SATYA NADELLA: Hi, everyone.

Welcome. It's great

to see all of you in Seattle in person.

We have an unbelievable show.

I see Scott Guthrie even wore his red shirt.

We welcome you to

the Azure Kubernetes Container Service 2023 launch.

No, don't worry. We'll have some fun.

Scott is not coming up to show you

code onscreen anytime soon,

but look, it's an exciting time in tech.

The broad contours of

this next platform are

just getting clearer and clearer each day.

The advances, what's possible.

That's what obviously excites us in our industry.

But they're also grounded

in what's happening in the broader world.

There's no question. There are

enormous challenges out there.

In fact, it reminds me and I've spoken about

this before of the very founding of Microsoft in 1975.

In fact, when the popular electronics cover

came out with the Altair,

which of course our founders

picked up and ran with it and

created essentially what's the software industry

as we know of it today.

That same week,

Newsweek had a cover with President Carter

trying to fight off

the three-headed monster of inflation,

recession, and an energy crisis.

Today you'd have something similar.

You will have AI on one cover and

then we'll have those three challenges

plus for good measure,

we can add a few more.

We, as Microsoft,

we as the tech industry have to really

ground ourselves in how do we relate one to the other?

In other words, can we use technology to overcome

the challenges that people and

organizations and countries face?

That's really the pursuit here.

In that context, I would say,

I just want to share a couple of

anecdotes which gives me great hope.

Quite honestly, it gives me personally a lot of

satisfaction around working at Microsoft,

working in this industry to

push the state of the art of technology.

The first one, obviously when Sam and

his team late last year launched ChatGPT,

that's the only thing anybody your friends and

family wanted to talk about throughout the holidays.

It was just crazy house.

It was like the Mosaic moment.

The closest we've come.

It's been 30 years now since when Mosaic launch,

which I distinctly remember.

It was very exciting time.

I went on a holiday first and then I was in

the first week of January I was in India.

On Jan 1st,

I look at my news feed and I see

this tweet that Andrej Karpathy put out.

Who is our ex OpenAI and Tesla,

and is now an independent AI developer.

He had this thing about the product that really he

was most excited about the previous year was

GitHub Copilot and he was saying how 80 percent of

his code was being generated by this,

I would say first at

scale product built on NLM technology.

This doesn't mean he's

80 percent somehow not doing his work.

In fact, he's getting so much more leverage.

In fact, recently we crossed

100 million developers on GitHub.

Think about this. There are

100 million developers on GitHub.

If we can improve their productivity,

just like how Andrej was able to observe.

Then let's say in the next decade we double that number,

may be double it again.

We get close to a half a billion developers,

what economic opportunity it would create.

Because there is not a meeting that I

go to today with any CEO,

CXO of any organization who's not

looking for more software developers,

more digital skills.

That's the currency in every sector of the economy,

in every country of the world.

That's the opportunity we

have to be able to take

this technology and make a difference.

But then the next day I went

to Mumbai and then I saw this demo.

This was just for me,

the most profound thing that I've seen in a long time.

The demo was actually built by

the Ministry of Electronics in

India because they are building

out a digital public goods.

Their idea is look, India has got

multiple official languages and they wanted to

democratize essentially access to language translation.

They're building it out as a digital public good.

In fact, Microsoft and Azure and

Microsoft Research are all involved in that project.

This is basically speech-to-text,

text-to-speech across all of the languages in India.

They showed me this demo where

a farmer speaking in

Hindi expresses a pretty complex thought

about how he had heard

about some government program and wants to

apply for a subsidy that he thinks he's eligible for.

It's a pretty complex prompt query.

There's technology,

that's a good job.

It goes to the bot, recognizes the speech,

comes back and says, you know what?

You should go to this portal,

fill out these forms,

then you'll get your subsidy.

He says, look, I'm not going to go into any portal.

I'm not going to fill out any forms,

can you help me? He does it.

Then I was told that a developer said, you know what?

That is Daisy Ching a model that was

trained on all of the documents of

the government of India using

GPT with this speech recognition software.

Basically two models coming together to really

help a rural farmer in India

trying to get access to a government program.

Look, I grew up in India.

I dreamt every day that someday

the industrial revolution will get evenly

distributed across the world.

Here I was,

seeing something so profound,

something that is developed by the folks at

OpenAI in the West Coast of

the United States a few months earlier,

used by a developer locally

to have an impact on a rural farmer.

That to me is what gives

me meaning and I think gives

us all meaning in our industry.

It is just fantastic to see that.

Now of course we've got to scale it and scale

it with a real understanding that we can break things.

It's about being also clear-eyed about

the unintended consequences of any new technology.

In fact, that's why way back in

2016 is when we came out with the AI principles.

You have both we as Microsoft and our partners at OpenAI,

deeply care about this.

In fact, the entire genesis of

OpenAI is from that foundation.

We built these principles,

but we've not just put those principles as a document,

but we've been practicing it because that's the only way

technology gets better in this particular case.

When you're talking about AI, it's about alignment with

human preferences and societal norms

and you're not going to do that in a lab.

You have to do that out there in the world.

It starts by the way with design decision ones makes.

When you think about AI, you

can have the human in the loop,

you can