WEBVTT

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Sata, you're the CEO of one of the most
valuable companies in the world. What's

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on your mind these days? What's top of

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mind for me is twofold, right? One is

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I'm grounded in the fact that in our

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business, in tech business, now this is

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my 32nd year at Microsoft. Um I know

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that for a fact that there's no such
thing as a franchise value. And so that

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means every day you have to get up
and hopefully you're doing things that are

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going to be relevant tomorrow. Um,
and so to me that's sort of perhaps the

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biggest lesson learned over all these

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decades and years. And so here we are on

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what is essentially a complete new

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platform shift uh that we are in the

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midstop right I kind of say it's my 32nd

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year but it's year two of my fourth

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platform shift and so what's on mind is

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like okay what is this platform shift

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really all about and as a company can we

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be allin and innovate right I mean at
the end of the day when I say there's no

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franchise value it also means that you

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get to play for it all up again even the

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battles we won and the battles we lost

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are all up for grabbed again. And so

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therefore, um there's a freshness to it.

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So what's on my mind is, you know, that

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ability to ground myself back yet
on another platform shift and it's

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exciting. The fact that there is no
franchise value, does that make you

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nervous? Absolutely. I mean, it should
make every one of us nervous, right? At

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least that's why I think tech is so
exciting, right? It's sort of it's kind

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of like the two sides to the same coin,
right? One is you're nervous and it's

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exciting. So therefore, you can't rest.

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Uh but at the same time, hey, who wants
to be in in a business that you know

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where you don't get to reinvent yourself
again and again? Absolutely. Do we think

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that we look at technology in very
narrow of a sense? I mean, how

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technology can be the driver for the
next economical growth? That's a great

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great question. I think a lot about that

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right which is uh in some sense if you

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think at a GDP level uh tech spend

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narrowly defined is probably four

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5%. Uh so the question is what is

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happening with the other 95%. So one of

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the ways I've always thought about the

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prospects of any new technological

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paradigm or platform shift like take AI

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let's I if AI is going to be the next

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big general purpose

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technology for me the real opportunity

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is let's say tech spend goes from 5 to

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10% over the next you know five years or

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what have you or 10 years then what

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happens to the other 90% and the pie
does it become bigger right? Does the

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you know do we have a breakthrough in

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healthcare driven by AI? Do we have a

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breakthrough in material science and

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energy transition because of uh AI? Uh

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and the list goes on, right? So to me

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that is fundamentally the way I think

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about it, right? is I I think that one

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of the things that might be most

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important for us is to consider how a

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general purpose technology somebody was

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telling me this right which is in the

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height of the industrial revolution

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uh in the United Kingdom they spent 10%

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of their GDP building the railroads and
obviously the railroads not just didn't

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is not about the railroads it was about
the entire economy of the United Kingdom

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and so something like that I think is

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what uh that's the unit of analysis at

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least for me as to how tech and its

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future will impact the you know broader

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society and economy. I mean Google had

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all the top AI people but suddenly you

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got ahead with your partnership
with open AI. How did that came about?

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I

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mean to me the way I came at this very

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simple which is obviously we mean like I

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think the very first thing Microsoft

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research did in 1995 when it was formed

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was some stuff around speech right in

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fact I think we hired a bunch of folks

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from CMU and so we've been at this AI

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thing in its variety of different forms

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forever um and so one of the things that

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when I met with the OpenAI folks and Sam

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and Greg and crew uh back in you know I

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would say when they were working even on

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the Dota 2 contest and what have you uh

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was to sort of say wow they have a new
different approach to things and we

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wanted to partner I mean you know one
of the things that I've always looked at

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over my years at Microsoft is look for

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high ambition um technology innovation

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companies right whether it you know and
partnership like whether it's Intel and

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Windows came together and and that was

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successful. SAP and SQL server it came

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together and it was successful. So I'm
always looking for partners that we can

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innovate with and that's what I found in

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uh Sam and team and uh and we you know

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at that time it was not like it was a it

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was a real uh shot in the dark right it

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is not like oh wow this is a sure sure

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thing everybody now talks about it as if
uh you know this is the issue with tech

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right which is uh long before it's

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conventional wisdom you have to be all

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in and hope it works um and this is one

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of those things where we backed it long

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before it was conventional wisdom. Um,
and here we are. But I don't take, you

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know, like there's going to be severe
amount of competition. You know,

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Google's a very competent company
and obviously they have uh both the talent

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and the compute and they have, you know,
they're the vertically integrated player

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in this, right? They have everything

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from data to silicon to models to have

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products and distribution and there's
others as well. And so yeah, we will

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have significant amount of competition

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and I think if anything Microsoft's

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partnership with open AI is bringing
more competition to otherwise what would

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have been a default Google should have

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be the default winner. Uh and if we

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partner well and we innovate well we
can bring some competition to that. So if

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you look three to five years where do

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you think Microsoft's going to be in the

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whole AI ecosystem? So to me I think

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about this in the fullness of the stack

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right so I want us to have first and

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foremost the best AI infrastructure

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uh so that means when it comes to Azure
whether it's for training whether it's

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for inference to have fantastic
infrastructure we'll partner with Nvidia

