WEBVTT

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So, what I think we've not yet
conceptually gotten right and a shared

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understanding is what is this future of

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work going to look like for a tech CEO?

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You have to be deep inside of what's the

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tech stack. AI is not a technology. It's

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the future of the firm. One of the

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dictums I have is don't use frontier

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models for non-frontier problems.
I think in the AI age that is going to be

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everything, right? I think I would be

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very surprised Reed if you were sitting

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here a year from now if the world is not

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completely turned on what
is my AI supply chain look like.

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>> I couldn't be more delighted to

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introduce a special episode of Possible

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with Satya Nadella, the chairman and CEO

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of Microsoft. Satya and I have known

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each other a long time, and part of in

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this is kind of AI revolution for

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humanity. Uh I thought this would be a

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great episode to get out. I mean, we

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covered all kinds of important topics.

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Satya, as always, is elegant, is

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cohesive, is smart, is comprehensive,

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and above all, humanist in what
is our AI future. This will be an amazing

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episode. Actually, one of the things
that's great with Satya about filming

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this here is it it reminds me of the
earliest days when we were talking about

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Microsoft and LinkedIn.
>> That's right, because we did one of our

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very, very great conversations here
in the Greylock office, so it's like just

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awesome to be back. >> I want
to start with something that I

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don't know as many people realize and

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appreciate about you, which is with how

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many books of poetry you have in your

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house. Um can you say a little bit
about your attraction to poetry, favorite

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poets, what what the engagement is there?

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>> Yeah, I mean, I I actually got into it

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in different times of my life. I

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remember that, you know, uh as a middle

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schooler we had this standard issue

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English poetry book, which I've been

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trying to reclaim and get all my life,

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but unfortunately it's out of print, but
it sort of had, you know, even getting,

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you know, introduced
to Shelley or Wordsworth and

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it had even sort of Indian authors like
Sarojini Naidu writing in English and it

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is I don't know. I felt maybe it was my

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attention span or what have you. I was
naturally drawn to poetry as a thing to

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enjoy and love and and I've always

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compared it even to code which is it's

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sort of compression in its best form. Um

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and so whenever I'm bored, I get to, you
know, go read. I'm not great actually at

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understanding deeply. I've never studied
it. I'm not like so so and so. That's

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why my reputation of knowing about

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poetry is far exceeds my knowledge of

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poetry. Uh

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but I still, you know, continue to

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um use poetry as perhaps the best

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expression of um uh
the human experience, right? I

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mean if you if you sort of broadly think

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about literature um as sort of what

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captures more than even history um

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the human experience, I think
poetry is the compressed form of it.

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>> I completely agree. Is there
a particular poet that you go back

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>> Yeah, so I the thing that I really

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I would say the poetry that probably
speaks to me most deeply is the Urdu

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poetry. That is sort of because I grew
up in Hyderabad in India and Urdu is

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sort of the in the air um and some of

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the Urdu poets both modern and sort of

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um you know, people in the 17th, 18th
century were just extraordinary. Uh and

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in particular, you know,
there's there's this

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poet uh whose name it goes by Ghalib as

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his name um and he's just uh

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extraordinary or even a modern poet like
Faiz. Um I was also very having grown up

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in you know, in Hyderabad, I was very

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influenced by Rumi. Um in fact, um you

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You I might the high school I went
to was fascinating. In fact, we had um you

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know, the number of languages which
were all taught was obviously there was

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English. Um there was Hindi, which
is the national language, and then um we

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had Sanskrit. Like basically,
you know, uh like Latin

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here, I guess. And then uh we
had the local language Telugu,

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which is my mother tongue.

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Um but we also had Urdu and uh Persian.

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And so, all of them like in fact, when

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we would break for what was called

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second language, I I my second language

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was Sanskrit. Um but you know, they I

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could see I had classmates who would go
to Persian, Urdu, Telugu, Hindi, and it

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was fascinating. >> Well,
among the experiences I'm

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experiencing a little bit of language
envy cuz I a joke that I like is you

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know, what do you call a person who
speaks three languages? Trilingual. Two

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languages, bilingual. One language,
American. And unfortunately, I I

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resemble this joke. Um all right. So,
let's uh we're just coming out of Build,

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which was amazing. Um let's kind of

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actually start with kind of going back

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in history. So, you know, Microsoft's

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first product was a basic interpreter.

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Um and 50 years later,
here at Build, framed

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the company's future on what others can
build with AI. So, what is the new basic

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interpreter >> Yeah. >> in this AI era?
That's a great one. So,

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in fact, it's it that that I think is

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the core of the DNA of the company,
right? Which is what can others build,

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and therefore what should we build? It's
sort of the two questions that we always

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ask ourselves. And in the AI era,

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to me, the answer is uh the basic
interpreter is the hill-climbing

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machine. Right? Because you think of you

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Now, bring all of this AI, what is it?

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It is basically taking an objective, an

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outcome, an eval that you have,

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and learning how to achieve that by

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learning uh using data,

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using some reinforcement reward. And so,

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the the entire conference was about not

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Hey, here is a new frontier model.

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It was about helping every developer,

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every company, whether it's a AI native

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startup or an enterprise, building their

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own hill-climbing machine so that they

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can operate at the frontier, right? So,
I think that that's it. They The idea In

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fact, the you know, being very clear

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about the evals and the objectives that

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you care deeply about, knowing how
to evaluate them, right? That it's in some

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sense is the most
and keeping That's the new IP.

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>> Yes. >> Because
everything else is pretty

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mechanical. Uh but knowing what is the

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set of data that you want to train a

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model on and how you reward it
is probably where the the next level of IP

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gets created. >> Well,
and one of the things let's dig

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into a couple different areas here
cuz you and I've had a number of

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conversations, you know, with Microsoft
strategy. And one of them is kind of

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this question about how enterprises
keep the advantage and integrity of

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their own data. And Microsoft is the most
natural company in the entire world

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to do this. So, what Say a little bit
about how enterprises should be thinking

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about like, "We need our own frontier
intel- intelligence, but we also need to

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maintain control of our data."

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>> Yeah, see that I think
is the the question, which is

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this economy is going
to be shaped going forward

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by both human capital

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and let's call it this token capital,
right? That is true for Microsoft, that

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is true for a new startup, that's going
to be true for any bank that's been in

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existence for 100 years, right? Doesn't

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matter. All of us will now need to sort

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of in fact the interplay between human

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capital and token capital
and compounding the returns of it is what

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you need to do. So, if you sort of frame
it that way, then one of the most

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important thing is not just even
thinking of data in its aggregate sense,

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right? See, what is the tacit knowledge

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of an enterprise or a firm? It's the

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unique ways that you're able to operate,

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pass judgment, have taste. All that's

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the tacit knowledge, mostly captured

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today in the tacit knowledge that is

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there with the human capital, and some

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artifacts that are digital.

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So, now when it comes to AI, the the

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model, in some sense, is able to

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extract, if anything, that tacit

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knowledge through human trajectories,
and then code it in a set of weights in

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a model. So, what I claim is that every

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enterprise now needs to be more mindful

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about that interplay of humans and their

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digital estate working together, those

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trajectories training essentially the

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models that they keep as IP versus leak

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it. Because if you leak it, it's
a one-way door. You're done in some sense,

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right? Which is what is unique, what you
may have spent a hundred years, right?

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In fact, we don't even know how to
articulate it, right? Nobody sort of has

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a line item in their balance sheet

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called tacit knowledge, but we take it

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for granted that because we have human

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capital, we have it. Now,
I believe that can leak. And in

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fact, it is leaking, right? If you
look at even the number how the model

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companies learn, they're essentially

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setting up these gyms with rewards,

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which are employing employees who worked
at your company previously. I mean,

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that's sort of should tell you
everything what what should not be

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happening. Which is So, that's one of
the fundamental reasons why we want this

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effectively the regime to change or the
paradigm to change, where you welcome

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the models to come in.
They should hill climb inside a machine

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that you control. Your data is your

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context you feed the model. You collect,

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in fact, these traces or trajectories of

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how work gets done between humans and

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agents inside the enterprise.

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But you have a continuous loop of that
and you're not letting that leak. That I

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think is the fundamental operation.

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>> Yep. And actually the related parallel
to the tacit knowledge is, and this is

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one of the conversations you and I had

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actually at the at the Microsoft board,

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which was what uh is the future role of

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AI employees? Right? Because part of
the question is say, "Hey, we're going to

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provision not just, you know, kind of uh
tools to amplify human work, great for

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you know, kind of AI companies,
but we're also going to provision at least

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specialist employees."
But the challenge with that is is that

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one of the things that really matters to

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enterprises is your employees embody a

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lot of, you know, tacit
knowledge, how to, what to do.

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Do you want that in other companies and
provision other places? What's the your

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and Microsoft view of the future of work

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when it comes down to how to think
about AI as also, maybe even specialist

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employees? >> Yeah. I mean,
I think that that that's

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right. So, the way to think about this,
you know, there are two ways I come at

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this, right? Which is let's let's take
one analogy and I'll come back to it,

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right? If somebody in the early '80s
had come to us and said, "You know what?

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There are going to be 4 billion typists
uh who are going to wake up every

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morning and start typing." And we would
say, "What for? I mean, it makes no

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sense, right? We have a typist pool, we

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have a slide pool, and we're you know,

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fine, uh you know, with that." But

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except we invented this complete new

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thing called knowledge work,
where everybody was typing and creating

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artifacts and so on.

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So, what I think we have not yet
conceptually gotten right, and our good

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understanding, and our shared
understanding, is what is this future of

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work going to look like when you have,
let's call it, you know, and let's take

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Microsoft, we have 20,000 employees and

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we have, let's say, 2 million uh agents

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or 20 million agents. All in a loop.

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What is happening?
Uh what is the tacit knowledge that gets

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created?

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Um what is, you know, the artifacts uh

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that are going between agents, between
agents and humans, and so on. All that's

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now going to be played out in the next
whatever even multiple years. You can

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see early forms of this in coding,

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right? It's a great place to observe,
in fact, the the the this even the sort of

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social change, right? I mean, think

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about We started in a good old IDE and

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said, "Hey, AI is in you know,
in there." Uh and it's doing code

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completion. That itself was useful, easy
to understand. We've always had spelling

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correction and IntelliSense was there

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for in VS Code, you know, from there
15 years ago and and it just got better.

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Then we said, "Oh, you know, instead of
going out of band and going to a browser

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and, you know, going to Stack Overflow
and searching, you can now bring all

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that coding knowledge to a chat session."
Well, that was also also easy

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to understand and you still were
in the IDE, you had sort of the chat.

