The following is a conversation with Kevin Scott,

the CTO of Microsoft.

Before that, he was the senior vice president

of engineering and operations at LinkedIn.

And before that, he oversaw mobile ads engineering

at Google.

He also has a podcast called Behind the Tech

with Kevin Scott, which I'm a fan of.

This was a fun and wide ranging conversation

that covered many aspects of computing.

It happened over a month ago,

before the announcement of Microsoft's investment

in OpenAI that a few people have asked me about.

I'm sure there'll be one or two people in the future

that'll talk with me about the impact of that investment.

This is the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube,

give it five stars on iTunes,

support it on Patreon,

or simply connect with me on Twitter at Lex Friedman,

spelled F R I D M A N.

And I'd like to give a special thank you

to Tom and Nelante Bighousen

for their support of the podcast on Patreon.

Thanks Tom and Nelante.

Hope I didn't mess up your last name too bad.

Your support means a lot

and inspires me to keep this series going.

And now, here's my conversation with Kevin Scott.

You've described yourself as a kid in a candy store

at Microsoft because of all the interesting projects

that are going on.

Can you try to do the impossible task

and give a brief whirlwind view

of all the spaces that Microsoft is working in?

Both research and product?

If you include research,

it becomes even more difficult.

I think broadly speaking,

Microsoft's product portfolio includes everything

from big cloud business,

like a big set of SaaS services.

We have sort of the original,

or like some of what are among the original

productivity software products that everybody uses.

We have an operating system business.

We have a hardware business where we make everything

from computer mice and headphones

to high end personal computers and laptops.

We have a fairly broad ranging research group

where we have people doing everything

from economics research.

So there's this really, really smart young economist,

Glenn Weil, who my group works with a lot,

who's doing this research on these things

called radical markets.

He's written an entire technical book

about this whole notion of radical markets.

So like the research group sort of spans from that

to human computer interaction to artificial intelligence.

And we have GitHub, we have LinkedIn,

we have a search advertising and news business

and like probably a bunch of stuff

that I'm embarrassingly not recounting in this list.

Gaming to Xbox and so on, right?

Yeah, gaming for sure.

Like I was having a super fun conversation this morning

with Phil Spencer.

So when I was in college,

there was this game that LucasArts made

called Day of the Tentacle

that my friends and I played forever.

And like we're doing some interesting collaboration now

with the folks who made Day of the Tentacle.

And I was like completely nerding out with Tim Schafer,

like the guy who wrote a Day of the Tentacle this morning,

just a complete fan boy,

which sort of it like happens a lot.

Like Microsoft has been doing so much stuff

at such breadth for such a long period of time

that like being CTO like most of the time,

my job is very, very serious.

And sometimes like I get caught up

in like how amazing it is to be able to have

the conversations that I have with the people

I get to have them with.

Yeah, to reach back into the sentimental.

And what's the radical markets and the economics?

So the idea with radical markets is like,

can you come up with new market based mechanisms to,

you know, I think we have this,

we're having this debate right now,

like does capitalism work like free markets work?

Can the incentive structures

that are built into these systems produce outcomes

that are creating sort of equitably distributed benefits

for every member of society?

You know, and I think it's a reasonable,

reasonable set of questions to be asking.

And so what Glenn, and so like, you know,

one mode of thought there,

like if you have doubts that the markets

are actually working, you can sort of like tip towards

like, okay, let's become more socialist

and, you know, like have central planning and, you know,

governments or some other central organization

is like making a bunch of decisions

about how, you know, sort of work gets done

and, you know, like where the, you know,

where the investments and where the outputs

of those investments get distributed.

Glenn's notion is like, lean more

into like the market based mechanism.

So like, for instance, you know,

this is one of the more radical ideas,

like suppose that you had a radical pricing mechanism

for assets like real estate where you were,

you could be bid out of your position

in your home, you know, for instance.

So like if somebody came along and said,

you know, like I can find higher economic utility

for this piece of real estate

that you're running your business in,

like then like you either have to, you know,

sort of bid to sort of stay

or like the thing that's got the higher economic utility,

you know, sort of takes over the asset

which would make it very difficult

to have the same sort of rent seeking behaviors

that you've got right now

because like if you did speculative bidding,

like you would very quickly like lose a whole lot of money.

And so like the prices of the assets

would be sort of like very closely indexed

to like the value that they could produce.

And like, because like you'd have this sort

of real time mechanism that would force you

to sort of mark the value of the asset to the market,

then it could be taxed appropriately.

Like you couldn't sort of sit on this thing and say,

oh, like this house is only worth 10,000 bucks

when like everything around it is worth 10 million.

That's really, so it's an incentive structure

that where the prices match the value much better.