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we'll partner with AMD we'll have
our own silicon uh we will have our own

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system architecture we will take the

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best system architecture innovation from

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Jensen and Lisa and others who may come

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along and make sure Azure is serving the

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needs of open AI serving the needs of

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Mistral uh serving the needs of FI that

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we are building right which is the small
language model so that's kind of the

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first uh thing that we want to do which

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is the best work um in being able to

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build the infrastructure out for both

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training and inference and then the next

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layer up we want to have like the ENT

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entire data tier right so you can

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imagine as these models and model

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capabilities become uh you know more

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capable I think the data tier will be

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completely uh redone uh right we've

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talked with the retrieval augmented
generation already you have all these

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things where whether it's embeddings
vector search how do you chunk data such

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that retrieval augmented generation can

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work well so that's an entire layer or
when context lens become bigger that's a

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different sort of data layer like what's

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the throughput between data and your

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inference fleet uh how do you sort of

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think about that so therefore we will

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innovate on the data layer and then of

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course on top of it is where um we will

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innovate on our uh co-pilots one of the

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first products we built was GitHub

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copilot in fact my entire confidence in

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this generation of AI started when I

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started seeing from GPD3 to 35
and that implementation in GitHub

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uh and so we now have you know not only
GitHub co-pilot we have co-pilot for m

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all knowledge work in Microsoft 365 we

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have co-pilots for these functions
whether it's service or sales or finance

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uh so we're going to innovate in our app
layer uh on our own and so that's I

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think fundamentally how I look at it
it's a full stack approach uh and each

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layer by the way we're we will innovate
we will have partners we will have

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others innovating there will be
competition even it's not like you know

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one of the of being a platform company

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is you got to be comfortable uh with

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many third parties competing with you on

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different layers because that to me is

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core otherwise you kind of try to you
know do everything in a monolithic way

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and at least what we've learned over the
years is the best thing to do is to keep

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each layer competitive on its own.
You said in the beginning that we are year

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two in this paradigm shift. How do you
see it compared to other technological

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breakthrough that you have witnessed? So

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at least the four I've seen is um I

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obviously PC client server both what

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happened on the you know PC and the
server side that was my first that's

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kind of when I joined 92 we're at the

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beginning of that uh then there was the

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web internet um and then there was

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mobile cloud and so AI is the fourth um

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I think one of the interesting things
is each one of these built on the previous

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right so I don't think the web would
have happened if there was not a

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ubiquitous PC. After all, the first time

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I saw Mosaic uh you know was as a

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browser on top of Windows, right? So,

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and then Netscape came about and then uh

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IE and what have you. And so therefore,

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I think um you sort of see each one of

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these births the next uh and then it

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goes beyond uh what birthed you, right?

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That's I think the the real thing, right?
Right now we're seeing that right

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which is the cloud um as we know of it

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and mobile uh and PCs on the edge have

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really birthed the AI age and the

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question is what happens next uh right
which is does it go beyond that and that

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I think is going I mean there's going to

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be AI that is not just about cognitive

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work AI that is also going to accelerate

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science so I think that that's exciting

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space uh AI that is going to be embodied

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in the real world so what may happen
in robotics is an exciting space and so

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there is a bunch of things that are
going to happen over time. Uh but

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clearly yes this is year two of a

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complete new paradigm that obviously
reinforces what happened pre or builds

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on what happened previously but also
also is showing early signs of what

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happens next. If there is no shortage
in chips would development have gone

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faster? That's a good
question in the sense of

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scaling laws, right? So there are two
sides to it, right? There's the the

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training side and the inference side. On

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the training side, clearly uh compute

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and compute scale and the scaling laws

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have proven to be very successful. U and

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the question is how long does that go?

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Is there going to be another model

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architecture breakthrough or what have

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you. Um I I think that one has to see.

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It's it's unclear. uh but we definitely

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are not going to bet against scaling
laws and nor are we going to bet against

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uh or nor are we going to say that
this is the last model architecture

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breakthrough. In fact, a great example
of it is even uh what we we've seen even

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with uh our small language model

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innovation like FI, right? Which is we

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are able to create what is significant

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um capabilities uh in a small language

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model uh which uh doesn't require
obviously the same amount of compute

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right which is uh like just like

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attention is everything attention is all

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you need textbooks is all you need. I

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mean that sort of intuitively speaks to

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uh how I think co you know learning can

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happen and so therefore I would say uh

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yes if more more more capacity there is

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in the world the chances are that we

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will be able to make progress but at the

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same time I wouldn't discount a real

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breakthrough

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uh in model architecture that doesn't

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perhaps require uh the same type of

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compute. So that's why I think um
at least I want to keep myself open-minded

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about it. Talking of small language model,
do you think a small country like

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the UEE should develop
their own language model?