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Then we said, "Okay, um you now
have reasoning models and some

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you know, primary you know, preliminary
agent loops and so on. And so you can

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assign tasks." So we had agent mode,
right? So you not only had chat, but you

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can give it small tasks
and you could see it complete

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um and then you could accept what it
did and then you could insert it and what

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have you.

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Then came the big breakthrough of total

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autonomy and agentic loop working
for long periods of time where you could

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literally fire forget, right? Where
you could assign a high-level intent and

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it'll go off and do the full PR and you

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would accept the PR.

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So that transition,

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right? Is what I think is going
to happen across all work, right? And then

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now we're seeing it with even in Copilot,
we now have chat and we have

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co-work and now in fact at build we
announced something called scout and

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autopilot. So, what is happening in coding
will happen even in knowledge work.

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Interestingly enough, one of the things

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we launched even at build in GitHub was

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a new feature called canvas because what

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has happened is

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as we've all gotten so good at using

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these coding agents. In fact, you know,
the biggest challenge we now have is I

00:13:53.600 --> 00:13:58.757
have a hundred CLI sessions open I'm
trying to operate these hundred you

00:13:58.840 --> 00:14:00.117
know, agents

00:14:00.200 --> 00:14:03.357
and now the cognitive load on me

00:14:03.440 --> 00:14:07.237
managing this is so high, right? I mean
first of all, I don't I mean they think

00:14:07.320 --> 00:14:11.757
about it, right? It's a linear chat
session in a command line and I have a

00:14:11.840 --> 00:14:13.997
hundred of them. So, guess what? We now

00:14:14.080 --> 00:14:17.117
have a back to an IDE, right? So, I have

00:14:17.200 --> 00:14:19.277
a new IDE we give it a new fancy name.

00:14:19.360 --> 00:14:22.437
It's called an ADE. It's an agentic
development environment. That's what the

00:14:22.520 --> 00:14:24.717
new GitHub app is. The GitHub app looks

00:14:24.800 --> 00:14:29.677
like an inbox except it's an inbox
of agents that are working across all the

00:14:29.760 --> 00:14:30.917
repos

00:14:31.000 --> 00:14:36.877
in in allowing me to do the micro
steering of the macro delegation I gave

00:14:36.960 --> 00:14:40.477
them. Uh but it's
a complete UI for the agents

00:14:40.560 --> 00:14:44.957
to deal with me and for me to deal with
agents. And so, I feel so we introduced

00:14:45.040 --> 00:14:49.357
this new feature called canvas in GitHub
where it can like for example, you know,

00:14:49.440 --> 00:14:51.917
sometimes it's easy to have a Kanban

00:14:52.000 --> 00:14:54.917
board visualization as a way to run down

00:14:55.000 --> 00:14:58.957
your PRs. Very useful
both for the agents and me

00:14:59.040 --> 00:15:01.357
to interact versus some chat session.

00:15:01.440 --> 00:15:04.277
So, I think that that type of innovation

00:15:04.360 --> 00:15:08.517
is how work will change. In fact, one of
the other fascinating things for me is a

00:15:08.600 --> 00:15:10.717
line of even AI research would be the

00:15:10.800 --> 00:15:14.477
models will also get much more tuned to

00:15:14.560 --> 00:15:18.797
learning how to stay the course

00:15:18.880 --> 00:15:22.917
um and understand human preferences in

00:15:23.000 --> 00:15:27.357
steerability, right? Because that's what
I want. And in fact, I want models that

00:15:27.440 --> 00:15:32.397
not only do instruction following,
but are also really steerable.

00:15:32.480 --> 00:15:35.317
Uh and that when you have that, that's
when you have confidence in it. So, I

00:15:35.400 --> 00:15:38.037
think the work of the future is about

00:15:38.120 --> 00:15:40.437
tacit knowledge that gets created by

00:15:40.520 --> 00:15:42.557
this interplay. Just like knowledge work

00:15:42.640 --> 00:15:46.677
got created because of digital artifacts

00:15:46.760 --> 00:15:48.597
and human capital,

00:15:48.680 --> 00:15:51.677
the new one would be this AI capital and

00:15:51.760 --> 00:15:56.157
the human capital working together,
creating in some sense the digital

00:15:56.240 --> 00:15:58.637
artifacts. >> And what do
you think are the things

00:15:58.720 --> 00:16:00.997
that people miss about how important it

00:16:01.080 --> 00:16:04.077
is to build out additional kind of

00:16:04.160 --> 00:16:06.797
structure for enabling enterprises. So,

00:16:06.880 --> 00:16:08.837
one of them is obviously the enabling

00:16:08.920 --> 00:16:11.917
humans canvas, you know, ADEs, etc. But

00:16:12.000 --> 00:16:17.357
what are the like, for example,
notions of like, what is uh security in an

00:16:17.440 --> 00:16:19.077
enterprise? >> a great point.
It's a great point. So,

00:16:19.160 --> 00:16:22.877
in fact, the to your point,
one is the experience layer.

00:16:22.960 --> 00:16:26.557
Clearly, that's important. And another
one which we referenced earlier was,

00:16:26.640 --> 00:16:30.437
hey, we need this hill climbing machine
as a concept that you needs to be

00:16:30.520 --> 00:16:34.757
instantiated. But the third is the
manageability of it and the security of

00:16:34.840 --> 00:16:36.517
it. Starting with observability, right?

00:16:36.600 --> 00:16:39.597
One of the first things we did even,
and we talked about it even yesterday, is

00:16:39.680 --> 00:16:43.797
something called Agent 365. I need
to know, I need to have an inventory. I

00:16:43.880 --> 00:16:47.877
said, oh, there may be 20 million agents
at Microsoft. I first need to know, what

00:16:47.960 --> 00:16:49.677
are these agents? What are they doing?

00:16:49.760 --> 00:16:53.117
What are their reasoning traces?
They need to be fully inspectable, fully

00:16:53.200 --> 00:16:55.957
auditable. Uh and by the way, when the

00:16:56.040 --> 00:16:58.157
agents also have this other attribute,

00:16:58.240 --> 00:17:00.597
they execute they are running, they can

00:17:00.680 --> 00:17:03.837
generate code and execute. So, you need

00:17:03.920 --> 00:17:08.077
the environment in which they are
executing code with maybe even file

00:17:08.160 --> 00:17:13.277
system access, network access, to be
think that is governed by policy. So,

00:17:13.360 --> 00:17:16.637
therefore, you now need to give them
identities, you need to give them

00:17:16.720 --> 00:17:19.237
sandboxes, then you need to set policies

00:17:19.320 --> 00:17:23.957
to govern them. And so we built this
entire thing called Agent 365, which

00:17:24.040 --> 00:17:27.277
really has, you know, we've extended
Entra so that you have an identity, we

00:17:27.360 --> 00:17:31.557
extended Defender so that you have
security, we extended Purview so that

00:17:31.640 --> 00:17:35.437
you can even label the data that is
getting created automatically so that

00:17:35.520 --> 00:17:39.397
you can have data protection.
So, I think security, containment,

00:17:39.480 --> 00:17:44.517
manageability, observability is the way
we will have confidence around these

00:17:44.600 --> 00:17:46.357
agents. The one other thing I'd say Reed

00:17:46.440 --> 00:17:49.037
also was you know salient in the

00:17:49.120 --> 00:17:53.637
developer conference was how important
it is when you're building these

00:17:53.720 --> 00:17:56.877
long-running agents.
All right, like if you think about

00:17:56.960 --> 00:18:02.157
right, we have always had
in programming languages,

00:18:02.240 --> 00:18:06.677
you know, programming
models for understanding the

00:18:06.760 --> 00:18:10.717
very, you know, the verifiability
of a program, right? And it's execu- at

00:18:10.800 --> 00:18:13.277
runtime. So, one of the attributes of

00:18:13.360 --> 00:18:15.437
these long-running agents that we added

00:18:15.520 --> 00:18:18.277
to Foundry was something called asserts.

00:18:18.360 --> 00:18:22.237
So, this allows us to assert what are

00:18:22.320 --> 00:18:26.757
the boundaries, right? So, instead of
talking about guardrails as a thing that

00:18:26.840 --> 00:18:28.677
is sort of just a classifier of some

00:18:28.760 --> 00:18:31.717
form, you really need to now have the

00:18:31.800 --> 00:18:34.517
ability during execution

00:18:34.600 --> 00:18:38.677
to really have that execution path not

00:18:38.760 --> 00:18:42.517
go off rails. And so, there's a lot
of engineering sophistication now that's

00:18:42.600 --> 00:18:46.397
emerging as we build out the platform, the

00:18:46.480 --> 00:18:51.277
runtime, the security layer, the
management layer, the observability layer.

00:18:51.360 --> 00:18:55.397
>> So, let's go to a little bit
of the role of CEO. And I think

00:18:55.480 --> 00:18:59.877
Fortune recently described you as acting
like a startup CEO inside of Microsoft's

00:18:59.960 --> 00:19:03.237
AI teams. But

00:19:03.320 --> 00:19:07.037
the more general question because I
think a lot of CEOs seek, you know, kind

00:19:07.120 --> 00:19:09.957
of technology strategy advice about

00:19:10.040 --> 00:19:11.797
what's going on in the world from you.

00:19:11.880 --> 00:19:17.077
What do you think the CEOs
should be doing around AI?

00:19:17.160 --> 00:19:20.717
What um you know,
kind of when they're like AI

00:19:20.800 --> 00:19:24.637
brings the kind of a re-founding moment
to lots of companies because of the

00:19:24.720 --> 00:19:29.437
nature. And so, what's your advice
to them for how they should engage?

00:19:29.520 --> 00:19:31.517
>> Great question because I've been
thinking about this, right? Because in

00:19:31.600 --> 00:19:33.917
some sense, you know, it clearly for if

00:19:34.000 --> 00:19:38.597
you sort of you're a tech CEO, you have
to be deep inside of what's the tech

00:19:38.680 --> 00:19:42.957
stack. There's no way you could be
a tech CEO and not essentially have a

00:19:43.040 --> 00:19:44.837
fundamental

00:19:44.920 --> 00:19:48.157
uh worldview on where the future is

00:19:48.240 --> 00:19:50.157
going and then to be long before it's

00:19:50.240 --> 00:19:55.277
conventional wisdom, you have to pass
judgment on uh you know, where the

00:19:55.360 --> 00:19:59.117
company's going, right? So, that's sort
of the our industry's sort of pretty

00:19:59.200 --> 00:20:03.277
binary transition. So, I think
there's no hope if you don't have a

00:20:03.360 --> 00:20:06.357
uh the CEO leading uh from the front and

00:20:06.440 --> 00:20:08.957
taking the shot on goal knowing that

00:20:09.040 --> 00:20:12.117
these are fairly uh harsh transitions.

