
The way we're thinking about
particularly the AI opportunity is

1165
01:13:07,620 --> 01:13:12,487
that it's just this fantastic way
in which people can do their job better.

1166
01:13:12,570 --> 01:13:14,857
They can do their job faster.
They can do it more safely.

1167
01:13:14,940 --> 01:13:16,657
They can do it more consistently.

1168
01:13:16,740 --> 01:13:20,257
And so the way we think about
AI is how do we get everybody set

1169
01:13:20,340 --> 01:13:22,177
up to use it, and how are we investing

1170
01:13:22,260 --> 01:13:25,147
in making sure they can use
it and know how to use it

1171
01:13:25,230 --> 01:13:26,737
and continue to improve how they use it?

1172
01:13:26,820 --> 01:13:29,227
So funny enough,
from my perspective, when we think

1173
01:13:29,310 --> 01:13:33,967
about AI, it's a technology, yes,
but it, really, it's a people issue.

1174
01:13:34,050 --> 01:13:38,227
It's a people challenge. And so how
well are we investing in our people?

1175
01:13:38,310 --> 01:13:39,637
How well are we training them?

1176
01:13:39,720 --> 01:13:42,667
How well are we leading them? How well
are we inspiring them and motivating them

1177
01:13:42,750 --> 01:13:45,727
to use this tool to improve how
they do things, day in, day out?

1178
01:13:45,810 --> 01:13:48,297
That's the way we're
thinking about it at Westpac.

1179
01:13:48,380 --> 01:13:50,457
JANE LIVESEY: Vicki?
VICKI BRADY: Yeah, so many parallels.

1180
01:13:50,540 --> 01:13:54,567
I guess I've been trying
to think about it in three ways.

1181
01:13:54,650 --> 01:14:00,177
So first off, as a telco,
lots of increasing demands,

1182
01:14:00,260 --> 01:14:02,997
expectation, just demand on our network.

1183
01:14:03,080 --> 01:14:06,927
So to continue to be
a leader in connectivity,

1184
01:14:07,010 --> 01:14:09,747
we've got to be a leader
in how we're embedding

1185
01:14:09,830 --> 01:14:12,937
and applying AI inside the business.

1186
01:14:13,020 --> 01:14:18,847
And that's a whole
of business transformation,

1187
01:14:18,930 --> 01:14:24,747
the way we engage with customers, the way
we empower our teams, even the way we run

1188
01:14:24,830 --> 01:14:26,187
and operate our network.

1189
01:14:26,270 --> 01:14:31,027
So the thing on my mind in that part
is always are we going fast enough?

1190
01:14:31,110 --> 01:14:34,207
I mean, the world is just changing
so fast, as Anthony just said.

1191
01:14:34,290 --> 01:14:35,707
It's sort of unprecedented.

1192
01:14:35,790 --> 01:14:40,987
I know that gets used a lot,
and I just think to be competitive,

1193
01:14:41,070 --> 01:14:43,867
we've got to be moving
at a scale and a pace.

1194
01:14:43,950 --> 01:14:46,267
The second way is very much the skills.

1195
01:14:46,350 --> 01:14:50,227
Are we giving sufficient opportunity
to our teams to grow those skills?

1196
01:14:50,310 --> 01:14:52,327
We use a data and AI academy.

1197
01:14:52,410 --> 01:14:55,537
We've just put in specific
learning pathways for jobs.

1198
01:14:55,620 --> 01:14:58,387
We're working with Microsoft
at the moment on a pilot

1199
01:14:58,470 --> 01:15:01,447
for an AI learning agent,
which would be great.

1200
01:15:01,530 --> 01:15:06,247
And then the final way we think about
it is really the national conversation

1201
01:15:06,330 --> 01:15:11,827
around how are the AI benefits
going to be shared across Australia?

1202
01:15:11,910 --> 01:15:15,967
Because AI is really
a whole-of-society transition.

1203
01:15:16,050 --> 01:15:18,307
It's not just affecting business.

1204
01:15:18,390 --> 01:15:19,717
It's affecting the way we work.

1205
01:15:19,800 --> 01:15:21,877
It affects the economy.

1206
01:15:21,960 --> 01:15:25,327
So we're thinking about things
that are important around trust,

1207
01:15:25,410 --> 01:15:29,707
around inclusion, around skills,
and we do think about some

1208
01:15:29,790 --> 01:15:31,537
of those groups that are
going to need more support:

1209
01:15:31,620 --> 01:15:34,147
small businesses, regional communities,

1210
01:15:34,230 --> 01:15:36,697
people facing barriers
to digital inclusion.