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You know, one of the things that I've
studied, there's an economist out of

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Dartmouth,

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uh, his name is Diego Comman, and he did

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one of the best longitudinal studies,

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uh, of technology diffusion. Uh and the

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fundamental conclusion um paraphrasing

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was that any country that wants to get

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ahead should one first make sure that

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they don't reinvent the wheel which is

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they import the best general purpose

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technology that is available in and then

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on top of it build value add. So there

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are ways you can add unique knowledge,

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unique value on top of even what exists

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uh before you go off and say let's build

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all the compute all of it and do the same
pre-training run u you know there's

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no nothing stopping any country from doing
any of this uh but the question is

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what is the value ad and so therefore
I think that you start from the value ad

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and then back into whatever is needed
for it when you think about how

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important the big technology companies
have become in geopolitics. What kind of

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reflections do you make? Well, I mean at

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the end of the day, um two things,
right? One is I'm very very grounded on

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the fact that we are a multinational
company that is a definitely in this

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case a US-based multinational company

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that has to earn permission and license

00:14:16.320 --> 00:14:18.637
to operate one country at a time. Right?

00:14:18.720 --> 00:14:21.917
So therefore, I I think of the uh we're

00:14:22.000 --> 00:14:24.717
not nation states are the ones that have

00:14:24.800 --> 00:14:28.557
power. we get to operate in any nation

00:14:28.640 --> 00:14:33.357
based on our ability to contribute
to that country's progress right so

00:14:33.440 --> 00:14:35.357
whenever I am right whether I'm in

00:14:35.440 --> 00:14:39.597
Norway or I'm in Jakarta or or New Delhi

00:14:39.680 --> 00:14:43.837
or wherever I I am always grounded on uh

00:14:43.920 --> 00:14:46.157
fundamentally the fact is are we able to

00:14:46.240 --> 00:14:48.877
make you know look the local politicians

00:14:48.960 --> 00:14:51.597
and political leadership and society at

00:14:51.680 --> 00:14:56.237
large in the eyes and say that we
contributed to their public sector

00:14:56.320 --> 00:15:00.397
becoming more efficient or large
multinational companies in the region

00:15:00.480 --> 00:15:04.637
becoming more globally competitive
because of some tech input education

00:15:04.720 --> 00:15:07.357
outcome health outcomes small businesses

00:15:07.440 --> 00:15:09.677
and their productivity at the end of the

00:15:09.760 --> 00:15:11.997
day you have your your social contract

00:15:12.080 --> 00:15:15.197
with the country uh comes from your

00:15:15.280 --> 00:15:19.517
ability to contribute to their local
progress and that so I don't think we we

00:15:19.600 --> 00:15:21.757
can ever be beyond geopolitics will

00:15:21.840 --> 00:15:25.037
exist with or without us and our goal

00:15:25.120 --> 00:15:29.837
has to be how do you participate and have
permission to operate in your book

00:15:29.920 --> 00:15:31.037
hit your fresh you talk about three

00:15:31.120 --> 00:15:35.197
areas um AI quantum computing and mixed

00:15:35.280 --> 00:15:39.517
reality what kind of opportunities do
you see in the quantum computing it's

00:15:39.600 --> 00:15:41.597
fascinating in fact I I think of all

00:15:41.680 --> 00:15:43.757
those three still right for example when

00:15:43.840 --> 00:15:46.557
I think about um mixed reality I think

00:15:46.640 --> 00:15:49.397
of it as that's an embodied AI right

00:15:49.480 --> 00:15:52.477
effectively whether it's autonomous uh

00:15:52.560 --> 00:15:56.157
vehicles robotics or people with glasses

00:15:56.240 --> 00:16:01.037
they're all seeing the real world is
the prompt effectively so your real world

00:16:01.120 --> 00:16:05.837
understanding so therefore I think
of in the generation of AI some of these

00:16:05.920 --> 00:16:09.677
things become even more interesting and
more important and we do need to broaden

00:16:09.760 --> 00:16:12.157
the aperture versus thinking narrowly of

00:16:12.240 --> 00:16:15.757
just one device or one form factor

00:16:15.840 --> 00:16:20.317
similarly quantum is a fascinating thing
when I I me mention science right one of

00:16:20.400 --> 00:16:21.837
the things I look at it and say is in

00:16:21.920 --> 00:16:24.077
order to make progress on science You

00:16:24.160 --> 00:16:27.757
need great incilico simulation. Quantum

00:16:27.840 --> 00:16:31.197
is the ultimate breakthrough. Right?
So when we have a complete new system

00:16:31.280 --> 00:16:33.437
architecture that go breaks free of the

00:16:33.520 --> 00:16:37.117
one nimon limitation. Uh you are then

00:16:37.200 --> 00:16:40.957
finally going to be able to simulate
something like the dynamics of a cell or

00:16:41.040 --> 00:16:44.877
a molecule. Right? So that I think when
you can do that then everything else