Yeah, and Glenn does a much better job than I do

at selling and I probably picked the world's worst example,

you know, and it's intentionally provocative,

so like this whole notion,

like I'm not sure whether I like this notion

that like we can have a set of market mechanisms

where I could get bid out of my property, you know,

but you know, like if you're thinking about something

like Elizabeth Warren's wealth tax, for instance,

like you would have, I mean, it'd be really interesting

in like how you would actually set the price on the assets

and like you might have to have a mechanism like that

if you put a tax like that in place.

It's really interesting that that kind of research,

at least tangentially is touching Microsoft research.

That you're really thinking broadly.

Maybe you can speak to, this connects to AI,

so we have a candidate, Andrew Yang,

who kind of talks about artificial intelligence

and the concern that people have about, you know,

automation's impact on society and arguably,

Microsoft is at the cutting edge of innovation

in all these kinds of ways and so it's pushing AI forward.

How do you think about combining all our conversations

together here with radical markets and socialism

and innovation in AI that Microsoft is doing

and then Andrew Yang's worry that that will result

in job loss for the lower and so on.

How do you think about that?

I think it's sort of one of the most important questions

in technology like maybe even in society right now

about how is AI going to develop

over the course of the next several decades

and what's it going to be used for

and what benefits will it produce

and what negative impacts will it produce

and who gets to steer this whole thing.

I'll say at the highest level,

one of the real joys of getting to do what I do at Microsoft

is Microsoft has this heritage as a platform company

and so Bill has this thing that he said a bunch of years ago

where the measure of a successful platform

is that it produces far more economic value

for the people who build on top of the platform

than is created for the platform owner or builder

and I think we have to think about AI that way.

As a platform.

Yeah, it has to be a platform that other people can use

to build businesses, to fulfill their creative objectives,

to be entrepreneurs, to solve problems that they have

in their work and in their lives.

It can't be a thing where there are a handful of companies

sitting in a very small handful of cities geographically

who are making all the decisions about what goes into the AI

and then on top of all this infrastructure,

then build all of the commercially valuable uses for it.

So I think that's bad from a sort of economics

and sort of equitable distribution of value perspective,

sort of back to this whole notion of did the markets work?

But I think it's also bad from an innovation perspective

because I have infinite amounts of faith

in human beings that if you give folks powerful tools,

they will go do interesting things

and it's more than just a few tens of thousands of people

with the interesting tools,

it should be millions of people with the tools.

So it's sort of like you think about the steam engine

in the late 18th century, like it was maybe the first

large scale substitute for human labor

that we've built like a machine

and in the beginning when these things are getting deployed,

the folks who got most of the value from the steam engines

were the folks who had capital

so they could afford to build them

and like they built factories around them and businesses

and the experts who knew how to build and maintain them.

But access to that technology democratized over time.

Like now, like an engine, it's not like a differentiated

thing, like there isn't one engine company

that builds all the engines

and all of the things that use engines

are made by this company

and like they get all the economics from all of that.

Like fully demarcated, like they're probably,

we're sitting here in this room

and like even though they're probably things

like the MEMS gyroscope that are in both of our phones,

like there's like little engines sort of everywhere.

They're just a component in how we build the modern world.

Like AI needs to get there.

Yeah, so that's a really powerful way to think.

If we think of AI as a platform

versus a tool that Microsoft owns,

as a platform that enables creation on top of it,

that's the way to democratize it.

That's really interesting actually.

And Microsoft throughout its history

has been positioned well to do that.

And the tie back to this radical markets thing,

like so my team has been working with Glenn on this,

and Jaren Lanier actually.

So Jaren is the sort of father of virtual reality.

Like he's one of the most interesting human beings on the planet,

like a sweet, sweet guy.

And so Jaren and Glenn and folks in my team have been working

on this notion of data as labor

or like they call it data dignity as well.

And so the idea is that if you,

again going back to this sort of industrial analogy,

if you think about data as the raw material that is

consumed by the machine of AI in order to do useful things,

then like we're not doing a really great job right now in having

transparent marketplaces for valuing those data contributions.

So and we all make them explicitly like you go to LinkedIn,

you sort of set up your profile on LinkedIn,

like that's an explicit contribution.

Like you know exactly the information

that you're putting into the system.

And like you put it there because you have

some nominal notion of what value you're going to get in return.

But it's like only nominal,

like you don't know exactly what value you're getting in return.

Like service is free,

like it's low amount of perceived debt.

And then you've got all this indirect contribution that you're

making just by virtue of interacting with all of

the technology that's in your daily life.

And so like what Glenn and

Jaren and this data dignity team are trying to do is like,

can we figure out a set of mechanisms that let us value

those data contributions so that you could create

an economy and like a set of controls and incentives that

would allow people to like maybe even in the limit,

like earn part of their living

through the data that they're creating.

And like you can sort of see it in explicit ways.

There are these companies like Scale AI,

and like there are a whole bunch of them in China

right now that are basically data labeling companies.

