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 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 in iTunes,
support it on Patreon,
or simply connect with me on Twitter at Lex Freedman,
spelled F-R-I-D-M-A-N.
And I'd like to give a special thank you
to Tom and Elanti Bighausen
for their support of the podcast on Patreon.
Thanks, Tom and Elanti.
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.
So, 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 use.
We have an operating system business.
We have a hardware business
where we make everything from computer mice
and headphones to high-end,
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 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, 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 probably a bunch of stuff
that I'm embarrassingly not recounting in this list.
On gaming, two 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 Lucas Arts made
called Day of the Tentacle,
that my friends and I played forever.
And we're doing some interesting collaboration now
with the folks who made Day of the Tentacle.
And I was completely nerding out with Tim Schaeffer,
like the guy who wrote Day of the Tentacle this morning,
just a complete fanboy,
which sort of it happens a lot.
Like, Microsoft has been doing so much stuff
at such breadth for such a long period of time
that being CTO, most of the time,
my job is very, very serious.
And sometimes I get caught up in how amazing it is
to be able to have the conversations
that I have with the people I get to have them with.
You had 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 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
and 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 can 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 interesting.
So it's an incentive structure
that where the prices match the value much better.
Yeah.
And Glenn does a much, much better job than I do
at selling and I probably picked the world's worst example,
you know, and, and, and, but like,
and it's intentionally provocative, you know,
so like this whole notion, like I, you know,
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, out of my property, you know,
but, 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 touching Microsoft research.
Yeah.
So I'm 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,
automations 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,
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 like what's it gonna be used for
and like what benefits will it produce
and what negative impacts will it produce
and you know, who gets to steer this whole thing?
You know, 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, you know, like 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.
Like 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 like,
and then on top of like all this infrastructure,
then build all of the commercially valuable uses for it.
So like, I think like that's bad from a, you know,
sort of, you know, economics
and sort of equitable distribution of value perspective,
like, you know, sort of back to this whole notion of,
you know, like, do the markets work?
But I think it's also bad from an innovation perspective
because like I have infinite amounts of faith
in human beings that if you, you know,
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 know,
you think about the steam engine
and the late 18th century, like it was, you know,
maybe the first large scale substitute for human labor
that we've built like a machine.
And, you know, 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 in 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 is not a,
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, no, like fully demarcated.
Like they're probably, you know,
we're sitting here in this room
and like even though they don't,
they're probably things, you know,
like the MIMS gyroscope that are in both of our,
like they're like little engines, you know,
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, you know, the tieback to this radical markets thing,
like the, so my team has been working with Glenn
on this and Jaren Lanier actually.
So Jaren is the like 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, you know,
again, going back to this, you know,
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 like, and we all make them like 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 like 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 services free, you know,
like it's low amount of like perceived.
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 they're 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 are getting like 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,
the data gets created and then it gets,
it's not valuable like the data exhaust that you give off
or the, you know, the explicit data that I am putting into the system
isn't value, valuable, it's super valuable atomically.
Like it's only valuable when you sort of aggregate it together
into, you know, sort of large numbers.
It's 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, you know,
a model that performs well.
And so, you know, I think that's one of the challenges.
It's like, how do you, you know, 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, 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 are thinking about
a Microsoft.
So if we go back to 89 when Microsoft released Office
or 1990 when they released Windows 3.0, how's the,
in your view, I know you weren't there the entire, you know,
through its history, but how's 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 like 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 changing a very interesting way.
So, you know, back in 89, 90, like they were still on the
original mission, which is, you know,
on the original mission, which was like put a PC on every desk
and in every home.
Like, 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 like people were building, you know,
homebrew computers, you know, from kits like the way people
build ham radios right now.
And I think this is sort of 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,
you know, when they started the company versus where things
are now, like in success, when you democratize 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, you know, when you see one, you know, fail,
like you just, you sort of understand, but like, you know,
it's not a thing where you're, you're not like waiting for,
you know, the next operating system thing in the same way
that you were in 1995, right?
Like in 1995, like, you know, we had rolling stones on the
stage with the windows 95 rollout.
Like it was like the biggest thing in the world.
Everybody would like lined up for it in 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, it, the success is that it's
sort of, it becomes ubiquitous.
It's like everywhere and 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 cloud or building
our 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, you know, like help those businesses using it to like
grow and be more successful.
So it's a, 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,
you know, that it, 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.
Yeah.
You know, that's, that's, that's probably early days or depends
how you think of it.
But if we think in a scale of centuries, it's the early days
of mixed reality.
Oh, for sure.
And so with how it lands, Microsoft is doing some really
interesting work there.
Do you, 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, they get it.
But like we also think of the, like mixed reality and quantum
is like these two interesting, you know, potentially.
Quantum computing.
Yeah.
Okay.
So let's get crazy then.
So, so you're talking about some futuristic things here.
Well, the mixed reality Microsoft is really not even
futuristic is here.
It is incredible stuff.
And look, and it's having an impact right now.
Like one of the, 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, a lot of units is for
these sorts of applications for these workers.
And then 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?
Sure.
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 want to do using neural networks.
Yep.
How do you think of AI infusing the, as a platform that
Microsoft can provide?
Yeah.
I mean, I think it's, it's super interesting.
It's like everywhere.
And like we, we run these, 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, 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 going to manifest and like an
experience for something that's going to make it better.
Like I think you don't want the, the AI-ness to be the first
order thing.
It's like whatever the product is and like the thing that it's
trying to help you do, like the AI just sort of makes it better.
And you know, this is a gross exaggeration, but like I, yeah,
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 are you using a hash table like in your code?
Like it's just another, it's a very interesting programming
tool, but it's sort of a, 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, you know, things like content
moderation on our Xbox and xCloud platform.