00:20:12.200 --> 00:20:14.397
But, the interesting thing that now I'm

00:20:14.480 --> 00:20:19.637
up to now if you are not a tech CEO,
you needed to be a great CEO, which means

00:20:19.720 --> 00:20:23.517
you need to be great at banking
or healthcare or whatever it is.

00:20:23.600 --> 00:20:28.717
And you could always have great partners
and technology advisers and what have

00:20:28.800 --> 00:20:33.197
you. But, now I'm changing my prior
on that, right? Because I think

00:20:33.280 --> 00:20:37.117
because I'm noticing even what's
happening and I don't think the the rest

00:20:37.200 --> 00:20:42.237
of the the the industry and the CEO
community has broadly woken up um to

00:20:42.320 --> 00:20:45.557
this and I'm like perplexed at it
because they're still you know, pretty

00:20:45.640 --> 00:20:49.957
happy doing a press release with a tech
company and saying, "Yeah, I got my AI

00:20:50.040 --> 00:20:53.357
strategy." And you know, pointing
to eight agents they built and some

00:20:53.440 --> 00:20:54.717
outcome.

00:20:54.800 --> 00:20:57.997
But, this is no longer AI is not like a

00:20:58.080 --> 00:21:02.357
sort of a technology.
It's the future of the firm.

00:21:02.440 --> 00:21:05.517
Right? It would be like saying, "Oh,
I don't know what the about the human

00:21:05.600 --> 00:21:10.117
capital in my firm." Right? So, I think
that this is where my thing would be

00:21:10.200 --> 00:21:13.437
that you have to now
get a deep understanding

00:21:13.520 --> 00:21:15.637
of what's your token capital. Can you

00:21:15.720 --> 00:21:17.517
answer that question? Right? Can you

00:21:17.600 --> 00:21:21.317
concretely say, "Oh, last night

00:21:21.400 --> 00:21:24.157
or based on all the work that happened

00:21:24.240 --> 00:21:27.077
across all of our operations,

00:21:27.160 --> 00:21:30.277
we were able to translate that into a

00:21:30.360 --> 00:21:33.717
set of knowledge that is somehow now

00:21:33.800 --> 00:21:38.197
part of my token capital.
It can be some context.

00:21:38.280 --> 00:21:42.557
It can be some skill.
It can be some weights in a model. It

00:21:42.640 --> 00:21:44.477
doesn't need to be any one thing, but

00:21:44.560 --> 00:21:46.437
you need to be able to specific you

00:21:46.520 --> 00:21:49.797
know, you need to be able to identify it

00:21:49.880 --> 00:21:53.717
clearly as something that you own, you

00:21:53.800 --> 00:21:56.517
control, you created, and you're put in

00:21:56.600 --> 00:21:59.917
place a system to have that compound.

00:22:00.000 --> 00:22:04.117
So, I think that that is going to be
probably the toughest change. So, this

00:22:04.200 --> 00:22:08.837
change is like unlike how this is like
going to a mobile phone era or a PC era

00:22:08.920 --> 00:22:12.917
or a cloud era where all I needed to do
is to have an IT department that knew

00:22:13.000 --> 00:22:15.597
how to deal with a bunch of vendors and

00:22:15.680 --> 00:22:20.517
did some smart things to reduce cost
or improve my efficiency. It should start

00:22:20.600 --> 00:22:21.717
there. I'm not saying that that's not

00:22:21.800 --> 00:22:24.517
the place, but this is about what what

00:22:24.600 --> 00:22:27.597
happens structurally in your industry

00:22:27.680 --> 00:22:30.957
when AI is sort of basically knows

00:22:31.040 --> 00:22:35.437
everything that it needs to know
about being in your industry.

00:22:35.520 --> 00:22:39.437
And if you start there, then I think
you will start understanding that this is

00:22:39.520 --> 00:22:43.117
not just a tech.
Uh it is really about a fundamental

00:22:43.200 --> 00:22:47.397
change uh to the firm.
>> And one of the things that I think

00:22:47.480 --> 00:22:52.197
thinking of the nature of the firm
uh is that you've done a great job in

00:22:52.280 --> 00:22:55.997
leading Microsoft. One of the many
different genius moments and skills is

00:22:56.080 --> 00:22:59.437
uh going through acquisitions and
partnerships, right? So, it's you know,

00:22:59.520 --> 00:23:02.877
whether it's LinkedIn, obviously
something close to our hearts.

00:23:02.960 --> 00:23:05.677
You know, GitHub, OpenAI, etc.

00:23:05.760 --> 00:23:08.677
And part of what I think Microsoft leads

00:23:08.760 --> 00:23:12.797
the entire world on is how do you build
trust in these other ecosystems? And in

00:23:12.880 --> 00:23:15.437
the world of AI, that's going to be
extremely important, especially as the

00:23:15.520 --> 00:23:19.837
firm changes. So, what are
some of that the kind of the

00:23:19.920 --> 00:23:23.437
um the kind of lessons you've learned, the

00:23:23.520 --> 00:23:26.277
things that you are advocating for in

00:23:26.360 --> 00:23:28.637
order to build and maintain that trust?

00:23:28.720 --> 00:23:31.317
>> so it's so key because in some sense

00:23:31.400 --> 00:23:33.917
I've always sort of thought about what's

00:23:34.000 --> 00:23:37.157
long-term stable, right? Um even from a

00:23:37.240 --> 00:23:41.597
Microsoft perspective. What is long-term
stable is for us to be a tools and a

00:23:41.680 --> 00:23:44.077
platform company where we fundamentally

00:23:44.160 --> 00:23:46.597
are defined by the amount of value that

00:23:46.680 --> 00:23:51.397
gets created on top of the platform,
which should far exceed

00:23:51.480 --> 00:23:54.797
anything that is captured in the platform,
right? It That's the only way

00:23:54.880 --> 00:23:56.917
to have stability.

00:23:57.000 --> 00:23:59.517
If you do that, then you have trust.

00:23:59.600 --> 00:24:01.957
Right? Because then the customer, the

00:24:02.040 --> 00:24:05.797
partner knows that it's not a zero-sum.

00:24:05.880 --> 00:24:08.637
Uh especially it's not a game theoretic

00:24:08.720 --> 00:24:11.597
z- you know, zero-sum with these games

00:24:11.680 --> 00:24:13.877
where, "Oh, I'm going to subsidize this

00:24:13.960 --> 00:24:16.757
until 2 years or 3 years only to then

00:24:16.840 --> 00:24:21.797
eat your lunch" type of games that
the tech people are really good at.

00:24:21.880 --> 00:24:24.717
Um and so, I've always felt like, "Hey,

00:24:24.800 --> 00:24:29.237
look, that is all not the way.
The way to think about it is to be very

00:24:29.320 --> 00:24:32.677
principled that you
are a platform company,

00:24:32.760 --> 00:24:38.237
that you will really live and die by
your ability to create success on top of

00:24:38.320 --> 00:24:40.677
the platform, and when they're
successful, you're going to be

00:24:40.760 --> 00:24:43.197
successful." And that is the equation

00:24:43.280 --> 00:24:45.397
that builds trust and long-term

00:24:45.480 --> 00:24:48.317
stability for both sides, right? Um

00:24:48.400 --> 00:24:53.077
I think in the AI age, that is going to be
everything, right? I think I would be

00:24:53.160 --> 00:24:57.197
very surprised, Reed, if you
were sitting here a year from now

00:24:57.280 --> 00:24:59.397
if the world is not completely turned

00:24:59.480 --> 00:25:03.157
on, what is my AI supply chain look

00:25:03.240 --> 00:25:07.797
like, where it is helping me as a firm

00:25:07.880 --> 00:25:11.197
compound the returns of AI that I can

00:25:11.280 --> 00:25:15.597
uniquely point to as my value in a world
where we know that these learning

00:25:15.680 --> 00:25:17.477
systems by definition don't have

00:25:17.560 --> 00:25:19.837
boundaries. They have to have boundaries

00:25:19.920 --> 00:25:23.197
because of mostly structures of markets

00:25:23.280 --> 00:25:26.557
and society and other mechanisms.

00:25:26.640 --> 00:25:29.477
>> Yep. So, we've talked
a lot about chatbots

00:25:29.560 --> 00:25:34.117
and code, um which have just
seen amazing explosive progress.

00:25:34.200 --> 00:25:38.557
Um what are some of the next frontiers
for LLMs over the next few years?

00:25:38.640 --> 00:25:42.037
>> Well, I mean, talking about that, in
fact, you know, one of the things that I

00:25:42.120 --> 00:25:44.517
know you have gotten very, very excited

00:25:44.600 --> 00:25:46.557
about, which I'm quite frankly, you

00:25:46.640 --> 00:25:49.357
know, excited for you, but it also has

00:25:49.440 --> 00:25:52.037
caused us to you to examine where you

00:25:52.120 --> 00:25:53.917
spend your time, and I'll let you speak

00:25:54.000 --> 00:25:56.917
to it, but it's sort of unbelievable,

00:25:57.000 --> 00:25:59.837
uh to see what's happening in science,

00:25:59.920 --> 00:26:04.637
um and and in fact, the company
that I, you know, you started with

00:26:04.720 --> 00:26:09.397
uh Sid and Monis, you know,
it's just a great example, um

00:26:09.480 --> 00:26:11.557
I'd say of

00:26:11.640 --> 00:26:14.477
what has happened in, let's call it,

00:26:14.560 --> 00:26:16.717
coding and knowledge work.

00:26:16.800 --> 00:26:19.157
If it start happening in science, in

00:26:19.240 --> 00:26:22.117
fact, I would even claim that I wish in

00:26:22.200 --> 00:26:24.397
fact, we did it in the reverse order, uh

00:26:24.480 --> 00:26:27.557
right? Because the um social permission

00:26:27.640 --> 00:26:30.837
for AI would be so much higher if we had

00:26:30.920 --> 00:26:33.357
started with some nice discoveries in

00:26:33.440 --> 00:26:37.957
science that were having great societal
benefits. Uh then people would have

00:26:38.040 --> 00:26:41.957
really thought of this as a thing that
is really going to be more helpful. So,

00:26:42.040 --> 00:26:46.037
I'm really excited uh for you, Reed,
what what you're doing. Obviously,

00:26:46.120 --> 00:26:49.037
you've spent a lot of time,
but maybe you want to talk about that.