So like you're doing supervised machine learning,

you need lots and lots of label training data.

And like those people who work for

those companies are getting compensated

for their data contributions into the system.

And so.

That's easier to put a number on

their contribution because they're explicitly labeling data.

Correct.

But you're saying that we're all

contributing data in different kinds of ways.

And it's fascinating to start to

explicitly try to put a number on it.

Do you think that's possible?

I don't know. It's hard. It really is.

Because we don't have

as much transparency as I think

we need in like how the data is getting used.

And it's super complicated.

Like we, I think as

technologists sort of appreciate

like some of the subtlety there.

It's like the data gets created and then it gets,

it's not valuable.

Like the data exhaust that you give off,

or the explicit data that I am putting into

the system isn't super valuable atomically.

Like it's only valuable when you sort of

aggregate it together into sort of large numbers.

This is true even for these like folks who are

getting compensated for like labeling things.

Like for supervised machine learning now,

like you need lots of labels to

train a model that performs well.

And so I think that's one of the challenges.

It's like how do you sort of figure

out like because this data is getting combined in

so many ways like through

these combinations like how the value is flowing.

Yeah, that's fascinating.

Yeah. And it's fascinating that you're thinking about this.

And I wasn't even going into this conversation expecting

the breadth of research really

that Microsoft broadly is thinking about,

you're thinking about at Microsoft.

So if we go back to 89 when Microsoft released Office,

or 1990 when they released Windows 3.0.

In your view, I know you weren't there through its history,

but how has the company changed in

the 30 years since as you look at it now?

The good thing is it's started off as a platform company.

Like it's still a platform company,

like the parts of the business that are thriving and

most successful are those that are building platforms.

Like the mission of the company now is,

the mission's changed.

It's like changed in a very interesting way.

So back in 89,

90 like they were still on the original mission,

which was like put a PC on every desk and in every home.

And it was basically about democratizing access to

this new personal computing technology,

which when Bill started the company,

integrated circuit microprocessors were a brand new thing.

And people were building homebrew computers from kits,

like the way people build ham radios right now.

I think this is the interesting thing

for folks who build platforms in general.

Bill saw the opportunity there and

what personal computers could do.

And it was like, it was sort of a reach.

Like you just sort of imagine like where things

were when they started the company

versus where things are now.

Like in success,

when you've democratized a platform,

it just sort of vanishes into the platform.

You don't pay attention to it anymore.

Like operating systems aren't a thing anymore.

Like they're super important,

like completely critical.

And like when you see one fail,

like you just sort of understand.

But like it's not a thing where you're not like

waiting for the next operating system thing

in the same way that you were in 1995, right?

Like in 1995, like we had

Rolling Stones on the stage with the Windows 95 rollout.

Like it was like the biggest thing in the world.

Everybody lined up for it the way

that people used to line up for iPhone.

But like, you know, eventually,

and like this isn't necessarily a bad thing.

Like it just sort of, you know,

the success is that it's sort of, it becomes ubiquitous.

It's like everywhere, like human beings,

when their technology becomes ubiquitous,

they just sort of start taking it for granted.

So the mission now that Satya

rearticulated five plus years ago now,

when he took over as CEO of the company.

Our mission is to empower every individual and

every organization in the world to be more successful.

And so, you know, again,

like that's a platform mission.

And like the way that we do it now is, is different.

It's like we have a hyperscale cloud that

people are building their applications on top of.

Like we have a bunch of AI infrastructure that

people are building their AI applications on top of.

We have, you know,

we have a productivity suite of software,

like Microsoft Dynamics, which, you know,

some people might not think is the sexiest thing in the world,

but it's like helping people figure out how to automate

all of their business processes and workflows

and to help those businesses using it to grow and be more.

So it's a much broader vision

in a way now than it was back then.

Like it was sort of very particular thing.

And like now, like we live in this world where

technology is so powerful and it's like

such a basic fact of life that it both exists

and is going to get better and better over time

or at least more and more powerful over time.

So like, you know, what you have to do as a platform player

is just much bigger.

Right. There's so many directions in which you can transform.

You didn't mention mixed reality, too.

You know, that's probably early days

or it depends how you think of it.

But if we think on a scale of centuries,

it's the early days of mixed reality.

Oh, for sure.

And so with HoloLens,

Microsoft is doing some really interesting work there.

Do you touch that part of the effort?

What's the thinking?

Do you think of mixed reality as a platform, too?

Oh, sure.

When we look at what the platforms of the future could be,

it's like fairly obvious that like AI is one.

Like you don't have to, I mean, like that's,

you know, you sort of say it to like someone

and you know, like they get it.

But like we also think of the like mixed reality

and quantum as like these two interesting,

you know, potentially.

Quantum computing?

Yeah.

Okay. So let's get crazy then.

So you're talking about some futuristic things here.

Well, the mixed reality, Microsoft is really,

it's not even futuristic, it's here.