00:16:44.960 --> 00:16:47.117
even in terms of biology becomes more

00:16:47.200 --> 00:16:50.077
feasible. The interesting thing is AI is

00:16:50.160 --> 00:16:51.997
kind of like an emulator of that

00:16:52.080 --> 00:16:54.317
simulator. In other words, you can kind

00:16:54.400 --> 00:16:57.677
of simplify the search space. One of the

00:16:57.760 --> 00:17:01.517
things we did recently uh was um we have

00:17:01.600 --> 00:17:05.357
a of a model uh for material science

00:17:05.440 --> 00:17:08.557
which we used to generate a new uh novel

00:17:08.640 --> 00:17:10.637
compound which we then went and

00:17:10.720 --> 00:17:12.477
manufactured. We worked with the Pacific

00:17:12.560 --> 00:17:15.677
National Lab locally here to effectively

00:17:15.760 --> 00:17:20.397
go reduce the uh the the lithium content

00:17:20.480 --> 00:17:23.917
uh by 70% in a new battery material uh

00:17:24.000 --> 00:17:26.157
and produced it right not just

00:17:26.240 --> 00:17:28.397
conceptualized it but simulated it

00:17:28.480 --> 00:17:31.837
produced it and so to me uh something

00:17:31.920 --> 00:17:35.437
where like quantum plus AI I think can

00:17:35.520 --> 00:17:38.557
be the ultimate accelerator of science

00:17:38.640 --> 00:17:42.477
uh and we are making progress we will
even On quantum we're taking a very full

00:17:42.560 --> 00:17:45.117
stack approach. Uh we have our software

00:17:45.200 --> 00:17:49.277
stack with uh you know our Q sharp which

00:17:49.360 --> 00:17:53.117
is our quantum programming stack. So we
are excited about sort of the progress

00:17:53.200 --> 00:17:55.277
we're making on quantum and how it

00:17:55.360 --> 00:17:57.677
complements AI and where does gaming fit

00:17:57.760 --> 00:18:02.637
into this? Yeah. So to me one of the
things in fact Microsoft was in gaming

00:18:02.720 --> 00:18:06.957
long before we you know we were into
Windows. In fact simulator I think was

00:18:07.040 --> 00:18:11.117
launched long before Windows was launched.
So uh we are very very excited

00:18:11.200 --> 00:18:14.957
obviously now with Activision as part
of Microsoft we have mobile gaming we have

00:18:15.040 --> 00:18:17.597
cloud gaming we have console gaming PC

00:18:17.680 --> 00:18:19.757
gaming so we're a full stack game pub

00:18:19.840 --> 00:18:22.677
publisher uh as well as a game systems

00:18:22.760 --> 00:18:26.637
provider um and so our goal there is one

00:18:26.720 --> 00:18:30.877
we're in gaming for our love of gaming
right so I always sort of say we should

00:18:30.960 --> 00:18:34.477
never be in businesses as a means
to some other end it has to be an end

00:18:34.560 --> 00:18:38.637
otherwise it's not a business so to me
gaming uh is something where we want to

00:18:38.720 --> 00:18:41.277
ing joy of gaming. That's the one pure

00:18:41.360 --> 00:18:44.477
consumer entertainment category. I love

00:18:44.560 --> 00:18:48.957
the fact that gaming on a secular basis
is probably going to be if not already

00:18:49.040 --> 00:18:53.357
the biggest entertainment category out
there. So that's one. And of course it

00:18:53.440 --> 00:18:55.837
has real implications on the rest of it.

00:18:55.920 --> 00:18:59.437
Right? If you think about even remember

00:18:59.520 --> 00:19:02.877
it's interesting I've not talked
to Jensen about it but one of the greatest

00:19:02.960 --> 00:19:05.877
sort of successes of uh

00:19:05.960 --> 00:19:09.517
GPUs was fostered by innovation in

00:19:09.600 --> 00:19:12.957
gaming right DX which was the Microsoft

00:19:13.040 --> 00:19:17.037
graphics stack is what made GPUs uh an

00:19:17.120 --> 00:19:19.677
accelerator uh right after all the GPUs

00:19:19.760 --> 00:19:22.397
were created for PC gaming you know

00:19:22.480 --> 00:19:25.677
gaming I think as both a an application

00:19:25.760 --> 00:19:28.957
of AI in terms of like game testing. One

00:19:29.040 --> 00:19:33.037
of the first areas I'm very excited
about is games are so complicated right

00:19:33.120 --> 00:19:35.037
now. One of the things that we want to

00:19:35.120 --> 00:19:37.437
use AI is to be able to find these bugs

00:19:37.520 --> 00:19:40.477
in these even closed worlds uh before

00:19:40.560 --> 00:19:44.717
they're out there. And so therefore we
have some very great use cases there. Uh

00:19:44.800 --> 00:19:47.117
but beyond that I think gaming as uh

00:19:47.200 --> 00:19:49.837
data in the context of some of the

00:19:49.920 --> 00:19:54.397
innovation uh in our models I think
is going to be important. Do you game

00:19:54.480 --> 00:19:56.237
yourself?

00:19:56.320 --> 00:19:59.517
I'm a, you know, a light gamer. I used

00:19:59.600 --> 00:20:02.557
to, uh, do a lot more of Civ was my

00:20:02.640 --> 00:20:04.557
favorite game. Age of Empires is a

00:20:04.640 --> 00:20:07.277
another great game that I enjoyed. I

00:20:07.360 --> 00:20:12.397
wish I could play more. Uh, but, you know,
from time to time I slip into it.

00:20:12.480 --> 00:20:15.517
You have really changed the culture
in Microsoft. When you look back, what are

00:20:15.600 --> 00:20:19.357
the most important changes you have made?

00:20:19.440 --> 00:20:21.677
Look, I mean, first of all, I've, as I

00:20:21.760 --> 00:20:23.997
said, I've grown up. All my professional

00:20:24.080 --> 00:20:26.397
career for most part is all Microsoft.