00:26:49.120 --> 00:26:52.237
>> Well, yeah, so, I mean, it was one
of the things that uh I realized over the

00:26:52.320 --> 00:26:57.717
last month was that um we're
seeing such progress with Manas in

00:26:57.800 --> 00:27:01.917
actually in fact we've got an internal
description of move 37 for chemistry cuz

00:27:02.000 --> 00:27:06.637
we're seeing chemistry that that might
actually in fact take shots at really

00:27:06.720 --> 00:27:08.797
interesting cancers and other things.

00:27:08.880 --> 00:27:10.677
And you know, it's very early, right?

00:27:10.760 --> 00:27:14.437
And so you know, these things take
a while with all the IDs, but the the fact

00:27:14.520 --> 00:27:17.077
that we are already beginning to see
like we've got some of the best

00:27:17.160 --> 00:27:18.517
computational chemists in the world.

00:27:18.600 --> 00:27:21.637
They looked and said that's
very interesting. We've never

00:27:21.720 --> 00:27:26.557
seen it before and that might work,
right? And so that kind of thing and so

00:27:26.640 --> 00:27:29.197
you know, Sid and Uzair and I were
talking about this and I said, look, I

00:27:29.280 --> 00:27:33.957
think I need to get back to founder mode
in terms of how to do this and so I need

00:27:34.040 --> 00:27:38.317
to be able to kind of focus on this
and then you know, part of the you know,

00:27:38.400 --> 00:27:41.797
conversation you and I have
had this week is to say, okay,

00:27:41.880 --> 00:27:46.677
um it's been 10 years on the Microsoft
board. It's been a huge honor and

00:27:46.760 --> 00:27:50.957
pleasure with you know, and not just
LinkedIn, but obviously Open AI, GitHub,

00:27:51.040 --> 00:27:54.557
a whole bunch of things.
But you know, at the end of the year I

00:27:54.640 --> 00:27:56.877
should really be transitioning right now

00:27:56.960 --> 00:27:58.757
to being founder mode. So like you know,

00:27:58.840 --> 00:28:02.277
it's like and you know, we'll always be

00:28:02.360 --> 00:28:06.277
working together. So it's like but but
you know, it's time to kind of dig back

00:28:06.360 --> 00:28:08.397
into the company. >> Now,
first of all, it's a been such a

00:28:08.480 --> 00:28:10.357
privilege to have you on the Microsoft

00:28:10.440 --> 00:28:12.597
board, work with you, obviously through

00:28:12.680 --> 00:28:15.717
all the partnerships and LinkedIn and

00:28:15.800 --> 00:28:19.677
you will definitely be missed on the
Microsoft board, but I know we'll be

00:28:19.760 --> 00:28:24.037
very connected, but I'm also excited for
you. You know, when you say you're going

00:28:24.120 --> 00:28:27.957
back into founder mode,
you know, that means I'm even more

00:28:28.040 --> 00:28:32.197
curious about what you're going to be
building because it's a as you I think you

00:28:32.280 --> 00:28:35.677
rightfully captured, this is the moment
where I think the impact of these

00:28:35.760 --> 00:28:39.957
technologies more broadly felt
in the society is the need of the hour.

00:28:40.040 --> 00:28:43.157
>> Yeah, no, exactly. [clears throat]
>> Let's come back to you know, kind of

00:28:43.240 --> 00:28:45.397
questions around

00:28:45.480 --> 00:28:47.877
the patterns of thinking

00:28:47.960 --> 00:28:51.237
that happen with AI because one of the

00:28:51.320 --> 00:28:55.437
things that I think like I kind
of roughly think about these things is

00:28:55.520 --> 00:28:57.597
you've got an alien intelligence, right?

00:28:57.680 --> 00:29:00.077
A different version of A that has been

00:29:00.160 --> 00:29:03.237
built to very much heavily mimic human

00:29:03.320 --> 00:29:07.197
intelligence which creates
a whole bunch of utility for us.

00:29:07.280 --> 00:29:12.317
But it's patterns of reasoning are
not the same as ours, right? And people

00:29:12.400 --> 00:29:14.557
frequently encounter that and they go,
"Oh, that's scary." You're like, "Well,

00:29:14.640 --> 00:29:17.717
no, actually that's that's Look,
you have to pay attention to make sure

00:29:17.800 --> 00:29:21.197
there's alignment, but it's also
wonderful because it enables new

00:29:21.280 --> 00:29:24.957
things." And so that's part of the thing
like the kinds of chemistry we're doing

00:29:25.040 --> 00:29:27.917
like the humans hadn't discovered
this chemistry at Manus,

00:29:28.000 --> 00:29:31.677
right? What are the other kind
of thinkings about this pattern of

00:29:31.760 --> 00:29:37.317
reasoning, right? That that that
kind of amplify human capability.

00:29:37.400 --> 00:29:38.677
>> It's a great one. Like, you know, I've

00:29:38.760 --> 00:29:41.837
not thought that deeply about sort of

00:29:41.920 --> 00:29:44.477
how it improves the I mean, if you sort

00:29:44.560 --> 00:29:46.797
of sort of say there's, you know,

00:29:46.880 --> 00:29:51.037
induction and deduction that we do.

00:29:51.120 --> 00:29:54.717
Being in this cognitive loop with an AI,

00:29:54.800 --> 00:29:55.717
um

00:29:55.800 --> 00:29:58.477
that is fascinating, right? Because my

00:29:58.560 --> 00:30:02.397
exploration space is changing. Um

00:30:02.480 --> 00:30:06.277
right? The you know, like even in coding,

00:30:06.360 --> 00:30:09.037
one of the most fascinating

00:30:09.120 --> 00:30:13.477
um things that one of my colleagues
introduced me to is he he wrote a new

00:30:13.560 --> 00:30:15.837
skill which I have in GitHub Copilot.

00:30:15.920 --> 00:30:18.277
It's called cognitive coverage, right?

00:30:18.360 --> 00:30:23.157
It's fascinating. What he's done
is you know, whenever an agent does some

00:30:23.240 --> 00:30:26.477
work for me, he says,
"Just like how we have test

00:30:26.560 --> 00:30:29.237
coverage, we now have this new concept

00:30:29.320 --> 00:30:31.957
called cognitive coverage where we we as

00:30:32.040 --> 00:30:35.877
humans are going to learn from what it

00:30:35.960 --> 00:30:36.917
did."

00:30:37.000 --> 00:30:39.677
And so it's just creates a quiz.

00:30:39.760 --> 00:30:44.397
So because I literally think of learning

00:30:44.480 --> 00:30:47.317
uh from and it's sort of it is me

00:30:47.400 --> 00:30:50.357
matching my ability

00:30:50.440 --> 00:30:52.277
to essentially form a deductive

00:30:52.360 --> 00:30:55.997
understanding in some sense um of what

00:30:56.080 --> 00:30:59.957
an agent did. Right?
So, to your point, it is um

00:31:00.040 --> 00:31:01.917
it's a basically it's mimicking. It's

00:31:02.000 --> 00:31:04.397
sort of an imitation game on one side.

00:31:04.480 --> 00:31:07.997
But, that imitation game does create so

00:31:08.080 --> 00:31:11.077
then my ability to then deductively

00:31:11.160 --> 00:31:15.037
understand it I think
is probably one of the more

00:31:15.120 --> 00:31:19.077
important human skills we have to develop.
So, this cognitive coverage is

00:31:19.160 --> 00:31:22.717
I'm like fascinated by it, right?
Which is oh I think we're going to have

00:31:22.800 --> 00:31:25.797
something like that, which is the agents
are working. They have to be aligned on

00:31:25.880 --> 00:31:28.357
one side. But, the other side of it is

00:31:28.440 --> 00:31:31.597
what is new will be us knowing, oh we've

00:31:31.680 --> 00:31:33.837
cognitively covered what AI did.

00:31:33.920 --> 00:31:38.397
>> Yeah. Well, I think that point
intersected with your earlier one about,

00:31:38.480 --> 00:31:42.917
you know, kind of token capital now AI
capital, is that part of the skill set

00:31:43.000 --> 00:31:45.037
for the modern you know, kind of human

00:31:45.120 --> 00:31:47.757
knowledge worker is to say, okay, how do

00:31:47.840 --> 00:31:52.317
I strategize on the use
of AI canvas orchestration?

00:31:52.400 --> 00:31:55.397
>> Yeah. >> But,
also within kind of capital

00:31:55.480 --> 00:31:58.637
allocation bounds. Right? Because part
of what we've seen is people can go

00:31:58.720 --> 00:32:01.117
crazy on spending lots of tokens in

00:32:01.200 --> 00:32:05.717
ineffective ways. It's like, you know,
buying, you know, go back to bad

00:32:05.800 --> 00:32:08.957
Pentagon days, toilet seats for a
million dollars. And so, putting your

00:32:09.040 --> 00:32:12.357
thinking together with
the cognition together with

00:32:12.440 --> 00:32:14.757
token management. And what do you think

00:32:14.840 --> 00:32:18.157
is part of the question around this

00:32:18.240 --> 00:32:22.117
skill of blending the cognitive coverage

00:32:22.200 --> 00:32:25.517
with also token intelligence in terms of

00:32:25.600 --> 00:32:27.597
how do you amplify the outcomes?

00:32:27.680 --> 00:32:31.637
>> one. In fact, I've been thinking
about like one of the things that um

00:32:31.720 --> 00:32:35.077
I think one has to study deeply is what
are all the things that become more

00:32:35.160 --> 00:32:39.637
valuable in the age of let's
call it token abundance.

00:32:39.720 --> 00:32:42.037
One is what you reference, which is how

00:32:42.120 --> 00:32:43.957
do you use tokens becomes very valuable.

00:32:44.040 --> 00:32:47.077
Whoever figures out that oh, I can use

00:32:47.160 --> 00:32:51.677
tokens more efficiently for an outcome
that matters in the world is going to

00:32:51.760 --> 00:32:55.237
get ahead, right? By definition. So,
then how does one build the intuition

00:32:55.320 --> 00:32:57.677
for it, right? Which is oh, like this is

00:32:57.760 --> 00:33:00.197
where what is that outcome? How do I

00:33:00.280 --> 00:33:02.717
measure it? What's a rubric? In fact,

00:33:02.800 --> 00:33:05.997
this is where I think the eval the you

00:33:06.080 --> 00:33:08.277
know, if it's it's fascinating.