It is.

It's incredible stuff.

And look, and it's having an impact right now.

Like one of the more interesting things

that's happened with mixed reality

over the past couple of years that I didn't clearly see

is that it's become the computing device

for folks who, for doing their work,

who haven't used any computing device at all

to do their work before.

So technicians and service folks and people

who are doing like machine maintenance on factory floors.

So like they, you know, because they're mobile

and like they're out in the world

and they're working with their hands

and, you know, sort of servicing these like

very complicated things, they're,

they don't use their mobile phone

and like they don't carry a laptop with them

and, you know, they're not tethered to a desk.

And so mixed reality, like where it's getting traction

right now, where HoloLens is selling a lot of units

is for these sorts of applications for these workers.

And it's become like, I mean, like the people love it.

They're like, oh my God, like this is like for them,

like the same sort of productivity boosts that,

you know, like an office worker had

when they got their first personal computer.

Yeah, but you did mention it's certainly obvious AI

as a platform, but can we dig into it a little bit?

How does AI begin to infuse some of the products

in Microsoft?

So currently providing training of,

for example, neural networks in the cloud

or providing pre trained models or just even providing

computing resources and whatever different inference

that you wanna do using neural networks.

How do you think of AI infusing as a platform

that Microsoft can provide?

Yeah, I mean, I think it's super interesting.

It's like everywhere.

And like we run these review meetings now

where it's me and Satya and like members

of Satya's leadership team and like a cross functional

group of folks across the entire company

who are working on like either AI infrastructure

or like have some substantial part of their product work

using AI in some significant way.

Now, the important thing to understand

is like when you think about like how the AI

is gonna manifest in like an experience

for something that's gonna make it better,

like I think you don't want the AIness

to be the first order thing.

It's like whatever the product is

and like the thing that is trying to help you do,

like the AI just sort of makes it better.

And this is a gross exaggeration,

but like people get super excited about like

where the AI is showing up in products and I'm like,

do you get that excited about like

where you're using a hash table like in your code?

Like it's just another.

It's just a tool.

It's a very interesting programming tool,

but it's sort of like it's an engineering tool.

And so like it shows up everywhere.

So like we've got dozens and dozens of features

now in Office that are powered by

like fairly sophisticated machine learning,

our search engine wouldn't work at all

if you took the machine learning out of it.

The like increasingly things like content moderation

on our Xbox and xCloud platform.

When you mean moderation,

you mean like the recommender is like showing

what you wanna look at next.

No, no, no, it's like anti bullying stuff.

So the usual social network stuff

that you have to deal with.

Yeah, correct.

But it's like really it's targeted,

it's targeted towards a gaming audience.

So it's like a very particular type of thing

where the line between playful banter

and like legitimate bullying is like a subtle one.

And like you have to like, it's sort of tough.

Like I have.

I'd love to if we could dig into it

because you're also,

you led the engineering efforts of LinkedIn.

And if we look at LinkedIn as a social network,

and if we look at the Xbox gaming as the social components,

the very different kinds of I imagine communication

going on on the two platforms, right?

And the line in terms of bullying and so on

is different on the platforms.

So how do you,

I mean, it's such a fascinating philosophical discussion

of where that line is.

I don't think anyone knows the right answer.

Twitter folks are under fire now, Jack at Twitter

for trying to find that line.

Nobody knows what that line is.

But how do you try to find the line

for trying to prevent abusive behavior

and at the same time, let people be playful

and joke around and that kind of thing?

I think in a certain way,

like if you have what I would call vertical social networks,

it gets to be a little bit easier.

So like if you have a clear notion

of like what your social network should be used for,

or like what you are designing a community around,

then you don't have as many dimensions

to your sort of content safety problem

as you do in a general purpose platform.

I mean, so like on LinkedIn,

like the whole social network

is about connecting people with opportunity,

whether it's helping them find a job

or to sort of find mentors

or to sort of help them like find their next sales lead

or to just sort of allow them to broadcast

their sort of professional identity

to their network of peers and collaborators

and sort of professional community.

Like that is, I mean, like in some ways,

like that's very, very broad,

but in other ways it's sort of, it's narrow.

And so like you can build AI's like machine learning systems

that are capable with those boundaries

of making better automated decisions

about like what is sort of inappropriate

and offensive comment or dangerous comment

or illegal content when you have some constraints.

You know, same thing with like the gaming social network.

So for instance, like it's about playing games,

not having fun.

And like the thing that you don't want to have happen

on the platform is why bullying is such an important thing.

Like bullying is not fun.

So you want to do everything in your power

to encourage that not to happen.

And yeah, but I think it's sort of a tough problem

in general and it's one where I think, you know,

eventually we're going to have to have some sort

of clarification from our policymakers about what it is

that we should be doing, like where the lines are,

because it's tough.