1211
01:15:36,780 --> 01:15:41,077
And again, Jane will announce something
today around small business, trying

1212
01:15:41,160 --> 01:15:43,267
to help small businesses
get greater access.

1213
01:15:43,350 --> 01:15:44,887
But I think businesses need to be

1214
01:15:44,970 --> 01:15:48,267
in that conversation alongside
government and industry,

1215
01:15:48,350 --> 01:15:51,247
and be helping contribute
to those solutions.

1216
01:15:51,330 --> 01:15:53,527
JANE LIVESEY: If we come
back into your own businesses,

1217
01:15:53,610 --> 01:15:55,627
you've both been quite ambitious in terms

1218
01:15:55,710 --> 01:15:58,837
of your AI transformations,
can you share maybe a little bit

1219
01:15:58,920 --> 01:16:02,017
about what you've learned and some
of the outcomes you've achieved?

1220
01:16:02,100 --> 01:16:02,947
VICKI BRADY: Yeah, absolutely.

1221
01:16:03,030 --> 01:16:05,977
I might start with
challenges and learnings.

1222
01:16:06,060 --> 01:16:10,927
Lots of those. Top of the list would
be just how important getting the

1223
01:16:11,010 --> 01:16:14,107
foundational tech and data is.

1224
01:16:14,190 --> 01:16:19,177
Just as an example, a few years ago, we
had 80 platforms where our data was held.

1225
01:16:19,260 --> 01:16:21,187
Our target is to get to three.

1226
01:16:21,270 --> 01:16:25,717
We're at 20 right now, so making progress,
but you've got to be relentless, I think,

1227
01:16:25,800 --> 01:16:30,087
relentless on removing complexity
and getting much simpler.

1228
01:16:30,170 --> 01:16:33,277
We had more than 400 software partners.

1229
01:16:33,360 --> 01:16:36,217
We consolidated down
to two to move faster.

1230
01:16:36,300 --> 01:16:39,757
And then it is skills and access.

1231
01:16:39,840 --> 01:16:43,897
And we've got a goal this year to get 85%

1232
01:16:43,980 --> 01:16:49,777
of our teams are using AI tools
at least weekly, and we're at 75% now.

1233
01:16:49,860 --> 01:16:53,047
So that's been important, foundationally.

1234
01:16:53,130 --> 01:16:55,987
If I think about outcomes,
I mean, everyone will talk

1235
01:16:56,070 --> 01:17:01,147
about getting better customer outcomes
alongside being more efficient.

1236
01:17:01,230 --> 01:17:05,797
But if I drill down and just
use one example, last November,

1237
01:17:05,880 --> 01:17:10,897
on our website, we launched
a generative-AI-powered assistant.

1238
01:17:10,980 --> 01:17:16,597
First time we had GenAI in that tool,
and we, in March, took that into our app.

1239
01:17:16,680 --> 01:17:19,597
So on our website,
it does pretty simple stuff,

1240
01:17:19,680 --> 01:17:22,537
be able to check plans, activate a SIM.

1241
01:17:22,620 --> 01:17:24,697
In the app, it can do
more sophisticated things

1242
01:17:24,780 --> 01:17:27,427
because we know it's who
it is, which customer,

1243
01:17:27,510 --> 01:17:31,857
so it can make changes to plans,
accounts, troubleshoot.

1244
01:17:31,940 --> 01:17:36,597
It's already, of all of that
volume coming in on the website

1245
01:17:36,680 --> 01:17:39,837
and our app,
it is dealing with 30% of that

1246
01:17:39,920 --> 01:17:44,737
without our teams needing to get involved,
and we continue to build that capability.

1247
01:17:44,820 --> 01:17:47,977
Of course, it hands off where
it's particularly sensitive

1248
01:17:48,060 --> 01:17:51,877
or there might be indicators
of vulnerable circumstances,

1249
01:17:51,960 --> 01:17:56,257
but it's allowing our teams
to spend more time with customers

1250
01:17:56,340 --> 01:17:59,887
on those more complex issues.
So that's one example.

1251
01:17:59,970 --> 01:18:01,177
ANTHONY MILLER: Well, I mean, Vicki,

1252
01:18:01,260 --> 01:18:04,837
I agree with almost
everything you said there.

1253
01:18:04,920 --> 01:18:06,727
That's the learnings we've had.

1254
01:18:06,810 --> 01:18:10,837
I think the challenge that or the learning

1255
01:18:10,920 --> 01:18:14,557
that I've really found
interesting with this tool,

1256
01:18:14,640 --> 01:18:18,187
this technology, is the challenge

1257
01:18:18,270 --> 01:18:21,577
that people need to be open-minded.