00:33:08.360 --> 00:33:12.837
How much time needs to be spent,
especially for an RL regime, I think

00:33:12.920 --> 00:33:14.637
it's been the clearest where if you

00:33:14.720 --> 00:33:16.917
really want you want to set up the

00:33:17.000 --> 00:33:20.877
rubric um and the eval dimensions or the

00:33:20.960 --> 00:33:23.237
rubrics scoring dimensions such that

00:33:23.320 --> 00:33:26.837
they're really capturing the high taste

00:33:26.920 --> 00:33:31.797
that you only can define. Because if
you did that, then you could be token

00:33:31.880 --> 00:33:33.477
efficient, right? In fact, the other

00:33:33.560 --> 00:33:36.637
side of the token efficiency is um in

00:33:36.720 --> 00:33:39.037
some of the examples we even showed at

00:33:39.120 --> 00:33:40.797
build was

00:33:40.880 --> 00:33:44.077
let's say you have a um I don't know um

00:33:44.160 --> 00:33:48.797
some you're a retailer or uh you're
a package goods company and one of the

00:33:48.880 --> 00:33:53.157
things you do is handle
sort of transactions around

00:33:53.240 --> 00:33:56.917
uh trade promotions. Right now,
you get all these claims from

00:33:57.000 --> 00:34:01.037
all the retailers and you have to process
them and the humans are doing it.

00:34:01.120 --> 00:34:05.117
You can easily automate
that using an agentic workflow.

00:34:05.200 --> 00:34:08.957
And you could say oh, I use a frontier
model for it. But then one of the

00:34:09.040 --> 00:34:13.757
dictums I have is don't use frontier
models for non-frontier problems, right?

00:34:13.840 --> 00:34:15.477
So, this is not a frontier problem.

00:34:15.560 --> 00:34:16.917
You're not trying to discover some new

00:34:17.000 --> 00:34:19.757
material science. This is a repeatable

00:34:19.840 --> 00:34:22.517
deterministic workflow,

00:34:22.600 --> 00:34:25.437
uh but that can benefit from all the

00:34:25.520 --> 00:34:30.037
mistakes humans can make and claims and
so on. So, therefore you do need uh you

00:34:30.120 --> 00:34:32.437
know, intelligence in it, but you can

00:34:32.520 --> 00:34:35.957
take, you know, a model like an MAI 5B

00:34:36.040 --> 00:34:39.597
and use the traces to hill climb in your

00:34:39.680 --> 00:34:43.437
RLE to perform even outperform even a

00:34:43.520 --> 00:34:46.277
frontier prompted. Right, so that to me

00:34:46.360 --> 00:34:49.477
is token efficiency. So that type of

00:34:49.560 --> 00:34:53.557
human understanding of both the limits

00:34:53.640 --> 00:34:58.757
of the system, the characteristics
of the system, I think become high premium

00:34:58.840 --> 00:35:02.637
at this stage. >> Yeah, I agree.
One of the other things that

00:35:02.720 --> 00:35:06.637
I think is important for people to grok,
as you know, cuz we've been in a lot of

00:35:06.720 --> 00:35:10.797
these conversations, there's a lot
of concern in other countries about, you

00:35:10.880 --> 00:35:15.637
know, kind of sovereign AI.
And I think that it may be useful here

00:35:15.720 --> 00:35:19.757
given we've just kind of gone in a depth
about companies and the sovereignty,

00:35:19.840 --> 00:35:22.797
like the companies,
their information, their employees.

00:35:22.880 --> 00:35:26.317
What are the parallels between what
we're doing for companies and how

00:35:26.400 --> 00:35:31.637
countries should think about kind of also
engaging and trust in the AI future?

00:35:31.720 --> 00:35:33.397
>> Yeah, I mean this is
interesting because I I

00:35:33.480 --> 00:35:36.197
one of the reasons why I have really

00:35:36.280 --> 00:35:40.997
pivoted to companies is because
companies exist all over the world.

00:35:41.080 --> 00:35:44.677
Right, that's the good news here, which
is because in some sense given countries

00:35:44.760 --> 00:35:47.837
thinking abstractly about sovereignty,

00:35:47.920 --> 00:35:50.557
they can even make big mistakes where

00:35:50.640 --> 00:35:53.917
they ultimately could erode their

00:35:54.000 --> 00:35:56.597
comparative advantage that is naturally

00:35:56.680 --> 00:35:59.437
there today embodied in the commercial

00:35:59.520 --> 00:36:04.277
activity happening in the country
through its company formation and

00:36:04.360 --> 00:36:05.917
thriving.

00:36:06.000 --> 00:36:09.477
So therefore preserving that is the best

00:36:09.560 --> 00:36:13.037
form of sovereignty. Sometimes I
think people think, oh, if only I had a

00:36:13.120 --> 00:36:17.197
firewall or all the data
was resident or what have you.

00:36:17.280 --> 00:36:22.117
You know, I'm not saying those are not
considerations, but this is not about

00:36:22.200 --> 00:36:26.037
even any of that, right, which is this is

00:36:26.120 --> 00:36:29.477
about making sure
that you have an economy.

00:36:29.560 --> 00:36:32.237
Uh that means you need to have
companies. That means that those

00:36:32.320 --> 00:36:35.357
companies have to thrive
in a token economy.

00:36:35.440 --> 00:36:39.037
Uh that means they need to be able
to build that IP. And so therefore, in

00:36:39.120 --> 00:36:41.477
fact, having partnerships even with

00:36:41.560 --> 00:36:44.397
companies outside that give you the

00:36:44.480 --> 00:36:47.437
ability, I think are more important than

00:36:47.520 --> 00:36:51.557
sort of suddenly falling behind some
frontier, right? And so, I think that

00:36:51.640 --> 00:36:54.517
therefore, I think that this is one
of those places where again, even the

00:36:54.600 --> 00:36:58.677
policy makers have to think
about infrastructure.

00:36:58.760 --> 00:37:03.077
Right? After all, you know, tokens,
they're electrons on one end and tokens

00:37:03.160 --> 00:37:04.917
on the other end. So, you want to be the

00:37:05.000 --> 00:37:07.957
cheapest, best, and environmentally good

00:37:08.040 --> 00:37:13.237
producers of those electrons to token
conversion machines called data centers,

00:37:13.320 --> 00:37:16.677
right? So, that one I think is going
to be very important for every country to

00:37:16.760 --> 00:37:19.837
prioritize. Uh of course,
the country should even

00:37:19.920 --> 00:37:23.317
prioritize whether that's their own
semiconductor production and what have

00:37:23.400 --> 00:37:25.477
you, right? That's are all very valid

00:37:25.560 --> 00:37:27.997
things to do. But beyond that, I think

00:37:28.080 --> 00:37:30.837
the most important thing is to take what

00:37:30.920 --> 00:37:33.357
Ricardo was always right on, which is

00:37:33.440 --> 00:37:36.157
countries have by definition their own

00:37:36.240 --> 00:37:38.597
comparative advantage, and now they need

00:37:38.680 --> 00:37:43.957
to amplify that using AI. That means
best thing is that whether it's a small

00:37:44.040 --> 00:37:47.077
business or whether it's the large
multinational or even the public sector

00:37:47.160 --> 00:37:51.357
efficiency in the country
is getting better

00:37:51.440 --> 00:37:54.597
uh and is operating at the frontier.
The worst thing for any country to in the

00:37:54.680 --> 00:37:56.637
name of sovereignty,

00:37:56.720 --> 00:37:59.317
if they're off frontier, then that makes

00:37:59.400 --> 00:38:01.877
no sense because you're falling behind.

00:38:01.960 --> 00:38:05.997
You have to but at the same time,
being dependent on one frontier model also

00:38:06.080 --> 00:38:08.637
makes no sense because
then you're not sovereign.

00:38:08.720 --> 00:38:11.637
So, what's the the way
to solve it is to be able to

00:38:11.720 --> 00:38:14.597
say, "No, we will use models to hill

00:38:14.680 --> 00:38:18.437
climb on our own one firm at a time."

00:38:18.520 --> 00:38:20.637
And at an economy at aggregate, that's I

00:38:20.720 --> 00:38:22.117
think the equation.

00:38:22.200 --> 00:38:25.597
>> The the gesture at Silicon actually uh

00:38:25.680 --> 00:38:30.077
I think is a good also bring
up both for companies and we are

00:38:30.160 --> 00:38:33.917
building Mayan Cobalt, but also
of course partnering well with

00:38:34.000 --> 00:38:35.437
Nvidia and AMD.

00:38:35.520 --> 00:38:38.677
So, first what's the strategic job of

00:38:38.760 --> 00:38:43.557
Microsoft's own Silicon? What might
companies or countries also learn from

00:38:43.640 --> 00:38:45.677
that? >> Yeah, so it's
interesting if you think

00:38:45.760 --> 00:38:50.317
about it right we want it's a great way
to observe it even because I what I mean

00:38:50.400 --> 00:38:54.037
if you look at what Nvidia has done or
what AMD has done or what Intel has done

00:38:54.120 --> 00:38:57.077
they built general purpose technology.

00:38:57.160 --> 00:39:00.717
Which you know, like in fact when I
look at what we're doing with GPUs today

00:39:00.800 --> 00:39:05.517
we're of course using GPUs to do model
training model inferencing. But we're

00:39:05.600 --> 00:39:08.917
also accelerating other workloads.
In fact one of the exciting announcements

00:39:09.000 --> 00:39:11.957
at Build was using GPUs to accelerate

00:39:12.040 --> 00:39:16.077
Nvidia GPUs to accelerate our fabric
data warehouse, right? In fact one of

00:39:16.160 --> 00:39:19.597
the things that's happening with the
agent take workloads is we need more

00:39:19.680 --> 00:39:22.637
more performance on everything. And so

00:39:22.720 --> 00:39:27.357
that's a great that's because of the
general purpose nature, right? GPUs have

00:39:27.440 --> 00:39:31.357
you know, Nvidia has CUDA CUDA can be
used as a programming model on top of

00:39:31.440 --> 00:39:35.357
those to accelerate a variety of
workloads. In fact we're using the older

00:39:35.440 --> 00:39:39.277
chips of Nvidia to accelerate and this
also works out economically for us,

00:39:39.360 --> 00:39:41.517
right? Which is a smart for us to take a

00:39:41.600 --> 00:39:46.397
fleet and keep using that fleet over
a lifetime where we are not only using it

00:39:46.480 --> 00:39:50.797
for some cutting-edge AI, but we're also
using it to in fact make an old workload

00:39:50.880 --> 00:39:54.757
even better performing, right?
That's great for Nvidia great for us.

00:39:54.840 --> 00:39:58.317
But that also speaks to what's happened
in terms of the new workloads, right?