Like you don't, like in democracy, right?

Like you don't want,

you want some sort of democratic involvement.

Like people should have a say

in like where the lines are drawn.

Like you don't want a bunch of people making

like unilateral decisions.

And like we are in a state right now

for some of these platforms

where you actually do have to make unilateral decisions

where the policymaking isn't going to happen fast enough

in order to like prevent very bad things from happening.

But like we need the policymaking side of that to catch up,

I think, as quickly as possible

because you want that whole process to be a democratic thing,

not a, you know, not some sort of weird thing

where you've got a non representative group

of people making decisions that have, you know,

like national and global impact.

And it's fascinating because the digital space is different

than the physical space in which nations

and governments were established.

And so what policy looks like globally,

what bullying looks like globally,

what's healthy communication looks like globally

is an open question and we're all figuring it out together,

which is fascinating.

Yeah, I mean with, you know, sort of fake news, for instance.

And...

Deep fakes and fake news generated by humans?

Yeah, so we can talk about deep fakes,

like I think that is another like, you know,

sort of very interesting level of complexity.

But like if you think about just the written word, right?

Like we have, you know, we invented papyrus,

what, 3,000 years ago where we, you know,

you could sort of put word on paper.

And then 500 years ago, like we get the printing press,

like where the word gets a little bit more ubiquitous.

And then like you really, really didn't get ubiquitous

printed word until the end of the 19th century

when the offset press was invented.

And then, you know, just sort of explodes

and like, you know, the cross product of that

and the Industrial Revolution's need

for educated citizens resulted in like

this rapid expansion of literacy

and the rapid expansion of the word.

But like we had 3,000 years up to that point

to figure out like how to, you know,

like what's journalism, what's editorial integrity,

like what's, you know, what's scientific peer review.

And so like you built all of this mechanism

to like try to filter through all of the noise

that the technology made possible

to like, you know, sort of getting to something

that society could cope with.

And like, if you think about just the piece,

the PC didn't exist 50 years ago.

And so in like this span of, you know,

like half a century, like we've gone from no digital,

you know, no ubiquitous digital technology

to like having a device that sits in your pocket

where you can sort of say whatever is on your mind

to like what did Mary have in her,

Mary Meeker just released her new like slide deck last week.

You know, we've got 50% penetration of the internet

to the global population.

Like there are like three and a half billion people

who are connected now.

So it's like, it's crazy, crazy, like inconceivable,

like how fast all of this happened.

So, you know, it's not surprising

that we haven't figured out what to do yet,

but like we gotta really like lean into this set of problems

because like we basically have three millennia worth of work

to do about how to deal with all of this

and like probably what, you know,

amounts to the next decade worth of time.

So since we're on the topic of tough, you know,

tough challenging problems,

let's look at more on the tooling side in AI

that Microsoft is looking at is face recognition software.

So there's a lot of powerful positive use cases

for face recognition, but there's some negative ones

and we're seeing those in different governments

in the world.

So how do you, how does Microsoft think about the use

of face recognition software as a platform

in governments and companies?

How do we strike an ethical balance here?

Yeah, I think we've articulated a clear point of view.

So Brad Smith wrote a blog post last fall,

I believe that sort of like outlined

like very specifically what, you know,

what our point of view is there.

And, you know, I think we believe

that there are certain uses

to which face recognition should not be put.

And we believe again,

that there's a need for regulation there.

Like the government should like really come in

and say that, you know, this is where the lines are.

And like, we very much wanted to like figuring out

where the lines are, should be a democratic process.

But in the short term, like we've drawn some lines

where, you know, we push back against uses

of face recognition technology, you know,

like the city of San Francisco, for instance,

I think has completely outlawed any government agency

from using face recognition tech.

And like that may prove to be a little bit overly broad.

But for like certain law enforcement things,

like you really, I would personally rather be overly

sort of cautious in terms of restricting use of it

until like we have, you know,

sort of defined a reasonable, you know,

democratically determined regulatory framework

for like where we could and should use it.

And, you know, the other thing there is like,

we've got a bunch of research that we're doing

and a bunch of progress that we've made on bias there.

And like, there are all sorts of like weird biases

that these models can have,

like all the way from like the most noteworthy one

where, you know, you may have underrepresented minorities

who are like underrepresented in the training data

and then you start learning like strange things.

But like there are even, you know, other weird things.

Like we've, I think we've seen in the public research,

like models can learn strange things,

like all doctors are men, for instance, just, yeah.

I mean, and so like, it really is a thing

where it's very important for everybody

who is working on these things before they push publish,

they launch the experiment, they, you know, push the code

to, you know, online, or they even publish the paper

that they are at least starting to think about

what some of the potential negative consequences are,

some of this stuff.

I mean, this is where, you know, like the deep fake stuff

I find very worrisome just because

there are going to be some very good beneficial uses

of like GAN generated imagery.