00:20:26.480 --> 00:20:29.677
So, uh, you know, when you say I've been

00:20:29.760 --> 00:20:31.997
at Microsoft for 32 years, all the good,

00:20:32.080 --> 00:20:34.877
the bad, uh, I was part of it, right?

00:20:34.960 --> 00:20:39.917
So, I don't sort of somehow think
that I'm I don't represent all errors of

00:20:40.000 --> 00:20:43.517
Microsoft. Yeah. Which even makes it
more incredible that you have made all

00:20:43.600 --> 00:20:47.277
these changes. Yeah. And and the way
though I came about it is quite frankly

00:20:47.360 --> 00:20:49.437
as a consumate insider I basically

00:20:49.520 --> 00:20:51.997
pattern matched as to hey what were the

00:20:52.080 --> 00:20:55.037
thing when were we at our best uh what

00:20:55.120 --> 00:20:57.677
was the cultural set of attributes that

00:20:57.760 --> 00:21:00.077
helped us succeed and then when we

00:21:00.160 --> 00:21:02.637
failed what were the cultural attributes

00:21:02.720 --> 00:21:05.117
that caused that failure and then you

00:21:05.200 --> 00:21:08.397
know dampen this ladder and amplify the

00:21:08.480 --> 00:21:10.717
former that was as simple as that. So

00:21:10.800 --> 00:21:13.997
one of the things I look back even in my

00:21:14.080 --> 00:21:17.997
you know career at Microsoft when we
first became the largest market cap

00:21:18.080 --> 00:21:22.717
company I forget I think in the early
2000s I think we crossed GE you know

00:21:22.800 --> 00:21:26.637
people on our campus probably were
walking around including me thinking oh

00:21:26.720 --> 00:21:31.277
we must be God's gifts uh because we're
so brilliant and what have you except

00:21:31.360 --> 00:21:33.277
what we needed to be grounded on that

00:21:33.360 --> 00:21:37.077
day was to say wow we now have a real

00:21:37.160 --> 00:21:40.397
responsibility to regground ourselves to

00:21:40.480 --> 00:21:44.717
innovate again uh so that we are
relevant in the future right and so

00:21:44.800 --> 00:21:47.357
that's why I I was lucky enough to have

00:21:47.440 --> 00:21:50.717
read Carol Dwek's book on mindset which

00:21:50.800 --> 00:21:55.597
is around child psychology called growth
mindset and I love that book I read it

00:21:55.680 --> 00:21:57.357
more on the context of sort of our

00:21:57.440 --> 00:22:00.077
children's education but I must say I

00:22:00.160 --> 00:22:03.117
got educated because I felt like this is

00:22:03.200 --> 00:22:06.397
uh what makes uh individuals children in

00:22:06.480 --> 00:22:10.957
school it's very clear right it's
better to be a learn it all versus a

00:22:11.040 --> 00:22:12.957
know-it-all because even if the knowit

00:22:13.040 --> 00:22:16.477
all has great innate capability

00:22:16.560 --> 00:22:21.037
uh the learn it all you know even if
they start from behind they will surpass

00:22:21.120 --> 00:22:23.437
the knowit-all right that's sort of uh

00:22:23.520 --> 00:22:25.517
you know true for children in school

00:22:25.600 --> 00:22:28.797
it's true for CEOs in my seat uh it's

00:22:28.880 --> 00:22:31.757
true for companies and so we took that

00:22:31.840 --> 00:22:35.197
approach we said let's be a learn it all

00:22:35.280 --> 00:22:39.437
versus a know-it-all uh and the A you

00:22:39.520 --> 00:22:41.557
say you've achieved that cultural

00:22:41.640 --> 00:22:43.757
transformation means you're a

00:22:43.840 --> 00:22:47.277
know-it-all. So therefore it is a good

00:22:47.360 --> 00:22:51.437
way to sort of say every day you make
a bunch of mistakes you at least have the

00:22:51.520 --> 00:22:56.317
courage to acknowledge them and continue
on it. And so it's a it's not a

00:22:56.400 --> 00:23:00.077
destination you ever reach. But how do
you get the organization to buy into it?

00:23:00.160 --> 00:23:02.877
It's a beautiful it's a great point. So

00:23:02.960 --> 00:23:07.437
I think the way I I I think see
the problem of corporations especially for

00:23:07.520 --> 00:23:11.997
non-founder companies right founders
have great power and great followership

00:23:12.080 --> 00:23:16.317
and that's why I think they're so
successful or at least you know at least

00:23:16.400 --> 00:23:18.077
we only talk about the successful

00:23:18.160 --> 00:23:20.237
founders but if you set that that class

00:23:20.320 --> 00:23:24.077
aside for me mortals like me uh it can't

00:23:24.160 --> 00:23:27.117
be like okay new dogma from a new CEO

00:23:27.200 --> 00:23:29.117
and more corporate speak it has to

00:23:29.200 --> 00:23:31.597
appeal to us as human beings that's why

00:23:31.680 --> 00:23:36.157
I you I credit more of this work uh by

00:23:36.240 --> 00:23:39.997
Carol and team and so on because it's
not like it was not like I don't think