00:39:58.400 --> 00:40:03.117
The new workloads of AI are these data
parallel synchronous workloads training

00:40:03.200 --> 00:40:05.237
inference as well as these new agent

00:40:05.320 --> 00:40:10.357
runtime workloads which have very
different call patterns. Very different

00:40:10.440 --> 00:40:13.797
and and they're highly constant like
there's this they didn't exist 3 years

00:40:13.880 --> 00:40:18.477
ago right at any scale and now they
exist at scale. So, it'd behooves us to

00:40:18.560 --> 00:40:21.437
start thinking whether it's not
even before we even get to the

00:40:21.520 --> 00:40:25.957
semiconductors. I'm building my data
center. My civil engineering is in

00:40:26.040 --> 00:40:31.237
force. My cooling system, my mechanical
systems. In fact, the DC to AC that they

00:40:31.320 --> 00:40:32.357
we're trying to make sure that the

00:40:32.440 --> 00:40:36.037
electrons are coming in in kilowatts, uh

00:40:36.120 --> 00:40:40.797
right? Hundreds of kilowatts straight
to the silicon without any losses, right?

00:40:40.880 --> 00:40:42.197
So, we're trying to minimize even the

00:40:42.280 --> 00:40:44.957
power distribution in a data center. So,

00:40:45.040 --> 00:40:49.997
we can optimize to the nth degree for
these workloads because they're at such

00:40:50.080 --> 00:40:53.157
scale. And that's what we're doing with
Maya, right? Maya, for example, is being

00:40:53.240 --> 00:40:57.477
co-designed with our MAI models
and the OpenAI models to get the best

00:40:57.560 --> 00:40:59.597
performance out of them. We're designing

00:40:59.680 --> 00:41:03.037
Cobalt, which is our arm-based uh core

00:41:03.120 --> 00:41:05.437
uh for compute, and we're designing it

00:41:05.520 --> 00:41:10.357
for, for example, using all the agentic
traces uh of GitHub, right? Coding, you

00:41:10.440 --> 00:41:12.317
know, the call pattern of a coding agent

00:41:12.400 --> 00:41:15.437
is pretty different than human apps or

00:41:15.520 --> 00:41:19.397
even asynchronous human apps. And so,
therefore, we're optimizing and getting

00:41:19.480 --> 00:41:23.637
massive latency gains, performance
gains, and what have you. And so,

00:41:23.720 --> 00:41:25.437
we're going to be a systems company that

00:41:25.520 --> 00:41:27.837
continuously to optimizes for the new

00:41:27.920 --> 00:41:30.797
at-scale workloads that we have while

00:41:30.880 --> 00:41:35.237
using general-purpose technology from
our partners uh to maximize the utility

00:41:35.320 --> 00:41:37.957
of those.
And that flexibility, by the way,

00:41:38.040 --> 00:41:41.157
uh I think is what really is good. And
that that's where I think your question

00:41:41.240 --> 00:41:44.517
was so good, which is countries should
think of that, right? Which is if you

00:41:44.600 --> 00:41:47.717
really think of there's one thing
that answers, right? If I said, "Oh,

00:41:47.800 --> 00:41:50.397
everything is Maya and everything
is Cobalt," that's probably not the right

00:41:50.480 --> 00:41:54.397
thing for Microsoft. Uh but at the
same time, if you said we're open to

00:41:54.480 --> 00:41:57.837
innovation from the outside,
we will innovate inside, we will in fact

00:41:57.920 --> 00:42:01.357
benchmark everything, we'll be
principled about it ultimately for

00:42:01.440 --> 00:42:05.037
better economics. >> Yes,
cuz cuz creating the efficient

00:42:05.120 --> 00:42:09.157
capital token factories is the create
human prosperity is the goal.

00:42:09.240 --> 00:42:12.437
>> Exactly. And enabling many
of companies to do it. So,

00:42:12.520 --> 00:42:15.797
uh different thread, um one
of the things that I know from various

00:42:15.880 --> 00:42:18.397
conversations with you
that you're very thoughtful on

00:42:18.480 --> 00:42:20.917
is the topic of children in the modern

00:42:21.000 --> 00:42:25.717
age because obviously we're creating
products for tutors, co-workers, etc.,

00:42:25.800 --> 00:42:28.637
etc. And one of the things we're all

00:42:28.720 --> 00:42:30.877
exploring together is what should we be

00:42:30.960 --> 00:42:35.597
doing as an industry to navigate kind of

00:42:35.680 --> 00:42:39.117
being of helping
children elevate the right

00:42:39.200 --> 00:42:43.357
way and also keeping them safe. What
are some of the principles that you know,

00:42:43.440 --> 00:42:47.077
Microsoft thinking about, you're
thinking about as ways to kind of be

00:42:47.160 --> 00:42:51.757
good as it regards, you know, the next
generation of humanity through children.

00:42:51.840 --> 00:42:55.317
>> Yeah, it's a great question.
I you know, the the there's

00:42:55.400 --> 00:42:57.997
obviously important things that we have

00:42:58.080 --> 00:43:01.077
to do around child safety, right? So,

00:43:01.160 --> 00:43:04.957
when any digital technology comes out, I

00:43:05.040 --> 00:43:06.917
think we have to now think as first

00:43:07.000 --> 00:43:09.997
class, right? There are AI safety issues

00:43:10.080 --> 00:43:15.117
around cyber, bio-weapons, alignment,
but it's also child safety. So, I think

00:43:15.200 --> 00:43:16.837
that that one thing that we want to make

00:43:16.920 --> 00:43:20.037
sure is some of the challenges

00:43:20.120 --> 00:43:22.677
uh of uh current sort of set of let's

00:43:22.760 --> 00:43:25.117
call it chatbots, right?

00:43:25.200 --> 00:43:28.197
And their conversations with children in

00:43:28.280 --> 00:43:29.997
particular is something that we need to

00:43:30.080 --> 00:43:32.237
be very mindful of and make sure that we

00:43:32.320 --> 00:43:35.877
are not uh having children uh not have

00:43:35.960 --> 00:43:38.997
that agency uh that they need to have in

00:43:39.080 --> 00:43:43.917
order to interact with this on their
terms versus um be persuaded, for

00:43:44.000 --> 00:43:46.877
example, right? So, those
are very important things.

00:43:46.960 --> 00:43:50.437
But, the other thing that you're pulling
on, which I think is a super important,

00:43:50.520 --> 00:43:52.757
is what does it mean to even be a child

00:43:52.840 --> 00:43:57.597
in a world like this where there
is all this abundance of tokens.

00:43:57.680 --> 00:44:00.517
And that I think is the the question,
right? How do children what should how

00:44:00.600 --> 00:44:03.717
should learning happen? Uh

00:44:03.800 --> 00:44:06.197
how should we inspire

00:44:06.280 --> 00:44:08.597
uh children? What's their sort of

00:44:08.680 --> 00:44:14.077
ability to go have a new pedagogical
system even that they can enter. Because

00:44:14.160 --> 00:44:19.317
I think even like if you take
the traditional ways, right? The anxiety

00:44:19.400 --> 00:44:24.517
that let's say I I had
growing up on learning.

00:44:24.600 --> 00:44:26.317
Interestingly enough,

00:44:26.400 --> 00:44:28.317
that's a sort of

00:44:28.400 --> 00:44:32.437
an artifact of the scarcity

00:44:32.520 --> 00:44:35.957
of opportunities, scarcity of good

00:44:36.040 --> 00:44:37.917
learning,

00:44:38.000 --> 00:44:40.117
and a variety of things which may or may

00:44:40.200 --> 00:44:43.157
not be true going forward. So, one of

00:44:43.240 --> 00:44:45.677
the first things I think is creating an

00:44:45.760 --> 00:44:49.277
learning environment where students and

00:44:49.360 --> 00:44:51.757
children in particular from the earliest

00:44:51.840 --> 00:44:55.077
of ages don't develop these phobias for

00:44:55.160 --> 00:44:57.557
math or science or what have you. In

00:44:57.640 --> 00:45:00.397
fact, because everyone's by definition a

00:45:00.480 --> 00:45:03.277
lot more curious. It's just that contact

00:45:03.360 --> 00:45:06.157
with the world is what, you know, erodes

00:45:06.240 --> 00:45:08.797
that curiosity and confidence that

00:45:08.880 --> 00:45:11.277
children have in Italy. How do we

00:45:11.360 --> 00:45:14.557
develop that even further by giving them

00:45:14.640 --> 00:45:17.477
the ability to explore, knowing that

00:45:17.560 --> 00:45:21.637
there's no anxiety because after all the
expertise is always going to be there in

00:45:21.720 --> 00:45:24.317
abundance. It's really your cognitive

00:45:24.400 --> 00:45:28.917
coverage of that expertise that's more
at a premium. Right? That's if I had

00:45:29.000 --> 00:45:31.277
sort of somebody said that to me

00:45:31.360 --> 00:45:33.677
as a five-year-old, I think I may have

00:45:33.760 --> 00:45:36.557
approached life very differently.

00:45:36.640 --> 00:45:40.877
And so, how do we as a society create
the necessary conditions for that? I

00:45:40.960 --> 00:45:43.357
think it would be probably very important.
And I think it's one of the

00:45:43.440 --> 00:45:47.117
things, you know, I know from
conversations with you intensely is like

00:45:47.200 --> 00:45:51.157
this is one of our responsibilities as
a tech industry. Right? This is like it's

00:45:51.240 --> 00:45:53.677
a No, no, we can't abrogate it. This is

00:45:53.760 --> 00:45:58.517
like given the ubiquity of the AI
technology, we have to be responsible

00:45:58.600 --> 00:46:00.517
for the next human generation. Exactly.

00:46:00.600 --> 00:46:02.477
Both both sort of the unintended

00:46:02.560 --> 00:46:05.077
consequences is something we think about

00:46:05.160 --> 00:46:08.317
from day one and really building safety

00:46:08.400 --> 00:46:11.597
guardrails and what have you. Then the

00:46:11.680 --> 00:46:16.957
great advantages of new technology,
right, have to be democratized.

00:46:17.040 --> 00:46:19.997
And then I think there needs to be
structural change, right? I don't think

00:46:20.080 --> 00:46:24.517
you can say education remains
exactly the same and we value the same

00:46:24.600 --> 00:46:27.717
credentials. I don't know.
I don't think so.

00:46:27.800 --> 00:46:31.197
Something's got to change.
>> So one of the things we haven't had a

00:46:31.280 --> 00:46:34.877
chance to talk about yet, which is very
natural in this context, is Pope Leo's

00:46:34.960 --> 00:46:40.077
encyclical. Which I thought was actually
in fact a magnificent part of leadership

00:46:40.160 --> 00:46:42.997
on the behalf on the behalf of the church.
I mean, Pope Francis had

00:46:43.080 --> 00:46:45.037
actually gotten me engaged in helping

00:46:45.120 --> 00:46:48.517
them talk about AI 10, 11 years ago,

00:46:48.600 --> 00:46:54.117
right? So like the church has been
amazingly front-footed on this.