1258
01:18:21,660 --> 01:18:25,447
I run -- and it is a very regulated
industry -- banking as appropriately

1259
01:18:25,530 --> 01:18:30,037
so banking license is a real privilege,
and we need to meet a certain standard.

1260
01:18:30,120 --> 01:18:32,767
And so therefore there's a real
culture, or that's the way we do it.

1261
01:18:32,850 --> 01:18:38,167
We must do it this way. And so the tool
asks you and gives you an opportunity

1262
01:18:38,250 --> 01:18:40,387
to think about doing things differently.

1263
01:18:40,470 --> 01:18:43,987
And so therefore, it's a very
big challenge for us to sort

1264
01:18:44,070 --> 01:18:46,567
of create an environment where
people feel they can step back

1265
01:18:46,650 --> 01:18:49,207
and go is there a different
way that I can do my job?

1266
01:18:49,290 --> 01:18:51,247
Is there a different way
that we can deliver this service

1267
01:18:51,330 --> 01:18:53,647
or product to the customer
and do it in a way

1268
01:18:53,730 --> 01:18:56,737
that meets the standards
rightly expected of us?

1269
01:18:56,820 --> 01:19:01,747
And that is not a skill
that people naturally challenge

1270
01:19:01,830 --> 01:19:04,567
to acquire as they
progress in an organization.

1271
01:19:04,650 --> 01:19:07,987
Equally, someone has a better
idea than you or a better way

1272
01:19:08,070 --> 01:19:10,897
of doing things is not way
people have classically been

1273
01:19:10,980 --> 01:19:13,867
rewarded to acknowledge
someone else has a better idea.

1274
01:19:13,950 --> 01:19:16,837
And so therefore this challenge
that sits in front of us is

1275
01:19:16,920 --> 01:19:20,557
that it asks us to think about things
differently and be more open-minded.

1276
01:19:20,640 --> 01:19:22,897
And so that's the learning we've taken.

1277
01:19:22,980 --> 01:19:24,277
And so hence my observation

1278
01:19:24,360 --> 01:19:27,997
around it being perhaps not
withstanding it's a technology.

1279
01:19:28,080 --> 01:19:31,267
It's one of the great people
issues or opportunities of all time.

1280
01:19:31,350 --> 01:19:33,757
We need to ask people to think
about things differently.

1281
01:19:33,840 --> 01:19:37,267
And thankfully, Jane, too,
your organization, with the Copilot,

1282
01:19:37,350 --> 01:19:41,457
we've given every single member
of the organization the tool,

1283
01:19:41,540 --> 01:19:43,077
every single member of the company.

1284
01:19:43,160 --> 01:19:47,007
Thirty-five thousand employees
is challenged, encouraged,

1285
01:19:47,090 --> 01:19:49,407
and motivated to try
and experiment and learn,

1286
01:19:49,490 --> 01:19:52,437
and then layering on top of that,
we have more ambition,

1287
01:19:52,520 --> 01:19:56,287
more goals, and more
opportunities as we go forward.

1288
01:19:56,370 --> 01:20:00,607
JANE LIVESEY: Amazing. I think as leaders,
we're all operating in the heart

1289
01:20:00,690 --> 01:20:02,047
of Australia at the moment.

1290
01:20:02,130 --> 01:20:04,297
What are some of the things
that you think we should be thinking

1291
01:20:04,380 --> 01:20:08,167
about and your hopes
as leaders for Australia?

1292
01:20:08,250 --> 01:20:10,537
VICKI BRADY: It's a big
question, isn't it?

1293
01:20:10,620 --> 01:20:13,717
I mean, the first thing I'd
say is I think we've, course,

1294
01:20:13,800 --> 01:20:16,867
we've got to consider
the risks and challenges,

1295
01:20:16,950 --> 01:20:20,527
but I really hope we grab the opportunity

1296
01:20:20,610 --> 01:20:23,857
because I just see it
as to remain competitive,

1297
01:20:23,940 --> 01:20:28,087
to remain relevant as companies,
but as a country,

1298
01:20:28,170 --> 01:20:31,567
grabbing that opportunity right
now and moving fast enough.