00:23:40.080 --> 00:23:42.077
anybody at Microsoft views growth

00:23:42.160 --> 00:23:44.797
mindset as some Microsoft dogma or

00:23:44.880 --> 00:23:47.757
definitely not Satia Nadella dogma it is

00:23:47.840 --> 00:23:49.917
something that speaks to them as humans

00:23:50.000 --> 00:23:51.917
right which is it's good for them as

00:23:52.000 --> 00:23:55.157
friends colleagues partners parents

00:23:55.240 --> 00:23:58.077
neighbors it integrates work and life

00:23:58.160 --> 00:24:00.797
they can bring their own personality and

00:24:00.880 --> 00:24:04.237
passion to it uh and benefit from it,

00:24:04.320 --> 00:24:07.837
right? I I always say like you're not
do like this Microsoft culture of growth

00:24:07.920 --> 00:24:11.677
mindset is not for Microsoft, it's for
you. And if you you should only practice

00:24:11.760 --> 00:24:13.997
it if you feel like it speaks to your

00:24:14.080 --> 00:24:17.837
own thriving at Microsoft and in life.

00:24:17.920 --> 00:24:22.477
Uh that's I think what I attribute it to,
right? It wouldn't have taken off if

00:24:22.560 --> 00:24:24.597
it was just another thing that is a

00:24:24.680 --> 00:24:27.197
top-down slogan.

00:24:27.280 --> 00:24:29.997
uh it is ultim I always have believed

00:24:30.080 --> 00:24:33.517
that which is ultimately people work um

00:24:33.600 --> 00:24:38.077
and find meaning in work uh only if they

00:24:38.160 --> 00:24:42.717
can find some true deeper meaning
for themselves and so that's I think what

00:24:42.800 --> 00:24:46.237
I've been always trying to invoke
and well they clearly also found a true

00:24:46.320 --> 00:24:50.637
deeper meaning in the concept of empathy
because you talk a lot about it and you

00:24:50.720 --> 00:24:54.477
say it's the key for innovation
and leadership and why do you think it's

00:24:54.560 --> 00:24:59.037
very important to you I think Most
people think of empathy as some kind of

00:24:59.120 --> 00:25:01.117
a soft skill that's interesting in the

00:25:01.200 --> 00:25:03.677
context of your family or personal life

00:25:03.760 --> 00:25:06.957
and work's all about hardcore you know

00:25:07.040 --> 00:25:09.277
whatever right but that but I I look at

00:25:09.360 --> 00:25:12.557
that and say again what is where does

00:25:12.640 --> 00:25:14.717
innovation come from right innovation

00:25:14.800 --> 00:25:18.557
comes from us being able to drive the

00:25:18.640 --> 00:25:22.637
solutions to unmet unarticulated needs

00:25:22.720 --> 00:25:25.197
of customers out there right so the key

00:25:25.280 --> 00:25:29.757
being unmet meta and unarticulated.
So that means you have to have a better

00:25:29.840 --> 00:25:31.597
sense when you're even looking at some

00:25:31.680 --> 00:25:35.517
log data or you know some customer

00:25:35.600 --> 00:25:40.397
interview data or whatever you it's not
just the words that they're saying but

00:25:40.480 --> 00:25:42.117
you got to be able to walk in their

00:25:42.200 --> 00:25:45.197
shoes. Uh and the good news there is

00:25:45.280 --> 00:25:47.517
this is innate in us all human beings.

00:25:47.600 --> 00:25:50.637
We have uh the ability to empathize with

00:25:50.720 --> 00:25:54.077
the other person. Um in fact design

00:25:54.160 --> 00:25:58.397
thinking is that right? So when people
go and say let's do learn about design

00:25:58.480 --> 00:26:00.357
thinking design thinking is applied

00:26:00.440 --> 00:26:04.397
empathy. Uh and so to me uh that's what

00:26:04.480 --> 00:26:08.157
I pulled the thread on which is let's
not think of empathy as something that

00:26:08.240 --> 00:26:11.677
you know is just a soft skill that you
reserve for your friends and family but

00:26:11.760 --> 00:26:13.677
it's I think it's at the root of all

00:26:13.760 --> 00:26:16.637
innovation. Uh it's about being able to

00:26:16.720 --> 00:26:19.437
meet the unmet unarticulated needs that

00:26:19.520 --> 00:26:21.837
comes from your unique I mean your

00:26:21.920 --> 00:26:24.477
innate ability to have curiosity to

00:26:24.560 --> 00:26:28.397
learn about others walk in their shoes

00:26:28.480 --> 00:26:31.117
innovate on their behalf uh and that I

00:26:31.200 --> 00:26:35.757
think is I think what we have to do
and that's why I I think empathy is an

00:26:35.840 --> 00:26:37.597
important important skill for all of us.