00:46:54.200 --> 00:46:56.517
And actually part of the thing
that didn't surprise me at all in the

00:46:56.600 --> 00:46:59.757
encyclical was the humanism of it,
right? Cuz people are thought, oh, it's

00:46:59.840 --> 00:47:02.997
about religion, it's about how you
how you pray and it's like, actually in

00:47:03.080 --> 00:47:07.197
fact, the the the at least the parts
of the church I've interfaced with have

00:47:07.280 --> 00:47:09.957
been humanists, right? And

00:47:10.040 --> 00:47:15.157
do you have any reflections on the Pope's
encyclical? To see the Pope weigh

00:47:15.240 --> 00:47:18.997
in and from what I understand
is historical precedent on this, right? I

00:47:19.080 --> 00:47:21.117
believe the Pope at the time of the

00:47:21.200 --> 00:47:26.077
Industrial Revolution also had weighed
in on the condition of labor and what

00:47:26.160 --> 00:47:27.797
should it do and what have you. And so

00:47:27.880 --> 00:47:31.517
to have the Pope sort of come out in

00:47:31.600 --> 00:47:35.077
defense of what I think all of us could

00:47:35.160 --> 00:47:39.957
be deeply care about, which
is human dignity and human agency

00:47:40.040 --> 00:47:41.997
in the age of AI,

00:47:42.080 --> 00:47:45.797
I think it's so important and I'm glad

00:47:45.880 --> 00:47:48.837
that he has advocated what he thinks is

00:47:48.920 --> 00:47:53.397
important. Um the other side of this to

00:47:53.480 --> 00:47:57.637
me is what is the society
like if I look at

00:47:57.720 --> 00:48:00.157
what is at least describing what what

00:48:00.240 --> 00:48:02.237
when I think about some of the greatest

00:48:02.320 --> 00:48:05.397
ways technological advances were

00:48:05.480 --> 00:48:07.477
harnessed

00:48:07.560 --> 00:48:12.597
to create great prosperity.
The story of the West is pretty

00:48:12.680 --> 00:48:16.597
unbelievable, right? There's a beautiful
book, in fact, that Joel Mokyr and a

00:48:16.680 --> 00:48:20.277
couple of other authors have written
called two It's called, I think, Two

00:48:20.360 --> 00:48:22.917
Paths to Prosperity, which describes the

00:48:23.000 --> 00:48:25.717
thousand-year history of China and the

00:48:25.800 --> 00:48:30.597
West, essentially. And you know,
I'm paraphrasing uh here,

00:48:30.680 --> 00:48:32.237
but fundamentally, some of the

00:48:32.320 --> 00:48:35.397
constructs, cultural, societal

00:48:35.480 --> 00:48:38.677
constructs in the West on how to use

00:48:38.760 --> 00:48:40.477
even the scientific revolution, the

00:48:40.560 --> 00:48:43.277
industrial revolution, this and they

00:48:43.360 --> 00:48:46.157
that necessitated a real change in how

00:48:46.240 --> 00:48:50.877
the society was organized so that it
could fundamentally take advantage of

00:48:50.960 --> 00:48:54.797
this new uh is sort of really massive.

00:48:54.880 --> 00:48:58.597
Uh Right? That in fact,
defined the modern world.

00:48:58.680 --> 00:49:00.917
And so, one of the things I feel we now

00:49:01.000 --> 00:49:04.237
need is a similar coming together from

00:49:04.320 --> 00:49:06.517
sort of both the moral philosophy. I

00:49:06.600 --> 00:49:08.477
think the Pope basically sort of said,

00:49:08.560 --> 00:49:11.317
"This needs to be the moral philosophy

00:49:11.400 --> 00:49:14.797
that guides uh us going forward." You

00:49:14.880 --> 00:49:17.437
combine that with even what is the

00:49:17.520 --> 00:49:21.997
market, what is the what is democracy,
uh and then what is the

00:49:22.080 --> 00:49:23.597
scientific/technological

00:49:23.680 --> 00:49:25.717
revolution. So, if we can somehow get

00:49:25.800 --> 00:49:30.037
into a virtuous cycle where the morality

00:49:30.120 --> 00:49:31.757
and the scientific sort of

00:49:31.840 --> 00:49:35.117
breakthroughs, the political system,

00:49:35.200 --> 00:49:39.197
and the markets are all reinforcing each

00:49:39.280 --> 00:49:42.917
other, then we will have abundance, and

00:49:43.000 --> 00:49:47.157
we will have many stakeholders all
benefiting. And if you don't, we are

00:49:47.240 --> 00:49:48.717
going to lose social permission, right?

00:49:48.800 --> 00:49:50.437
So, I just don't think I I this is what

00:49:50.520 --> 00:49:55.917
the narrow understanding of the success
of the West as just a technological

00:49:56.000 --> 00:49:57.397
breakthrough

00:49:57.480 --> 00:49:59.357
I think is sort of not the case right

00:49:59.440 --> 00:50:03.197
it's a unbelievable coming together

00:50:03.280 --> 00:50:06.677
of a multitude of sort of forces in a

00:50:06.760 --> 00:50:11.197
virtual cycle it also
had really bad parts to it

00:50:11.280 --> 00:50:15.237
right we know that and so you have
to avoid that right which is and so that I

00:50:15.320 --> 00:50:18.837
think was also some things that the Pope
himself wrote in the encyclical which I

00:50:18.920 --> 00:50:22.837
thought was great which is even to think
about sort of what are the bad parts how

00:50:22.920 --> 00:50:28.477
do we not repeat it what is the way to be
you know advocating for this positive

00:50:28.560 --> 00:50:32.797
cycle I think it's beautifully captured
>> and I thought it was great that it was

00:50:32.880 --> 00:50:37.277
kind of a focus on how do you keep
humans at the center how do you elevate

00:50:37.360 --> 00:50:39.597
human dignity how do you

00:50:39.680 --> 00:50:42.117
address needs not just of the wealthy

00:50:42.200 --> 00:50:47.157
countries but the entire world is I
think you know a great you know beacon

00:50:47.240 --> 00:50:48.397
in terms of how we
started to think about it

00:50:48.480 --> 00:50:51.677
>> you know I think deeply about
that right for example I've always

00:50:51.760 --> 00:50:56.077
you know you know been like someone who
cared deeply about the global south in

00:50:56.160 --> 00:51:01.037
some sense finally having their moment
where they can be real catch-up growth

00:51:01.120 --> 00:51:05.157
and so I think that there's a real
danger now in the age of AI for even

00:51:05.240 --> 00:51:07.917
that convergence growth to slow and in

00:51:08.000 --> 00:51:10.317
fact go the other way and so what are

00:51:10.400 --> 00:51:13.277
the again the global structures because

00:51:13.360 --> 00:51:17.837
by the way someone sitting in the United
States in you know in Palo Alto may

00:51:17.920 --> 00:51:21.557
think that somehow it doesn't impact
them but there's no such thing right you

00:51:21.640 --> 00:51:24.077
know we share this planet and our

00:51:24.160 --> 00:51:25.877
destinies are a lot more tied than we

00:51:25.960 --> 00:51:28.997
think we are just because of you know

00:51:29.080 --> 00:51:33.157
being far away from what's happening >>
well um last question before we get to

00:51:33.240 --> 00:51:35.317
rapid fire

00:51:35.400 --> 00:51:37.757
as you and I both know

00:51:37.840 --> 00:51:40.597
you know in the US and in Europe there's

00:51:40.680 --> 00:51:44.957
a lot of AI backlash now part
of how as you know I've I've

00:51:45.040 --> 00:51:48.677
been trying to address it is like with
books like Super Agency, trying to say,

00:51:48.760 --> 00:51:52.037
"No, no, this is an opportunity
to gain agency. The transition will be

00:51:52.120 --> 00:51:55.997
difficult, but embracing
the agency and the transformation

00:51:56.080 --> 00:51:58.877
ultimately you have to, but if you do it

00:51:58.960 --> 00:52:03.637
you with forethought and and kind
of like leaning into it, it can be greatly

00:52:03.720 --> 00:52:05.077
helpful.

00:52:05.160 --> 00:52:07.757
Uh what do you think we should be trying

00:52:07.840 --> 00:52:10.917
to help people in the US and the West

00:52:11.000 --> 00:52:14.197
understand about why it is that actually

00:52:14.280 --> 00:52:18.797
embracing AI is more important. And it
doesn't mean that there aren't genuine

00:52:18.880 --> 00:52:22.397
issues in the backlash and obviously
work transition all the rest will be

00:52:22.480 --> 00:52:26.757
real issues, but how do we help people
see that this could be an important part

00:52:26.840 --> 00:52:27.677
of their future?

00:52:27.760 --> 00:52:30.557
>> Yeah, it's I think really at least now

00:52:30.640 --> 00:52:33.837
I've come to the conclusion that um

00:52:33.920 --> 00:52:39.600
there needs to be real tangible

00:52:41.840 --> 00:52:46.637
um outcomes that speak
for themselves. Because I

00:52:46.720 --> 00:52:50.477
think what has happened, and this is one
of those places where quite frankly our

00:52:50.560 --> 00:52:53.277
industry even, you know, the way we have

00:52:53.360 --> 00:52:58.397
talked about it, I mean when you go out
and say, "Hey, you know, all economic

00:52:58.480 --> 00:53:00.237
opportunity will go away for knowledge

00:53:00.320 --> 00:53:02.317
workers." or you know, "White collar

00:53:02.400 --> 00:53:05.557
jobs are gone." or you know, I

00:53:05.640 --> 00:53:10.037
And and you And then you're saying, "I'm
excited about building that technology."

00:53:10.120 --> 00:53:13.757
right? Why would anyone want you to be

00:53:13.840 --> 00:53:17.877
successful? Right? I mean, I don't want
you to be successful. I mean, this just

00:53:17.960 --> 00:53:23.277
makes no you know,
no social sense. So, I feel

00:53:23.360 --> 00:53:26.277
now um like when, you know, when you have

00:53:26.360 --> 00:53:28.237
someone in a college commencement be

00:53:28.320 --> 00:53:30.797
booed because they're saying AI is a you

00:53:30.880 --> 00:53:35.597
know, means we've now crossed
over to people don't believe us.