When you mean moderation, do you mean like the recommenders
is like showing what you want to 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, you know, the 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 love to, if we could dig into it, because
you're also, you led the engineering efforts of LinkedIn.
Yep.
And if we look at, 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?
Yep.
And the line in terms of bullying and so on is different
on the two 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, you know, 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, you know, 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 know, 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, you know, sort of
find mentors or to, you know, sort of help them, like, find
their next sales lead or to just sort of allow them to
broadcast their, you know, sort of professional identity to
their network of peers and collaborators and, you know,
sort of professional community.
Like, that is, I mean, in some ways, like, that's very, very
broad, but in other ways, it's sort of, you know, it's narrow.
And so, like, you can build AIs, like, machine learning
systems that are, you know, capable with those boundaries
of making better automated decisions about, like, what is,
you know, sort of inappropriate and offensive comment or
dangerous comment or illegal content when you have some
constraints.
You know, same thing with, you know, same thing with, like,
the gaming social network, so for instance, like, it's about
playing games, about 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 policy makers 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, we're in a state right now for some
of these platforms where you actually do have to make
unilateral decisions where the policy making isn't going to
happen fast enough in order to, like, prevent very bad things
from happening.
But, like, we need the policy making 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, 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 healthy communication looks
like globally is an open question.
And we're all figuring it out together, which is fascinating.
I mean, with, with, you know, sort of fake news, for
instance, and...
Deep fakes and fake news generated by humans?
Yeah.
So, I mean, 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, on paper. And then 500 years ago, like, we, 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, 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 would Mary
have?
And 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, 3.5 billion people who are
connected now.
So it's like, it's crazy.
Crazy.
Like, inconceivable, like, how fast all of this happens.
So, you know, it's not surprising that we haven't
figured out what to do yet.
But, like, we got to, like, we got to 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
space 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?
Yeah, 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 this 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, they're 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 or men, for instance.
So, 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
they're going to be some very good beneficial
uses of, like, GAN-generated imagery.
And, like, 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 saying, 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, uh, 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.
Yeah.
Right.
And the idea is 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.
Yeah.
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, you know, 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, you know, from this source
and it's, like, signed by, like, someone whose
identity I trust.
Yeah.
Yeah, I think having, 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 deep fake
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.
Right.
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 democracy where people will
be skeptical about the thing they watch.
Yeah.
In general.
So, you know, which is good.
Skepticism, in general, is good for your
personal content.
So, deep fakes in that sense are creating
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.
Yeah.
Supposed to just trusting anyone's source.
Well, like, I think it's one of the reasons why
the, you know, the scientific method and
our apparatus of modern science is so good.
Like, because you don't have to trust anything.
Like, you, like, the whole notion of, you know,
like, modern science beyond the fact that, you
know, this is a hypothesis and this is an
experiment to test the hypothesis and, you
know, like, this is a peer review process for
scrutinizing published results.
But, like, stuff's also supposed to be
reproducible.
So, like, you know it's been vetted by this
process, but, like, you also are expected to
publish enough detail where, you know, if you
are sufficiently skeptical of the thing, you
can go try to, like, 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,
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?
Do you see it...
I mean, I remember looking forward to XP.
It was an exciting...
Yep.
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,
okay, well, it's nice.
It's a nice improvement.
But...
Yeah.
So, what do you see in the future of these
products?
Yeah, I think there's a bunch of excitement.
I mean, on the Office front, there's going to be
this, like, increasing productivity winds that
are coming out of some of these AI-powered
features that are coming.
Like, the products sort of get smarter and
smarter in a very subtle way.
Like, there's not going to be this big bang
moment where, you know, Clippy is going to
reemerge and it's going to be...
Wait a minute.
Okay, well, I have to...
Wait, wait, wait.
It's coming back?
Yeah.
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's...
There's a possibility of that in the future?
Yeah.
So, I think there are a bunch of, like, very
small ways in which, like, machine learning
power and 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, you know, it
can auto-respond with, like, okay, let, you
know, this person is 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 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, you know,
potentially in other products, and, like,
we have this thing called the Microsoft
Graph that is basically an API
Federator that, you know, sort of, like,
gets you hooked up across the entire
breadth of, like, all of the, you know,
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 the, into some of these
auto-response things where you're going
to 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, like, a fully, fully powered assistant.
So, I think that's, that's definitely
coming out, like, all of the, you know,
collaboration co-authoring stuff's getting
better, you know, it's, like, really
interesting, like, if you look at how
we use, like, 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 co-authoring.
That's awesome. So, in, is there a cloud
component to that? So, on the web, or is it,
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.
We're already 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 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 years.
So, like, I know the internal code name
for it, but I think when we launched it
at Bill, 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,
you know, a 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 in, like, all of the
changes that you're making to the,
to this object or, 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, 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. This is
more than 10 or 15.
What, what,
I mean, you've,
you've led
different sizes, mostly large
sizes of engineers. What does it take
to lead such a large
group into
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 going to 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 going to
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 I, like, I'm not just talking about storage systems
or cloud APIs. It's also, like,
what does your development process look like?
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, to clean up.
So, like, that's one
bundle of things. Like, the other
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
Yval 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
you can just brute force
things when you're less than
120, 150, uh, 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.
Yval's 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, like, our currency, for instance, is a
story. Our Constitution is a
story. Our laws are
a story. 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, 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? Yeah.
Platforms in 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 got some really big
social problems, like
society scale problems
that we have to
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, in 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,
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?
You know, like, when I was a kid
in the 70s and 80s, like, we talked
all the time about, like, oh, the, 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,
you know, 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,
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 Harari, 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 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, Kellen, thank you so much for
talking to me. Thank you so much for having me. It was a pleasure.