00:26:37.680 --> 00:26:39.277
When did you first discover the power of

00:26:39.360 --> 00:26:42.157
empathy? Yeah. And you know, I think

00:26:42.240 --> 00:26:45.277
that one of the things that I I feel

00:26:45.360 --> 00:26:49.037
like all of us learn how to turn on this

00:26:49.120 --> 00:26:50.957
bit of empathy through life's

00:26:51.040 --> 00:26:53.357
experience, right? So, in some sense,

00:26:53.440 --> 00:26:58.157
every day you get confronted with

00:26:58.240 --> 00:27:00.157
different circumstances, not just

00:27:00.240 --> 00:27:02.397
yourself personally, but people around

00:27:02.480 --> 00:27:06.077
you. Uh for me obviously the birth of my

00:27:06.160 --> 00:27:09.757
son uh for both my wife and me was uh

00:27:09.840 --> 00:27:11.997
was a life-changing event and it was

00:27:12.080 --> 00:27:14.877
something that uh you know over the

00:27:14.960 --> 00:27:17.997
years I at least learned a lot because I

00:27:18.080 --> 00:27:22.397
I I remember in the early days it
was all about sort of my son was born with

00:27:22.480 --> 00:27:24.157
cerebal palsy. He passed away a few

00:27:24.240 --> 00:27:27.917
years ago. Uh and um uh but you know in

00:27:28.000 --> 00:27:32.157
the when the when he was born it
was a lot about what happened to me. I was

00:27:32.240 --> 00:27:33.997
sort of you know essentially quote

00:27:34.080 --> 00:27:38.077
unquote um you know in you know all

00:27:38.160 --> 00:27:42.637
about why did this happen to us,
why did it happen to me or how and then I

00:27:42.720 --> 00:27:44.957
realized after watching in some sense my

00:27:45.040 --> 00:27:48.157
wife uh who was there as a caregiver, as

00:27:48.240 --> 00:27:51.517
a parent uh you know taking him up and

00:27:51.600 --> 00:27:54.237
down Seattle to every therapy possible.

00:27:54.320 --> 00:27:55.997
Uh quite frankly you know it took me

00:27:56.080 --> 00:27:58.637
years not you know days or months or

00:27:58.720 --> 00:28:02.637
weeks. Um and um and then I realized

00:28:02.720 --> 00:28:05.837
that it nothing had happened to me
but something had happened to my son and I

00:28:05.920 --> 00:28:09.197
needed to be there for my son and that

00:28:09.280 --> 00:28:13.837
is the experience I talk a lot about but
there's experiences like that every day

00:28:13.920 --> 00:28:15.917
right some colleague of mine comes with

00:28:16.000 --> 00:28:19.277
some you know parent of theirs who needs

00:28:19.360 --> 00:28:22.877
care right that sort of you know I learn

00:28:22.960 --> 00:28:25.037
from it I or at least let me put it this

00:28:25.120 --> 00:28:28.717
way I am more attuned to learning from

00:28:28.800 --> 00:28:33.597
other people's experiences today than I
was in the beginning of my career and I

00:28:33.680 --> 00:28:35.117
think that happens to all of us right

00:28:35.200 --> 00:28:38.637
which is life's experiences if they they

00:28:38.720 --> 00:28:42.877
acrewue uh that ability to build a

00:28:42.960 --> 00:28:45.037
deeper empathy for other people and that

00:28:45.120 --> 00:28:47.277
also helps you be a better manager a

00:28:47.360 --> 00:28:50.317
better co-worker a better uh innovator

00:28:50.400 --> 00:28:52.317
thank you for sharing but do you think

00:28:52.400 --> 00:28:54.397
there's a contradiction between empathy

00:28:54.480 --> 00:28:58.157
and execution I don't right I think that

00:28:58.240 --> 00:29:01.677
at the end end of the day to me you have

00:29:01.760 --> 00:29:04.157
to take accountability right so this is

00:29:04.240 --> 00:29:07.997
one of the things that uh in business um

00:29:08.080 --> 00:29:11.277
like in life too right uh you have to be

00:29:11.360 --> 00:29:14.157
accountable for making real progress uh

00:29:14.240 --> 00:29:16.317
one of the things I think about why do

00:29:16.400 --> 00:29:21.277
businesses exist businesses exist at least
I like this colin mayor definition

00:29:21.360 --> 00:29:23.157
that you have to create profitable

00:29:23.240 --> 00:29:25.677
solution to challenges of people and

00:29:25.760 --> 00:29:28.637
planet uh because that's at least a good

00:29:28.720 --> 00:29:31.437
way to allocate the global resources

00:29:31.520 --> 00:29:36.237
that are available, right? The profit
motive is a good motive because it means

00:29:36.320 --> 00:29:38.157
you're competing and allocating

00:29:38.240 --> 00:29:41.357
resources in the most efficient ways and

00:29:41.440 --> 00:29:45.757
uh and face competition and so therefore
you have to have great execution. have

00:29:45.840 --> 00:29:46.837
to have great

00:29:46.920 --> 00:29:48.957
accountability. And so I think of

00:29:49.040 --> 00:29:53.077
empathy as a necessary condition to

00:29:53.160 --> 00:29:57.437
create great solutions that are
profitable and that are competitive

00:29:57.520 --> 00:30:01.117
solutions that are winning in the
marketplace as opposed to somehow this

00:30:01.200 --> 00:30:04.637
being a trade-off. When you look at your
skills and personality as a leader, what

00:30:04.720 --> 00:30:07.757
do you think that makes you most
effective as a leader? I mean, first of