00:53:35.680 --> 00:53:39.077
And and rightfully so. So, therefore I
think what do we need to do now is time

00:53:39.160 --> 00:53:43.197
to do the hard work. The hard work is if
you're building a data center, let's

00:53:43.280 --> 00:53:47.797
make sure that that community believes
that this data center is great for them.

00:53:47.880 --> 00:53:50.077
It's the for their tax base, for their

00:53:50.160 --> 00:53:52.317
community efforts, their real estate

00:53:52.400 --> 00:53:57.237
value, their schools,
their water use, right? Their

00:53:57.320 --> 00:54:00.877
electricity prices. It can't be again
like, "Oh, I said something." No, it has

00:54:00.960 --> 00:54:04.517
to be real, right? That's kind of what
the way we earn social permission. Same

00:54:04.600 --> 00:54:07.237
thing I would say with employment. We

00:54:07.320 --> 00:54:11.677
can't abstractly even say, "Hey, lump
of labor fallacy, always there's going to

00:54:11.760 --> 00:54:14.157
be new jobs." What are the new jobs?

00:54:14.240 --> 00:54:16.997
What are the wages of the new jobs?

00:54:17.080 --> 00:54:19.517
Uh that I can now go apply for, train

00:54:19.600 --> 00:54:24.117
myself for, and and how do I really
start a new company or what have you,

00:54:24.200 --> 00:54:27.797
right? I think unless we really get
clear. All the third thing which we

00:54:27.880 --> 00:54:32.877
unpacked a lot, every firm needs
to participate in the frontier ecosystem.

00:54:32.960 --> 00:54:36.757
Oh, it's not like, "Oh, I'm just
a feeder of data to some foundation

00:54:36.840 --> 00:54:39.197
model." Like that is like talk about

00:54:39.280 --> 00:54:41.877
sovereignty and dignity both being lost,

00:54:41.960 --> 00:54:44.397
right? At simultaneously, right? By for

00:54:44.480 --> 00:54:46.677
countries, communities, and companies.

00:54:46.760 --> 00:54:51.677
So, I think now we have to go all
the way and say, okay, this is a positive

00:54:51.760 --> 00:54:55.997
sum. This is in fact
the you know, challenges

00:54:56.080 --> 00:54:57.797
of some of the technology. We are going

00:54:57.880 --> 00:55:00.837
to really actively work it. We can

00:55:00.920 --> 00:55:05.317
articulate the tangible benefits and
this is where again, what you will do at

00:55:05.400 --> 00:55:08.837
Manas and others are also going to be
very, very helpful because the world

00:55:08.920 --> 00:55:11.157
needs more proof points that this

00:55:11.240 --> 00:55:16.397
technology is ultimately helping human
condition and our societies broadly, not

00:55:16.480 --> 00:55:20.157
narrowly. >> Yeah.
No, AI for humanity. So,

00:55:20.240 --> 00:55:24.437
you don't have to answer rapidly, but we
ask the same questions of all our guests.

00:55:24.520 --> 00:55:28.517
So, the first one is, is there a movie,
song, or book that fills you with

00:55:28.600 --> 00:55:32.317
optimism for the future? >> It's this

00:55:32.400 --> 00:55:34.357
parallel paths to prosperity, all right?

00:55:34.440 --> 00:55:38.597
Because I I the reason why I like that
is because it's a good call to action.

00:55:38.680 --> 00:55:42.917
For the next thousand years,
what's the path to prosperity? There was

00:55:43.000 --> 00:55:46.397
a blueprint um at least
that at the part of the

00:55:46.480 --> 00:55:49.437
world got it right in the last thousand.

00:55:49.520 --> 00:55:53.797
Can we now as a an entire planet get it

00:55:53.880 --> 00:55:56.957
uh right for the next thousand?
I think that this is where

00:55:57.040 --> 00:55:59.797
uh some of our very
best work has to be done.

00:55:59.880 --> 00:56:04.877
>> Agreed. What's a question that you
wish people would ask you more often?

00:56:04.960 --> 00:56:08.517
>> Ah, that's a good one. I would love
for people to ask me what am I not excited

00:56:08.600 --> 00:56:11.997
about. Because I'm excited
about a lot of things.

00:56:12.080 --> 00:56:16.277
Uh but I'm not excited, for example,
about us losing permission on

00:56:16.360 --> 00:56:20.397
AI by you know, saying all the wrong
things or doing the wrong things even

00:56:20.480 --> 00:56:25.517
and not having a a complete thought
on how to truly have a positive sum

00:56:25.600 --> 00:56:29.757
construct. >> Yep. 100%. So,
where do you see progress or

00:56:29.840 --> 00:56:34.597
momentum outside of your industry? And
given that Microsoft powers a lot of the

00:56:34.680 --> 00:56:37.797
world's industry, um but you know,

00:56:37.880 --> 00:56:41.997
uh it could be robotics, AI, etc. But
where do you see progress that inspires

00:56:42.080 --> 00:56:46.597
you? >> I mean I mean
to some degree you know,

00:56:46.680 --> 00:56:48.677
the work in bio I mean, if I think about

00:56:48.760 --> 00:56:51.237
it, right? One of the most complex

00:56:51.320 --> 00:56:54.797
systems um that we have to have a better

00:56:54.880 --> 00:56:57.077
understanding of is human biology. So,

00:56:57.160 --> 00:57:01.717
anything any tool that can help humans

00:57:01.800 --> 00:57:04.917
take care of humans is probably the

00:57:05.000 --> 00:57:08.237
thing that will have uh it's it's

00:57:08.320 --> 00:57:10.357
awe-inspiring. There's this one thing uh

00:57:10.440 --> 00:57:12.637
we I recently came across, right? Which

00:57:12.720 --> 00:57:15.357
is this for immunotherapy,

00:57:15.440 --> 00:57:17.997
I believe there's a test um

00:57:18.080 --> 00:57:20.357
that is uh you know, basically a a

00:57:20.440 --> 00:57:23.397
complex costly test that figures out

00:57:23.480 --> 00:57:25.677
whether a particular immunotherapy will

00:57:25.760 --> 00:57:28.637
work on that tumor or not. Um and so,

00:57:28.720 --> 00:57:31.797
Providence and uh some researchers at U

00:57:31.880 --> 00:57:34.477
Dub and Microsoft Research came together

00:57:34.560 --> 00:57:36.997
and built this thing called Giga time,

00:57:37.080 --> 00:57:39.357
which is a cool model, which basically

00:57:39.440 --> 00:57:44.277
is a simulation of that task and reduces
the cost of what could get done only at

00:57:44.360 --> 00:57:48.277
sort of, you know, for took a lot of time
and a lot of money, now can be done

00:57:48.360 --> 00:57:51.117
by any tertiary sort of hospital in a,

00:57:51.200 --> 00:57:54.597
you know, in any city. And that type of

00:57:54.680 --> 00:57:57.517
economic sort of availability of sort of

00:57:57.600 --> 00:58:01.957
medicine in and and medical practice,
I think is just breakthrough.

00:58:02.040 --> 00:58:05.597
>> So, last question. Can you leave us
with a final thought on what you think is

00:58:05.680 --> 00:58:10.077
possible to achieve if everything breaks
humanity's way in the next 15 years? And

00:58:10.160 --> 00:58:12.717
what's our first step? >> Mhm.

00:58:12.800 --> 00:58:15.117
I think if

00:58:15.200 --> 00:58:17.237
everything breaks our way, I've always

00:58:17.320 --> 00:58:19.797
gone back like what's the dream

00:58:19.880 --> 00:58:22.877
um of the world

00:58:22.960 --> 00:58:27.037
compounding at 10% GDP growth.

00:58:27.120 --> 00:58:31.637
Right? Um that is what
can happen, right? You mean

00:58:31.720 --> 00:58:35.717
one thought experiment is if
the industrial revolution had reached all

00:58:35.800 --> 00:58:39.437
corners of the world at the same time.

00:58:39.520 --> 00:58:43.157
And every country could
express their comparative

00:58:43.240 --> 00:58:46.117
advantage fully.

00:58:46.200 --> 00:58:51.117
Right? That's the maximalist
positive sum construct.

00:58:51.200 --> 00:58:55.277
Can we do it? Right? Because we are kind
of captive to this, you know, what we

00:58:55.360 --> 00:58:59.237
will describe as, "Hey, that's not
how the world works. History is about

00:58:59.320 --> 00:59:02.277
dominance and dominant powers." And And

00:59:02.360 --> 00:59:06.437
I'm not saying we will defy all that,
but the bottom line is you should since

00:59:06.520 --> 00:59:09.197
you asked me to dream, I'm dreaming that

00:59:09.280 --> 00:59:11.437
if humanity can get back past their

00:59:11.520 --> 00:59:14.317
bounded rationality and only sort of

00:59:14.400 --> 00:59:16.837
say, "Oh, history repeats itself and we

00:59:16.920 --> 00:59:21.797
are only never going to get better than,
you know, I know fighting these wars and

00:59:21.880 --> 00:59:25.437
and what have you." And say, "No,
no, no, let's maybe we can change the

00:59:25.520 --> 00:59:28.797
course." Uh then the first step would be

00:59:28.880 --> 00:59:33.717
to accept that that possibility exists
is what I would say is what will get us

00:59:33.800 --> 00:59:37.557
down. Otherwise, we're going to back to
re-litigating, oh, we are Let me look at

00:59:37.640 --> 00:59:40.357
history and then just try to match.

00:59:40.440 --> 00:59:43.917
>> Beautiful dream. Sateer, always
a pleasure, and I'll see you next week at

00:59:44.000 --> 00:59:47.117
the board meeting. >> Thank you
so much, Reid. Thank you so much.

00:59:47.200 --> 00:59:50.157
>> This is [laughter] awesome.
Such a fun >> Yes, >> conversation.

00:59:50.240 --> 00:59:53.197
>> Yes, exactly. >> Possible
is produced by Pallet Media.

00:59:53.280 --> 00:59:57.877
It's hosted by Ara Finger and me, Reid
Hoffman. Our show runner is Shawn Young.

00:59:57.960 --> 00:59:59.837
Possible is produced by Tanaz Deelo,

00:59:59.920 --> 01:00:02.197
Katie Sanders, Spencer Strassmore, Imo

01:00:02.280 --> 01:00:05.237
Zoo, Aman Souri, Lexi Kevin, Danny

01:00:05.320 --> 01:00:09.397
Garrison, Trent Barbosa,
and Tafadzwa Nemarundwe.

01:00:09.480 --> 01:00:11.637
>> Special thanks to Surya Yalamanchili,

01:00:11.720 --> 01:00:18.560
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