And funny enough, like one of the places

where it's actually useful is we're using the technology

right now to generate synthetic visual data

for training some of the face recognition models

to get rid of the bias.

So like, that's one like super good use of the tech,

but like, you know, it's getting good enough now

where, you know, it's going to sort of challenge

a normal human being's ability to,

like now you're just sort of say,

like it's very expensive for someone

to fabricate a photorealistic fake video.

And like GANs are going to make it fantastically cheap

to fabricate a photorealistic fake video.

And so like what you assume you can sort of trust is true

versus like be skeptical about is about to change.

And like, we're not ready for it, I don't think.

The nature of truth, right.

That's, it's also exciting because I think both you and I

probably would agree that the way to solve,

to take on that challenge is with technology, right?

There's probably going to be ideas of ways to verify

which kind of video is legitimate, which kind is not.

So to me, that's an exciting possibility,

most likely for just the comedic genius

that the internet usually creates with these kinds of videos

and hopefully will not result in any serious harm.

Yeah, and it could be, you know,

like I think we will have technology to,

that may be able to detect whether or not

something's fake or real.

Although the fakes are pretty convincing,

even like when you subject them to machine scrutiny.

But, you know, we also have these increasingly

interesting social networks, you know,

that are under fire right now

for some of the bad things that they do.

Like one of the things you could choose to do

with a social network is like you could,

you could use crypto and the networks

to like have content signed

where you could have a like full chain of custody

that accompanied every piece of content.

So like when you're viewing something

and like you want to ask yourself,

like how much can I trust this?

Like you can click something

and like have a verified chain of custody

that shows like, oh, this is coming from this source.

And it's like signed by like someone

whose identity I trust.

Yeah, I think having that, you know,

having that chain of custody,

like being able to like say, oh, here's this video.

Like it may or may not have been produced

using some of this deepfake technology,

but if you've got a verified chain of custody

where you can sort of trace it all the way back

to an identity and you can decide whether or not

like I trust this identity.

Like, oh no, this is really from the White House

or like this is really from the, you know,

the office of this particular presidential candidate

or it's really from, you know, Jeff Wiener, CEO of LinkedIn

or Satya Nadella, CEO of Microsoft.

Like that might be like one way

that you can solve some of the problems.

So like that's not the super high tech.

Like we've had all of this technology forever.

And, but I think you're right.

Like it has to be some sort of technological thing

because the underlying tech that is used to create this

is not going to do anything but get better over time

and the genie is sort of out of the bottle.

There's no stuffing it back in.

And there's a social component,

which I think is really healthy for a democracy

where people will be skeptical

about the thing they watch in general.

So, you know, which is good.

Skepticism in general is good for content.

So deepfakes in that sense are creating a global skepticism

about can they trust what they read.

It encourages further research.

I come from the Soviet Union

where basically nobody trusted the media

because you knew it was propaganda.

And that kind of skepticism encouraged further research

about ideas as opposed to just trusting any one source.

Well, look, I think it's one of the reasons why

the scientific method and our apparatus

of modern science is so good.

Like, because you don't have to trust anything.

Like, the whole notion of modern science

beyond the fact that this is a hypothesis

and this is an experiment to test the hypothesis

and this is a peer review process

for scrutinizing published results.

But stuff's also supposed to be reproducible.

So you know it's been vetted by this process,

but you also are expected to publish enough detail

where if you are sufficiently skeptical of the thing,

you can go try to reproduce it yourself.

And like, I don't know what it is.

Like, I think a lot of engineers are like this

where like, you know, sort of this,

like your brain is sort of wired for skepticism.

Like, you don't just first order trust everything

that you see and encounter.

And like, you're sort of curious to understand,

you know, the next thing.

But like, I think it's an entirely healthy thing.

And like, we need a little bit more of that right now.

So I'm not a large business owner.

So I'm just a huge fan of many of Microsoft products.

I mean, I still, actually in terms of,

I generate a lot of graphics and images

and I still use PowerPoint to do that.

It beats Illustrator for me.

Even professional sort of, it's fascinating.

So I wonder, what is the future of,

let's say Windows and Office look like?

Is, do you see it?

I mean, I remember looking forward to XP.

Was it exciting when XP was released?

Just like you said, I don't remember when 95 was released.

But XP for me was a big celebration.

And when 10 came out, I was like, oh, okay.

Well, it's nice.

It's a nice improvement.

So what do you see the future of these products?

I think there's a bunch of excite.

I mean, on the Office front,

there's gonna be this like increasing productivity wins

that are coming out of some of these AI powered features

that are coming.

Like the products will sort of get smarter and smarter

in like a very subtle way.

Like there's not gonna be this big bang moment

where like Clippy is gonna reemerge and it's gonna be.

Wait a minute.

Okay, we'll have to wait, wait, wait.

Is Clippy coming back?

