Kevin Scott: Microsoft CTO
Guests: Kevin Scott
Full transcript · Read the full conversation in order, with its original context.
§01Transcript · 00:00–09:36
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 @LexFriedman,
spelled F-R-I-D-M-A-N.
And I'd like to give a special thank you
to Tom and Nalanti Bighausen
for their support of the podcast on Patreon.
Thanks, Tom and Nalanti.
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 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 smart young economist, Glenn Weil,
who my group works with a lot,
who's doing this research
on these things called radical markets.
Like he's written an entire technical book
about this whole notion of a radical market.
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 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 Day of the Tentacle this morning,
just a complete fan boy,
which, you know, sort of, it like happens a lot.
Like, you know, Microsoft has been doing so much stuff
at such breadth for such a long period of time
that, you know, 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.
- You have 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
that's 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, you 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 interesting.
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, and, and,
but like, and it's intentionally provocative,
you know, so like this whole notion,
like I'm not sure whether I like this notion
that like we could have a set of market mechanisms
where I could get bid 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, the, 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.
- Yeah.
- 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 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 going to be used for?
And like, what, what benefits will it produce
and what negative impacts will it produce?
And, you know, how, who, who,
who gets to steer this whole thing?
You know, I'll say at a, at the highest level,
one of the real joys of, of getting to do what I do
at Microsoft is Microsoft has this heritage
as a platform company.
And so, you know, like Bill,
Bill has this thing that he said, you know,
a bunch of years ago where, you know,
the measure of a successful platform
is that it produces far more economic value
§02Transcript · 09:39–18:32
for the people who build on top of the platform
than is created for the, the platform owner or, or builder.
And I think we have to think about AI that way.
Like-- - As a platform.
- Yeah, it has to, 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, and, 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, the steam engine
in 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 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, no, no, like fully democratized.
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 MEMS gyroscope that are in both of our phones.
Like there's 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 tie back
to this radical markets thing,
like the, so my team has been working with Glenn
on this and Jaron Lanier actually.
So Jaron 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 Jaron 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 gonna 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 Jaron
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 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
'cause they're explicitly labeling data.
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, you know, we don't have as much transparency
as I think we need in like how the data is getting used.
And it's, you know, super complicated.
Like, you know, we, you know,
I think as technologists sort of appreciate
like some of the subtlety there.
It's like, you know, the data gets created
and then it gets, you know, 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 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, 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 fascinating.
- Tough.
- 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,
how's the, in your view, I know you weren't there
the entire, you know, 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 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 changed in a very interesting way.
So, you know, 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,
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 a, 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'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.
§03Transcript · 18:35–27:22
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 was 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, 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 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, you know,
like help those businesses using it to like grow
and be more successful.
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, you know, 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 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 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?
- 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.
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 AI-ness
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, you know,
things like content moderation
on our Xbox and xCloud platform.
- Yeah, when you mean moderation,
do you mean like the recommenders
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, you know, the line between playful banter
and like legitimate bullying is like a subtle one.
And like you have to, it's sort of tough.
Like I have--
- I'd love to, if we could dig into it,
'cause you're also, you led the engineering efforts
at 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 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
§04Transcript · 27:25–37:49
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, 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, like 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.
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 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.
- 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. - Yeah, I mean,
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 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 happens.
So, you know, it's not surprising
that we haven't figured out what to do yet.
But like we gotta 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 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?
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 outline 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.
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 gonna 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 gonna 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 gonna 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.
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.
§05Transcript · 37:52–47:20
- Yeah.
- 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,
like I think we will have technology
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 we also have these increasingly interesting
social networks 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 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 wanna ask yourself,
"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 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 office
"of this particular presidential candidate."
Or it's really from Jeff Weiner, 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.
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 gonna 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.
- Yeah. - In general.
So, which is good, skepticism in general is good
for your personal content. - It is good, I think.
- 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 it was propaganda.
And that kind of skepticism encouraged further research
about ideas. - Yeah.
- As opposed to just trusting any one source.
- Well, I think it's one of the reasons why
the scientific method and our apparatus
of modern science is so good.
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 I don't know what it is.
I think a lot of engineers are like this
where your brain is sort of wired for skepticism.
You don't just first order trust everything
that you see and encounter.
And you're sort of curious to understand the next thing.
But I think it's an entirely healthy thing.
And 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.
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 exciting,
I mean on the Office front there's gonna be this
increasing productivity wins that are coming out
of some of these AI powered features that are coming.
Like the products are 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
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 a 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 powered assistant.
So I think 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
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 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, no, no.
- So this is.
- 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 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 at Build,
it's called the Fluid Framework.
And, but like what Fluid lets you do is like,
§06Transcript · 47:25–56:59
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 is 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 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 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, it's more than 10 or 15.
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 I'm not just talking
about storage systems or cloud APIs,
it's also like, what is 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, 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 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 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.
- Yuval'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 sort of really think about it,
like our currency, for instance, is a story.
Our constitution is a story.
Our laws are, 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?
Yeah, 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'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 get some really big social problems,
like society scale problems that we have to tackle.
And so, I think we're gonna 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 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 gonna make it increasingly
difficult to feed the global population in particular,
like in this place where you're gonna have
like the biggest population boom.
I think we, like AI is gonna,
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 gonna be largely retired.
And like, you're gonna have more retired people
than working age people.
And then like, you've got, you know,
sort of natural questions about who's gonna take care
of all the old folks and who's gonna 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 gonna 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, like population boom, population boom,
like we're gonna, like we're not gonna be able
to like feed the planet.
And like, we were like right in the middle
of the green revolution where like this,
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 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 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
§07Transcript · 57:00–57:36
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've said,
except I do hope Clippy comes back.
(Colin laughs)
We miss him.
I speak for the people.
So Colin, thank you so much for talking to me.
- Thank you so much for having me.
It was a pleasure.
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