00:30:07.840 --> 00:30:10.997
all, right, I I I don't think of this

00:30:11.080 --> 00:30:15.477
as I I I don't know. I have causality

00:30:15.560 --> 00:30:19.677
here well understood because quite
frankly it's so much easier for others

00:30:19.760 --> 00:30:21.597
to opine on this than or rather it's

00:30:21.680 --> 00:30:24.077
it's just really for others to judge and

00:30:24.160 --> 00:30:27.357
assign uh causality there but the way at

00:30:27.440 --> 00:30:31.277
least I come at this is I don't start

00:30:31.360 --> 00:30:37.717
with uh what am I good at I am very

00:30:37.800 --> 00:30:42.077
keenly shaped by what am I not good at

00:30:42.160 --> 00:30:45.277
uh in other words. I'm always looking

00:30:45.360 --> 00:30:49.837
what can I learn from someone else.
So if there's one attribute I have I don't

00:30:49.920 --> 00:30:51.757
start each day by thinking oh all the

00:30:51.840 --> 00:30:53.597
stuff I know and I'm good at. I'm like

00:30:53.680 --> 00:30:56.877
wow what am I weak at whom do I talk to

00:30:56.960 --> 00:30:59.917
whom do I meet? How do I really shape

00:31:00.000 --> 00:31:02.557
the colleagues around me who have better

00:31:02.640 --> 00:31:08.157
skills than me on many fronts? uh that's
what I'm wired like maybe that helps but

00:31:08.240 --> 00:31:10.957
I don't know whether that's
the causality but I don't start each day

00:31:11.040 --> 00:31:14.637
with saying wow I'm so good at this so
therefore I'm going to go do this is

00:31:14.720 --> 00:31:17.197
that something that you learned from
Bill Gates because he said something

00:31:17.280 --> 00:31:19.277
very similar it's a good point both Bill

00:31:19.360 --> 00:31:23.277
and Steve uh there is uh there's a sense

00:31:23.360 --> 00:31:26.797
at Microsoft I think that that's an
interesting thing there is you know what

00:31:26.880 --> 00:31:28.797
Andy Grove would talk the paranoid

00:31:28.880 --> 00:31:32.237
survive or what have you um and I don't

00:31:32.320 --> 00:31:37.037
come at it though with paranoia right
I I mean I don't like paranoia. I like

00:31:37.120 --> 00:31:41.277
this just that's why I go back to my own
words for this is that's why the the

00:31:41.360 --> 00:31:45.757
growth mindset or confronting your own
fixed mindset having confidence that wow

00:31:45.840 --> 00:31:50.077
what an unbelievable world we live
in that every customer can teach me every

00:31:50.160 --> 00:31:51.997
partner can teach me every colleague can

00:31:52.080 --> 00:31:55.037
teach me like what more can I ask for in

00:31:55.120 --> 00:31:58.157
life uh so it's not paranoia it's not

00:31:58.240 --> 00:32:01.757
like oh wow we have to go in every day

00:32:01.840 --> 00:32:05.597
uh that if I don't learn something I'll
you know fail I'm more about like what

00:32:05.680 --> 00:32:10.477
do I learn so that I can innovate maybe
uh that's how I come at it and that's

00:32:10.560 --> 00:32:15.037
right Bill and Steve in their own unique
way uh had that mindset and so I've

00:32:15.120 --> 00:32:19.197
grown up around it how do you solve this
in an organization you know humbleness

00:32:19.280 --> 00:32:20.957
is an interesting word right I I always

00:32:21.040 --> 00:32:24.317
say that you you need confidence

00:32:24.400 --> 00:32:28.477
um and uh humility

00:32:28.560 --> 00:32:31.517
uh and not hubris right because there is

00:32:31.600 --> 00:32:34.797
sometimes confidence with humility can

00:32:34.880 --> 00:32:39.277
allow you to really make great progress
but confidence that translates into

00:32:39.360 --> 00:32:41.117
hubris can bring you know it's a

00:32:41.200 --> 00:32:43.837
downfall of you know civilizations

00:32:43.920 --> 00:32:46.477
empires and individuals right and from

00:32:46.560 --> 00:32:48.397
ancient Greece to modern Silicon Valley

00:32:48.480 --> 00:32:53.597
and so that's why I think uh you have to
sort of really get that calibration that

00:32:53.680 --> 00:32:57.357
you got to have some confidence in your
own capability you said in a podcast

00:32:57.440 --> 00:32:59.997
with Adam Grad that your father has a

00:33:00.080 --> 00:33:05.277
list of people that he met and a list
of ideas generated do you have a list of

00:33:05.360 --> 00:33:08.957
people that you would like to meet.
you know he had this note you know his

00:33:09.040 --> 00:33:11.917
diaries were full of that schema uh

00:33:12.000 --> 00:33:15.277
which is uh people met uh ideas

00:33:15.360 --> 00:33:17.517
generated and tasks completed which I

00:33:17.600 --> 00:33:20.077
love which is a beautiful way each day

00:33:20.160 --> 00:33:23.277
to keep a count of uh and absolutely uh

00:33:23.360 --> 00:33:27.037
so that's sort of like I I took that u

00:33:27.120 --> 00:33:33.040
to heart and that's essentially
how that's my framework for life as