1299
01:20:31,650 --> 01:20:38,557
And I think, for me, that means choices
we make today as businesses, as a country,

1300
01:20:38,640 --> 01:20:40,447
around some of those foundational pieces

1301
01:20:40,530 --> 01:20:44,637
about investing in skills
in Australia, building trust

1302
01:20:44,720 --> 01:20:47,487
through the risk
and governance frameworks,

1303
01:20:47,570 --> 01:20:51,207
inclusion so people can
share in those benefits,

1304
01:20:51,290 --> 01:20:53,397
and getting the digital
infrastructure right

1305
01:20:53,480 --> 01:20:56,227
so we unlock those productivity benefits.

1306
01:20:56,310 --> 01:20:59,767
I just think it's a huge
opportunity, and those things

1307
01:20:59,850 --> 01:21:03,307
to position us well are
necessary and they're urgent.

1308
01:21:03,390 --> 01:21:04,690
Yeah.

1309
01:21:05,940 --> 01:21:07,147
ANTHONY MILLER: I think, I agree.

1310
01:21:07,230 --> 01:21:11,857
I mean, the real challenge
or what I really want to see is

1311
01:21:11,940 --> 01:21:14,137
like let's have some ambition.

1312
01:21:14,220 --> 01:21:17,077
The current discourse
publicly is so negative.

1313
01:21:17,160 --> 01:21:23,137
It's so informed by what's not
going well or what is a challenge,

1314
01:21:23,220 --> 01:21:28,117
as opposed to there are opportunities this
country has today which are extraordinary,

1315
01:21:28,200 --> 01:21:30,307
and I think this technology
is a contributor

1316
01:21:30,390 --> 01:21:34,207
to what we should be aiming for,
which is to really take Australia forward.

1317
01:21:34,290 --> 01:21:37,537
And so without wanting to be
too dramatic about it, I mean,

1318
01:21:37,620 --> 01:21:41,107
one of the challenges that faces
the country is lifting productivity.

1319
01:21:41,190 --> 01:21:44,887
And classically, to grow productivity,
it's about investment in things

1320
01:21:44,970 --> 01:21:47,347
such as technology and innovation.

1321
01:21:47,430 --> 01:21:49,267
And normally, that's
technology and innovation,

1322
01:21:49,350 --> 01:21:52,447
it only drives productivity
when it's diffused right

1323
01:21:52,530 --> 01:21:54,967
across the community
and right across society.

1324
01:21:55,050 --> 01:21:57,757
And so think electricity,
it's only when everyone's using it

1325
01:21:57,840 --> 01:22:01,027
that we truly realized its benefits.

1326
01:22:01,110 --> 01:22:03,217
And funny enough, this technology has

1327
01:22:03,300 --> 01:22:07,387
so quickly diffused and so
quickly being adopted and used

1328
01:22:07,470 --> 01:22:11,557
by everyone that it sets us up quicker
than we've ever had the opportunity to,

1329
01:22:11,640 --> 01:22:15,727
so how are we embracing it? And so
we need an ambition for the country.

1330
01:22:15,810 --> 01:22:17,707
We need a public discourse that's focused

1331
01:22:17,790 --> 01:22:20,167
on how do we improve
the country using this?

1332
01:22:20,250 --> 01:22:22,897
And it's not the panacea
for everything we need to do, but,

1333
01:22:22,980 --> 01:22:24,277
gee, it's got a big role to play.

1334
01:22:24,360 --> 01:22:27,697
And I think things like
regulators, governments,

1335
01:22:27,780 --> 01:22:32,047
businesses, have a real
obligation to encourage

1336
01:22:32,130 --> 01:22:35,797
and motivate and invest
in and challenge people to use it,

1337
01:22:35,880 --> 01:22:39,217
to improve how they do their job, deliver
better outcomes for their customers.

1338
01:22:39,300 --> 01:22:41,947
And I think that's that
catalyst will then drive some

1339
01:22:42,030 --> 01:22:43,417
of what I think we can realize from it.

1340
01:22:43,500 --> 01:22:47,617
And as I say, I just wish we were much
more positive and ambitious around it.

1341
01:22:47,700 --> 01:22:48,187
JANE LIVESEY: Amazing.

1342
01:22:48,270 --> 01:22:51,667
So Vicki, Anthony, I can't thank
you enough for your partnership

1343
01:22:51,750 --> 01:22:54,037
and for sharing your insights today.

1344
01:22:54,120 --> 01:22:58,177
We're really excited about the opportunity
that AI brings to all Australians.

1345
01:22:58,260 --> 01:23:01,807
Thank you all for joining us today, and we
hope you enjoy the rest of the AI tour.

1346
01:23:01,890 --> 01:23:03,437
Thank you. ANTHONY MILLER: Thank you.

1347
01:23:03,520 --> 01:23:10,520
[Music]