But quite seriously, so injection of AI.

There's not much, or at least I'm not familiar,

sort of assistive type of stuff going on

inside the Office products.

Like a Clippy style assistant, personal assistant.

Do you think that there's a possibility

of that in the future?

So I think there are a bunch of like very small ways

in which like machine learning powered assistive things

are in the product right now.

So there are a bunch of interesting things.

Like the auto response stuff's getting better and better.

And it's like getting to the point

where it can auto respond with like,

okay, this person's clearly trying to schedule a meeting.

So it looks at your calendar and it automatically

like tries to find like a time and a space

that's mutually interesting.

Like we have this notion of Microsoft search

at a Microsoft search where it's like not just web search,

but it's like search across like all of your information

that's sitting inside of like your Office 365 tenant

and like potentially in other products.

And like we have this thing called the Microsoft Graph

that is basically an API federator that sort of like

gets you hooked up across the entire breadth

of like all of the, like what were information silos

before they got woven together with the graph.

Like that is like getting increasing,

with increasing effectiveness,

sort of plumbed into some of these auto response things

where you're gonna be able to see the system

like automatically retrieve information for you.

Like if, you know, like I frequently send out,

you know, emails to folks where like I can't find a paper

or a document or whatnot.

There's no reason why the system

won't be able to do that for you.

And like, I think the, it's building towards

like having things that look more like,

like a fully integrated, you know, assistant,

but like you'll have a bunch of steps

that you will see before you,

like it will not be this like big bang thing

where like Clippy comes back and you've got this like,

you know, manifestation of, you know,

like a fully, fully powered assistant.

So I think that's, that's definitely coming in,

like all of the, you know, collaboration,

coauthoring stuff's getting better.

You know, it's like really interesting.

Like if you look at how we use

the Office product portfolio at Microsoft,

like more and more of it is happening inside of

like Teams as a canvas.

And like, it's this thing where, you know,

you've got collaboration is like at the center

of the product and like we built some like really cool stuff

that's some of, which is about to be open source

that are sort of framework level things

for doing, for doing coauthoring.

That's awesome.

So in, is there a cloud component to that?

So on the web, or is it,

and forgive me if I don't already know this,

but with Office 365, we still,

the collaboration we do if we're doing Word,

we still send the file around.

No, no.

So this is.

We're already a little bit better than that.

A little bit better than that and like, you know,

so like the fact that you're unaware of it means

we've got a better job to do,

like helping you discover, discover this stuff.

But yeah, I mean, it's already like got a huge,

huge cloud component.

And like part of, you know, part of this framework stuff,

I think we're calling it, like I,

like we've been working on it for a couple of years.

So like, I know the internal code name for it,

but I think when we launched it to build,

it's called the Fluid Framework.

And, but like what Fluid lets you do is like,

you can go into a conversation that you're having in Teams

and like reference like part of a spreadsheet

that you're working on where somebody's like sitting

in the Excel canvas,

like working on the spreadsheet with a, you know,

chart or whatnot,

and like you can sort of embed like part of the spreadsheet

in the Teams conversation where like you can dynamically

update it and like all of the changes that you're making

to the, to this object are like, you know,

coordinate and everything is sort of updating in real time.

So like you can be in whatever canvas is most convenient

for you to get your work done.

So I, out of my own sort of curiosity as an engineer,

I know what it's like to sort of lead a team

of 10, 15 engineers.

Microsoft has, I don't know what the numbers are,

maybe 50, maybe 60,000 engineers, maybe 40.

I don't know exactly what the number is, it's a lot.

It's tens of thousands.

Right, so it's more than 10 or 15.

What, I mean, you've led different sizes,

mostly large size of engineers.

What does it take to lead such a large group

into a continue innovation,

continue being highly productive

and yet develop all kinds of new ideas and yet maintain,

like what does it take to lead such a large group

of brilliant people?

I think the thing that you learn

as you manage larger and larger scale

is that there are three things

that are like very, very important

for big engineering teams.

Like one is like having some sort of forethought

about what it is that you're gonna be building

over large periods of time.

Like not exactly, like you don't need to know

that like, you know, I'm putting all my chips

on this one product and like this is gonna be the thing,

but like it's useful to know like what sort of capabilities

you think you're going to need to have

to build the products of the future.

And then like invest in that infrastructure,

like whether, and like I'm not just talking

about storage systems or cloud APIs,

it's also like what does your development process look like?

What tools do you want?

Like what culture do you want to build around?

Like how you're, you know, sort of collaborating together

to like make complicated technical things.

And so like having an opinion and investing in that

is like, it just gets more and more important.

And like the sooner you can get a concrete set of opinions,

like the better you're going to be.

Like you can wing it for a while at small scales,

like, you know, when you start a company,

like you don't have to be like super specific about it,

but like the biggest miseries that I've ever seen

as an engineering leader are in places

where you didn't have a clear enough opinion

about those things soon enough.

And then you just sort of go create a bunch

of technical debt and like culture debt

that is excruciatingly painful to clean up.

So like, that's one bundle of things.

Like the other, you know, another bundle of things

is like, it's just really, really important

to like have a clear mission

that's not just some cute crap you say

because like you think you should have a mission,

but like something that clarifies for people

like where it is that you're headed together.

Like, I know it's like probably like a little bit

too popular right now,

but Yuval Harari's book, Sapiens,

one of the central ideas in his book is that

like storytelling is like the quintessential thing

for coordinating the activities of large groups of people.

Like once you get past Dunbar's number,

and like I've really, really seen that

just managing engineering teams.

Like you can just brute force things

when you're less than 120, 150 folks

where you can sort of know and trust

and understand what the dynamics are

between all the people, but like past that,

like things just sort of start to catastrophically fail

if you don't have some sort of set of shared goals

that you're marching towards.

And so like, even though it sounds touchy feely

and you know, like a bunch of technical people

will sort of balk at the idea that like,

you need to like have a clear, like the missions,

like very, very, very important.

You're always right, right?

Stories, that's how our society,

that's the fabric that connects us,

all of us is these powerful stories.

And that works for companies too, right?

It works for everything.

Like, I mean, even down to like, you know,

you sort of really think about it,

like our currency, for instance, is a story.

Our constitution is a story.

Our laws are stories.

I mean, like we believe very, very, very strongly in them.

And thank God we do.

But like they are,

they're just abstract things.

Like they're just words.

Like if we don't believe in them, they're nothing.

And in some sense, those stories are platforms

and the kinds, some of which Microsoft is creating, right?

They have platforms on which we define the future.

So last question, what do you,

let's get philosophical maybe,

bigger than even Microsoft,

what do you think the next 20, 30 plus years

looks like for computing, for technology, for devices?

Do you have crazy ideas about the future of the world?

Yeah, look, I think we, you know,

we're entering this time where we've got,

we have technology that is progressing

at the fastest rate that it ever has.

And you've got,

you've got some really big social problems,

like society scale problems that we have to tackle.

And so, you know, I think we're going to rise to the challenge

and like figure out how to intersect

like all of the power of this technology

with all of the big challenges that are facing us,

whether it's, you know, global warming,

whether it's like the biggest remainder of the population boom

is in Africa for the next 50 years or so.

And like global warming is going to make it increasingly difficult

to feed the global population in particular,

like in this place where you're going to have

like the biggest population boom.

I think we, you know, like AI is going to,

like if we push it in the right direction,

like it can do like incredible things to empower all of us

to achieve our full potential and to, you know,

like live better lives.

But like that also means focus on like

some super important things.

Like how can you apply it to healthcare to make sure that,

you know, like our quality and cost of

and sort of ubiquity of health coverage is better

and better over time.

Like that's more and more important every day is like

in the United States and like the rest of the industrialized world,

so Western Europe, China, Japan, Korea,

like you've got this population bubble of like aging,

working, you know, working age folks who are,

you know, at some point over the next 20, 30 years,

they're going to be largely retired.

And like you're going to have more retired people

than working age people.

And then like you've got, you know,

sort of natural questions about who's going to take care of

all the old folks and who's going to do all the work.

And the answers to like all of these sorts of questions,

like where you're sort of running into, you know,

like constraints of the, you know,

the world and of society has always been like

what tech is going to like help us get around this?

Like when I was a kid in the 70s and 80s,

like we talked all the time about like population boom,

population boom, like we're going to,

like we're not going to be able to like feed the planet.

And like we were like right in the middle of the Green Revolution

where like this massive technology driven increase

in crop productivity like worldwide.

And like some of that was like taking some of the things

that we knew in the West and like getting them distributed

to the, you know, to the developing world.

And like part of it were things like, you know,

just smarter biology like helping us increase.

And like we don't talk about like overpopulation anymore

because like we can more or less,

we sort of figured out how to feed the world.

Like that's a technology story.

And so like I'm super, super hopeful about the future

and in the ways where we will be able to apply technology

to solve some of these super challenging problems.

Like I've, like one of the things that I'm trying to spend

my time doing right now is trying to get everybody else

to be hopeful as well because, you know, back to Harare,

like we are the stories that we tell.

Like if we, you know, if we get overly pessimistic right now

about like the potential future of technology,

like we, you know, like we may fail to get all of the things

in place that we need to like have our best possible future.

And that kind of hopeful optimism, I'm glad that you have it

because you're leading large groups of engineers

that are actually defining, that are writing that story,

that are helping build that future, which is super exciting.

And I agree with everything you said except I do hope

Clippy comes back.

We miss him. I speak for the people.

So, Galen, thank you so much for talking to me.

Thank you so much for having me. It was a pleasure.